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Exploring Behavioral Biases in Stock Market: Evidence from India


Affiliations
1 Assistant Professor, Delhi School of Management, Delhi Technological University, India
 

Due to the positive association between investments and the development of the economy, the rise of investment will progressively influence the economy's overall growth and vice versa. Thus, investors’ decisions play a significant role in describing the market trend, which in turn affects the economy. Individuals invest with unique planning or no planning at all based on their available funds, time span, and financial goal. Ultimately, the majority of them want high returns that will make them wealthy overnight Regardless of how strong the company fundamentals are, strong negative emotions can wreck down a robust bullish market trend. The investor behavior is guided by many factors, such as investment horizons, other investors' actions, risk capacity, personality, and level of volatility in equity markets. Past studies have highlighted that individuals commit various behavioral anomalies due to incomplete information, shortage of technical skills, and belief in their competencies to invest while investing. This study has empirically tried to determine the presence of the predominant behavioral anomalies; the herding bias, overconfidence bias, disposition effect, and noise trading in the Indian stock market. Herding has been tested using the cross-sectional absolute deviation methodology as described by Chang et al. (2000). The other biases have been tested using a time-series regression model, such as VAR and Granger causality. Our sample consists of Nifty 50 companies for 21 years (January, 2000-December, 2020). The research shows that Indian stock markets are efficient as we fail to validate the herding bias for the overall market. However, herd mentality exists in crisis and extreme market conditions. The results also validate the existence of anomalies, such as the disposition effect, overconfidence, and noise trading in the Indian stock market.

Keywords

Inclusive Leadership, Thriving At Work, Innovative Work Behavior, Hospitality Employees

JEL Classification- G1,G4,G5

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  • Abdullahi, S. A., Kouhy, R., & Muhammad, Z. (2014). Trading volume and return relationship in the crude oil futures markets. Studies in Economics and Finance. https://doi.org/10.1108/SEF-08-2012-0092

  • Al-Samman, H., & Al-Jafari, M. K. (2015). Trading volume and stock returns volatility. Evidence from industrial firms of Oman. Asian Social Science, 11, 139–146. https://doi.org/10.5539/ass.v11n24p139

  • Barber, B.M., & Odean,T. (2000). Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors. Journal of Finance, LV, 773–806. https://doi.org/10.1111/0022-1082.00226

  • Barberis, N., & Xiong, W. (2009). What Drives the Disposition Effect? An Analysis of a Long‐Standing Preference‐Based Explanation. The Journal of Finance, 64(2), 751-784. https://doi.org/10.1111/j.1540-6261.2009.01448.x

  • Bailey, W., Kumar, A., & Ng, D. (2011). Behavioral biases of mutual fund investors. Journal of Financial Economics, 102(1), 1-27. https://doi.org/10.1016/j.jfineco.2011.05.002

  • Buffett, M. & Clark, D. (2008). Warren Buffett and the Interpretation of Financial Statements: The Search for the Company with a Durable Competitive Advantage. Scribner, NY.

  • Chang, E. C., Cheng, J. W., & Khorana, A. (2000). An examination of herd behavior in equity markets: An international perspective. Journal of Banking & Finance, 24(10), 1651-1679. https://doi.org/10.1016/S0378-4266(99)00096-5

  • Chen, J., Hong, H., & Stein, J. C. (2001). Forecasting crashes: Trading volume, past returns, and conditional skewness in stock prices. Journal of financial Economics, 61(3), 345-381. https://doi.org/10.1016/S0304-405X(01)00066-6

  • Chiang, T. C., & Zheng, D. (2010). An empirical analysis of herd behavior in global stock markets. Journal of Banking & Finance, 34(8), 1911-1921. https://doi.org/10.1016/j.jbankfin.2009.12.014

  • Chiang, T. C., Li, J., & Tan, L. (2010). Empirical investigation of herding behavior in Chinese stock markets: Evidence from quantile regression analysis. Global Finance Journal, 21(1), 111-124. https://doi.org/10.1016/j.gfj.2010.03.005

  • Chuang, W. I., & Lee, B. S. (2006). An empirical evaluation of the overconfidence hypothesis. Journal of Banking & Finance, 30(9), 2489-2515. https://doi.org/10.1016/j.jbankfin.2005.08.007

  • Daniel, K., Hirshleifer, D., & Subrahmanyam, A. (1998). Investor Psychology and Security Market Under- and Overreactions. Journal of Finance, LIII, 1839–1885. https://doi.org/10.1111/0022-1082.00077

  • De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990). Noise trader risk in financial markets. Journal of political Economy, 98(4), 703-738. http://dx.doi.org/10.1086/261703

  • Dhall, R., & Singh, B. (2020). The COVID-19 pandemic and herding behaviour: Evidence from India’s stock market. Millennial Asia, 11(3), 366-390. https://doi.org/10.1177/0976399620964635

  • Frazzini, A. (2006). The disposition effect and underreaction to news. The Journal of Finance, 61(4), 2017-2046. https://doi.org/10.1111/j.1540-6261.2006.00896.x Ganesh, R., Naresh, G., &

  • Thiyagarajan, S. (2016). Industry herding behaviour in Indian stock market. American Journal of Finance and Accounting, 4(3-4), 284-308. https://doi.org/10.1504/AJFA.2016.10001602

  • Gervais, S., & Odean, T. (2001). Learning to be overconfident. Review of Financial studies, 14(1), 1-27. https://doi.org/10.1093/rfs/14.1.1 Ghazali, R., Hussain,

  • A. J., & Liatsis, P. (2011). Dynamic Ridge Polynomial Neural Network: Forecasting the univariate non-stationary and stationary trading signals. Expert Systems with Applications, 38(4), 3765-3776. https://doi.org/10.1016/j.eswa.2010.09.037

  • Grinblatt, M., & Keloharju, M. (2001). What makes investors trade?. The Journal of Finance, 56(2), 589-616. https://doi.org/10.1111/0022-1082.00338

  • Gunduz, L., & Hatemi-J, A. (2005). Stock price and volume relation in emerging markets. Emerging Markets Finance and Trade, 41(1), 29-44. https://doi.org/10.1080/1540496X.2005.11052599

  • Hiemstra, C., & Jones, J. D. (1994). Testing for linear and nonlinear Granger causality in the stock price‐volume relation. The Journal of Finance, 49(5), 1639-1664. https://doi.org/10.1111/j.1540-6261.1994.tb04776.x

  • Jain, P. C., & Joh, G. H. (1988). The dependence between hourly prices and trading volume. Journal of Financial and Quantitative Analysis, 269-283. https://doi.org/10.2307/2331067

  • Jose, B., Varghese, J., & Surendran, A. (2018). Investors’ Herding In Indian Stock Market; An Empirical Analysis. IOSR Journal of Business and Management, 20(1), 76-86. https://doi.org/10.9790/487X-2001047686

  • Kanojia, S., Singh, D., and Goswami, A. (2020), “Impact of herding on the returns in the Indian stock market: an empirical study”, Review of Behavioral Finance, Vol. 14 No. 1, pp. 115-129. https://doi.org/10.1108/RBF-01-2020-0017

  • Khan, H., Hassairi, S. A., & Viviani, J. L. (2011). Herd behavior and market stress: The case of four European countries. International Business Research, 4(3), 53. https://doi.org/10.5539/ibr.v4n3p53

  • Lakshman, M.V., Basu, S., & Vaidyanathan, R. (2011). Market wide herding and the impact of institutional investors in the Indian capital market. Working Paper 327, Indian Institute of Management, Banglore. https://doi.org/10.1177/0972652713494046

  • Lao, P., & Singh, H. (2011). Herding behaviour in the Chinese and Indian stock markets. Journal of Asian Economics, 22(6), 495-506. https://doi.org/10.1016/j.asieco.2011.08.001

  • Lindhe, E. (2012). Herd behavior in stock markets: A Nordic study. Retrieved from http://lup.lub.lu.se/student-papers/record/3046944

  • Mackay, C. (1841). Memoirs of Extraordinary Popular Delusions and the Madness of Crowds. (1 ed.). London: Richard Bentley.

  • Mahajan, S., & Singh, B. (2009). The empirical investigation of relationship between return, volume and volatility dynamics in Indian stock market. Eurasian Journal of Business and Economics, 2(4), 113-137. Retrieved from https://ejbe.org/index.php/EJBE/article/view/27

  • Odean, T. (1999). Do Investors Trade Too Much?. American Economic Review, 1279–1298. https://doi.org/10.1257/aer.89.5.1279 Prosad, J. M., Kapoor, S., &

  • Sengupta, J. (2012). An examination of herd behavior: An empirical evidence from Indian equity market. International Journal of Trade, Economics and Finance, 3(2), 154. https://doi.org/10.7763/IJTEF.2012.V3.190

  • Prosad, J. M., Kapoor, S., & Sengupta, J. (2013). Impact of overconfidence and the disposition effect on trading volume: An empirical investigation of Indian Equity Market. International Journal of Research in Management and Technology, 3(4), 109-116. https://doi.org/10.1177/0972150917726660

  • Sewell, M. (2007). Behavioral Finance. Retrieved from http://www.behaviouralfinance.net/behavioural-finance.pdf.

  • Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. The Journal of finance, 40(3), 777-790. https://doi.org/10.1111/j.1540-6261.1985.tb05002.x

  • Shrestha, S. (2017). Contemporaneous Relationship between Trading Volume and Stock Returns Volatility: Evidence from Nepalese Stock Market. PYC Nepal Journal of Management, 10(1), 40-63. https://doi.org/10.3126/pycnjm.v10i1.36067

  • Shumway, T., & Wu, G. (2006). Does Disposition Drive Momentum? University of Michigan working paper.

  • Sonavane, R.N. (2021, September 16). Indian stock market overtakes France; becomes sixth biggest. Livemint.com. Retrieved from https://www.livemint.com/market/stock-market-news/indian-stock-market-overtakes-france-becomes-sixth-biggest-11631730375607.html

  • Statman, M., Thorley, S., & Vorkink, K. (2006). Investor overconfidence and trading volume. Review of Financial Studies, 19(4), 1531-1565. https://doi.org/10.1093/rfs/hhj032

  • Tapia, W., & Yermo, J. (2007). Implications of Behavioural Economics for Mandatory Individual Account Pension Systems. https://doi.org/10.1787/19936397

  • Tariq, B., & Ullah, N. (2013). Investor overconfidence and stock returns: Evidence from pakistan. IOSR Journal of Business and Management, 8(1), 77-84. https://doi.org/10.9790/487X-0817784

  • Weber, M., & Camerer, C. F. (1998). The disposition effect in securities trading: An experimental analysis. Journal of Economic Behavior & Organization, 33(2), 167-184. https://doi.org/10.1016/S0167-2681(97)00089-9

  • Zaiane, S. (2013). Overconfidence, trading volume and the disposition effect: Evidence from the

  • Shenzhen Stock Market of China. Journal Issues ISSN, 2350, 157X. Available online at http://www.journalissues.org/IBME/

  • Zia, L., Ilyas Sindhu, M., & Haider Hashmi, S. (2017). Testing overconfidence bias in Pakistani stock market. Cogent Economics & Finance, 5(1), 1289656. https://doi.org/10.1080/23322039.2017.1289656


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  • Exploring Behavioral Biases in Stock Market: Evidence from India

Abstract Views: 119  |  PDF Views: 79

Authors

Deepali Malhotra
Assistant Professor, Delhi School of Management, Delhi Technological University, India

Abstract


Due to the positive association between investments and the development of the economy, the rise of investment will progressively influence the economy's overall growth and vice versa. Thus, investors’ decisions play a significant role in describing the market trend, which in turn affects the economy. Individuals invest with unique planning or no planning at all based on their available funds, time span, and financial goal. Ultimately, the majority of them want high returns that will make them wealthy overnight Regardless of how strong the company fundamentals are, strong negative emotions can wreck down a robust bullish market trend. The investor behavior is guided by many factors, such as investment horizons, other investors' actions, risk capacity, personality, and level of volatility in equity markets. Past studies have highlighted that individuals commit various behavioral anomalies due to incomplete information, shortage of technical skills, and belief in their competencies to invest while investing. This study has empirically tried to determine the presence of the predominant behavioral anomalies; the herding bias, overconfidence bias, disposition effect, and noise trading in the Indian stock market. Herding has been tested using the cross-sectional absolute deviation methodology as described by Chang et al. (2000). The other biases have been tested using a time-series regression model, such as VAR and Granger causality. Our sample consists of Nifty 50 companies for 21 years (January, 2000-December, 2020). The research shows that Indian stock markets are efficient as we fail to validate the herding bias for the overall market. However, herd mentality exists in crisis and extreme market conditions. The results also validate the existence of anomalies, such as the disposition effect, overconfidence, and noise trading in the Indian stock market.

Keywords


Inclusive Leadership, Thriving At Work, Innovative Work Behavior, Hospitality Employees

JEL Classification- G1,G4,G5


References





DOI: https://doi.org/10.58426/cgi.v4.i2.2022.25-46