Using Neural Networks to Support Early Warning System for Financial Crisis Forecasting Kyong Joo Oh1, Tae Yoon Kim2, Hyoung Yong Lee3, and Hakbae Lee4 1
Dept. of Information and Industrial Engineering, Yonsei University, Seoul, Korea
[email protected] 2 Dept. of Statistics, Keimyung University, Daegu, Korea
[email protected] 3 Dept. of Management Engineering, Korea Advanced Institute of Science and Technology, Seoul, Korea
[email protected] 4 Dept. of Statistics, Yonsei University, Seoul, Korea
[email protected]
Abstract. This study deals with the construction process of a daily financial condition indicator (DFCI), which can be used as an early warning signal using neural networks and nonlinear programming. One of the characteristics in the proposed indicator is to establish an alarm zone in the DFCI, which plays a role of predicting a potential financial crisis. The previous financial condition indicators based on statistical methods are developed such that they examine whether a crisis will be break out within 24 months. In this study, however, the alarm zone makes it possible for the DFCI to forecast an unexpected crisis on a daily basis and then issue an early warning signal. Therefore, DFCI involves daily monitoring of the evolution of the stock price index, foreign exchange rate and interest rate, which tend to exhibit unusual behaviors preceding a possible crisis. Using nonlinear programming, the procedure of DFCI construction is completed by integrating three sub-DFCIs, based on each financial variable, into the final DFCI. The DFCI for Korean financial market will be established as an empirical study. This study then examines the predictability of alarm zone for the financial crisis forecasting in Korea.
1 Introduction Financial crises that have swept across many developing countries in the 1990s, e.g. the Asian crisis in 1997, have imposed severe economic and social costs on the inflicted financial markets and threatened the stability of the international monetary system. Since these financial crises begin in the shape of a foreign exchange crisis, a variety of theoretical and empirical works have been done to search for early warning signals from various financial variables [2, 3]. Most of these works, however, have focused on long term prediction (e.g., a possible crisis within two years) based on financial variables observed over a rather “long period of time,” which implies loss of vast, useful information that comes directly from dynamic daily movements of financial markets. This study is intended to utilize such daily-basis dynamic information in searching for early warning signals of crisis. For this purpose, the daily financial condition indicator (DFCI), which monitors the financial markets on a daily S. Zhang and R. Jarvis (Eds.): AI 2005, LNAI 3809, pp. 284 – 296, 2005. © Springer-Verlag Berlin Heidelberg 2005
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basis with the purpose of providing early warning signs, is introduced, and its construction procedure is discussed. Traditionally, the studies on financial crises have concentrated on the fundamentals of financial market since it was believed that the weak fundamentals of financial market eventually induce crises [1, 5, 6]. However, recent crises have evidently shown another style, i.e., a crisis may develop without a significant deterioration in the fundamentals of financial market since it is often self-fulfilling under complicated situations and uncertainty of financial market [7, 13]. This new perspective of a financial crisis highlights the importance of financial variables observed daily in studying crises. The stock price index (SPI), foreign exchange rates (FER) and interest rates (INT) are major financial variables that reflect daily dynamic movements of the financial markets. In addition, Kim et al. [4] demonstrated neural networks (NN) are more efficient in monitoring the situation of financial market and providing early warning signals than other artificial intelligence (AI) tools, such as neuro-fuzzy model and inductive learning. In this study, therefore, all these three major daily financial variables are considered to construct a DFCI using NN and nonlinear programming (NLP). One of characteristics in the proposed indicator is to establish an alarm zone in the DFCI, which plays a role of predicting a potential financial crisis. The previous financial condition indicators based on statistical methods [2, 3] are developed such that they examine whether a crisis will break out within 24 months. In this study, however, the alarm zone makes it possible for the DFCI to forecast an unexpected crisis daily and then issue an early warning signal. To give a specific illustration of DFCI construction, a DFCI is constructed for the Korean financial market, which experienced a severe financial crisis in 1997. This study then examines the predictability of alarm zone for financial crisis forecasting. This study consists of five sections. Following Section 1, the introduction, in Section 2 discusses the detailed procedure of DFCI construction, and Section 3 explains the DFCI construction case study for the Korean financial market. The conclusion is given in Section 4.
2 Model Specification In this section, we discuss the DFCI construction procedure, which consists of three phases. Its basic architecture, integrating NN and NLP, is given by Figure 1. As an NN algorithm in this study, BPN is introduced, which is the most widely used one suitable for nonlinear data analysis in science, engineering, finance and other fields [8, 10, 11]. For DFCI construction, BPN is used to train sub-DFCIs based on each of the three financial variables. In financial time series analysis, NLP provides more accurate and less ambiguous results [9], which means that NLP can be useful in financial time series forecasting [12]. Using NLP, therefore, the procedure of DFCI construction is completed by integrating three sub-DFCIs, based on each financial variable, into the final DFCI. Throughout this study, we classify the conditions of financial market into three phases according to the level of its volatilities: ( ) the stable period (SP), ( ) the the crisis period (CP). In our model, SP, UP and CP are unstable period (UP), (
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NN trained SPI-DFCI
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Phase 3 Optimizing the weights of each trained NN indicator using NLP
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Fig. 1. DFCI construction architecture
denoted by 1, 2 and 3, respectively. SP is literally a stable period while UP is a phase, which is characterized by a sudden increase of volatility and rapid swings in market sentiment and plays a role of an alarm zone mentioned in Section 2.2. In CP, the financial market recognizes the occurrence of the crisis and adapts itself to the crisis. Since these three phases or patterns are structurally different from each other, they are considered as a pattern set to be classified and thus serve as a basis of the DFCI. The terms “stable, unstable and crisis” are used as descriptive terminologies to describe the financial market responses to the given conditions of financial market. 2.1 The Construction Process of the DFCI The DFCI is established by the following phases: In Phase 1, input variables for each index are selected appropriately. Using NN, then, three sub-DFCIs are constructed in Phase 2 while they are integrated into the final DFCI using NLP in Phase 3. Phase 1: Selecting and examining of input variables. Input and target data for each financial variable should be selected with due consideration paid to the final task that an individual sub-DFCI is to perform. Indeed, the selection of input and target variables should be viewed as the model construction process, through which SP, UP and CP could be defined such that each of them has its own distinctive feature. Thus, it is crucial to select a set of proper input variables which could measure the volatility level of the financial market and sensitively detect its changes. This is an essential phase for defining an individual sub-DFCI successfully. Transformation of the original daily financial variables (SPI, FER and INT) and expert opinion always prove quite useful in this phase.
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Phase 2: Sub-DFCI Construction for Each Financial Variable. In this phase, NN is designed and trained to develop a sub-DFCI for each financial variable. The design stage of working with NN involves a number of aspects: designing the network structure, selecting neuron transfer functions, selecting a method for updating the weights and a training cessation scheme. After being trained on the given training data, each sub-DFCI is to be tested and adjusted by applying it to test data and checking its performance. Performance will be measured as a degree of consistency between the output of each sub-DFCI and the real development of financial market. If performance achieves a desired level, each NN will be labeled as the sub-DFCI for each financial variable. Phase 3: Integrating Individual Sub-DFCIs into the Final DFCI. In this phase, using NLP, the individually trained NNs (or sub-DFCIs) are combined into the final DFCI as follows:
DECI t = w1 St + w2 Ft + w3 I t ,
t = 1, 2, K , n
(1)
where w1 , w2 and w3 are weights, and St , Ft and I t are the trained sub-DFCIs for the SPI, FER and INT, respectively. Note that St , Ft and I t take on values 1, 2, or 3. Finding the optimal weights is resolved by NLP. For this, it is essential to define an objective function E ( w) and then find an optimal way to produce the desired final DFCI. Indeed, the objective function E ( w) under consideration is defined as:
E ( w) =
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which is minimized over w1 , w2 , w3 ≥ 0 satisfying w1 + w2 + w3 = 1 . Of course, ' 2 ’ in (2) means the objective function is constructed such that the alarm zone probes into the condition of financial market. 2.2 The Role of the Alarm Zone in DFCI
There is no clear classified consensus about the definition of a financial crisis. However, we establish our definition in terms of volatility. These three phases or patterns are used as an output. The UP above, usually occurring just prior to a crisis, could be interpreted as a phase through which the financial market makes a transition from a stable condition to a crisis. Often, it is called a grey zone where self-correcting mechanisms of the financial market deteriorate [4]. In this study, it is defined an alarm zone. There is no consensus about how long it lasts, but it is usually expected to be a very short period because of an abrupt reversal of market sentiment. In the meantime, an alarm zone may shift to either a stable condition or a crisis. In other words, even though the financial market reaches an alarm zone, it may get back to a stable one through recovery or reform measures. One of the main contributions of the DFCI developed here is that the crisis trained DFCI is able to provide a decision as to whether the financial market has entered an alarm zone and hence an appropriate
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warning signal. Thus, an alarm zone can allow DFCI to perform the role of forecasting an unexpected crisis. The role of the alarm zone is evaluated by applying the DFCI to real data, and examining its performance which might be measured by matching its final output signal with the event of financial market at that time.
3 An Empirical Study The Korean financial crisis that occurred in late 1997 and persisted for over a year brought massive bankruptcies in financial and industrial systems. It was a quite new experience for Korea, which had become accustomed to a steady growth track until then. This unprecedented and peculiar crisis brought large scale changes to the Korean financial market, and since then much attention has been focused on the study of financial crisis. Especially, much effort has been made to build an early warning system on a long-term basis. However, one on short-term basis is strongly demanded by quite a few members of the Korean financial market since there is a strong argument that the crisis that the financial market had experienced was abrupt and unexpected, i.e., seemingly not a result of long term aggravation of financial market. Indeed, such demand is partially met by the early warning system based on the Korean stock price index (KOSPI) by Kim et al. [4] whose construction steps will be helpful here. Throughout this section, the variable names given in Table 1 are used. Table 1. Input variables considered Variable name
Numerical formula
Description
IND
xt
Index or Rate
DRF
pt =
2
Daily rise and fall rate
xt −1 pm , t =
MA( m )
MV( m )
xt − xt −1
sm , t =
1
1
t
∑
m i =t − ( m −1) t
∑
m i =t − ( m −1)
pi
( pi − pm , t )
m -day moving average 2
m -day moving variance
The KOSPI 200, Korea won/U.S. dollar exchange rate, and Korea Treasury bill rate with 3-year maturity are considered as the three major financial variables. In order to establish training data with a set of proper input variables, the movements of 1997 financial markets are examined closely. A rough look at the three major variables (IND, Figure 2) in 1997 reveals that all three variables appear to change their movements around October 1997. To check structural changes of three variables during the crisis, DRF pt is calculated. The main reason for choosing pt is that it actively responds to the increased instability or volatility of the financial market due to the potential crisis, which would lead to a sudden increase of frequency and amplitude of pt . As expected, such a phenomenon is easy to notice in Figure 3 (See
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Appendix). Around October or November of 1997, indeed, there was a strong signal of a volatility increase in each DRF movement. Note that, in December 1997, Korea was officially put in the IMF (International Monetary Fund) financial rescue program. ͢͟͡ ͪ͟͡ ͩ͟͡ ͨ͟͡ ͧ͟͡
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Fig. 2. Korea stock price index, foreign exchange rates and interest rates of 1997, which are scaled from 0 to 1
To investigate DRF further, its 5-day moving average pt , (MA(5)) and 5-day 2
moving variance st (MV(5), Figure 4 (See Appendix), another measure of volatility) are studied for each variable. A rather short period of 5 days was chosen here for the moving average to take into account the visibly clear non-stationarity of DRF from Figure 3 (See Appendix). Figure 4 (See Appendix) shows that all three MV(5)’s have sudden increases in around October 1997. To construct the training data, the 1997 and the early 1998 period, during which Korea experienced the crisis, is considered. Since a sudden increase of moving 2
variance st is a signal of the volatility increase, UPs are first established around that point. In fact, a UP for each financial variable is established as Set. 19 – Oct. 21 for SPI, Oct. 27 – Nov. 30 for FER, and Nov. 13 – Dec. 12 for INT, each of which contains the point of sudden volatility increase. SP and CP are established before and after a UP, respectively, which gives training data sets for each variable as in Table 2. The length of the periods for each variable is adjusted and corrected such that its 2
training error rate is optimized. It is interesting to observe that the signal in st
(sudden increase in volatility) for each variable appears in the order of SPI, FER and INT in 1997. This confirms that the stock market responds quickly to the condition changes of financial market while the exchange rate and bond market are slow in responding. In addition to the main input variables (IND, DRF and MV( m )), which
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are basically introduced by Kim et al. [4], MA( m ) ( m = 5, 20, 60, 120 or 240 ) are considered (it would make more sense to have the m -day moving average, a past history, instead of having just current daily data as input variables), and among them, some variables are heuristically selected such that the resulting NN is well fitted to the training data set (Figure 5 (See Appendix)). Input and output variables selected are shown in Table 3 where SP, UP and CP are encoded by 1, 2 and 3, respectively. Note that training is done for the well-known BPN. The number of hidden layers of NN used for SPI ranges from 2 to 4 while those for FER and INT range from 3 to 7. As an activation function, the logistic function is used with the learning rate, momentum and initial weights given by 0.1, 0.1 and 0.3, respectively. Table 2. Specific dates of SP, UP and CP for training data Index
SP Apr. 22, 97 – Sep. 18, 97 Jul. 1, 97 – Oct. 26, 97 Aug. 21, 97 – Nov. 12, 97
SPI FER INT
UP Sep. 19, 97 – Oct. 21, 97 Oct. 27, 97 – Nov. 30, 97 Nov. 13, 97 – Dec. 12, 97
CP Oct. 22, 97 – Mar. 10, 98 Dec. 1, 97 – Mar. 20, 98 Dec. 13, 97 – Mar. 16, 98
Table 3. List of input and output variables for each sub-DFCI Input Variables DFCI (SPI) DFCI (FER) DFCI (INT)
IND, DRF, MA(5), VA(5) DRF, MA(5), VA(5), MA(20), VA(20), MA(60), VA(60) DRF, MA(5), VA(5), MA(20), VA(20), MA(60), VA(60)
Output Variable SP: 1 UP: 2 CP: 3
Table 4. The coefficients obtained for three sub-DFCI
SPI FER INT
Coefficients 0.4959 0 0.5041
The Individually trained NN (sub-DFCI) is applied to the period other than the training data set. The overall classification results of each sub-DFCI are given in Figure 6 (See Appendix), which shows that the sub-DFCI for SPI has higher fluctuations than other variables. Indeed, in Figure 6 (See Appendix), the sub-DFCI of SPI moves quickly between the patterns. We combine the three trained sub-DFCIs into the final DFCI by NLP. Table 4 shows coefficients obtained for the three subDFCIs when they are integrated by NLP. It is noticed that FER contributes the least among the three major financial variables, which was somewhat anticipated since FER in 1997 was largely controlled by the policy authority.
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In the reflection on the periods having “2” (i.e., UP) or more values at which the alarm zone operates, in Figure 7, the Korean financial market suffered a severe recession and went through strong financial reform programs during the 1997 – 1998 crisis periods. As a result of successful reform programs coupled with the steady U.S. and world financial expansion, it returned to a stable condition in the early 1999. Note that the Korean financial market is typically opened. In 1999, however, it bumped into trouble due to the severe liquidity shortage of Daewoo, one of the major Korean conglomerates. In 2000, the Korean financial market faced more severe difficulty due to liquidity problems of Hyundai Engineering & Construction Co., Hyundai Investment & Trust Co., and Hynix Semiconductor Co., which together constitute the gigantic Korean conglomerate enterprise. Then, the slump of the real U.S. financial market started in 2001, and on Sep. 11 of that year, terrorists attacked the World Trade Center in New York City, U.S. In 2002, external condition of financial became worse due to increased tension between North Korea and the U.S. When one examines the behaviors of the DFCI with respect to the difficulties of financial market that Korea experienced, one may note that the DFCI produces meaningful and adequate warning signals, i.e., UP. Thus, it is obvious that the DFCI explains the Korean financial market well and the alarm zone operates responsively for predicting some crisis status.
4 Conclusion The financial crises in many parts of the world for the 1990s sparked interest in establishing financial condition indicators. However, most indicators developed so far measure only the present condition instead of forecasting the future status of financial market. Although some indicators can provide notification of a crisis in advance, they
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do not issue the warning signals abruptly since they are built on a long-term basis. Thus, these indicators are ineffective for forecasting the future state of financial market. In order to overcome this problem, this study suggests a DFCI to involve an alarm zone which allows it to forecast ongoing crises responsively. Early warning signals are produced daily by establishing the DFCI that provides a signal for potential crisis based on its daily monitoring of major financial variables (e.g., the stock price index, exchange rate, and interest rate). Three sub-DFCIs are constructed using NN, and then they are integrated to build up the final DFCI using NLP. Then, the usefulness of the alarm zone is evaluated by matching the final output signals with the events of financial market in test data, which also becomes the performance of the DFCI. An empirical study is done for the Korean financial market which had experienced a financial crisis in 1997. It turns out that the DFCI is desirable for the Korean financial market and the alarm zone plays an important role in improving the performance of the DFCI.
Acknowledgment This work was supported by Yonsei University Research Fund of 2005.
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ͪͪͩͣͣ͢͢͞͡͞
ͪͪͨͣͣͣ͢͢͞͞
ͪͪͨͣͣ͢͢͢͞͞
ͪͪͨͣͣ͢͢͞͡͞
ͪͪͨͪͣͣ͢͞͡͞
ͪͪͨͩͣͣ͢͞͡͞
ͪͪͨͨͣͣ͢͞͡͞
ͪͪͨͧͣͣ͢͞͡͞
ͪͪͨͦͣͣ͢͞͡͞
ͪͪͨͥͣͣ͢͞͡͞
͡
(b) Foreign exchange rates ͥ
ͤ Σ Ζ Ε ΚΔ Υ Ζ Ε ͑ · Β ΝΦ Ζ Τ ͣ
͵ Ζ Τ ΚΣ Ζ Ε ͑ Φ Υ Ρ Φ Υ Τ
͢
ͪͩͤ͢͞͡͞͡
ͪͩͣͣͥ͞͡͞
ͪͩͣ͢͞͡͞͡
ͪͩͣͨ͢͞͡͞
ͪͩͤ͢͢͞͡͞
ͪͨͣͤ͢͞͞͡
ͪͨͣͧ͢͢͞͞
ͪͨͣͣ͢͞͞͡
ͪͨͩ͢͢͢͞͞
ͪͨͥ͢͢͞͞͡
ͪͨͣ͢͢͞͡͞
ͪͨͨ͢͞͡͞͡
ͪͨͪͣͤ͞͡͞
ͪͨͪͪ͞͡͞͡
ͪͨͩͣͧ͞͡͞
ͪͨͩͣ͢͞͡͞
ͪͨͨͣͪ͞͡͞
ͪͨͨͦ͢͞͡͞
ͪͨͨ͢͞͡͞͡
͡
(c) Interest rates ͥ
ͤ Σ Ζ Ε ΚΔ Υ Ζ Ε ͑ · Β ΝΦ Ζ Τ ͣ
͵ Ζ Τ ΚΣ Ζ Ε ͑ Φ Υ Ρ Φ Υ Τ
͢
ͪͩͤͦ͞͡͞͡
ͪͩͣͪ͢͞͡͞
ͪͩͣͦ͞͡͞͡
ͪͩͣͣ͢͞͡͞
ͪͩͩ͢͞͡͞͡
ͪͨͣͣͦ͢͞͞
ͪͨͣ͢͢͢͞͞
ͪͨͣͨ͢͢͞͞
ͪͨͤ͢͢͢͞͞
ͪͨͤ͢͞͡͞͡
ͪͨͧ͢͢͞͡͞
ͪͨͣ͢͞͡͞͡
ͪͨͪͩ͢͞͡͞
ͪͨͪͥ͞͡͞͡
ͪͨͩͣ͢͞͡͞
͡
Fig. 5. Classification results of individual sub-DFCI for each training dataset (1, 2 and 3 denotes SP, UP and CP, respectively)
296
K.J. Oh et al.
(a) Stock price index 4
3
2
1
0
k c to S
4 2 0 1 6 9 9 1
1 2 1 0 7 9 9 1
4 1 4 0 7 9 9 1
9 0 7 0 7 9 9 1
7 0 0 1 7 9 9 1
6 0 1 0 8 9 9 1
1 0 4 0 8 9 9 1
5 2 6 0 8 9 9 1
6 1 9 0 8 9 9 1
9 0 2 1 8 9 9 1
2 1 3 0 9 9 9 1
4 0 6 0 9 9 9 1
5 2 8 0 9 9 9 1
7 1 1 1 9 9 9 1
4 1 2 0 0 0 0 2
2 1 5 0 0 0 0 2
8 0 8 0 0 0 0 2
3 0 1 1 0 0 0 2
5 0 2 0 1 0 0 2
0 3 4 0 1 0 0 2
4 2 7 0 1 0 0 2
8 1 0 1 1 0 0 2
1 1 1 0 2 0 0 2
1 1 4 0 2 0 0 2
8 0 7 0 2 0 0 2
1 0 0 1 2 0 0 2
4 2 2 1 2 0 0 2
(b) Foreign exchange rates 4
3
2
1
0
R E F
4 2 0 1 6 9 9 1
1 2 1 0 7 9 9 1
4 1 4 0 7 9 9 1
9 0 7 0 7 9 9 1
7 0 0 1 7 9 9 1
6 0 1 0 8 9 9 1
1 0 4 0 8 9 9 1
5 2 6 0 8 9 9 1
6 1 9 0 8 9 9 1
9 0 2 1 8 9 9 1
2 1 3 0 9 9 9 1
4 0 6 0 9 9 9 1
5 2 8 0 9 9 9 1
7 1 1 1 9 9 9 1
4 1 2 0 0 0 0 2
2 1 5 0 0 0 0 2
8 0 8 0 0 0 0 2
3 0 1 1 0 0 0 2
5 0 2 0 1 0 0 2
0 3 4 0 1 0 0 2
4 2 7 0 1 0 0 2
8 1 0 1 1 0 0 2
1 1 1 0 2 0 0 2
1 1 4 0 2 0 0 2
8 0 7 0 2 0 0 2
1 0 0 1 2 0 0 2
4 2 2 1 2 0 0 2
9 0 7 0 7 9 9 1
7 0 0 1 7 9 9 1
6 0 1 0 8 9 9 1
1 0 4 0 8 9 9 1
5 2 6 0 8 9 9 1
6 1 9 0 8 9 9 1
9 0 2 1 8 9 9 1
2 1 3 0 9 9 9 1
4 0 6 0 9 9 9 1
5 2 8 0 9 9 9 1
7 1 1 1 9 9 9 1
4 1 2 0 0 0 0 2
2 1 5 0 0 0 0 2
8 0 8 0 0 0 0 2
3 0 1 1 0 0 0 2
5 0 2 0 1 0 0 2
0 3 4 0 1 0 0 2
4 2 7 0 1 0 0 2
8 1 0 1 1 0 0 2
1 1 1 0 2 0 0 2
1 1 4 0 2 0 0 2
8 0 7 0 2 0 0 2
1 0 0 1 2 0 0 2
4 2 2 1 2 0 0 2
(c) Interest rates 4
3
2
1
0
T N I
4 2 0 1 6 9 9 1
1 2 1 0 7 9 9 1
4 1 4 0 7 9 9 1
Fig. 6. Classification results of individual sub-DFCI from May 1996 to March 2003
