Investor attention and currency performance ...

Applied Economics

ISSN: 0003-6846 (Print) 1466-4283 (Online) Journal homepage: http://www.tandfonline.com/loi/raec20

Investor attention and currency performance: international evidence Liyan Han, You Wu & Libo Yin To cite this article: Liyan Han, You Wu & Libo Yin (2018) Investor attention and currency performance: international evidence, Applied Economics, 50:23, 2525-2551, DOI: 10.1080/00036846.2017.1403556 To link to this article: https://doi.org/10.1080/00036846.2017.1403556

Published online: 20 Nov 2017.

Submit your article to this journal

Article views: 39

View related articles

View Crossmark data

Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=raec20

APPLIED ECONOMICS, 2018 VOL. 50, NO. 23, 2525–2551 https://doi.org/10.1080/00036846.2017.1403556

Investor attention and currency performance: international evidence Liyan Hana, You Wua and Libo Yinb a School of Economics and Management, Beihang University, Beijing, China; bSchool of Finance, Central University of Finance and Economics, Beijing, China

ABSTRACT

KEYWORDS

This article investigates the relationship between investor attention measured by Google search volume index and the performance of several currencies. We find that currency performance is remarkably responsive to changes in investor attention. These impacts, generated rapidly, are present over the relatively long term, especially for emerging currencies, and are intensified during periods of high uncertainty. We also demonstrate that there is a prominent asymmetric effect for the impact of attention, as past currency performance also influences attention. Typically, past currency performance can determine the magnitude of the impact on current currency performance. Moreover, we confirm that investor attention has a predictive power for forecasting emerging currency performance in the out-of-sample analysis. Further, these forecasts generate substantial economic value in the framework of asset allocation. By contrast, statistical predictability and economic value do not exist in the currencies from developed markets. These results indicate that investor attention can alter currency performance and its predictability. More broadly, our study emphasizes the potential of employing investor attention for emerging currency performance forecasting applications.

Investor attention; currency performance; predictability; asymmetric effect; out-of-sample forecast

I. Introduction Classical asset pricing models have difficulty in explaining some stylized empirical facts on price dynamics that are unrelated to fundamentals. For example, high levels of attention cause buying pressure and sudden price reactions (Barber and Odean 2008; Barber, Odean, and Zhu 2009), whereas low levels generate under-reaction to announcements (Dellavigna and Pollet 2009). This phenomenon is especially remarkable for foreign exchange (FX) markets. Since the seminal work of Meese and Rogoff (1983), who put forward the ‘exchange rate puzzle’, many academic studies have documented that macroeconomic variables cannot consistently outperform a simple random walk in terms of out-of-sample prediction; thus, exchange rates seemed to be disconnected from fundamentals (Obstfeld and Rogoff 2000; Andersen et al. 2003; Rogoff 2007; Engel, Mark, and West 2007; Balke, Ma, and Wohar 2013; Bacchetta and Van Wincoop 2013). These findings motivate a growing body of literature concerned with the implications of limited attention for asset pricing since the seminal work CONTACT Libo Yin [email protected] Road, HaiDian District, Beijing 100081, China

JEL CLASSIFICATION

G15; G12

of Merton (1987). In reality, given an abundance of information, investors with limited attention must allocate their attention efficiently across different assets and over time. In line with Huberman and Regev (2001), prices react to new information only when investors pay attention to it. The allocation of attention precedes portfolio allocation and can lead to infrequent portfolio decisions, affecting aspects of the dynamics of asset prices such as return predictability. Empirical evidence has been proven to be statistically and economically significant in the field of stock market volatility (Andrei and Hasler 2014; Vlastakis and Markellos 2012), earnings announcements (Drake, Roulstone, and Thornock 2011), liquidity and returns (Bank, Larch, and Peter 2011), trading volume (Tantaopas, Padungsaksawasdi, and Treepongkaruna 2016) and prediction of firms’ future cash flows (Da, Engelberg, and Gao 2010). However, little research has been undertaken in the context of currency markets. Even in highly liquid markets such as the FX market, information acquisition may still be important for asset price dynamics. As Bacchetta and Van Wincoop (2005)

School of Finance, Central University of Finance and Economics, Beijing, China, No. 39 South College

© 2017 Informa UK Limited, trading as Taylor & Francis Group

2526

L. HAN ET AL.

note, rational inattention slows down the process whereby new information becomes impounded into the exchange rate, leading to infrequent portfolio allocation decisions and predictable excess returns. This statement can also be verified by the fact that only a small fraction of international financial holdings are actively managed (Sager and Taylor 2006; Bacchetta and Van Wincoop 2010). Yu (2013) shows that sentiment provides a solution to the forward discount puzzle. Two studies resemble ours. Smith (2012) and Goddard, Kita, and Wang (2015) suggest that investor attention as proxied by the Google searches commove with contemporaneous FX market volatility and predicts subsequent FX market volatility. Following Da et al. (2011, 2015), Vlastakis and Markellos (2012), and Vozlyublennaia (2014), among others, in this study we proceed to further explore the international evidence investor attention effects on exchange rate returns.1 In contrast to these two studies that focus on developed FX markets, we examine emerging FX markets, which creates a naturally ideal environment to test the predictions of the limited attention theory. As conventional wisdom suggests, in an emerging market, the lack of affluent and professional institution investors leads to insufficient role of information diffusion, therefore more inattention and more behaviour bias than that of developed countries. Our results confirm this viewpoint. Moreover, to disentangle the effects of investor attention from those of macroeconomic uncertainty, liquidity risk, and fear of financial markets, we provide predictions on the joint impact of time-varying attention and uncertainty on FX returns. The main results of our study are (i) Investor attention significantly positive influences almost all of the representative currency returns based on the in-sample analysis. The impact, which manifests rapidly, presents a relatively long-term and higher quantitative effect for emerging currencies and can be strengthened during periods of high uncertainty. Additionally, past exchange rates should determine the magnitude of the impact of attention on current exchange rates. (ii) Investor attention can effectively predict emerging currency returns based on the outof-sample analysis, and these forecasts also have 1

substantial economic value by performing asset allocation exercises. Generally, investor attention has significant explanatory power for the movements of emerging currency returns. Our results have broad practical implications: investors may be able to forecast the emerging currency returns by employing investor attention obtained from online search queries for profitable trading. The remainder of the article is organized as follows. Section II provides a brief review of related literature. Section III describes and summarizes our data. Sections IV and V report empirical results for in-sample and out-of-sample analysis, respectively. Section VI explores the economic value. Section VII discusses robustness checks, and Section VIII concludes. II. Literature review and contributions Previous research provides a profound theoretical framework in which limited attention can affect asset pricing statics as well as dynamics (Merton 1987; Sims 2003; Peng and Xiong 2006). Many empirical studies also find consistent evidence to support the theory that investor attention has a significant impact on determining asset prices (Barber and Odean 2008; Da, Engelberg, and Gao 2011; Yuan 2015; Da, Engelberg, and Gao 2015). Our article contributes to a growing literature on the role of investor attention in the following streams. First, our article contributes to a growing body of work that sheds light on the importance of attention allocation in FX markets. Although a rich group of studies on the implications of investor attention for the dynamics of equity prices has emerged in the last two decades (Merton 1987; Sims 2003; Peng and Xiong 2006; Barber and Odean 2008; Da, Engelberg, and Gao 2015), there is a dearth of studies on attention in FX markets with few exceptions. In one intriguing study, Goddard, Kita, and Wang (2015) suggest that investor attention co-moves with contemporaneous FX market volatility and predicts subsequent FX market volatility for four currencies of developed countries (namely, the US dollar, Euro, Japanese yen and British pound), after controlling for macroeconomic

Because online queries reflect investors’ active attention to information, we refer to investor attention and information acquisition interchangeably in this article.

APPLIED ECONOMICS

fundamentals. Smith (2012) provides similar evidence for four more currencies: the Australian dollar, Canadian dollar, New Zealand dollar and Swiss franc. He also reports that Google search volume index (SVI) has incremental predictive ability beyond GARCH (1, 1). There is limited empirical evidence, however, concerning the impact of investors’ information acquisition on the dynamics of currency prices. Moreover, in contrast to these two studies that focus on developed FX markets, we examine emerging FX markets, which creates a naturally ideal environment to test the predictions of the limited attention theory. As conventional wisdom suggests, in a less developed or illiquid FX market, such as FX markets in emerging countries, the lack of affluent and professional institutional investors leads to the insufficient role of information diffusion in dealer-dominated FX markets, as well as more inattention and more behaviour bias than occurs in developed countries (Burnside et al. 2011; Loring and Lucey 2013). To better compare and discriminate among these differences, we investigate 10 selected currencies circulated in BRICS countries and G7 countries. These currencies were selected because their corresponding issuing countries are typically viewed as the most representative of emerging and developed countries (Fratzscher 2009; Sui and Sun 2016; Galloppo and Paimanova, Forthcoming). Based on limited attention theory, rational inattention slows down the process whereby new information becomes impounded into the exchange rate, leading to predictable excess returns. In other words, the attentive investors immediately incorporate new information into prices, therefore leading to failure prediction or an insignificant effect of attention on future prices. Our results confirm this viewpoint. Attention exhibits significant spillover effects and predictive power for both in-sample and out-of-sample analysis on FX returns for emerging markets, while that behaviour is much poorer for developed countries. Further, exchange rates are unlikely to be driven by private information, and the marginal investor is not subject to any short-selling constraints in FX markets. This is helpful for the investigation of information-driven trades in the absence of private information, which can disentangle the effects of investor attention from those of corruption, credit history and order flow (Vega 2006; Agarwal and Hauswald 2010; Jiang and Lo 2014).

2527

Second, our article contributes to this literature by providing predictions on the joint impact of timevarying attention and uncertainty on FX returns. To disentangle the effects of investor attention from those of macroeconomic uncertainty, liquidity risk, and fear of financial markets, we investigate the joint effects of these variables. Although a positive association between investor attention and performance of markets measured by returns is intuitive, several findings suggest mixed results. For example, Freixas and Kihlstrom (1984) and Huang and Liu (2007) argue that when there is uncertainty concerning the value of information, information acquisition is less frequent because it is costly, thus reducing the benefit of more frequent information updates. Vlastakis and Markellos (2012) and Goddard, Kita, and Wang (2015), however, indicate a positive association between the intensity of information acquisition and the variance risk premium for the S&P 500 index and FX markets, respectively. The intuition for this can be understood as follows. When investors pay more attention to news, the information is immediately incorporated into prices, and thus, high attention induces high return volatility. Conversely, high attention generates high return volatility and thus a larger risk premium since investors require a large risk premium to bear this attention-induced risk. When considering the learning mechanism, however, the effects of fluctuations in attention on the risk premium may be contaminated. When attention is high, learning is fast, and uncertainty about the future fundamentals, therefore, tends to be low, which generates low levels of volatility and risk premia. Therefore, we would expect similar effects of investor attention in FX markets: the FX performance should improve not only with attention but also with fundamentals. Third, we consider past returns as a part of the information the market receives (and to which it reacts). Inspired by Andrei and Hasler (2014), attention is state dependent, and can be influenced by past asset performance. In particular, a decreasing or negative return on an asset could be perceived as ‘‘bad news’’ by investors. During the bad times, investors become increasingly worried about their investments and are likely to draw considerable attention to the respective asset about fundamentals. Therefore, the sign of the past return or its recent

2528

L. HAN ET AL.

increase/decline can indicate the nature of the information received by investors. To account for this effect, we include the interaction terms between lagged attention and returns in the model in order to incorporate both possibilities of what the market may consider as “news.” III. Data We investigate 10 currencies circulated in G7 group and BRICS countries. They are the Brazilian real (BRL), Canadian dollar (CAD), Chinese yuan (CNY), Euro (EUR), Pound sterling (GBP), Indian rupee (INR), Japanese yen (JPY), Russian ruble (RUB), South African rand (ZAR) and United States dollar (USD). The returns of these currencies are measured by the nominal exchange rates at a weekly frequency, which can be readily downloaded from Quandl (https://www.quandl.com). For the sake of unified specification, the exchange rates are measured via US dollar quotations except for the Euro, Pound sterling and United States dollar. Specifically, the exchange rates of the Euro and Pound sterling are quite the opposite forms against US dollar quotation. This means that the exchange rate of Pound sterling is the number of Pounds equal to one unit US dollar, which is similar to measuring the exchange rate of the Euro in the context of this article. For the exchange rate of US dollar, we denote it using the US dollar index (USDX). Note that the exchange rate of Chinese yuan is not completely floating, but rather is partly managed by the central bank of China. Thus, the exchange rate of offshore Chinese yuan (CNH) has entered into the scope of our examination, which is not subject to the central bank’s intervention or stipulation of a daily trading band in the rate movement (Funke et al. 2015). We eventually use these exchange rates: USD/BRL, USD/ CAD, USD/CNH, USD/EUR, USD/GBP, USD/INR, USD/JPY, USD/RUB, USD/ZAR and USDX. The corresponding investor attention is denoted by SVI, derived from Google Trends (http://www. Google.com/trends). The Google SVI shows the percentage of search volumes for certain keywords relative to the total number of searches over a given period. In our analysis, we utilize the official names of specific currencies and their closely related terms as Google search keywords. For example, we view United States dollar as search keywords of ‘USD’ or

‘US dollar’, etc., in Google Trends and thus obtain the weekly SVI of USD. Searching these keywords has the advantage of avoiding the potential problem of ambiguity. If we use the currency code as the keyword, the SVI may contain multiple meanings, which is probably not related to investor attention of specific currency. For instance, a search for the keyword ‘CAD’ may have information about the design software. By contrast, searching ‘Canadian dollar’ clearly expresses investors’ demand for currencyspecific information on Google. We download SVI at a weekly frequency spanning from January 2007 to September 2015 for these currencies. Specifically, the SVI of CNH we have chosen runs from May 2012 to September 2015 as this is when data on USD/CNH is available. All data are employed to the forms of log-difference. Concerning fundamental variables about uncertainty, we include economic policy uncertainty (EPU), TED spread (TED) and volatility index (VIX) as variables (Baker, Bloom, and Davis 2016; Baruník, Kočenda, and Vácha 2016; Leduc and Liu 2016). The EPU, TED and VIX can disentangle the effects of investor attention from macroeconomic uncertainty, liquidity risk and fear of financial markets, respectively. Because EPU is available daily from http://www.policyuncertainty.com, we convert it into a weekly frequency by simple arithmetic average. TED is defined as the difference between the 3-month LIBOR and 3-month US Treasury Bill rate. It can be collected from Federal Reserve Economic Data. VIX is derived from the Chicago Board Options Exchange (CBOE). Lastly, it should be noted that all data of fundamental variables should also transform to the log-difference form as investor attention and exchange rates. Descriptive statistics for investor attention and exchange rates are shown in Table 1. Concerning the indicators of investor attention, the dispersion degree and the differences between Max and Min show that investor attention on BRICS countries appears to be more volatile than that of the G7 group. Similar characteristics can also be found in exchange rates, which are reported in panel B. These phenomena may imply there is a link between investor attention and exchange rates. Additionally, panel B conveys the information that the exchange rates of all currencies against the US dollar experienced depreciation over the sample period, as the mean

APPLIED ECONOMICS

2529

Table 1. Descriptive statistics of investor attention and exchange rates. Mean Panel A: investor attention BRL 0.001 CAD 0.001 CNH −0.006 EUR −0.000 GBP −0.000 INR 0.001 JPY 0.001 RUB −0.000 ZAR −0.000 USD −0.000 Panel B: exchange rate USD/BRL 0.001 USD/CAD 0.000 USD/CNH 0.000 USD/EUR 0.000 USD/GBP 0.001 USD/INR 0.001 USD/JPY 0.000 USD/RUB 0.002 USD/ZAR 0.002 USDX 0.000

SD

Median

Max

Min

Skewness

Kurtosis

Jarque–Bera

0.076 0.073 0.128 0.068 0.051 0.122 0.096 0.116 0.075 0.062

0.001 −0.002 0 −0.002 0 0 0.001 0.001 0 0

0.308 0.461 1.081 0.450 0.217 0.690 0.671 1.043 0.277 0.350

−0.387 −0.297 −0.627 −0.521 −0.574 −1.067 −0.718 −1.388 −0.398 −0.446

−0.116 1.225 2.193 −0.078 −2.569 −0.669 −0.278 −1.684 −0.019 −0.417

6.098 10.953 34.907 17.797 37.485 20.759 20.645 68.757 6.272 13.913

183.321 1315.663 7693.387 4160.275 23,096.312 6026.107 5921.548 82,371.543 203.379 2276.073

0.021 0.014 0.003 0.014 0.013 0.013 0.013 0.023 0.022 0.012

−0.001 −0.001 −0.000 −0.000 −0.000 0.000 0.000 0.000 −0.001 −0.000

0.162 0.056 0.028 0.051 0.062 0.060 0.055 0.187 0.116 0.048

−0.094 −0.050 −0.009 −0.091 −0.039 −0.077 −0.051 −0.214 −0.123 −0.042

1.424 0.364 3.816 −0.381 0.619 −0.136 −0.032 0.028 0.497 0.345

11.574 4.885 34.690 7.694 5.642 8.670 4.899 32.442 7.842 4.249

1550.789 77.561 7880.115 429.745 161.791 612.258 68.599 16,469.499 464.217 38.702

This table reports some basic statistical characteristics for investor attention and exchange rates of all sample currencies. Here, Google search volume indexes are employed to describe investor attention. All exchange rates are priced on the basis of the US Dollar Quotation in addition to the exchange rates of EUR and GBP, which are defined as USD/EUR and USD/GBP. They are quite opposite against the US Dollar Quotation. Besides, the exchange rate of USD is represented by the US dollar index (USDX). Panels A and B summarize the results of descriptive statistics of investor attention and exchange rates, respectively. The sample of investor attention and exchange rates of all currencies except CNH is from January 2007 to September 2015 at a weekly frequency, while that of CNH ranges from May 2012 to September 2015 at a weekly frequency. All data are turned into the forms of log-difference.

value of changes in the exchange rate is positive. The excess kurtosis and non-zero skewness exhibited in all variables show the investor attention and exchange rates are common financial time series. Finally, the Jarque–Bera statistic listed in the last column rejects the null hypothesis that the investor attention and exchange rates are estimated with a good fit by the normal distribution.

IV. In-sample analysis of investor attention and currency performance Granger causality test between investor attention and exchange rate

We first employ the Granger causality test to investigate the possible causal relationships between exchange rate (Re) and investor attention (Atten). The constructed models are as follows: Ret ¼ α01 þ α11 Ret1 þ    þ αn1 Retn þ β11 Attent1 þ   βn1 Attentn þ et ;

(1)

Attent ¼ α02 þ α12 Ret1 þ    þ αn2 Retn þ β12 Attent1 þ    þ βn2 Attentn þ et : (2)

where the null hypothesis of the Equation (1) is Atten does not Granger cause Re (β11 ¼    ¼ βn1 ¼ 0). The null hypothesis of the Equation (2) is that Re does not Granger cause Atten (α12 ¼    ¼ αn2 ¼ 0). The F-test is used to check whether the null hypothesis should be rejected, which means changes in Atten (Re) lead to the changes of Re (Atten). In other words, we depend on the significance value of the F-test to determine the Granger cause between investor attention and the exchange rate. Table 2 reports the significance value (p-value) for Equations (1) and (2) based on pairwise Granger causality tests of the relationships between investor attention and the corresponding exchange rates. Additionally, four lag specifications are considered according to the AIC information criterion. As revealed in Table 2, there are two rows contained in each cross between investor attention and exchange rate of specific currency. The first row denotes the first p-value, which refers to the null hypothesis: exchange rate does not Granger cause investor attention. The second row denotes the second p-value that refers to the null hypothesis: investor attention does not Grange cause the exchange rate. Overall, changes in investor attention can clearly cause the following changes in the exchange rate. All currencies except EUR and GBP follow this causal

2530

L. HAN ET AL.

Table 2. Pairwise Granger causality tests between investor attention and exchange rates. Attention Exchange Rate BRL CAD CNH EUR GBP INR JPY RUB ZAR USD

BRL

CAD

CNH

EUR

GBP

INR

JPY

RUB

ZAR

USD

0.971 0.000*** 0.128 0.031** 0.869 0.499 0.103 0.178 0.535 0.823 0.672 0.000*** 0.453 0.423 0.064* 0.005*** 0.294 0.001*** 0.083* 0.006***

0.537 0.051* 0.079* 0.044** 0.136 0.161 0.116 0.108 0.020** 0.059* 0.579 0.574 0.179 0.043** 0.003*** 0.046** 0.302 0.002*** 0.949 0.829

0.221 0.685 0.399 0.218 0.193 0.000*** 0.611 0.010** 0.566 0.731 0.635 0.362 0.537 0.437 0.000*** 0.312 0.382 0.694 0.194 0.142

0.465 0.516 0.060* 0.111 0.549 0.673 0.005*** 0.757 0.179 0.195 0.037** 0.128 0.541 0.352 0.171 0.023** 0.732 0.009*** 0.511 0.160

0.654 0.371 0.495 0.006*** 0.053* 0.068* 0.084* 0.268 0.159 0.254 0.590 0.767 0.510 0.234 0.219 0.073* 0.568 0.093* 0.884 0.114

0.693 0.000*** 0.865 0.214 0.309 0.132 0.534 0.646 0.434 0.987 0.976 0.000*** 0.563 0.047** 0.378 0.029** 0.723 0.007*** 0.718 0.368

0.197 0.415 0.291 0.087* 0.127 0.398 0.281 0.780 0.094* 0.030** 0.789 0.395 0.038** 0.001*** 0.492 0.032** 0.217 0.468 0.515 0.701

0.862 0.182 0.924 0.454 0.962 0.467 0.982 0.572 0.922 0.380 0.711 0.661 0.896 0.468 0.449 0.000*** 0.902 0.453 0.677 0.449

0.764 0.233 0.001*** 0.053* 0.020** 0.724 0.112 0.975 0.011** 0.416 0.813 0.028** 0.254 0.073* 0.005*** 0.142 0.088* 0.000*** 0.052* 0.987

0.926 0.001*** 0.038** 0.108 0.612 0.021** 0.048** 0.753 0.122 0.296 0.979 0.003*** 0.129 0.000*** 0.048** 0.000*** 0.402 0.000*** 0.478 0.018**

This table reports the p-values for Granger causality tests on investor attention and exchange rates. As shown in the table, there are two rows contained in each cross between investor attention and exchange rate of specific currency. The first row denotes the first p-value, which refers to the null hypothesis: exchange rate does not Granger cause investor attention. And the second row denotes the second p-value, which refers to the null hypothesis: investor attention does not Grange cause the exchange rate. Additionally, the related results are obtained from Equations (1) and (2) containing four lag specification according to AIC. The sample of investor attention and exchange rates of all currencies except CNH is from January 2007 to September 2015 at a weekly frequency, while that of CNH ranges from May 2012 to September 2015 at a weekly frequency. *, **, *** Denote significance at 10%, 5% and 1% level, respectively.

relationship pattern. Specifically, the Granger causality relationships in terms of the attention of BRL, CNH, JPY, RUB and ZAR on the corresponding exchange rates are confirmed at the 1% significance level, while the Granger causality relationships from the attention of CAD and USD to their exchange rates are verified at the 5% significance level. For EUR and GBP, the results of the Granger causality test fail to reject the null hypothesis, which means that investor attention on the two currencies does not Granger cause their corresponding exchange rates. The reverse Granger causality relationships expressed by Equation (2), however, present a slightly different way. Although the reverse relationships exist in four currencies, namely, CAD, EUR, JPY and ZAR, the statistical significance is much weaker than the relationships of attention on exchange rates. In other words, changes in the exchange rate are unlikely to cause changes in investor attention. This phenomenon is mainly attributed to the psychological bias of overconfidence according to the interpretation of Zhang et al. (2013). In particular, the individual investor can obtain information from the public channels and then generate heterogeneous private information. The price movement may not reflect the public information if the investors overestimate the accuracy of their private

information and underestimate the precision of the public information. Overconfidence results in this dominant causal relationship pattern, namely, investor attention Granger causes currency return. Moreover, the table also indicates that investor attention regarding BRL significantly affects not only the currency return of BRL, but also many other currency returns such as CAD, INR, RUB, ZAR and USD. Similar cross effects are also observed with investor attention regarding other currencies. These strong cross effects show that investor attention is an important channel through which contagion spillover arises in FX markets, which is partly supported by Mondria and Quintana-Domeque (2013) and Hasler and Ornthanalai (2015) in that their studies indicate increased attention in one sector of stock market positively affects the return of the other sector that is fundamentally unrelated. Sign, timing, and persistence of investor attention effects

To obtain the sign, timing and persistence of the relationship between investor attention and the exchange rate, especially the impact of investor attention on the exchange rate, we employ a VAR

APPLIED ECONOMICS

model because the Granger causality test cannot convey this information. We find the results of VAR estimation and the corresponding impulse response functions by using the following model: Xt ¼ α0 þ α1 Xt1 þ    þ αn Xtn þ et ;

(3)

where vector X contains exchange rates and the appropriate investor attention variables. The VAR specification helps us to inquire as to the reaction of each investor attention/exchange rate pair to the shocks under the remaining investor attention and exchange rates over time within the impulse response function framework. Moreover, it must be

2531

noted that we only consider the relationships between investor attention and the corresponding exchange rates, while the relationships regarding cross-variables are not taken into account in our VAR specifications. Table 3 exhibits the estimation results of the VAR model for each currency. As shown in Table 3, the results for each currency are arranged in two columns, the first of which presents all the regression coefficients when the exchange rate is the dependent variable in our VAR model, while the second column contains the regression coefficients with investor attention as the dependent variable. For the sake

Table 3. VAR for investor attention and exchange rates. BRL Intercept Ret1 Ret2 Ret3 Ret4 Attent1 Attent2 Attent3 Attent4 R2

Ret 0.001 (0.001) −0.041 (0.047) 0.065 (0.047) 0.082 (0.047) 0.023 (0.047) 0.067*** (0.013) 0.040*** (0.014) 0.027* (0.015) 0.008 (0.014) 0.069

CAD Attent 0.001 (0.003) −0.065 (0.165) −0.005 (0.166) 0.079 (0.165) −0.062 (0.163) −0.279*** (0.047) −0.162*** (0.051) 0.007 (0.051) −0.046 (0.048) 0.089

Ret 0.000 (0.001) 0.099** (0.047) −0.136*** (0.047) 0.041 (0.047) −0.072 (0.047) −0.009 (0.009) 0.003 (0.009) 0.022*** (0.009) −0.007 (0.009) 0.050

Attent 0.001 (0.006) −0.196 (0.477) 0.030 (0.478) 0.246 (0.476) 0.065 (0.459) −0.022 (0.047) −0.095 (0.049) −0.034 (0.049) −0.061 (0.049) 0.014

Ret 0.000 (0.001) 0.082* (0.047) 0.075 (0.047) −0.062 (0.047) −0.000 (0.047) −0.025*** (0.006) −0.006 (0.007) 0.005 (0.007) 0.007 (0.006) 0.050

INR Intercept Ret1 Ret2 Ret3 Ret4 Attent1 Attent2 Attent3 Attent4 R2

Ret 0.001 (0.001) 0.107** (0.047) −0.013 (0.047) 0.054 (0.047) −0.073 (0.045) 0.027*** (0.005) 0.010** (0.005) −0.000 (0.005) 0.008 (0.005) 0.105

CNH Attent 0.001 (0.003) −0.127 (0.253) −0.573** (0.251) −0.272 (0.252) −0.312 (0.252) −0.141*** (0.047) −0.165*** (0.047) −0.146*** (0.046) −0.118** (0.047) 0.071

Ret 0.000 (0.000) 0.242*** (0.075) 0.141* (0.078) −0.083 (0.078) 0.222*** (0.072) 0.015*** (0.002) 0.007*** (0.002) 0.003 (0.002) 0.000 (0.002) 0.410

Attent 0.001 (0.004) 0.301 (0.346) −0.551 (0.347) 0.937 (0.347) −0.056 (0.346) −0.185 (0.047) −0.109 (0.048) −0.068 (0.048) −0.041 (0.047) 0.065

Ret 0.001 (0.001) −0.043 (0.047) 0.251*** (0.047) 0.008 (0.046) 0.095** (0.044) 0.067*** (0.009) −0.032*** (0.009) 0.007 (0.009) 0.005 (0.009) 0.244

JPY

EUR Attent −0.009 (0.009) 0.524 (3.523) −1.045 (3.654) −6.027 (3.674) −2.591 (3.379) −0.384*** (0.076) −0.086 (0.092) −0.257*** (0.094) −0.123 (0.090) 0.193

Ret 0.000 (0.001) 0.103** (0.047) −0.009 (0.047) 0.079* (0.048) −0.060 (0.048) 0.010 (0.010) 0.007 (0.010) 0.007 (0.009) −0.001 (0.009) 0.023

Attent −0.002 (0.005) 0.029 (0.257) −0.081 (0.257) 0.275 (0.254) 0.393 (0.239) −0.226*** (0.047) −0.189*** (0.051) 0.096* (0.052) −0.073 (0.050) 0.101

Ret 0.001 (0.001) −0.031 (0.047) −0.086* (0.047) −0.019 (0.047) 0.131*** (0.046) 0.061*** (0.014) 0.045*** (0.014) 0.029** (0.014) −0.003 (0.014) 0.078

RUB

GBP Attent −0.000 (0.003) 0.311 (0.231) −0.854*** (0.233) 0.052 (0.236) −0.127 (0.235) −0.067 (0.047) −0.056 (0.047) 0.037 (0.046) −0.060 (0.046) 0.045

Ret 0.001 (0.001) 0.015 (0.047) −0.065 (0.046) 0.188*** (0.046) 0.001 (0.047) 0.011 (0.012) 0.001 (0.012) 0.018 (0.012) 0.020* (0.012) 0.053

Attent 0.001 (0.003) −0.204 (0.162) −0.350** (0.163) −0.237 (0.162) −0.106 (0.159) −0.179*** (0.047) −0.086* (0.049) −0.141*** (0.049) 0.013 (0.048) 0.072

Ret 0.000 (0.001) 0.031 (0.047) 0.051 (0.047) −0.082* (0.047) 0.046 (0.047) 0.025*** (0.009) −0.005 (0.009) 0.012 (0.009) 0.014 (0.009) 0.036

ZAR

Attent 0.000 (0.002) −0.138 (0.187) −0.302 (0.184) 0.188 (0.184) −0.261 (0.187) −0.168*** (0.047) −0.075 (0.048) 0.012 (0.047) −0.090* (0.047) 0.057 USD Attent 0.000 (0.003) −0.215 (0.248) −0.331 (0.247) −0.175 (0.247) −0.117 (0.247) −0.084* (0.048) −0.127*** (0.048) −0.032 (0.048) −0.105** (0.048) 0.036

This table reports VAR estimation results for investor attention (Attent ) and exchange rate (Ret ). Here, Google search volume indexes are employed to depict investor attention. All exchange rates are priced on the basis of the US Dollar Quotation in addition to the exchange rates of EUR and GBP, which are defined as the quite opposite forms against the US Dollar Quotation. Besides, the exchange rate of USD is represented by the US dollar index (USDX). The sample of investor attention and exchange rates of all currencies except CNH is from January 2007 to September 2015 at a weekly frequency, and that of CNH ranges from May 2012 to September 2015 at a weekly frequency. The results about VAR estimations only contain four lag specifications according to AIC. The VAR estimation for each currency is arranged in two columns: The left column is used to portray the exchange rate equation and the right column is used to portray the investor attention equation. The standard errors, which are corresponded to the estimate coefficients, are presented in parenthesis. *, **, *** Denote significance at 10%, 5% and 1% level, respectively.

2532

L. HAN ET AL.

of consistency of the context, the lag length in the model is still set as a four lag specification. Figure 1 portrays the impulse response functions, which correspond to the results from Table 3. In general, the results revealed in Table 3 suggest that investor attention can strikingly influence the future exchange rate. This impact is clear in almost all currencies. Specifically, the first lag of attention in BRL, CNH, INR, JPY, RUB, ZAR and USD has a noted impact on future exchange rates. Furthermore, the attention of all these currencies positively affects their corresponding exchange rates in the first lag, while that of JPY presents the completely opposite effect. In some extent, this phenomenon can be explained in light of Da, Engelberg, and Gao (2011) and Dimpfl and Jank (2016). Investors may be inclined to act and trade immediately or wait until the following week after paying attention to a specific asset and the trading trigger price pressure that persists over a period. In other words, incremental attention often leads to increased buying pressure that pushes asset prices up temporarily. Moreover, the results from Table 3 summarize an interesting phenomenon, namely, that the impact of investor attention on the returns of currencies circulated in G7 group (namely, CAD, EUR, GBP, JPY and USD) is clearly short-lived. Unlike in the G7 group, the impact of attention on the returns of emerging currencies (namely, BRL, CNH, INR, RUB and ZAR) appears to last relatively, generally two or three weeks. Furthermore, the quantitative effects of attention on the emerging currency returns are obviously higher than those on the returns of currencies from G7 group. We extrapolate that it is mainly related to the market efficiency hypothesis. In particular, the information originating from the developed FX markets is more likely to be quickly digested by investors for a high level of market efficiency. Due to the immaturity of these markets, however, the information generated from the emerging FX markets may elapse quite slowly, and then the related currency returns are more likely to be forecast. Figure 1 also verifies this phenomenon by converging to zero after a few periods. Thus, investor attention could be useful to help forecast exchange rates in the representative emerging FX markets. As for the impact of exchange rates on investor attention, we find that three out of the 10 currencies (CAD, EUR and ZAR) appear to manifest a

significant negative effect, which can be explained by loss aversion. In financial markets, investors typically pay more attention to ‘bad news’ than ‘good news’. Furthermore, the negative effect does not usually appear immediately because the impact is only significant at the second lag.

Asymmetric effects

Investor attention measured by SVI has a noted impact on future exchange rates, especially in the emerging FX markets. The dynamic effect from investor attention to the exchange rate we have analysed so far, however, is not intact and accurate in that changes in attention could be affected by past currency returns. For the sake of intactness and accuracy, we must take into account the influence from past returns when investigating the impact of attention on current returns. Moreover, Vozlyublennaia (2014) and Andrei and Hasler (2014) note that decreasing or negative changes in past asset performance perceived as ‘bad news’ could draw considerable attention to the asset. Following this insight, we also consider the nature of the information received by investors in the FX market, measured by the sign of changes in past currency returns, to further investigate the impact of investor attention. To measure the effect of the two possibilities, we incorporate the interaction terms between lagged investor attention and exchange rate into our model in the remaining analysis. We consider two kinds of interaction terms, the first of which is the interaction between lagged investor attention and the lagged exchange rate, and the second of which is the interaction between lagged investor attention and an exchange rate dummy (the numerical value is defined as 1 if the change in lagged exchange rate is negative and 0 otherwise). They can be viewed as ‘news’ released from the FX market influencing the impact of attention. Specifically, the first interaction accounts for the impact of attention conditional on changes in the past exchange rate. The second one estimates the effect of a negative or decreasing change in past returns on the impact of attention. We can also observe the difference in the impact of attention when the change in past returns is negative versus when it is positive. The two related models are listed as follows:

APPLIED ECONOMICS

Response of USD/BRL to BRL search

2533

Response of BRL search to USD/BRL .01

.006

.005

.004

0

.002

-.005

0

-.01

-.002 0

5

10

15

0

20

5

10

Response of USD/CAD to CAD search

15

20

Response of CAD search to USD/CAD

.004

.005

0 .002

-.005

0 -.01

-.002

-.015 0

5

10

15

20

0

5

Response of USD/CNH to CNH search

10

15

20

Response of CNH search to USD/CNH .02

.002

.001

0

0

-.02

-.001

-.04 0

5

10

15

20

0

5

10

15

20

15

20

15

20

Response of EUR search to USD/EUR

Response of USD/EUR to EUR search .002

.01

.001

0

0

-.01

-.001 -.02 0

5

10

15

0

20

5

Response of USD/GBP to GBP search

10

Response of GBP search to USD/GBP .01

.002

.005 .001

0 0 -.005

-.001 -.01 0

5

10

15

20

0

5

10

Figure 1. Impulse response functions for VAR of investor attention and exchange rate. This figure plots impulse response to Cholesky one standard deviation innovations ±2 standard errors (grey area). The left side reports impulse response of exchange rates to the corresponding investor attention, the right side reports impulse response of investor attention to the corresponding exchange rates. All impulse response functions contain in the VAR analysis of four lag specification. The sample of investor attention and exchange rates for all currencies except CNH is from January 2007 to September 2015 at a weekly frequency, and that of CNH ranges from May 2012 to September 2015 at a weekly frequency.

2534

L. HAN ET AL.

Response of USD/INR to INR search

Response of INR search to USD/INR .02

.004

.01 .002

0 0

-.01 -.002 0

5

10

15

20

0

5

Response of USD/JPY to JPY search

10

15

20

15

20

15

20

Response of JPY search to USD/JPY

.002

.02

.01 0

0 -.002 -.01

-.004

-.02 0

5

10

15

20

0

5

Response of USD/RUB to RUB search

10

Response of RUB search to USD/RUB

.01

.02

.005

.01

0

0

-.005 -.01 0

5

10

15

20

0

5

10

Response of USD/ZAR to ZAR search

Response of ZAR search to USD/ZAR .005

.005

0

-.005

0

-.01

-.005

-.015 0

5

10

15

20

0

5

10

15

20

Response of USD search to USDX

Response of USDX to USD search .004

.005

.002

0

0

-.005

-.01

-.002 0

Figure 1. (Continued).

5

10

15

20

0

5

10

15

20

APPLIED ECONOMICS

Reit ¼ ϕi þ

4 X

αil Reitl þ

l¼1

þ

4 X

4 X

βil Attenitl

l¼1

λil Attenitl  Reitl þ eit ;

(4)

l¼1

Reit ¼ ϕi þ

4 X

πil Reitl þ

l¼1

þ

4 X

4 X

γil Attenitl

l¼1

ψil Attenitl  DðReitl < 0Þ þ eit :

l¼1

(5) where coefficient λ on the interaction term of Equation (4) measures the magnitude of the impact of investor attention on the future exchange rate based on per unit change in the past exchange rate. In other words, it is an indicator used to measure the asymmetric effect of the impact of attention. Coefficient ψon the interaction term of Equation (5), however, typically has a different meaning in comparison with the coefficient λ in Equation (4). It measures the magnitude of the impact of investor attention on the future exchange rate when the change in the past exchange rate is negative. D in the interaction term of Equation (5) is the dummy variable with a value equal to 1 if the change in the exchange rate of currency i at period t is negative and 0 otherwise. Similar to the preceding setting, we consider a four lag specification. We further provide the prediction model of this effect without investor attention terms, which is expressed as the following equation: Reit ¼ ϕi þ

4 X

ωil Reitl þ eit :

(6)

l¼1

It is an AR (4) model. By comparing the regression results of Equations (4) and (5) with that of Equation (6), we can obtain information about whether the combined effects of the terms related to investor attention are statistically significant and approximately how much the addition of the terms increases predictability. Table 4 reports the performance comparisons of the asymmetric effects for prediction models with and without investor attention terms. As revealed 2

2535

in this table, the predictability by incorporating the investor attention sharply increases compared to the predictability without them. In particular, the results derived from Table 4 show that the past performances of all currencies except JPY generate remarkable predictability for explaining their corresponding future performances when the investor attention is not included in the prediction models. The results also report that almost all investor attention appears to be statistically significant for predicting the future performances of all currencies based on attention being viewed as the key explanatory variables. It should also be noted that the quantitative effects of attention on the emerging currencies are evidently larger than that on the currencies from G7 group, which is consistent with the results presented in subsection ‘Sign, timing, and persistence of investor attention effects’. Moreover, the interaction terms related to the investor attention also play an important role in forecasting the currency performances revealed in the table. Furthermore, the addition of the investor attention greatly promotes predictability, which is mainly reflected as the increased R-squared statistics for all currencies. They are also higher in the currencies from emerging FX markets compared to that from developed FX markets. This indicates that the addition of investor attention generates better predictive power for forecasting the future performance of emerging currencies. Meanwhile, detailed regression results of model (5) for EUR and GBP2 and the results of model (4) for the other currencies can be derived from Table 4. The results show that the interaction terms in model (4) are significant for all currencies except EUR and GBP, revealing that changes in past exchange rate have a remarkable influence on the magnitude of the impact of attention on the current exchange rate. In other words, the asymmetric effect of investor attention conditional on the changes in past returns clearly exists in all these currencies. Specifically, the first lag of the interaction term for these currencies except CNH has a prominent positive impact on their corresponding returns, which means that the asymmetric effects for these currencies are generated rapidly. In other words, the information that

In consideration of prohibiting the impact of leap and breakpoint on the sample data of EUR and GBP, we then employ model (5) to empirically gauge the asymmetric effect for these two currencies.

2536

L. HAN ET AL.

Table 4. The performance comparisons of the asymmetric effects for the impacts with and without investor attention. USDBRL Intercept Ret1 Ret2 Ret3 Ret4 Attent1 Attent2 Attent3 Attent4 Interactt1 Interactt2 Interactt3 Interactt4 R2 Intercept Ret1 Ret2 Ret3 Ret4

0.001 (0.001) −0.004 (0.047) 0.074 (0.048) 0.080* (0.048) 0.020 (0.048)

0.001 (0.001) 0.098** (0.048) −0.020 (0.047) 0.180*** (0.046) −0.061 (0.045) 0.064*** (0.012) 0.044*** (0.013) 0.033** (0.013) 0.013 (0.012) 4.216*** (0.484) 1.053* (0.552) 3.040*** (0.549) −1.055* (0.550) 0.012 0.278 USDINR

0.001 (0.001) 0.136*** (0.047) −0.022 (0.048) 0.066 (0.048) −0.072 (0.047)

Attent1 Attent2 Attent3 Attent4 Interactt1 Interactt2 Interactt3 Interactt4 R2

0.025

0.001 (0.001) 0.139*** (0.048) 0.036 (0.048) 0.011 (0.048) −0.097** (0.045) 0.020*** (0.005) 0.009* (0.005) 0.003 (0.005) 0.007 (0.005) 2.052*** (0.298) 0.873*** (0.315) −0.140 (0.316) 0.243 (0.312) 0.202

USDCAD −0.000 (0.001) 0.107** (0.047) −0.130*** (0.048) −0.027 (0.049) −0.094** (0.048) −0.008 (0.009) 0.001 (0.008) 0.019** (0.008) −0.006 (0.009) 3.158*** (0.577) 0.795 (0.592) 0.318 (0.582) 0.901 (0.586) 0.030 0.127 USDJPY

0.000 (0.001) 0.092* (0.047) −0.141*** (0.047) 0.055 (0.047) −0.081* (0.047)

−0.000 (0.001) 0.076 (0.047) 0.056 (0.047) −0.055 (0.047) −0.016 (0.047)

0.012

−0.000 (0.001) 0.153*** (0.050) 0.015 (0.050) −0.000 (0.050) −0.038 (0.049) −0.019*** (0.007) −0.012 (0.007) 0.011 (0.007) −0.001 (0.007) 0.985*** (0.369) −0.722* (0.376) 1.038*** (0.377) −0.742** (0.373) 0.089

USDEURΔ

USDCNH −0.000 (0.000) 0.248*** (0.078) 0.166** (0.079) −0.195** (0.081) 0.138* (0.079)

0.000 (0.000) 0.162* (0.095) 0.195** (0.093) −0.023 (0.095) 0.298*** (0.090) 0.015*** (0.002) 0.005** (0.002) 0.003 (0.002) 0.001 (0.002) −0.335 (0.286) 0.162 (0.276) 0.279 (0.277) 0.438* (0.246) 0.116 0.429 USDRUB

0.001 (0.001) −0.108** (0.047) 0.215*** (0.047) 0.067 (0.047) 0.109** (0.047)

0.082

0.001 (0.001) 0.156*** (0.050) 0.212*** (0.051) 0.017 (0.048) 0.056 (0.044) 0.033*** (0.009) −0.004 (0.010) −0.011 (0.010) 0.001 (0.009) 1.623*** (0.151) −0.232 (0.174) 0.582*** (0.172) 0.033 (0.166) 0.404

0.000 (0.001) 0.107** (0.047) −0.005 (0.047) 0.073 (0.047) −0.065 (0.047)

0.000 (0.001) 0.113** (0.048) −0.003 (0.048) 0.072 (0.049) −0.058 (0.048) 0.042*** (0.014) 0.007 (0.014) 0.020 (0.014) −0.004 (0.014) −0.063*** (0.019) −0.005 (0.019) −0.027 (0.019) 0.007 (0.019) 0.019 0.051 USDZAR

0.001 (0.001) 0.020 (0.047) −0.079* (0.047) −0.047 (0.047) 0.095** (0.047)

0.019

0.001 (0.001) −0.022 (0.048) −0.078 (0.048) −0.096** (0.047) 0.108** (0.045) 0.046*** (0.013) 0.045*** (0.013) 0.038*** (0.013) 0.000 (0.013) 3.732*** (0.456) 1.447*** (0.494) −1.183** (0.493) −0.305 (0.480) 0.224

USDGBPΔ 0.000 (0.001) 0.021 (0.047) −0.068 (0.046) 0.194*** (0.046) 0.002 (0.047)

0.042 0.000 (0.001) 0.028 (0.047) 0.049 (0.047) −0.080* (0.047) 0.038 (0.047)

0.010

0.000 (0.001) 0.008 (0.048) −0.088* (0.047) 0.163*** (0.047) −0.004 (0.047) 0.033** (0.015) 0.013 (0.015) 0.023 (0.015) 0.033** (0.015) −0.064*** (0.025) −0.033 (0.025) −0.020 (0.025) −0.048* (0.025) 0.080 USDX 0.000 (0.001) 0.005 (0.048) 0.020 (0.048) −0.108** (0.048) 0.044 (0.047) 0.015 (0.009) −0.006 (0.009) 0.010 (0.009) 0.014 (0.009) 1.988*** (0.623) 0.510 (0.637) 1.853*** (0.639) −0.184 (0.638) 0.079

This table reports the performance comparisons of prediction models without investor attention terms and models for the asymmetric impacts of investor attention (Attent ) on exchange rates (Ret ) conditional on the past exchange rates. As shown in the table, the left column reports the estimation result of prediction model without investor attention term, while the right column reports that of prediction model with it for each currency. Specifically, the results hinge upon model (4) for all currencies except EUR and GBP. And lagged investor attention and lagged exchange rate consists of the interaction term (Interacttl ) in model (4). However, the estimation results of the currencies marked by triangle symbol, namely, EUR and GBP, are obtained from the regression of model (5). It reports the impact of investor attention on exchange rate conditional on the sign of past exchange rate. And the related interaction term of model (5) is made up of lagged investor attention and a dummy variable of exchange rate, which is equal to 1 if the lagged exchange rate is negative and 0 otherwise. The sample for investor attention and exchange rates of all currencies except CNH is from January 2007 to September 2015 at a weekly frequency, and that of CNH ranges from May 2012 to September 2015 at a weekly frequency. The standard errors, which are corresponded to the estimate coefficients, are presented in parenthesis. *, **, *** Denote significance at 10%, 5% and 1% level, respectively.

emerged in the immediate past, namely, the previous week, easily attracts considerable attention from investors that further influences future returns.

Additionally, the asymmetric effects for all these currencies not including CAD and CNH are maintained for at least two weeks, when considering them

APPLIED ECONOMICS

at a four lag specification. This means that the asymmetric effect for the impact of attention on most currencies can persist for a long period. Comparing the immediate past with the distant past of the asymmetric effect, we find the signs of coefficients λ for INR, RUB and USD generally remain significantly positive, which indicates that the corresponding FX markets are dominated by momentum investors. For these currencies, positive or increasing changes in the past exchange rates are achieved by the dominant investors as a useful indicator that the exchange rates probably continue to appreciate in the future. The consequent buying intention and pressure bring about the appreciation of all these currencies and the appreciation extent of the exchange rates hinges upon how much investor attention was devoted to the corresponding currency performance. Contrary to the identical sign for the preceding currencies (INR, RUB and USD), the sign of coefficient λ for BRL, JPY and ZAR presents a reverse trend in the distant past. The positive asymmetric effects for the three currencies, however, are obviously superior to the negative effects because positive effects that usually exist in the immediate past are highly significant, while the opposite effects appearing in the distant past are generally only marginally significant. For CNH, there is only a marginally significant positive effect presenting at the fourth lag of the interaction term. This suggests that the asymmetric effect for CNH does not appear instantly, which usually occurs in the previous four weeks. Further, the majority momentum investors in the CNH market anticipate an increase in the exchange rate when changes in the past exchange rate are positive in this setting. Furthermore, the asymmetric effects for EUR and GBP expressed by the interaction terms in model (5) have similar conclusions to those of the other currencies. The dominant investors in the FX markets of EUR and GBP are also momentum investors as the signs of coefficients ψ in the interaction terms are significantly negative. We interpret this evidence as follows. Investors expect the currency performances will continue to decrease based on the information that the sign of past performances are negative, especially when additional investor attention is attracted to the currency performance. Then, the rational investors likely sell these currencies, which leads to a depreciation. Moreover, the

2537

asymmetric effects for EUR and GBP immediately appear at the first lag and show no reversal trend at the follow-up lags.

The joint impact of investor attention and uncertainty

We have already demonstrated that the impact of investor attention on future currency returns is prominent. Notice, however, that macroeconomic fundamentals may also simultaneously influence these currency returns. In this subsection, we investigate that whether there are macroeconomic variables that significantly enhance the return predictability, and even obviously alter the impact of attention. Hence, we next empirically examine this effect by incorporating macro variables as new terms into the following model (7) to find more accurate information about the joint impact of investor attention and uncertainty for all currencies. The related model lies in the following equation: Reit ¼ ϕi þ

4 X

μil Reitl þ

l¼1

þ

4 X

σ il Attenitl þ

l¼1

4 X

δil Unceritl

l¼1 4 X

θil Attenitl

l¼1

 Unceritl þ eit :

(7)

where coefficient θ on the interaction term estimates the quantitative effect of investor attention on the future exchange rate conditional on the per unit increase/decrease in the past uncertainty variable. This means that the coefficient is devoted to gauging the magnitude of the joint impact of attention and uncertainty (Uncer) expressed by several macro variables, namely, EPU, TED and VIX. As previously, we consider a four lag specification. In addition, we also consider the performance comparisons of the prediction model of this joint effect with and without investor attention terms to obtain the evidence that incorporating them indeed increases the predictability. The specified expression of the model without attention is presented as follows: Reit ¼ ϕi þ

4 X l¼1

il Reitl þ

4 X

$il Unceritl þ eit :

l¼1

(8)

2538

L. HAN ET AL.

Table 5 reports the results of these performance comparisons. They indicate that the predictive power of the models based on the joint impact of investor attention and uncertainty variables is obviously superior to that of the models without the investor attention terms. In particular, almost all of the attention terms of the joint impact have significant predictability for currency returns, especially in the joint impact of attention and TED on emerging currency returns. Additionally, the R-squared statistics of prediction models with investor attention have been largely augmented by comparison with that of models without attention, which is also more prominent for all emerging currencies. In terms of the detailed results of the prediction model including investor attention, it is clear that uncertainty variables have a remarkable influence on the impact of attention. In particular, the joint impact of attention and EPU on exchange rate appears in four currencies: CNH, EUR, RUB and ZAR. The signs of the impact on CNH and RUB are prominently negative in the first lag, while those of the impact on EUR and ZAR are positive. In other words, the impact of attention on CNH and RUB is readily contorted by EPU, different from the promotion effect on EUR and ZAR. The reason for these antipodal results is that the FX markets of EUR and ZAR are more inclined to be market-oriented, while that of RUB is government-managed. As a result, the impact of attention could be reversed due to the increase in uncertainty about the monetary or fiscal policy, tax or regulatory regime and electoral outcomes of Russia. Additionally, searching for valuable information in the FX markets of CNH and RUB maybe become more costly under the circumstances of high EPU; thus, the impact of attention tends to decrease. For the joint impact of attention and TED, it is clearly emerging in all currencies. That of the first lag is significantly positive for almost all currencies, which powerfully supports the argument of Vlastakis and Markellos (2012) and Goddard, Kita, and Wang (2015). In other words, more TED spread improves the performance of the impact of attention for most currencies. The intuition for this phenomenon can be understood as follows. TED is viewed as a proxy of liquidity in financial markets. When liquidity is shrinking in the FX markets, higher investor attention induces higher return volatility and then higher risk premiums for returns to bear

the increased liquidity risk to a large extent. Thus, we can take it for granted that high TED enlarges the impact of attention. For the joint impact of attention and VIX, there exists a remarkable positive effect in four currencies. The impacts on BRL, INR and RUB are significantly positive in the first lag, and that on ZAR is positive in the second lag. Because VIX is often referred to as the ‘investor fear gauge’ by financial practitioners, a high degree of VIX indicates that panic sentiment is continuing to rise, which means that the global financial market is at stake. For the sake of financial assets’ safety, surplus capital will withdraw from the emerging FX markets and then inflow to developed FX markets. Hence, high VIX can strengthen the impact of attention, especially in emerging markets. Meanwhile, Table 5 also shows that the impact of the attention terms within uncertainty variables is almost completely identical to that without uncertainty variables as shown in subsection ‘Sign, timing, and persistence of investor attention effects’. In summary, the coefficients of interaction terms between attention and uncertainty variables are mainly positive and statistically significant, suggesting that investor attention exerts a remarkable impact on exchange rates during periods of high uncertainty. Ultimately, the joint impact of attention and uncertainty conspicuously improve the primary impact of attention. V. Investor attention and out-of-sample predictability of currency performance Generally, we have already demonstrated that investor attention has a significant influence on the future exchange rate in the preceding in-sample analysis. In other words, there exists a remarkable predictive power of investor attention on the exchange rate. According to the comprehensive study of Welch and Goyal (2008), however, many conventional forecast variables perform poorly out-of-sample. Thus, we cannot ensure that this forecasting ability of investor attention in the in-sample analysis will also appear in the out-of-sample analysis. We next turn to the out-of-sample analysis to investigate the predictability of investor attention on future exchange rates. In the remaining analysis, we include several statistical indicators based on existing classical literature to quantifiably test the predictive power

APPLIED ECONOMICS

2539

Table 5. The performance comparisons of the joint effects with and without investor attention. Panel A: Reit ¼ ϕi þ

Intercept Ret1 Ret2 Ret3 Ret4 EPUt1 EPUt2 EPUt3 EPUt4 Attent1 Attent2 Attent3 Attent4 Attent1  EPUt1 Attent2  EPUt2 Attent3  EPUt3 Attent4  EPUt4 R2 Intercept Ret1 Ret2 Ret3 Ret4 EPUt1 EPUt2 EPUt3 EPUt4 Attent1 Attent2 Attent3 Attent4 Attent1  EPUt1 Attent2  EPUt2 Attent3  EPUt3 Attent4  EPUt4 R2

4 P l¼1

μil Reitl þ

4 P

δil EPUitl þ

l¼1

4 P l¼1

σil Attenitl þ

4 P

θil Attenitl  EPUitl þ eit

l¼1

USDBRL 0.001 0.001 (0.001) (0.001) −0.010 −0.049 (0.048) (0.048) 0.066 0.055 (0.048) (0.049) 0.068 0.068 (0.048) (0.048) 0.011 0.014 (0.048) (0.048) 0.002 0.003 (0.004) (0.004) 0.006 0.006 (0.004) (0.004) 0.008* 0.008* (0.004) (0.004) 0.003 0.003 (0.004) (0.004) 0.065*** (0.014) 0.040*** (0.015) 0.030** (0.015) 0.007 (0.014) 0.010 (0.039) 0.001 (0.039) −0.009 (0.039) 0.036 (0.039) 0.022 0.079

USDCAD 0.000 0.000 (0.001) (0.001) 0.087* 0.085* (0.047) (0.048) −0.145*** −0.144*** (0.048) (0.048) 0.046 0.033 (0.048) (0.048) −0.083* −0.072 (0.047) (0.048) 0.001 0.001 (0.002) (0.002) 0.004 0.004 (0.003) (0.003) 0.005* 0.005* (0.003) (0.003) 0.001 0.001 (0.002) (0.002) −0.011 (0.009) 0.002 (0.009) 0.022** (0.009) −0.010 (0.009) 0.041 (0.027) 0.041 (0.027) 0.007 (0.027) −0.013 (0.027) 0.038 0.069

USDCNH −0.000 0.000 (0.000) (0.000) 0.210*** 0.177** (0.078) (0.078) 0.190** 0.132 (0.079) (0.080) −0.144* −0.110 (0.081) (0.081) 0.182** 0.235*** (0.079) (0.074) −0.002* −0.001* (0.001) (0.001) −0.003** −0.001 (0.001) (0.001) −0.003*** −0.002** (0.001) (0.001) −0.001 −0.001 (0.001) (0.001) 0.015*** (0.002) 0.008*** (0.002) 0.006** (0.002) 0.002 (0.002) −0.012** (0.005) −0.001 (0.005) −0.012** (0.005) −0.006 (0.005) 0.179 0.475

USDEUR 0.000 0.000 (0.001) (0.001) 0.100** 0.088* (0.047) (0.048) 0.001 0.003 (0.047) (0.048) 0.077 0.100** (0.047) (0.049) −0.061 −0.056 (0.047) (0.048) 0.004 0.004* (0.002) (0.003) 0.006** 0.006** (0.003) (0.003) 0.001 0.001 (0.003) (0.003) −0.004 −0.004* (0.002) (0.003) 0.002 (0.010) 0.012 (0.011) 0.010 (0.010) −0.002 (0.011) 0.049* (0.027) −0.018 (0.027) −0.023 (0.027) −0.007 (0.027) 0.042 0.057

USDINR 0.001 0.001 (0.001) (0.001) 0.138*** 0.106** (0.048) (0.048) −0.019 −0.001 (0.048) (0.048) 0.064 0.049 (0.048) (0.048) −0.076 −0.080* (0.048) (0.046) −0.001 −0.001 (0.002) (0.002) −0.000 0.001 (0.003) (0.002) 0.001 0.002 (0.003) (0.002) 0.001 0.002 (0.002) (0.002) 0.029*** (0.005) 0.014*** (0.005) −0.003 (0.005) 0.005 (0.005) −0.021 (0.014) −0.023 (0.014) 0.020 (0.014) 0.021 (0.014) 0.027 0.123

USDJPY 0.000 −0.000 (0.001) (0.001) 0.071 0.086* (0.048) (0.049) 0.056 0.082* (0.048) (0.049) −0.067 −0.082* (0.048) (0.049) −0.033 −0.026 (0.048) (0.049) −0.001 −0.002 (0.002) (0.002) −0.001 −0.001 (0.003) (0.003) −0.005* −0.004* (0.003) (0.003) −0.005** −0.004* (0.002) (0.002) −0.021*** (0.007) −0.005 (0.007) 0.002 (0.007) 0.007 (0.007) −0.032 (0.025) −0.010 (0.025) 0.018 (0.025) 0.003 (0.025) 0.027 0.069

USDRUB 0.001 0.001 (0.001) (0.001) −0.102** −0.022 (0.047) (0.048) 0.207*** 0.230*** (0.048) (0.049) 0.072 0.043 (0.048) (0.048) 0.102** 0.080* (0.048) (0.045) −0.003 −0.003 (0.004) (0.004) 0.003 0.002 (0.004) (0.004) 0.002 0.003 (0.004) (0.004) 0.005 0.002 (0.004) (0.004) 0.077*** (0.011) −0.058*** (0.012) 0.019 (0.012) 0.014 (0.012) −0.078*** (0.023) 0.092*** (0.024) −0.027 (0.024) −0.029 (0.024) 0.089 0.290

USDZAR 0.001 0.001 (0.001) (0.001) 0.004 −0.047 (0.047) (0.048) −0.083* −0.087* (0.047) (0.047) −0.052 −0.029 (0.047) (0.047) 0.088* 0.129*** (0.047) (0.046) 0.003 0.003 (0.004) (0.004) 0.008** 0.008** (0.004) (0.004) 0.014*** 0.014*** (0.004) (0.004) 0.009** 0.009** (0.004) (0.004) 0.066*** (0.014) 0.048*** (0.014) 0.024* (0.014) 0.001 (0.014) −0.048 (0.040) −0.013 (0.040) 0.116*** (0.040) −0.022 (0.041) 0.045 0.126

USDGBP 0.000 0.000 (0.001) (0.001) 0.019 0.010 (0.048) (0.048) −0.065 −0.065 (0.046) (0.047) 0.192*** 0.190*** (0.046) (0.047) 0.007 0.005 (0.047) (0.048) 0.001 0.002 (0.002) (0.002) 0.005** 0.005** (0.003) (0.003) 0.004 0.004* (0.003) (0.003) −0.002 −0.002 (0.002) (0.002) 0.013 (0.014) 0.010 (0.015) 0.017 (0.015) 0.017 (0.014) 0.001 (0.030) −0.018 (0.030) 0.003 (0.030) 0.005 (0.030) 0.062 0.073 USDX 0.000 (0.001) 0.018 (0.048) 0.045 (0.048) −0.077 (0.047) 0.044 (0.047) 0.004* (0.002) 0.004* (0.002) 0.002 (0.002) 0.000 (0.002)

0.020

0.000 (0.001) 0.017 (0.048) 0.042 (0.048) −0.084* (0.048) 0.051 (0.048) 0.005** (0.002) 0.004* (0.002) 0.002 (0.002) 0.000 (0.002) 0.025** (0.010) −0.008 (0.010) 0.009 (0.010) 0.012 (0.010) 0.011 (0.026) 0.016 (0.026) 0.020 (0.025) 0.009 (0.025) 0.050 (Continued)

2540

L. HAN ET AL.

Table 5. (Continued).

Panel B: Reit ¼ ϕi þ

4 P l¼1

μil Reitl þ

4 P

δil TEDitl þ

l¼1

4 P

σil Attenitl þ

l¼1

USDBRL Intercept Ret1 Ret2 Ret3 Ret4 TEDt1 TEDt2 TEDt3 TEDt4 Attent1 Attent2 Attent3 Attent4 Attent1  TEDt1 Attent2  TEDt2 Attent3  TEDt3 Attent4  TEDt4 R2 Intercept Ret1 Ret2 Ret3 Ret4 TEDt1 TEDt2 TEDt3 TEDt4

0.001 (0.001) −0.032 (0.048) 0.053 (0.048) 0.071 (0.048) 0.030 (0.048) 0.018** (0.009) 0.021** (0.009) 0.019** (0.009) −0.019** (0.009)

0.001 (0.001) −0.029 (0.048) 0.026 (0.048) 0.046 (0.048) 0.025 (0.046) 0.013 (0.008) 0.015* (0.008) 0.015* (0.008) −0.021** (0.008) 0.055*** (0.013) 0.032** (0.014) 0.015 (0.014) 0.005 (0.013) 0.590*** (0.099) −0.030 (0.103) 0.226** (0.102) 0.030 (0.103) 0.055 0.177 USDINR

0.001 (0.001) 0.130*** (0.048) −0.032 (0.048) 0.061 (0.048) −0.074 (0.048) 0.001 (0.005) 0.011** (0.005) 0.006 (0.005) −0.004 (0.005)

Attent1 Attent2 Attent3 Attent4 Attent1  TEDt1 Attent2  TEDt2 Attent3  TEDt3 Attent4  TEDt4 R2

0.040

0.001 (0.001) 0.114** (0.048) −0.013 (0.048) 0.055 (0.048) −0.081* (0.045) 0.001 (0.005) 0.013*** (0.005) 0.004 (0.005) −0.006 (0.005) 0.032*** (0.005) 0.010** (0.005) −0.002 (0.005) 0.005 (0.005) 0.174*** (0.034) 0.008 (0.035) 0.001 (0.035) −0.025 (0.035) 0.171

4 P

θil Attenitl  TEDitl þ eit

l¼1

USDCAD 0.000 (0.001) 0.076 (0.047) −0.151*** (0.047) 0.054 (0.047) −0.080* (0.047) 0.002 (0.005) 0.015*** (0.005) 0.009 (0.005) −0.000 (0.005)

0.000 (0.001) 0.075 (0.048) −0.142*** (0.048) 0.036 (0.048) −0.063 (0.047) 0.001 (0.005) 0.012** (0.005) 0.007 (0.005) 0.001 (0.005) −0.007 (0.009) 0.004 (0.009) 0.021** (0.009) −0.008 (0.009) 0.220*** (0.072) 0.098 (0.074) 0.140* (0.074) 0.062 (0.074) 0.055 0.101 USDJPY

−0.000 (0.001) 0.079* (0.048) 0.054 (0.048) −0.058 (0.048) −0.013 (0.047) −0.009* (0.005) 0.001 (0.005) −0.005 (0.005) −0.000 (0.005)

0.023

0.000 (0.001) 0.089* (0.048) 0.080* (0.048) −0.066 (0.048) 0.001 (0.048) −0.005 (0.005) 0.002 (0.005) −0.007 (0.005) −0.003 (0.005) −0.022*** (0.007) −0.003 (0.007) 0.006 (0.007) 0.007 (0.007) −0.105** (0.045) −0.041 (0.047) 0.044 (0.047) 0.028 (0.046) 0.076

USDCNH −0.000 (0.000) 0.243*** (0.078) 0.169** (0.080) −0.171** (0.082) 0.148* (0.079) 0.003 (0.003) −0.004 (0.003) −0.007** (0.003) −0.002 (0.003)

0.000 (0.000) 0.143* (0.079) 0.204*** (0.077) −0.030 (0.078) 0.179** (0.070) 0.001 (0.003) 0.000 (0.003) −0.002 (0.003) 0.001 (0.002) 0.010*** (0.002) 0.003 (0.002) 0.001 (0.002) −0.001 (0.002) 0.105*** (0.021) 0.027 (0.022) −0.067*** (0.020) −0.045** (0.021) 0.151 0.535 USDRUB

0.001 (0.001) −0.107** (0.047) 0.217*** (0.047) 0.065 (0.048) 0.108** (0.047) 0.001 (0.009) 0.005 (0.009) −0.003 (0.009) −0.001 (0.009)

0.083

0.001 (0.001) −0.042 (0.049) 0.266*** (0.049) −0.000 (0.048) 0.101** (0.046) 0.000 (0.008) 0.005 (0.008) −0.004 (0.008) 0.003 (0.008) 0.066*** (0.009) −0.038*** (0.010) 0.007 (0.010) −0.000 (0.010) 0.051 (0.120) −0.243** (0.123) 0.067 (0.123) −0.132 (0.122) 0.253

USDEUR 0.000 (0.001) 0.116** (0.047) −0.019 (0.048) 0.077 (0.047) −0.061 (0.047) −0.005 (0.006) 0.018*** (0.005) 0.000 (0.006) −0.000 (0.006)

0.000 (0.001) 0.117** (0.048) −0.011 (0.049) 0.072 (0.049) −0.049 (0.048) −0.006 (0.006) 0.017*** (0.006) −0.001 (0.006) 0.001 (0.006) 0.018* (0.010) 0.005 (0.010) 0.008 (0.010) 0.002 (0.010) 0.243*** (0.088) 0.014 (0.091) 0.081 (0.091) 0.047 (0.090) 0.044 0.065 USDZAR

0.002 (0.001) −0.006 (0.047) −0.086* (0.048) −0.055 (0.047) 0.083* (0.047) 0.020** (0.009) 0.021** (0.009) 0.008 (0.009) −0.003 (0.009)

0.046

0.001 (0.001) −0.072 (0.048) −0.116** (0.048) −0.039 (0.048) 0.097** (0.047) 0.014 (0.009) 0.018** (0.009) 0.006 (0.009) −0.003 (0.009) 0.057*** (0.014) 0.042*** (0.014) 0.022 (0.014) −0.007 (0.014) 0.235** (0.104) 0.256** (0.106) 0.247** (0.107) −0.069 (0.106) 0.130

USDGBP 0.001 (0.001) 0.027 (0.047) −0.075 (0.047) 0.200*** (0.046) −0.001 (0.047) −0.006 (0.005) 0.010* (0.005) −0.000 (0.005) 0.005 (0.005)

0.001 (0.001) 0.027 (0.048) −0.070 (0.047) 0.209*** (0.047) 0.005 (0.048) −0.006 (0.005) 0.008 (0.005) −0.001 (0.005) 0.007 (0.005) 0.020 (0.012) −0.003 (0.012) 0.021* (0.012) 0.017 (0.012) 0.277*** (0.104) −0.109 (0.106) 0.017 (0.106) −0.226** (0.105) 0.056 0.094 USDX

0.000 (0.001) 0.022 (0.048) 0.044 (0.047) −0.078 (0.047) 0.034 (0.047) 0.010** (0.005) 0.001 (0.005) 0.006 (0.005) −0.002 (0.005)

0.027

0.000 (0.001) 0.026 (0.048) 0.064 (0.048) −0.089* (0.048) 0.040 (0.048) 0.012** (0.005) 0.000 (0.005) 0.001 (0.005) −0.001 (0.005) 0.024*** (0.009) −0.009 (0.009) 0.012 (0.009) 0.014 (0.009) 0.168*** (0.064) −0.060 (0.067) 0.074 (0.067) 0.095 (0.066) 0.075 (Continued)

2541

APPLIED ECONOMICS

Table 5. (Continued).

Panel C: Reit ¼ ϕi þ

4 P l¼1

μil Reitl þ

4 P

δil VIXitl þ

l¼1

USDBRL Intercept Ret1 Ret2 Ret3 Ret4 VIXt1 VIXt2 VIXt3 VIXt4 Attent1 Attent2 Attent3 Attent4 Attent1  VIXt1 Attent2  VIXt2 Attent3  VIXt3 Attent4  VIXt4 R2 Intercept Ret1 Ret2 Ret3 Ret4 VIXt1 VIXt2 VIXt3 VIXt4 Attent1 Attent2 Attent3 Attent4 Attent1  VIXt1 Attent2  VIXt2 Attent3  VIXt3

0.001 (0.001) −0.044 (0.050) 0.076 (0.050) 0.070 (0.050) 0.023 (0.049) 0.022*** (0.007) 0.003 (0.008) 0.004 (0.008) 0.003 (0.007)

0.001 (0.001) −0.054 (0.050) 0.061 (0.050) 0.086* (0.050) 0.011 (0.048) 0.018** (0.007) 0.003 (0.007) 0.002 (0.007) 0.004 (0.007) 0.060*** (0.014) 0.033** (0.015) 0.019 (0.015) 0.006 (0.014) 0.182** (0.084) −0.005 (0.084) 0.195** (0.084) −0.154* (0.083) 0.034 0.113 USDINR

0.001 (0.001) 0.106** (0.049) −0.035 (0.049) 0.060 (0.049) −0.060 (0.048) 0.012*** (0.004) 0.004 (0.004) 0.006 (0.004) −0.002 (0.004)

0.001 (0.001) 0.085* (0.049) −0.011 (0.049) 0.037 (0.049) −0.076* (0.046) 0.008* (0.004) 0.004 (0.004) 0.006 (0.004) 0.000 (0.004) 0.027*** (0.005) 0.010* (0.005) −0.003 (0.005) 0.006 (0.005) 0.051*** (0.019) 0.023 (0.019) −0.019

4 P

σil Attenitl þ

l¼1

4 P

θil Attenitl  VIXitl þ eit

l¼1

USDCAD 0.000 (0.001) 0.022 (0.050) −0.163*** (0.050) 0.044 (0.050) −0.071 (0.049) 0.016*** (0.005) 0.010** (0.005) 0.007 (0.005) 0.007 (0.005)

0.000 (0.001) 0.031 (0.050) −0.151*** (0.050) 0.019 (0.051) −0.069 (0.050) 0.017*** (0.005) 0.009* (0.005) 0.006 (0.005) 0.008 (0.005) −0.014 (0.009) −0.002 (0.009) 0.019** (0.009) −0.016* (0.009) 0.036 (0.050) 0.004 (0.050) 0.001 (0.049) 0.078 (0.049) 0.062 0.091 USDJPY

0.000 (0.001) 0.030 (0.049) 0.064 (0.049) −0.052 (0.049) −0.021 (0.048) −0.015*** (0.004) −0.005 (0.005) −0.000 (0.005) −0.002 (0.004)

0.000 (0.001) 0.057 (0.050) 0.060 (0.050) −0.056 (0.050) −0.026 (0.049) −0.011** (0.004) −0.004 (0.005) −0.001 (0.005) −0.003 (0.004) −0.017** (0.007) −0.005 (0.007) 0.010 (0.007) 0.007 (0.007) −0.112*** (0.037) −0.001 (0.037) −0.044

USDCNH −0.000 (0.000) 0.269*** (0.078) 0.139* (0.083) −0.120 (0.083) 0.154* (0.080) −0.000 (0.002) −0.004** (0.002) −0.001 (0.002) −0.004** (0.002)

0.000 (0.000) 0.231*** (0.077) 0.134 (0.082) −0.030 (0.083) 0.278*** (0.081) −0.000 (0.001) −0.003** (0.002) −0.002 (0.002) −0.001 (0.001) 0.012*** (0.002) 0.005** (0.002) 0.002 (0.002) 0.002 (0.002) 0.014 (0.016) 0.006 (0.014) −0.013 (0.014) −0.032** (0.014) 0.177 0.451 USDRUB

0.001 (0.001) −0.121** (0.048) 0.252*** (0.048) 0.068 (0.048) 0.113** (0.047) 0.033*** (0.007) −0.017** (0.008) −0.010 (0.008) 0.004 (0.007)

0.001 (0.001) 0.038 (0.049) 0.258*** (0.048) 0.018 (0.045) 0.109*** (0.042) 0.019*** (0.006) −0.008 (0.007) −0.011* (0.007) −0.001 (0.006) 0.027*** (0.010) 0.016 (0.010) −0.010 (0.010) 0.004 (0.009) 0.140*** (0.037) −0.353*** (0.040) 0.216***

USDEUR 0.000 (0.001) 0.100** (0.048) −0.012 (0.048) 0.074 (0.048) −0.071 (0.048) 0.003 (0.005) 0.005 (0.005) 0.000 (0.005) 0.005 (0.005)

0.000 (0.001) 0.095* (0.048) −0.015 (0.049) 0.085* (0.050) −0.067 (0.049) 0.003 (0.005) 0.003 (0.005) −0.001 (0.005) 0.003 (0.005) 0.011 (0.010) 0.007 (0.010) 0.007 (0.010) −0.000 (0.010) −0.002 (0.048) 0.037 (0.048) 0.041 (0.047) 0.063 (0.047) 0.025 0.035 USDZAR

0.002 (0.001) −0.051 (0.050) −0.118** (0.050) −0.057 (0.050) 0.092* (0.049) 0.025*** (0.008) 0.024*** (0.008) 0.008 (0.008) 0.005 (0.008)

0.001 (0.001) −0.082 (0.050) −0.106** (0.050) −0.043 (0.049) 0.100** (0.048) 0.015** (0.008) 0.012 (0.008) 0.004 (0.008) 0.007 (0.008) 0.051*** (0.014) 0.027* (0.014) 0.021 (0.014) −0.009 (0.014) 0.101 (0.070) 0.236*** (0.069) −0.004

USDGBP 0.000 (0.001) 0.002 (0.048) −0.076 (0.047) 0.213*** (0.047) 0.017 (0.048) 0.009** (0.004) 0.013*** (0.004) −0.000 (0.004) 0.001 (0.004)

0.000 (0.001) −0.003 (0.048) −0.076 (0.047) 0.209*** (0.047) 0.006 (0.048) 0.009** (0.004) 0.013*** (0.004) −0.002 (0.004) 0.000 (0.004) 0.007 (0.012) −0.003 (0.013) 0.014 (0.013) 0.024* (0.012) 0.127 (0.081) 0.032 (0.081) 0.028 (0.081) −0.043 (0.081) 0.066 0.085 USDX

0.000 (0.001) 0.013 (0.048) 0.051 (0.048) −0.077 (0.048) 0.028 (0.048) 0.011*** (0.004) −0.001 (0.004) 0.001 (0.004) 0.005 (0.004)

0.000 (0.001) 0.022 (0.048) 0.055 (0.048) −0.083* (0.048) 0.032 (0.048) 0.009** (0.004) −0.001 (0.004) −0.000 (0.004) 0.005 (0.004) 0.022** (0.009) −0.007 (0.010) 0.013 (0.010) 0.010 (0.009) 0.052 (0.041) −0.021 (0.041) 0.023

(Continued )

2542

L. HAN ET AL.

Table 5. (Continued). Panel C: Reit ¼ ϕi þ

4 P l¼1

μil Reitl þ

4 P

δil VIXitl þ

l¼1

4 P

σil Attenitl þ

l¼1

R2

0.048

θil Attenitl  VIXitl þ eit

l¼1

USDBRL Attent4  VIXt4

4 P

USDCAD

(0.019) −0.004 (0.019) 0.137

USDCNH

(0.037) 0.008 (0.037) 0.090

0.037

0.146

USDEUR

(0.043) 0.023 (0.040) 0.409

USDGBP

(0.070) −0.073 (0.069) 0.130

0.054

(0.041) 0.028 (0.041) 0.058

0.030

This table reports the performance comparisons of prediction models without investor attention terms and models for the joint impacts of investor attention (Attent ) and uncertainty variables on exchange rates (Ret ). As shown in the table, the left column reports the estimation result of prediction model without investor attention term, while the right column reports that of prediction model with it for each currency. Specifically, uncertainty variables include these follow variables: Economic Policy Uncertainty (EPU), TED spread (TED) and Volatility Index (VIX). As revealed in the table, panel A, B and C display the detailed results for the joint impact incorporating EPU, TED and VIX, respectively. In particular, the results contain estimations of lagged exchange rates (Retl ), lagged investor attention (Attentl ), lagged uncertainty variables (EPUtl ,TEDtl and VIXtl ), as well as the interaction terms between lagged investor attention and uncertainty variables (Attentl  EPUtl ,Attentl  TEDtl andAttentl  VIXtl ). The sample for investor attention, exchange rates and uncertainty variables of all currencies except CNH is from January 2007 to September 2015 at a weekly frequency, and that of CNH ranges from May 2012 to September 2015 at a weekly frequency. The standard errors, which are corresponded to the estimate coefficients, are presented in parenthesis. *, **, *** Denote significance at 10%, 5% and 1% level, respectively.

of investor attention for all currencies in the out-ofsample analysis. The related indicators applied to the out-of-sample analysis are presented as follows: PT ðRet  Ret Þ R2 ¼ 1  Pt¼1 2 T t¼1 ðRet  Ret Þ ^

Ret ¼ ϕ þ

t¼1

4 X

ρl Attentl þ et :

(13)

l¼1

By incorporating the estimation coefficients τ, ρ and ϕ into Equation (12), which is obtained from the OLS regression of Equation (13) based on the data from week 1 to week t, we can find the forecast

(9)

ENCNEW ¼ T 

τl Retl þ

l¼1

2

PT

4 X



^

2



ðRet  Ret Þ  ðRet  Ret ÞðRet  Ret Þ PT t¼1

^

2

;

(10)

ðRet  Ret Þ

^

exchange rate at week t þ 1, namely, Retþ1. Then, t¼1 ðRet  Ret Þ  t¼1 ðRet  Ret Þ ; we construct a series of forecast exchange rates in MSEF ¼ T  ^ 2 PT the out-of-sample analysis by constantly recalculatt¼1 ðRet  Ret Þ ing and updating the coefficients until completely (11) computing the out-of-sample forecasts. Here, we select the data from January 2007 to December where Ret is the exchange rate at week t, which is the 2009 at a weekly frequency for all currencies except same as the previous in-sample analysis. Let the CNH, a total of 155 observations, as the initial estiforecasted exchange rate of the unrestricted model mation interval of equation (12). The initial interval at week t be obtained from the following equation: of coefficient estimation for CNH is defined as being available from May 2012 through December 2012 at 4 4 X^ X^ ^ ^ Ret ¼ ϕ þ τl Retl þ ρl Attentl : (12) a weekly frequency, a total of 34 observations. Thus, l¼1 l¼1 we predict returns for all currencies except CNH at the first week of January 2010 based on the initial The coefficients of this equation depend on the estimation interval. We also achieve forecast returns recursive OLS estimation of the predictive Equation for CNH at the first week of January 2012. Using this (13) by using data from the initial week up to week re-estimation procedure, we construct 143 forecasts t  1. In particular, the predictive Equation (13) is of future returns for CNH and 300 forecasts for the stated as follows: PT

2

PT

^

2

APPLIED ECONOMICS

other currencies. As for Ret , defined as the historical average return, it derives from the forecasted exchange rate of the benchmark model at week t. Moreover, it must be noted that Tin Equations (9) to (11) are all the size of the out-of-sample forecast. We next interpret in detail the specific meanings of the out-of-sample statistical indicators. First, Equation (9) defined as the out-of-sample R2 is raised by Welch and Goyal (2008) and Campbell and Thompson (2008), which indicates the comparison of the mean squared forecast error of the unrestricted model with that of the benchmark model. A positive out-of-sample R2 statistic denotes that the forecast error reduced by the benchmark model is less than the unrestricted model, while a negative R2 indicates the opposite. Second, the ENCNEW statistic depicted by Equation (10) is rooted in Clark and McCracken (2001). The null hypothesis of this statistic is the predictive power of the benchmark model is superior to the unrestricted model. Third, the forecast accuracy of the MSE-F statistic is equal to the statistical approach verified by McCracken (2007), and the null hypothesis of this statistic is that the mean squared forecast error of the unrestricted model exceeds that of the benchmark model. Hence, the null hypothesis should be rejected when both the ENC-NEW statistic and the MSE-F statistic are significant. Finally, we also include the MSFE-adjusted statistic proposed by Clark and West (2007), which is a t-statistic based on regression for ^

the equation of ft ¼ ðRet  Ret Þ2  ½ðRet  Ret Þ2  ^

ðRet  Ret Þ2  on a constant. For the null hypothesis of the MSFE-adjusted statistic, it indicates that the MSFE of the benchmark model is equal to that of the unrestricted model, while the alternative hypothesis is that the unrestricted model has less MSFE than the benchmark model. Moreover, the one-sided p-value is provided to evaluate the significance of this statistic. If the one-sided p-value of the MSFE-statistic is smaller than 10%, the null hypothesis should be rejected. We further provide the out-of-sample forecast performance comparisons of prediction models with investor attention versus models without them, which is similar to the above in-sample analysis. Here, Equation (6) is viewed as the prediction

2543

model without attention in the out-of-sample forecast, while Equation (13) is the basic prediction model with attention, as previously mentioned. The related results of these performance comparisons are summarized in Table 6. As shown in the table, we can find that prediction models incorporating investor attentions have good predictability in the out-of-sample forecast evaluations for all emerging currencies, while in the models without attention, it is almost impossible to predict the future performances of all currencies. It should be noted that the prediction model without attention for CNH can significantly forecast its future performance in a four lag specification. The predictive power, however, is evidently weaker than that of the prediction model with attention. Meanwhile, similar findings emerge for the two lag specification. In addition, we can also obtain detailed results about the out-of-sample analysis for the predictive power of the impact of investor attention on the currency performance from Table 6. Panel A of the table exhibits the four statistics for all currencies based on the four lag specification forecast model. Generally, investor attention has noteworthy predictive power on future returns for emerging currencies, while this phenomenon does not exist in the currencies from the developed FX markets of the G7 group. As shown in panel A of Table 6, the R2 statistics of the forecast models for the emerging currencies are all positive, indicating that the unrestricted forecast models for these currencies decrease more forecast errors than the benchmark forecast models. It implies that the forecast models for the emerging currencies beat the historical average forecasts based on the out-of-sample predictability analysis. The ENC-NEW statistics and the MSE-F statistics are all significant at the 1% level for the emerging currencies, which means that the prediction performance of the unrestricted forecast model is better than the benchmark model for these currencies. In other words, it actually suggests that there is great predictability in investor attention regarding the corresponding emerging currency returns. For the MSFE-adjusted statistics, the one-sided p-values are all less than 10% for the emerging currencies. Specifically, the p-values of USD/BRL, USD/CNH,

2544

L. HAN ET AL.

Table 6. The performance comparisons of the out-of-sample forecasts on exchange rates with and without investor attention. Panel A: Reit ¼ ϕi þ

4 P

τ il Reitl þ

l¼1

4 P l¼1

Ⅰ. USD/BRL WoA Atten Ⅱ. USD/CAD WoA Atten Ⅲ. USD/CNH WoA Atten Ⅳ. USD/EUR WoA Atten Ⅴ. USD/GBP WoA Atten Ⅵ. USD/INR WoA Atten Ⅶ. USD/JPY WoA Atten Ⅷ. USD/RUB WoA Atten Ⅸ. USD/ZAR WoA Atten Ⅹ. USDX WoA Atten Panel B: Reit ¼ ϕi þ Ⅰ. USD/BRL WoA Atten Ⅱ. USD/CAD WoA Atten Ⅲ. USD/CNH WoA Atten Ⅳ. USD/EUR WoA Atten Ⅴ. USD/GBP WoA Atten Ⅵ. USD/INR WoA Atten Ⅶ. USD/JPY WoA Atten Ⅷ. USD/RUB WoA Atten Ⅸ. USD/ZAR WoA Atten Ⅹ. USDX WoA Atten

2 P l¼1

τ il Reitl þ

2 P l¼1

ρil Attenitl þ eit R2 (%)

ENC-NEW

MSE-F

MSFE-adjusted

−1.698 5.781

−0.065 17.619***

−5.025 18.467***

−0.028 3.233***

−6.376 −14.383

−0.544 3.533***

3.330 9.115

12.098*** 20.168***

−18.042 −37.848 4.960*** 14.442***

−0.109 0.513 1.965** 2.467***

−4.091 −5.009

0.037 0.207

−11.831 −14.357

0.009 0.048

−12.704 −14.026

−5.894 −5.243

−33.930 −37.026

−1.219 −0.963

−0.124 7.390

3.794*** 34.479***

−1.370 −6.108

0.296 9.071***

−13.053 3.857

−0.372 24.018***

0.922 2.779***

−4.067 −17.328

0.122 1.034

9.159*** 24.902***

−34.754 12.074***

0.381 1.298*

−1.253 6.209

1.578* 26.652***

−3.725 19.925***

−1.037 −3.638

0.412 2.344***

−3.091 −10.565

R2 (%)

ENC-NEW

MSE-F

MSFE-adjusted

−0.272 5.913

0.592 15.753***

−0.817 18.916***

0.424 3.437***

0.569 3.841*** 0.196 0.515

ρil Attenitl þ eit

−3.013 −10.010

0.066 −2.183

4.510 11.744

11.976*** 19.182***

0.369 0.014

2.278** 2.479***

−8.805 −27.389 6.801*** 19.162***

0.017 −0.390 1.841** 2.653***

1.113 0.042

1.150 1.012 −1.263 −1.510

−2.520 −3.227

−2.382 −2.940

−7.398 −9.407

1.706 10.616

3.814*** 33.426***

5.226*** 35.751***

−0.370 −4.472

0.825 9.714***

−6.028 9.829

1.725** 3.273***

−1.110 −12.885

0.462 1.096

14.049 32.733***

−17.114 32.811***

0.616 1.629*

−1.508 5.840

−0.757 19.901***

−4.473 18.669***

−0.405 3.458***

−1.503 −0.439

−0.716 2.838***

−4.458 −1.316

−0.377 0.998

This table reports the out-of-sample performance comparisons of prediction models on exchange rates with and without investor attention. As shown in the table, the first row reports the out-of-sample forecast performance of model without investor attention (WoA), while the second row reports that of model with investor attentions (Atten) for each currency pair. Moreover, panel A summarizes the above-mentioned out-of-sample performance comparisons on four lag specification, and panel B further presents those on two lag specification. The column headings denote statistical indicators of the out-of-sample forecast performance. Specifically, the R2 statistic is derived from Campbell and Thompson (2008) and Welch and Goyal (2008). The ENC-NEW statistical indicator is the forecast statistic for encompassing test suggesting by Clark and McCracken (2001). The MSE-F statistical indicator is the equal forecasting statistic for accuracy test proposed by McCracken (2007). The MSFE-adjusted statistical indicator is the forecast error test statistic of Clark and West (2007). *, **, *** Denote significance at 10%, 5% and 1% level, respectively.

APPLIED ECONOMICS

USD/INR and USD/ZAR are all highly significant at the 1% level, while that of USD/RUB is marginally significant at the 10% level. It eventually accepts the alternative hypothesis that the unrestricted forecast model has less MSFE than the benchmark model. The four statistics presented at panel A, however, do not simultaneously exhibit positive R2, significant ENC-NEW statistic, MSE-F statistic and MSFEadjusted statistic for the currencies from the developed FX markets of the G7 group. We attempt to interpret these results below. The market efficiency of developed markets is relatively stronger than that of the emerging markets. According to Malkiel and Fama (1970), the returns are hardly predictable and are likely to follow a random walk in that the information flows are usually transparent and immediate in these efficient markets. It implies that the unpredictability of currency returns in the developed markets may be ascribed to the relatively higher market efficiency. Thus, only the investor attention on emerging currencies has remarkable predictive power. It sheds light on better performance than that of the currencies from the developed FX markets, where out-of-sample return predictability does not exist. We also take account of the two lag specification as a robustness check. The related results are presented in panel B of Table 6, which supports the conclusions we summarize in the four lag specification. For all emerging currencies, the R2 statistics are positive, while both the ENC-NEW statistics and the MSE-F statistics are highly significant, and the onesided p-values of the MSFE-adjusted statistics are all less than 10%. For the currencies circulated in the developed FX markets, the above-mentioned statistical characteristics are not concurrently established. In general, the predictive power of investor attention on the future exchange rate is statistically significant in the emerging FX markets when using the outof-sample analysis. According to the results of the R2 statistic, the ENC-NEW statistic, the MSE-F statistic and the MSFE-adjusted statistic for all currencies, both the four lag specification and the two lag specification forecast models perfectly verify the results. Furthermore, the results of the out-of-sample analysis are slightly different from the in-sample analysis. All investor attention can forecast the corresponding exchange rates in the in-sample analysis, while the

2545

predictive power of attention only exists in the emerging markets based on the out-of-sample analysis. VI. The economic value of currency performance forecasts based on investor attention Following Ferreira and Santa-Clara (2011), Neely et al. (2014) and Yin and Yang (2016), among others, we perform a rigorous analysis of the economic value of the predictive power of investor attention on currency performances by calculating the certainty equivalent return (CER), which is interpreted as the risk-free rate of return that a riskaverse investor with mean-variance preference, who allocates across currencies and risk-free assets weekly by employing the currency return forecasts, is willing to accept rather than adopting the given risky currency portfolio. We then consider the CER gain to measure the difference between the CER of predictive regression forecast of the currency returns based on incorporating the important variables for investor attention and that of the historical average forecast. To more completely gauge the economic value of these currency return forecasts, we further include the Sharpe ratio and relative average turnover. Specifically, the weekly Sharpe ratio is the mean risky portfolio return based on return forecast in excess of the risk-free rate divided by the standard deviation of the excess risky portfolio return. The relative average turnover is derived from the average weekly turnover for the portfolio based on the predictive regression forecast of currency returns divided by the average weekly turnover for the portfolio based on the historical average forecast. The related results of economic value for the currency performance forecasts based on investor attention are presented in Table 7. It reveals that the portfolios based on the predictive regression forecast of currency returns with attention clearly outperform those without attention in the first place. Specifically, the CER gains for the predictive regression forecasts of emerging currency returns with attention are obviously larger than those without attention. Similar phenomena also exist in the comparisons of the Sharpe ratios and the net-of-transactions-costs CER gains for these emerging currency return

2546

L. HAN ET AL.

Table 7. The comparisons of portfolio performance measures with and without investor attention. Predictor Ⅰ. USD/BRL HA WoA Atten Ⅱ. USD/CAD HA WoA Atten Ⅲ. USD/CNH HA WoA Atten Ⅳ. USD/EUR HA WoA Atten Ⅴ. USD/GBP HA WoA Atten Ⅵ. USD/INR HA WoA Atten Ⅶ. USD/JPY HA WoA Atten Ⅷ. USD/RUB HA WoA Atten Ⅸ. USD/ZAR HA WoA Atten Ⅹ. USDX HA WoA Atten

Δ (ann.) (%)

Sharpe ratio

Relative average turnover

Δ (ann.), cost = 10 bps (%)

−1.530 0.858 1.660

0.134 0.101 0.136

1.114 12.791 9.256

−1.588 0.189 1.187

−4.206 −0.884 0.477

0.058 −0.028 0.065

0.595 17.424 23.556

−4.236 −1.384 −0.228

−0.313 0.671 1.389

−0.027 0.028 0.081

1.057 11.850 9.892

−0.362 0.085 0.897

−3.386 −2.348 −2.078

0.060 0.005 0.005

2.296 15.286 8.830

−3.503 −4.068 −3.022

−6.763 −0.927 −1.943

0.013 −0.008 −0.019

2.953 21.587 16.637

−6.920 −4.118 −4.333

−1.637 1.153 1.264

0.089 0.103 0.103

23.922 1.979 1.662

−2.847 0.064 0.291

−4.559 −0.132 −0.179

−0.074 −0.024 −0.079

0.134 39.320 19.032

−4.566 −0.390 −0.302

2.269 1.718 2.455

0.130 0.138 0.146

9.643 4.229 4.563

1.776 0.065 0.631

0.520 1.333 2.329

0.121 0.138 0.160

2.553 13.742 9.300

0.389 −0.331 1.246

−3.878 0.447 −0.424

0.044 0.053 0.028

1.542 19.029 13.008

−3.952 −0.953 −1.357

This table reports the comparisons of portfolio performance measures for a mean-variance investor with and without investor attention. Here, we hypothesize the investor with a relative risk-aversion coefficient of five who weekly allocates across currencies and risk-free assets choose a historical average forecast (HA), or predictive regression currency return without and with investor attention forecast. As shown in the table, the first row reports the portfolio performance measure of historical average forecast, while the second and third row reports that of prediction model without and with investor attention (WoA andAtten) for each currency pair, respectively. The column headings denote economic indicators of these portfolio performance measures. The Δ (ann.) statistic in the second column presents the annualized certainty equivalent return (CER) gains for an investor who using either the predictive regression forecast with investor attention or that without investor attention rather than the historical average forecast. For the historical average forecast, the table reports the CER level. The Sharpe ratio in the third column is the mean portfolio return in excess of the risk-free rate divided by the standard deviation of the excess portfolio return. The relative average turnover in the fourth column reports that the average weekly turnover for the portfolio based on the predictive regression forecast divided by the average turnover for the portfolio based on the historical average forecast. The Δ (ann.), cost = 10 bps statistic reports the net annualized CER gains by assuming a proportional transactions cost of 10 basis points per transaction.

forecasts. Moreover, the portfolio performance measures also demonstrate that the currency return forecasts with attention have substantial economic value,

especially for the emerging currencies, which is identical to the results of the out-of-sample forecast summarized in Section V. In particular, the Δ (ann.) statistic reports the annualized CER gains for an investor with a relative risk coefficient of five who depends on the predictive regression forecast of currency returns with investor attention instead of the historical average forecast. As shown in the table, the CER gains in the second column are all positive for the predictive regression forecasts of emerging currency returns with attention. Meanwhile, these forecasts provide CER gains of more than 100 basis points, with the forecast of USD/RUB generating a maximum of 245.5 basis points. Moreover, the Sharpe ratios focusing on the emerging currency return forecasts with attention exhibited in the third column produce higher ratios than their corresponding historical average forecasts, reaching the highest ratio of 0.160 for the performance forecast of USD/ZAR. The average turnovers of the risky portfolios based on the emerging currency return forecasts with attention appear to be approximately 2–10 times higher than their corresponding historical average portfolios. Despite the relatively high turnover, the net-of-transactions-costs CER gains summarized in the final column, where the costs are computed using the weekly turnover measures and assuming a proportional transaction cost equal to 10 basis points per transaction, are still positive and as high as 124.6 basis points for all of the emerging currency return forecasts with attention. Generally, the improvements of the predictive power for incorporating attention to forecast emerging currency returns are also economically significant from the perspective of the asset allocation exercise.

VII. Robustness check The analysis of real exchange rate

In what follows, we investigate the impact of investor attention on the real exchange rate. Our robustness check indicates that the main results of the article are robust to this alternative specification. We repeat our in-sample regression analysis and out-of-sample predictability analysis using the alternative real exchange rate. In summary, the predictive performance comparisons of the in-sample and outof-sample analysis for the real exchange rate indicate

APPLIED ECONOMICS

that it is necessary to incorporate the investor attention terms in the prediction models instead of simply relying on the models without attention, which is almost consistent with the results of the nominal exchange rate. Moreover, we find that the in-sample analysis of the real exchange rate, presented in panel A of Table 8, generates similar results to that of the nominal exchange rate. In particular, the alternative results are summarized as follows. Investor attention has a prominent effect on the real exchange rate for almost all currencies. The effect, emerging as statistically significant, is mostly positive in the first lag and can persist for a long time (at least two weeks), especially for emerging currencies. In terms of the real exchange rate forecast, panel B of Table 8 reports that attention exhibits a noted predictive power on the real exchange rate for emerging currencies, while this role is not embodied in currencies from developed FX markets. In other words, the predictability of attention on the nominal exchange rate argued in Section V is robust according to this analysis. The predictive power of orthogonalized attention

For robustness, we further checked whether the orthogonalized investor attention, obtained by taking the residuals from the regressions of the attention on lagged exchange rates, retains predictive power based on the in-sample and out-of-sample analysis. Generally, the estimation results (not reported) are similar to those for the impacts of investor attention illustrated in subsections ‘Sign, timing, and persistence of investor attention effects’ to ‘The joint impact of investor attention and uncertainty’. Specifically, the results regarding the performance comparisons of prediction models with and without orthogonalized attention indicate that the predictive power of models with orthogonalized attention is evidently superior to that of models without them, especially for emerging currencies. Further, almost all of the orthogonalized attention has a significantly positive impact on their corresponding exchange rates according to the detailed results of the insample VAR analysis. Analogous evidence is also embodied in the asymmetric effects and the joint effects of the in-sample analysis. For almost all of the currencies, highly pronounced predictive power

2547

clearly appears for the impacts of orthogonalized attention based on discriminating among the movements of past exchange rates. It also exerts a reinforced impact on exchange rates during periods of high uncertainty in line with the joint effects of orthogonalized attention and uncertainty variables. For the out-of-sample evaluations and portfolio performance measures, it has statistical significance and economic implications for the good predictability of orthogonalized attention, which dominate in the emerging currencies. Furthermore, we also consider the predictive power of the impact of orthogonalized attention on real exchange rates. Apparently, it generates similar results to those of nominal exchange rates. Thus, it is robust for the related results about the predictive power of investor attention on exchange rates according to the in-sample and outof-sample analysis for estimating the specific performance of prediction models with orthogonalized investor attention. VIII. Conclusion In this article, we investigate empirically whether investor attention measured by Google SVI has an impact on the performance and predictability of several currencies. We first demonstrate that investor attention significant influences the currency returns based on the in-sample analysis. First, investor attention Granger causes the returns of all currencies except EUR and GBP. Second, the impact of attention on returns is sufficiently remarkable for all currencies, not including EUR. Moreover, the effect is predominantly positive and is typically long-term for the emerging currencies. Third, there exists an asymmetric effect of the impact for all currencies based on the changes in past currency return. It must be noted that the asymmetric effect emerges rapidly and lasts for a long period under normal circumstances. Fourth, the joint impact of attention and uncertainty is significantly positive in general. It denotes that the uncertainty variables can effectively drive the impact of attention. Aside from the inspiring results of the in-sample analysis, we also confirm the significant predictability of investor attention on future currency returns in the out-of-sample analysis. Specifically, for the representative emerging currencies, the level of attention devoted to them significantly affects the

2548

L. HAN ET AL.

Table 8. The performance comparisons of prediction models on real exchange rates with and without investor attention. Panel A: in sample VAR analysis USDBRL Intercept Ret1 Ret2 Ret3 Ret4 Attent1 Attent2 Attent3 Attent4 R2 Intercept Ret1 Ret2 Ret3 Ret4

0.001 (0.001) −0.008 (0.047) 0.069 (0.048) 0.081* (0.048) −0.028 (0.048)

0.001 (0.001) −0.023 (0.048) 0.066 (0.048) 0.081* (0.048) −0.032 (0.047) 0.056*** (0.016) 0.029* (0.017) 0.025 (0.017) 0.005 (0.016) 0.012 0.042 USDINR

0.001 (0.001) 0.135*** (0.047) −0.021 (0.048) 0.066 (0.048) −0.072 (0.047)

Attent1 Attent2 Attent3 Attent4 R2

0.025

0.001 (0.001) 0.106** (0.047) −0.012 (0.047) 0.054 (0.047) −0.073 (0.045) 0.027*** (0.005) 0.010** (0.005) −0.000 (0.005) 0.008 (0.005) 0.105

Panel B: out-of-sample forecast analysis Ⅰ. USD/BRL R2 (%) WoA −1.762 Atten 2.319 Ⅱ. USD/CAD WoA −6.305 Atten −14.558 Ⅲ. USD/CNH WoA 2.976 Atten 7.444 Ⅳ. USD/EUR WoA −2.175 Atten −3.376 Ⅴ. USD/GBP WoA −11.814 Atten −13.897 Ⅵ. USD/INR WoA −0.131 Atten 7.374 Ⅶ. USD/JPY WoA −1.369 Atten −6.100 Ⅷ. USD/RUB WoA −13.110 Atten 3.844 Ⅸ. USD/ZAR WoA −1.235 Atten 6.179

USDCAD 0.000 (0.001) 0.099** (0.047) −0.137*** (0.047) 0.052 (0.047) −0.084* (0.047)

0.000 (0.001) 0.106** (0.047) −0.133*** (0.047) 0.039 (0.047) −0.075 (0.047) −0.008 (0.009) 0.002 (0.009) 0.023** (0.009) −0.008 (0.009) 0.030 0.050 USDJPY

−0.000 (0.001) 0.076 (0.047) 0.056 (0.047) −0.055 (0.047) −0.016 (0.047)

0.012

−0.000 (0.001) 0.082* (0.048) 0.075 (0.048) −0.062 (0.048) −0.000 (0.047) −0.025*** (0.006) −0.006 (0.007) 0.005 (0.007) 0.007 (0.006) 0.050

USDCNH −0.000 (0.000) 0.239*** (0.078) 0.162** (0.080) −0.163** (0.081) 0.119 (0.079)

0.000 (0.000) 0.247*** (0.077) 0.131 (0.081) −0.080 (0.081) 0.227*** (0.075) 0.015*** (0.002) 0.007*** (0.002) 0.003 (0.002) 0.002 (0.002) 0.106 0.395 USDRUB

0.001 (0.001) −0.108** (0.047) 0.215*** (0.047) 0.067 (0.047) 0.109** (0.047)

0.082 ENC-NEW −0.555 7.803*** −0.553 3.493*** 10.833*** 16.804*** 2.444*** 2.687*** −6.049 −4.458 3.786*** 34.489***

0.001 (0.001) −0.044 (0.047) 0.251*** (0.047) 0.008 (0.047) 0.094** (0.044) 0.067*** (0.009) −0.032*** (0.009) 0.007 (0.010) 0.005 (0.009) 0.244

USDEUR 0.000 (0.001) 0.124*** (0.047) −0.019 (0.047) 0.058 (0.047) −0.071 (0.047)

0.000 (0.001) 0.125*** (0.047) −0.025 (0.048) 0.068 (0.049) −0.077 (0.048) 0.010 (0.010) −0.003 (0.010) 0.011 (0.010) −0.004 (0.010) 0.022 0.027 USDZAR

0.001 (0.001) 0.018 (0.047) −0.079* (0.047) −0.047 (0.047) 0.094** (0.047)

0.019 MSE-F −5.213 7.147*** −17.854 −38.252

0.001 (0.001) −0.033 (0.047) −0.086* (0.048) −0.019 (0.047) 0.130*** (0.047) 0.061*** (0.014) 0.045*** (0.014) 0.029** (0.014) −0.003 (0.014) 0.078

USDGBP 0.001 (0.001) 0.030 (0.047) −0.081* (0.046) 0.176*** (0.046) −0.030 (0.047)

0.001 (0.001) 0.029 (0.047) −0.076 (0.047) 0.168*** (0.046) −0.036 (0.047) 0.023* (0.013) −0.010 (0.013) 0.018 (0.013) 0.020 (0.013) 0.038 0.056 USDX

0.000 (0.001) 0.028 (0.047) 0.049 (0.047) −0.079* (0.047) 0.038 (0.047)

0.010

0.000 (0.001) 0.031 (0.047) 0.051 (0.047) −0.082* (0.047) 0.046 (0.047) 0.025*** (0.009) −0.005 (0.009) 0.012 (0.009) 0.014 (0.009) 0.036 MSFE-adjusted −0.281 2.228** −0.110 0.508

4.417*** 11.582***

1.818** 2.046**

−6.407 −9.829

0.676 0.651

−31.802 −36.725

−1.425 −0.825

−0.392 23.962***

0.920 2.779***

−4.064 −17.305

0.122 1.034

9.044*** 24.880***

−34.888 12.032***

0.377 1.298*

1.545* 26.473***

−3.673 19.824***

0.295 9.080***

0.562 3.839***

(Continued )

APPLIED ECONOMICS

2549

Table 8. (Continued). Panel B: out-of-sample forecast analysis Ⅹ. USDX WoA −1.041 Atten −3.641

−3.102 −10.574

0.410 2.340***

0.195 0.514

This table reports the in-sample and out-of-sample performance comparisons of prediction models on real exchange rates with and without investor attention. Specifically, panel A summarizes the in-sample VAR estimations for these comparisons. The detailed regression results hinge upon the following model: 4 4 X X

Reit ¼ ϕi þ

τil Reitl þ

l¼1

ρil Attenitl þ eit :

l¼1

Here, Reit and Attenit denotes real exchange rate and investor attention of currency i at week t, respectively. The standard errors, which are corresponded to the estimate coefficients, are presented in parenthesis. As before, the results of panel B summarize the out-of-sample forecast performances for these comparisons based on four lag specification. In particular, the first row of panel B reports the out-of-sample performance without investor attention (WoA), while the second row reports that with investor attention (Atten) for each currency pair. The definitions of all forecast performance indicators in panel B, namely, the R2, ENC-NEW, MSE-F and MSFE-adjusted, are identical to those in Table 6. *, **, *** Denote significance at 10%, 5% and 1% level, respectively.

return predictability, while this regulation is not suitable for the representative currencies from developed FX markets. We attribute the unpredictably of currency returns to relatively strong market efficiency. To sum up, these findings emphasize the importance of incorporating investor attention when forecasting emerging currency returns. In closing, we believe that our efforts provide further evidence for the literature on attention where investor attention is an important factor in explaining financial phenomena in the markets. In future studies, we plan to carefully investigate the cross effects of investor attention for specific currencies on other currency returns, especially the contagion spillover of attention from developed FX markets on emerging currency returns, in the hopes that it will be helpful to search out new channels through which financial contagion arises in the FX markets. Acknowledgement This research is financially supported by the National Natural Science Foundation of China under projects Nos. 71671193, 71401193, and 71371022, and the Program for Innovation Research in Central University of Finance and Economics.

Disclosure statement No potential conflict of interest was reported by the authors.

Funding This research is financially supported by the National Natural Science Foundation of China under projects Nos.

71671193, 71401193, and 71371022, and the Program for Innovation Research in Central University of Finance and Economics.

References Agarwal, S., and R. Hauswald. 2010. “Distance and Private Information in Lending.” Review of Financial Studies 23: 2757–2788. Andersen, T. G., T. Bollerslev, F. X. Diebold, and C. Vega. 2003. “Micro Effects of Macro Announcements: Real-Time Price Discovery in Foreign Exchange.” American Economic Review 93: 38–62. Andrei, D., and M. Hasler. 2014. “Investor Attention and Stock Market Volatility.” Review of Financial Studies 28: 33–72. Bacchetta, P., and E. Van Wincoop, 2005. Rational Inattention: A Solution to the Forward Discount Puzzle. NBER Working Paper, No. 11633. Bacchetta, P., and E. Van Wincoop. 2010. “Infrequent Portfolio Decisions: A Solution to the Forward Discount Puzzle.” American Economic Review 100: 870–904. Bacchetta, P., and E. Van Wincoop. 2013. “On the Unstable Relationship between Exchange Rates and Macroeconomic Fundamentals.” Journal of International Economics 91: 18–26. Baker, S. R., N. Bloom, and S. J. Davis. 2016. “Measuring Economic Policy Uncertainty.” The Quarterly Journal of Economics 131: 1593–1636. Balke, N. S., J. Ma, and M. E. Wohar. 2013. “The Contribution of Economic Fundamentals to Movements in Exchange Rates.” Journal of International Economics 90: 1–16. Bank, M., M. Larch, and G. Peter. 2011. “Google Search Volume and Its Influence on Liquidity and Returns of German Stocks.” Financial Markets and Portfolio Management 25: 239–264. Barber, B. M., and T. Odean. 2008. “All that Glitters: The Effect of Attention and News on the Buying Behavior of

2550

L. HAN ET AL.

Individual and Institutional Investors.” Review of Financial Studies 21: 785–818. Barber, B. M., T. Odean, and N. Zhu. 2009. “Do Retail Trades Move Markets?” Review of Financial Studies 22: 151–186. Baruník, J., E. Kočenda, and L. Vácha. 2016. “Asymmetric Connectedness on the U.S. Stock Market: Bad and Good Volatility Spillovers.” Journal of Financial Markets 27: 55–78. Burnside, C., B. Han, D. Hirshleifer, and T. Wang. 2011. “Investor Overconfidence and the Forward Premium Puzzle.” The Review of Economic Studies 78: 523–558. Campbell, J. Y., and S. B. Thompson. 2008. “Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?” Review of Financial Studies 21: 1509–1531. Clark, T. E., and K. D. West. 2007. “Approximately Normal Tests for Equal Predictive Accuracy in Nested Models.” Journal of Econometrics 138: 291–311. Clark, T. E., and M. W. McCracken. 2001. “Tests of Equal Forecast Accuracy and Encompassing for Nested Models.” Journal of Econometrics 105: 85–110. Da, Z., J. Engelberg, and P. Gao, 2010. In Search of Fundamentals. Working paper, University of Notre Dame and University of North Carolina at Chapel Hill. http://dx. doi.org/10.2139/ssrn.1589805 Da, Z., J. Engelberg, and P. Gao. 2011. “In Search of Attention.” The Journal of Finance 66: 1461–1499. Da, Z, J Engelberg, and P. Gao. 2011. “In Search Of Attention [j].” The Journal Of Finance 66 (5): 1461-1499. Da, Z, J Engelberg, and P. Gao. 2015. “In Search Of Attention [j].” The Journal Of Finance 66 (5): 1461-1499. Da, Z., J. Engelberg, and P. Gao. 2015. “The Sum of All FEARS Investor Sentiment and Asset Prices.” Review of Financial Studies 28: 1–32. Dellavigna, S., and J. M. Pollet. 2009. “Investor Inattention and Friday Earnings Announcements.” The Journal of Finance 64: 709–749. Dimpfl, T., and S. Jank. 2016. “Can Internet Search Queries Help to Predict Stock Market Volatility?” European Financial Management 22: 171–192. Drake, M. S., D. T. Roulstone, and J. R. Thornock. 2011. “Investor Information Demand: Evidence from Google Searches around Earnings Announcements.” Journal of Accounting Research 50: 1001–1040. Engel, C., N. C. Mark, and K. D. West, 2007. Exchange Rate Models are not as Bad as You Think. NBER Working Paper, No. 13318, 22: 381-473. Available at http://www. journals.uchicago.edu/doi/abs/10.1086/ma.22.25554969 Ferreira, M. A., and P. Santa-Clara. 2011. “Forecasting Stock Market Returns: The Sum of the Parts Is More than the Whole.” Journal of Financial Economics 100: 514–537. Fratzscher, M. 2009. “How Successful Is the G7 in Managing Exchange Rates?” Journal of International Economics 79: 78–88. Freixas, X., and R. Kihlstrom. 1984. “Risk Aversion and Information Demand.” In Bayesian Models in Economic Theory, Eds. M. Boyer and R. E. Kihlstrom. Amsterdam, North Holland: Elsevier.

Funke, M., C. Shu, X. Cheng, and S. Eraslan. 2015. “Assessing the CNH-CNY Pricing Differential: Role of Fundamentals, Contagion and Policy.” Journal of International Money and Finance 59: 245–262. Galloppo, G., and V. Paimanova. forthcoming. “The Impact of Monetary Policy on BRIC Markets Asset Prices during Global Financial Crises.” The Quarterly Review of Economics and Finance. Goddard, J., A. Kita, and Q. Wang. 2015. “Investor Attention and FX Market Volatility.” Journal of International Financial Markets, Institutions and Money 38: 79–96. Hasler, M., and C. Ornthanalai, 2015. Fluctuating Attention and Contagion: Theory and Evidence from the U.S. Equity Market. Working Paper.Available at SSRN: https://ssrn. com/abstract=2551085 or http://dx.doi.org/10.2139/ssrn. 2551085 Huang, L., and H. Liu. 2007. “Rational Inattention and Portfolio Selection.” The Journal of Finance 62: 1999– 2040. Huberman, G., and T. Regev. 2001. “Contagious Speculation and a Cure for Cancer: A Nonevent that Made Stock Prices Soar.” The Journal of Finance 56: 387–396. Jiang, G. J., and I. Lo. 2014. “Private Information Flow and Price Discovery in the US Treasury Market.” Journal of Banking & Finance 47: 118–133. Leduc, S., and Z. Liu. 2016. “Uncertainty Shocks are Aggregate Demand Shocks.” Journal of Monetary Economics 82: 20–35. Loring, G., and B. Lucey. 2013. “An Analysis of Forward Exchange Rate Biasedness across Developed and Developing Country Currencies: Do Observed Patterns Persist Out of Sample?” Emerging Markets Review 17: 14–28. Malkiel, B. G., and E. F. Fama. 1970. “Efficient Capital Markets: A Review of Theory and Empirical Work.” The Journal of Finance 25: 383–417. McCracken, M. W. 2007. “Asymptotic for Out of Sample Tests of Granger Causality.” Journal of Econometrics 140: 719–752. Meese, R. A., and K. Rogoff. 1983. “Empirical Exchange Rate Models of the Seventies: Do They Fit Out of Sample?” Journal of International Economics 14: 3–24. Merton, R. C. 1987. “A Simple Model of Capital Market Equilibrium with Incomplete Information.” The Journal of Finance 42: 483–510. Mondria, J., and C. Quintana-Domeque. 2013. “Financial Contagion and Attention Allocation.” The Economic Journal 123: 429–454. Neely, C. J., D. E. Rapach, J. Tu, and G. Zhou. 2014. “Forecasting the Equity Risk Premium: The Role of Technical Indicators.” Management Science 60: 1772–1791. Obstfeld, M., and K. Rogoff. 2000. “New Directions for Stochastic Open Economy Models.” Journal of International Economics 50: 117–153. Peng, L., and W. Xiong. 2006. “Investor Attention, Overconfidence and Category Learning.” Journal of Financial Economics 80: 563–602.

APPLIED ECONOMICS

Rogoff, K. 2007. “Global Imbalances and Exchange Rate Adjustment.” Journal of Policy Modeling 29: 705–709. Sager, M. J., and M. P. Taylor. 2006. “Under the Microscope: The Structure of the Foreign Exchange Market.” International Journal of Finance & Economics 11: 81–95. Sims, C. A. 2003. “Implications of Rational Inattention.” Journal of Monetary Economics 50: 665–690. Smith, G. P. 2012. “Google Internet Search Activity and Volatility Prediction in the Market for Foreign Currency.” Finance Research Letters 9: 103–110. Sui, L., and L. Sun. 2016. “Spillovers Effects between Exchange Rates and Stock Prices: Evidence from BRICS around the Recent Global Financial Crisis.” Research in International Business and Finance 36: 459–471. Tantaopas, P., C. Padungsaksawasdi, and S. Treepongkaruna. 2016. “Attention Effect via Internet Search Intensity in Asia-Pacific Stock Markets.” Pacific-Basin Finance Journal 38: 107–124. Vega, C. 2006. “Stock Price Reaction to Public and Private Information.” Journal of Financial Economics 82: 103–133.

2551

Vlastakis, N., and R. N. Markellos. 2012. “Information Demand and Stock Market Volatility.” Journal of Banking & Finance 36: 1808–1821. Vozlyublennaia, N. 2014. “Investor Attention, Index Performance, and Return Predictability.” Journal of Banking & Finance 41: 17–35. Welch, I., and A. Goyal. 2008. “A Comprehensive Look at the Empirical Performance of Equity Premium Prediction.” Review of Financial Studies 21: 1455–1508. Yin, L., and Q. Yang. 2016. “Predicting the Oil Prices: Do Technical Indicators Help?” Energy Economics 56: 338–350. Yu, J. 2013. “A Sentiment-Based Explanation of the Forward Premium Puzzle.” Journal of Monetary Economics 60: 474–491. Yuan, Y. 2015. Market-wide attention, trading, And Stock Returns[j]. Journal Of Financial Economics, 2015, 116(3): 548-564. Zhang, W., D. Shen, Y. Zhang, and X. Xiong. 2013. “Open Source Information, Investor Attention, and Asset Pricing.” Economic Modelling 33: 613–619.