Behavioral Risk Measurement: Empirical Evidence From NYSE

Yıl 2023, , 1 - 25, 30.06.2023

Sezgin Demir , Mustafa Ünlü

https://doi.org/10.25229/beta.1189408

Öz

This study aims to create a risk measure based on systematic investor behavior. For this purpose, as an alternative to the classical risk measure, volatility, the empirical validity of the downside risk measure, which includes skewness and kurtosis values, was tested. Standard deviation, skewness, and kurtosis differences are used to explain the returns of portfolios created using data from stocks listed on the New York Stock Exchange (NYSE) between 1982 and 2020 depending on different risk concepts. Risk definitions are based on the previous period's skewness and kurtosis coefficients of stock returns. Based on the determined measures, stocks are classified according to their risk level. The relationship between returns and risk measures was examined by regression analysis. According to the results, negative skewness did not provide a higher return than positive skewness. In addition, a higher kurtosis value did not provide higher returns than a lower kurtosis value. As a result, the concept of risk, which represents the loss of the investor, emerges as a result of irrational systematic investor behavior and can be modeled with the skewness coefficient of the return distribution. However, taking a risk in this sense does not promise a reward.

Anahtar Kelimeler

Systematic Investor Behavior, Psychological Biases, Skewness and Kurtosis

Davranışsal Risk Ölçüsü: NYSE’den Ampirik Kanıtlar

Öz

Bu çalışmanın amacı sistematik yatırımcı davranışlarına dayalı risk ölçüsü oluşturmaktır. Bu amaç doğrultusunda, klasik risk ölçüsü olan volatiliteye alternatif olarak, çarpıklık ve basıklık değerlerinin dahil olduğu aşağı yönlü risk ölçüsünün ampirik olarak geçerliliği test edilmiştir. 1982 – 2020 yılları arasında NYSE’de listelenen hisse senetlerine ait verilerden yararlanarak ve farklı risk kavramları baz alınarak oluşturulan portföylerin getirilerini açıklamak için standart sapma, çarpıklık ve basıklık farklılıkları kullanılmıştır. Risk tanımlamaları, hisse senetleri getirilerinin bir önceki dönem ait çarpıklık ve basıklık katsayıları üzerinden yapılmıştır. Belirlenen ölçüler üzerinden hisse senetleri risklerine göre sınıflandırılmıştır. Getiriler ile riski tanımlayan ölçüler arasındaki ilişki regresyon analizi ile incelenmiştir. Elde edilen sonuçlara göre negatif çarpıklığın pozitif çarpıklığa göre, daha yüksek basıklık değerinin de daha düşük basıklık değerine göre daha yüksek getiri sağlamadığı görülmüştür. Sonuç olarak, yatırımcının kaybını temsil eden risk kavramı, irrasyonel sistematik yatırımcı davranışının sonucu olarak ortaya çıkmakta ve getiri dağılımının çarpıklık katsayısı ile modellenebilmektedir. Ancak bu anlamdaki riskin üstlenilmesi bir ödül vaat etmemektedir.

Anahtar Kelimeler

Sistematik Yatırımcı Davranışı, Psikolojik Yanılsamalar, Çarpıklık ve Basıklık

Kaynakça

• Aggarwal, R., Rao, R. P., & Hiraki, T. (1989). Skewness and Kurtosis in Japanese Equity Returns: Empirical Evidence. Journal of Financial Research, 12(3), 253-260.
• Albuquerque, R. (2012). Skewness in Stock Returns: Reconciling the Evidence on Firm Versus Aggregate Returns. The Review of Financial Studies, 25(5), 1630–1673.
• Andersen, T.J., Denrell, J., Betti, R.A., (2007). Strategic Responsiveness And Bowman's Risk-Return Paradox. Strategic Management Journal, 28, 407-429.
• Arditti, F.D. (1967), Risk and the Required Return on Equity. The Journal of Finance, 22: 19-36.
• Arditti, F.D., Levy, H. (1972). Distribution Moments and Equilibrium: A Comment. Journal of Financial and Quantitative Analysis, 7, 1429-1433.
• Baker, M., Wurgler, J., (2006). Inverstor Sentiment and the Cross-Section of Stock Returns. The Journal of Finance, 61, 1645-1680.
• Baker, S. D., Hollifield, B., & Osambela, E. (2016). Disagreement, Speculation, and Aggregate Investment. Journal of Financial Economics, 119(1), 210-225.
• Bali, T.G., Cakici, N., Whitelaw, R.F., (2011). Maxing Out: Stocks as Lotteries and the Cross-Section of Expected Returns. Journal of Financial Economics, 99, 427-446.
• Barberis, N., Huang, M., (2008). Stocks as Lotteries: The implications of Probability Weighting for Security Prices. American Economic Review, 98, 2066-2100.
• Birru, J., Wang, B. (2016). Nominal Price Illusion, Journal of Financial Economics, 119(3), 578-598.
• Bowden, M. P. (2015). A model of Information Flows and Confirmatory Bias in Financial Markets. Decisions in Economics and Finance, 38(2), 197-215.
• Bowman, E.H., (1980). A risk/return paradox for strategic management. MIT Sloan Management Review, 1-25.
• Boyer, B., Mitton, T., Vorkink, K., (2010). Expected Idiosyncratic Skewness. The Review of Financial Studies, 23, 169-202.
• Brunnermeier, M.K., Parker, J.A., (2005). Optimal Expectations. American Economic Review, 95, 1092-1118.
• Chou, P.H., Chou, R.K., Ko, K.C., (2009). Prospect Theory And The Risk-Return Paradox: Some Recent Evidence. Review of Quantitative Finance and Accounting, 33, 193-208.
• Conrad, J., Kapadia, N., & Xing, Y. (2014). Death and Jackpot: Why Do Individual Investors Hold Overpriced Stocks? Journal of Financial Economics, 113(3), 455-475.
• De Bondt, W.F., (1998). A Portrait of The Individual Investor. European Economic Review 42, 831-844.
• Dillenberger, D., & Rozen, K. (2015). History-Dependent Risk Attitude. Journal of Economic Theory, 157, 445-477.
• Fama, E. F., & French, K. R. (1992). The Cross-Section of Expected Stock Returns. The Journal of Finance, 47(2), 427–465.
• Fama, E. F., & French, K. R. (1993). Common Risk Factors in the Returns on Stocks and Bonds, Journal of Financial Economics, 33(1), 13-56.
• Fama, E. F., & French, K. R. (1995), Size and Book-to-Market Factors in Earnings and Returns. The Journal of Finance, 50, 131-155.
• Fama, E. F., & French, K. R. (1996), Multifactor Explanations of Asset Pricing Anomalies. The Journal of Finance, 51, 55-84.
• Fama, E. F., & French, K. R. (2015). A Five-Factor Asset Pricing Model, Journal of Financial Economics, 116(1), 1-22.
• Fiegenbaum, A., & Thomas, H., (1988). Attitudes Toward Risk And The Risk/Return Paradox: Prospect Theory Explanations. Academy of Management Journal 31, 85-106.
• Francis, J. C. (1975). Skewness and Investors’ Decisions. The Journal of Financial and Quantitative Analysis, 10(1), 163–172.
• Goetzmann, W.N., Kumar, A., (2008). Equity Portfolio Diversification. Review of Finance, 12, 433-463.
• Harvey, C.R. & Siddique, A. (2000), Conditional Skewness in Asset Pricing Tests. The Journal of Finance, 55, 1263-1295.
• Jean, W.H., (1971). The Extension of Portfolio Analysis to Three or More Parameters. Journal of Financial and Quantitative Analysis, 6, 505-515.
• Jondeau, E., & Rockinger, M. (2003). Conditional Volatility, Skewness, and Kurtosis: Existence, Persistence, and Comovements. Journal of Economic Dynamics and Control, 27(10), 1699-1737.
• Jurczenko, E., & Maillet, B. B., & Merlin, P. (2005). Hedge Fund Portfolio Selection with Higher-order Moments: A Nonparametric Mean-Variance-Skewness- Kurtosis Efficient Frontier. SSRN Electronic Journal. 10.2139/ssrn.676904.
• Kim, J. S., Ryu, D., & Seo, S. W. (2014). Investor Sentiment and Return Predictability of Disagreement. Journal of Banking & Finance, 42, 166-178.
• Kumar, A., (2009). Who Gambles in the Stock Market? The Journal of Finance, 64, 1889-1933.
• Lai, J. Y. (2012). An Empirical Study of The Impact of Skewness and Kurtosis on Hedging Decisions. Quantitative Finance, 12(12), 1827-1837.
• Lee, C.M., Ready, M.J., (1991). Inferring Trade Direction from Intraday Data. The Journal of Finance, 46, 733-746.
• Levy, H. (1969). A Utility Function Depending on the First Three Moments. The Journal of Finance, 24(4), 715–719.
• Levy, H., (1973). The Demand for Assets Under Conditions of Risk. The Journal of Finance, 28, 79-96.
• Luchtenberg, K. F., & Seiler, M. J. (2014). Do Institutional and Individual Investors Differ in Their Preference for Financial Skewness? Journal of Behavioral Finance, 15(4), 299-311.
• Mitton, T., Vorkink, K., (2007). Equilibrium Underdiversification and The Preference for Skewness. The Review of Financial Studies, 20, 1255-1288.
• Peiro, A. (1999), Skewness in Financial Returns, Journal of Banking & Finance, 23, (6), 847-862
• Prakash, A. J., Chang, C. H., & Pactwa, T. E. (2003). Selecting a Portfolio with Skewness: Recent Evidence from US, European, and Latin American Equity Markets. Journal of Banking & Finance, 27(7), 1375-1390.
• Premaratne, G., & Bera, A. K. (2000). Modeling Asymmetry and Excess Kurtosis in Stock Return Data. SSRN Electronic Journal. 10.2139/ssrn.259009.
• Simkowitz, M. A., & Beedles, W. L. (1978). Diversification in a Three-Moment World. The Journal of Financial and Quantitative Analysis, 13(5), 927–941.
• Simkowitz, M. A., & Beedles, W. L. (1980). Asymmetric Stable Distributed Security Returns. Journal of the American Statistical Association, 75(370), 306–312.
• Singleton, J. C., & Wingender, J. (1986). Skewness Persistence in Common Stock Returns. The Journal of Financial and Quantitative Analysis, 21(3), 335–341.
• Sun, Q., & Yan, Y. (2003). Skewness Persistence with Optimal Portfolio Selection. Journal of Banking & Finance, 27(6), 1111-1121.
• Tversky, A., Kahneman, D., (1992). Advances in Prospect Theory: Cumulative Representation of Uncertainty. Journal of Risk and Uncertainty, 5, 297-323.
• Wen, F., Tao, M., He, Z., & Chen, X. (2013). The Impact of Investors’ Risk Attitudes on Skewness of Return Distribution. Procedia Computer Science, 17, 664-670.
• Yang, C., & Zhou, L. (2015). Investor Trading Behavior, Investor Sentiment and Asset Prices. The North American Journal of Economics and Finance, 34, 42-62.