Risk Tolerance and the Adoption of Stop-Loss Strategies among Retail Investors in the Indian Stock Market
DOI:
https://doi.org/10.69980/kp4q9a68Keywords:
Risk Tolerance, Stop-Loss Strategy, Retail Investors, Behavioural Finance, AdoptionAbstract
This paper examines the association between risk tolerance and the usage of stop-loss strategies by retail investors in the Indian stock market. The paper uses primary data collected from 72 investors using a structured questionnaire with five-point Likert scales and employs correlation analysis and ordinal logistic regression to examine the hypotheses. The results indicate that higher risk as a predictor of stop-loss usage is significant, but self-identified risk profile is not significant. Moreover, behavioral response to market downturns is identified as a robust predictor of disciplined stop-loss strategy, accounting for a larger variance than general risk tolerance metrics. The results indicate that dynamic behavioral responses to market volatility have a more significant impact on the adoption of formal risk management strategies than static risk identity. This paper makes a contribution to the literature on behavioral finance by indicating that disciplined protective action is linked to active risk engagement rather than risk tolerance.
References
1.Aren, S., & Zengin, A. N. (2016). Influence of financial literacy and risk perception on choice of investment. Procedia – Social and Behavioral Sciences, 235, 656–663.
2.Baker, H. K., & Ricciardi, V. (2016). Understanding behavioral aspects of financial planning and investing. Journal of Financial Planning, 29(3), 22–26.
3.Baker, H. K., Kumar, S., Goyal, N., & Gaur, V. (2020). How financial literacy and demographic variables relate to behavioral biases. Managerial Finance, 46(6), 771–794.
4.Barber, B. M., & Odean, T. (2017). Trading is hazardous to your wealth: The common stock investment performance of individual investors. The Journal of Finance, 72(2), 773–806.
5.Bouri, E., Gupta, R., & Roubaud, D. (2017). Herding behaviour in cryptocurrencies. Finance Research Letters, 20, 1–8.
6.Chang, T. Y., Solomon, D. H., & Westerfield, M. M. (2016). Looking for someone to blame: Delegation, cognitive dissonance, and the disposition effect. The Journal of Finance, 71(1), 267–302.
7.Duong, T. H., Kalev, P. S., & Krishnamurti, C. (2019). The impact of stop-loss strategies on trading performance. Journal of Behavioral and Experimental Finance, 23, 100–112.
8.Grable, J. E. (2016). Financial risk tolerance: A psychometric review. Journal of Financial Service Professionals, 70(2), 59–66.
9.Hoffmann, A. O. I., & Post, T. (2016). How does investor confidence lead to trading? Linking investor return experiences, confidence, and investment beliefs. Journal of Behavioral and Experimental Finance, 12, 65–78.
10.Hoffmann, A. O. I., Post, T., & Pennings, J. M. E. (2017). Individual investor perceptions and behavior during the financial crisis. Journal of Banking & Finance, 80, 1–16.
11.Kumar, S., & Goyal, N. (2016). Evidence on rationality and behavioural biases in investment decision making. Qualitative Research in Financial Markets, 8(4), 270–287.
12.Nguyen, L., Gallery, G., & Newton, C. (2019). The joint influence of financial risk perception and risk tolerance on individual investment decision-making. Accounting & Finance, 59(1), 747–771.
13.Statman, M. (2019). Behavioral finance: The second generation. CFA Institute Research Foundation.
14.Toma, F. M. (2015). Behavioral biases of the investment decisions of Romanian investors on the Bucharest Stock Exchange. Procedia Economics and Finance, 32, 200–207.




