Intellectual Capital Disclosure in IPO Prospectuses: Evidence From Technology Companies Listed on NASDAQ Tatiana Garanina1 and Alexandra Manuilova2 1 Department of Finance and Accounting, Graduate School of Management, St.Petersburg University, Russia 2 Graduate of Master in International Business Program, Graduate School of Management, St.Petersburg University, Russia
[email protected] [email protected] Abstract: There is quite a number of papers, concerning Intellectual Capital itself, research on why the company should disclose the information on Intellectual Capital (IC), in which way, who is the target audience and why this information is of interest to participants of stock market. Mainly research covers official annual reporting and separate IC Statements. Much less time and attention was dedicated to IPO prospectuses IC information disclosure and even less to post‐issue stock performance in connection to the disclosure. This paper extends this line of investigation. It follows the existing research conducted by Singh and Van der Zahn (2009) in the index chosen and time of observation. Although, while the academics were concentrating on Singapore exchange, the focus of this paper are the IPOs of Technology companies on NASDAQ before and after the crisis 2008. Findings from this study provide a broader, long‐term image of the potential consequences of Intellectual Capital disclosure in IPO prospectuses and share price returns of Technology companies, which is of particular interest to investors due to sadly famous events of 2001. To measure the level of disclosure of Intellectual Capital, Disclosure Index is used. Post‐issue stock‐performance is calculated as buy‐and‐hold return for 500 days after listing. The sample consists of the technology companies (according to NASDAQ) that were listed on NASDAQ from 2002 to 2010. The goal of the paper is to define the relationship between the disclosure of information on Intellectual Capital and the 500‐day post‐issue stock performance on example of NASDAQ companies; The main objectives are to create a regression model with Intellectual Capital elements that will best possibly reflect the connection between Intellectual Capital disclosure and post‐issue stock performance, to compare the results with other similar studies and to interpret the differences. The main results are: Intellectual Capital disclosure has a positive effect on post‐issue stock performance; The influence of Intellectual Capital disclosure is higher for non‐manufacturing firms, small companies and firms that issue American Depositary Shares. Keywords: intellectual capital, initial public offering (IPO), intellectual capital information disclosure, NASDAQ, technology companies, IPO prospectus
1. Introduction: Reasons for intellectual capital information disclosure For decades the researches are trying to understand what are the main factors influencing the stock of the price and its movement. Academics have debated the relevance of accounting information for valuing firms. Most of the studies show a low earnings‐return association (Amir, Lev, 1996) and a lowering association between stock prices, earnings and book values (Brown, et al., 1999; Core, et al., 2003; Ely, Waymire, 1999a; Lev, et al., 1999). The higher is the awareness among investors about the importance of Intellectual Capital, the more they request the disclosure of such information. As a result, managers started to provide information related to Intellectual Capital voluntarily, seeking for lower average cost of capital, more accurate analyst forecasts, higher liquidity in capital markets and credibility among investors (Garcı´a‐Meca, et al., 2005). While many researchers dedicate their time to the problem of Intellectual Capital transparency and the factors that influence the decision of the company to disclose such information, very limited attention is paid to the consequences of Intellectual Capital disclosure. This work addresses this issue by examining the connection between the Intellectual Capital disclosure in the initial public offering (IPO) prospectuses and post‐issue stock performance. The goal of the research is to define the relationship between the disclosure of information on Intellectual Capital and the 500‐day post‐issue stock performance on example of NASDAQ companies.
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Tatiana Garanina and Alexandra Manuilova To be able to compare the results with similar research conducted on another market (Singapore Stock Exchange) alike research methodology is chosen. Research on Intellectual Capital disclosure has witnessed a significant growth in the past two decades (Garcı´a‐ Meca, et al., 2005). Stages of Intellectual Capital development, IC application to business and management issues and the application of IC research in different cultural contexts are traced in Tan (Tan, et al., 2008). Most of the studies are dedicated to a specific country, for example Australia (Guthrie, et al., 2000), Canada (Bontis, 2003), India ((Kamath, 2007); (Kamath, 2008)), Japan (Mavridis, 2004), Malaysia (Goh, et al.), Singapore (Tan, et al., 2007), Spain (Oliveras, et al., 2008), Taiwan (Chen, et al., 2005), UK (Williams, 2001). These studies give an overview of Intellectual Capital reporting in different countries across sectors. When disclosing the information on Intellectual Capital to the market the company achieves two goals: reducing information asymmetry amongst market actors and attaining market valuations that better reflect the risk profile of the firm (Dumay, et al., 2007). The empirical evidence also supports the advantages of the reporting of Intellectual Capital to external stakeholders. For example, the number of companies who are now reporting their Intellectual Capital increases and the frameworks for doing so are developing further (Edvinsson, 1997); (Meritum, 2002); (Mouritsen, 2002); (Mouritsen, et al., 2003). Studies show that financial analysts pay attention to Intellectual Capital information to some extent compensate themselves for the Intellectual Capital‐related information deficit of financial reports (Amir, et al., 2003) especially in non‐manufacturing industries. Though, on average, financial market specialists base their judgments on limited information ‐ based mainly on financial performance and tangible assets – with level of disclosure on intangible assets quite low (Lev, 1999). However this creates a potential problem in forecasting – the easily available financial information does not provide a feeling of what intellectual is all about: about future prospects, future growth and potential. When the economy moves to an increased reliance on intangible assets, the use and recognition of these assets becomes very important for the study. The research has identified the effects of Intellectual Capital on company/ stock performance (mainly R&D expenses or R&D intensity) in the context of capital markets. For instance, Lev and Sougiannis (1996) write about a significant inter‐temporal association between company’s research and development capital and subsequent stock returns. Aboody and Lev (1998) examined the performance of listed chemical companies from 1980‐1999 and show that a dollar invested in chemical R&D increases, on average, current and future operating income by $2. Translated to annual rate of return on investment, the after‐tax rate of return on investments in R&D in chemical industry is about 17%, indicating a very significant contribution of chemical R&D to corporate value (typical weighted‐average cost of chemical R&D is 8‐10 percent). Thus, in stock performance, chemical companies collectively outpaced the S&P 500 companies during 1985‐1998 (Ghosh, et al., 2007).
2. Literature gap: Hypothesis formulation There is quite a number of literature, concerning Intellectual Capital itself, research on why the company should disclose the information on IC, in which way, who is the target audience and why this information is of interest to participants of stock market. Mainly research covers official annual reporting and separate IC Statements. Much less time and attention was dedicated to IPO prospectuses IC information disclosure and even less to post‐issue stock performance in connection to the disclosure. This paper extends this line of investigation. It follows the existing research conducted by Singh and Van der Zahn in the index chosen and time of observation. Although, while the academics were concentrating on Singapore exchange, the focus of this paper are the IPOs of Technology companies on NASDAQ before and after the crisis 2008. Findings from this study provide a broader, long‐term image of the potential consequences of Intellectual Capital disclosure in IPO prospectuses and share price returns of Technology companies, which is of particular interest to investors due to sadly famous events of 2001. There was no research of this kind conducted before.
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Tatiana Garanina and Alexandra Manuilova In arguing that IC disclosure is diminishing the uncertainty, which always accompanies IPO, the result should be a smaller effect of post‐issue price drop. Nevertheless the research conducted by Singh and Van der Zahn shows exactly the opposite. The goal of this paper is to duplicate the research using different sample to check the results as this can become a new trigger for the development of the topic. The specific research hypothesis, therefore, is formalized as: H1: The level of IC disclosure in the prospectus of an IPO is inversely associated with the post‐issue stock performance. Additional attention would be paid to pre‐ and post‐crisis results and the fact that the sample consists of Technology companies.
3. Research methodology In order to conduct the research content analysis (and more specifically Disclosure Index) was employed for evaluating the level of IC disclosure. The regression model of Singh and van der Zahn (Singh, et al., 2009) was modified on the one hand, to include all the major variables in order to be able to compare the results, on the other hand, to take into account special features of the companies that get listed on NASDAQ and SEC regulation. To assess post‐issue stock performance market‐adjusted buy‐and‐hold returns applying a 500 trading‐day observation window is used. The following procedures are employed to measure various buy‐and‐hold returns. First, the basic raw buy‐and‐hold return for IPOj (Rj) is computed as: (1) where T = daily trading day holding period (for main observations 500 days); and Rjt = return on IPO share j during the trading period t inclusive of cash dividends paid during the trading period t. (Singh, et al., 2009) The average buy‐and‐hold return for the trading period t (Rt) is computed as: (2) where N ‐ number of IPOs included in the portfolio. Consequently, the mean market‐adjusted buy‐and‐hold return (MBHAR) for the daily trading period t (denoted as MBHART) is calculated based on the formula: (3) where rmt ‐ return on the market portfolio during the trading period t. MBHAR measures the compounded buy‐and‐hold returns an investor could earn from a portfolio of IPO stocks held till a given trading day T in excess of the buy‐and‐hold return the investor could have earned if holding the market portfolio for the same trading day period. 500 For the multivariate regression analysis the dependent variable proxy denoted BHARj is the 500 trading day compounded market‐adjusted buy‐and‐hold return for IPOj. Multivariate regression analysis is used as the main statistical test of the study’s prime conjecture of a negative association between the extent of Intellectual Capital disclosure in the prospectus and the post‐issue stock performance of NASDAQ IPOs. We posit the following regression model to test the primary conjecture:
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Tatiana Garanina and Alexandra Manuilova
(4)
where
Number of Intellectual Capital items disclosed voluntarily in the prospectus of IPOj divided by the total number of items from the 81‐item index relevant to IPOj αj is calculated as α=(N–Np–Ns)/N where N = number of common outstanding of IPOj after listing, Np = number of primary common outstanding shares offered by IPOj and Ns = number of secondary common outstanding shares offered by IPOj. Lnαj is then calculated as Lnαj =αj+ln(1 – αj) Indicator variable where IPOj is scored one (1) if it engages either of the top two underwriter firms (based on frequency) in the year of listing; otherwise scored zero (0) The ratio of proceeds reported in the prospectus of IPOj that is to be allocated to meet immediate working capital needs following listing to total proceeds to be raised by IPOj on listing as reported in the prospectus Square root of the total number of shares traded for IPOj during the 500 trading day observation window (less the first day of trading) divided by number of days shares in IPOj actively traded during the 500 trading day observation window Mom60j = percentage change in the NASDAQ Composite Index 60 days prior to the day of listing for IPOj and the day of listing for IPOj Natural logarithm of the difference between the closing price on the first day of trading and the initial offering price for firm j, expressed as a percentage of the initial offering price Natural logarithm of the projected market capitalization of IPOj on the day of listing derived from the total number of outstanding common shares of the firm on listing multiplied by offer price Natural logarithm of the net proceeds (based on prospectus projections) to be received by IPOj (expressed in USD) Square root of the number of days from the date of incorporation of IPOj to the date of listing for IPOj Indicator variable where the IPOj is scored one (1) if from the manufacturing industry as determined by NASDAQ specifications; other scored zero (0) Indicator variable where the IPOj is scored one (1) if the company is registered in an offshore; other scored zero (0) Indicator variable where the IPOj is scored one (1) if the company is doing business via Internet (meaning business model implies that business – sales, providing services, etc. – is done via Internet); other scored zero (0) Indicator variable where the IPOj is scored one (1) if the company has operations abroad; other scored zero (0)
Control variables are included in equation (4) to control for cross‐sectional variations for such characteristics as:
firm quality (i.e. ownership retention, underwriter reputation);
intended use of proceeds (i.e. ratio working capital to total proceeds);
investor interest post‐listing (i.e. level of underpricing, trading volume);
capital market sentiment (i.e. market momentum 60 days before listing);
information asymmetry (i.e. firm size); and
ex‐ante uncertainty (i.e. offering size and firm age).
Controls for industry sector influences are also included.
4. Sample To check the results of (Singh, et al., 2009), sample from another stock exchange was taken – from NASDAQ Stock Exchange. NASDAQ is famous for being an exchange for technology companies. As there is a lot of discussion going on about technology companies’ Intellectual Capital and disclosure of IC by technology
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Tatiana Garanina and Alexandra Manuilova companies, not only NASDAQ was chosen, but also the companies that NASDAQ classifies as “Technology companies”. To determine the timeframe for the sample the following logic was used:
As the observation window equals to 500 days the company has to be listed before October 2010 (+500 days=April 2012);
Due to time and resources limitations IPOs from the beginning of 2000s were taken
To avoid the effects of dot.com bubble, listings after 2002 were taken
The recent crisis of 2008 should be taken into account (officially the crisis continued from December 2007 to June 2009). All the IPOs are divided into 3 groups: those that were listed before, after and during the crisis (with consideration of 500‐day observation window):
Companies listed before the crisis – listing before August 2006
Companies listed during the crisis – listing from Dec 2007 to February 2008
Companies listed after crisis – listing from July 2009 to October 2010
Taking all those constraints into account we got the list of companies consisting out of 65 technology firms. Table 1 presents descriptive statistics for the sample. Table 1: Description of the sample
2002
2003
2004
2005
2006
2007
2009
Number of companies
4
3
12
12
4
3
4
% of manufacturing companies
25,0%
0,0%
41,7%
41,7%
25,0%
33,3%
50,0%
% of ADS
25,0%
0,0%
33,3%
41,7%
25,0%
0,0%
0,0%
% of Internet companies
25,0%
33,3%
41,7%
25,0%
25,0%
33,3%
50,0%
Average % of IC items disclosed
22%
19%
23%
22%
19%
26%
22%
% of offshore companies
0,0%
0,0%
33,3%
33,0%
25,0%
0,0%
25,0%
201 0 23 21, 7% 39, 1% 56, 5% 28 % 30, 4%
5. Research description As mentioned above the Disclosure Index used in the (Singh, et al., 2009) was applied for the research to be able to compare the results on 2 different markets. Some of the elements of the index were quite disputable. For example, some of the positions were too broad and obscure like “Description of the network of distributors and suppliers” could potentially score 1 just for the information about distributors or could score 1 for the information about both distributors and suppliers. Likewise, the description of the network could mean more or less detailed information – this is the limitation of this approach, this makes it more subjective. Due to this subjectivity and possibility of different interpretations, there was a set of rules established for conducting the Disclosure Index (Table 2): The only element that was mentioned by each company out of the sample is Executive stock‐incentive programme (falls under “Remuneration and incentive systems”). Technology companies are different from all the other companies, because their most valuable assets are intangible, including the research they make, software they develop etc. Therefore Human Capital is of extreme importance for such companies – stock‐incentive programme is a way for them to make sure the key personnel does not move to competitors because of the higher salary.
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Tatiana Garanina and Alexandra Manuilova Table 2: Rules designed for filling in the disclosure Index with data Disclosure Index position Employee breakdown by nationality Employee breakdown by level of education All the elements for IT capital External sharing of knowledge and information Details of future prospects regarding R&D
Rules for filling in the element of Disclosure Index More topical for Asian context, especially Singapore; for NASDAQ breakdown by number of employees in different countries (geographical) taken In all companies was used only for Researchers – if mentioned for researchers, then the company scores 1 When the company is the software developer, apriori it has IT systems developed inhouse. Because for some companies this is just one of the businesses and the case is disputable, to avoid subjectivity, every company that invests in IT gets scores for IT capital measures Software developers and IT systems users who work with open platforms, get 1
As the level of detail is not specified, the data on future spending (in USD), direction of future research, expected future results of R&D get scored 1
6. Research results In the section below main results of the research are presented – concerning Disclosure Index and regression analysis. In Table 3 the results of analysis of Disclosure Index are presented: Table 3: Number of disclosure index items disclosed
Year
Average for Disclosure Index overall Average for Human Resource Capital
2002
2003
2004
2005
2006
2007
2009
2010
Overall Average
17,8
15,0
18,0
17,7
15,0
20,3
17,5
21,8
19,0
4,5
3,3
4,8
5,0
3,3
4,7
3,5
5,3
4,7
Average for Customer capital
4,5
4,3
3,9
4,3
3,0
2,7
3,5
4,0
3,9
Average for IT Capital Average for Processes Capital
1,8
1,3
2,0
1,3
1,3
1,0
2,8
2,7
2,0
0,8
0,0
0,5
0,3
0,5
1,3
0,3
0,7
0,5
Average for R&D capital
3,5
4,0
2,9
3,1
3,3
5,7
3,0
4,3
3,7
Average for Strategic Capital
2,8
2,0
3,8
3,8
3,8
5,0
4,5
5,0
4,2
Number of Companies
4
3
12
12
4
3
4
23
65
The results also showed that there is no linear dependence of the level of disclosure and the year. The level of overall disclosure rises in 2004, 2007 and 2010 and fall in all other years during the observation window. We also found out that in 2010 on average there was more information disclosed on Intellectual Capital than during all other years (2002‐2009). On average, the companies are more ready to present information on Human Recourse capital (6% out of overall average 24%) than on any other type of capital. These results are consistent with what (Singh, et al., 2009) found during their study on Singapore Stock Exchange. Then follow Customer, Strategic and R&D capital with 5%. The last place is occupied by the Processes Capital. It is interesting to note that since 2002 to 2010 the level of disclosure on Strategic Capital disclosure doubled. It reached this level in 2007 and remains 6% since that time. There was a drop in disclosure level in the pre‐ crisis year 2007 for Customer ad IT Capital, but the growth for 3 other types of capital. Interestingly enough the least disclosed type of Capital – Processes Capital was best presented in 2007 – could be related to the growing risks for investors in anticipation of the crisis and an attempt to reassure them in company’s reliability. In Table 4 the results of the regression analysis are presented. The results of the regression analysis show that the significance of the model equals to 54% ‐ R Squared – 54% of the 500‐day returns fluctuations are explained by the factors in the model.
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Tatiana Garanina and Alexandra Manuilova Table 4: Statistical results of regression analysis in Excel Regression Statistics Multiple R
0,732184
R Square
0,535687
Adjusted R Square
0,282
Standard Error
1,01521
Observations
65
ANOVA
df
SS
MS
F
Significance F
Regression
14
47,67068
2,073049
2,036575
0,02633611
Residual
50
41,22647
1,031689
Total
64
88,96515
Coefficients
Standard Error
t Stat
P‐value
Lower 95%
Upper 95%
Lower 95,0%
Upper 95,0%
Intercept
‐0,734
1,416
‐0,518
0,607
‐3,579
2,111
‐3,579
2,111
Disclosure Working capital
0,052
0,030
1,722
0,033
‐0,009
0,112
‐0,009
0,112
0,139
0,584
0,237
0,813
‐1,034
1,311
‐1,034
1,311
Ln alpha
0,232
0,466
0,498
0,621
‐0,703
1,167
‐0,703
1,167
SqrtAge
0,000
0,008
‐0,012
0,990
‐0,015
0,015
‐0,015
0,015
LnUP
0,123
0,108
1,141
0,259
‐0,093
0,339
‐0,093
0,339
FSize*LnGP
‐0,001
0,004
‐0,229
0,820
‐0,009
0,007
‐0,009
0,007
MOM60
4,013
2,232
1,797
0,078
‐0,472
8,497
‐0,472
8,497
ManInd
‐0,121
0,409
‐0,297
0,768
‐0,943
0,700
‐0,943
0,700
Underwriter
0,565
0,342
1,654
0,104
‐0,121
1,252
‐0,121
1,252
SqrtATV
0,000
0,000
‐2,007
0,050
‐0,001
0,000
‐0,001
0,000
International
0,562
0,355
1,581
0,120
‐0,152
1,276
‐0,152
1,276
Offshore
‐0,081
0,446
‐0,183
0,856
‐0,977
0,814
‐0,977
0,814
Internet
0,124
0,368
0,338
0,737
‐0,615
0,864
‐0,615
0,864
ADS
0,615
0,452
1,360
0,180
‐0,293
1,523
‐0,293
1,523
The most significant result is the fact that, in contrast to the research conducted by (Singh, et al., 2009), the Disclosure Index has a positive influence on the 500‐day returns, which is an opinion supported by majority of literature. From all the factors included in the model, the significant ones (checked by p‐value – should be
