Volatility Effectiveness for Selected Penny Stocks in the Indian Stock Market
Authors
-
Sandeep Bhattacharjee
Assistant Professor (Corresponding Author), Amity University, Major Arterial Road, Action Area II, Kadampukur Village, Rajarhat, Newtown, Kolkata - 700 135, West Bengal
https://orcid.org/0000-0002-6686-3947
DOI:
https://doi.org/10.17010/ijrcm/2025/v12i2/175456Keywords:
penny stocks, volatility analysis, Bollinger bands, intramonthly returns, beta values.JEL Classification Codes : G11, G12, G14, G15
Publication Chronology: Paper Submission Date : February 5, 2025 ; Paper sent back for Revision : May 20, 2025 ; Paper Acceptance Date : May 25, 2025
Abstract
Purpose : The study investigated the volatility efficacy of specific penny stocks in the Indian stock market. The research emphasized an urgent need for efficient volatility assessment tools in emerging markets, considering the 78% year-on-year increase in penny stock trading volumes (National Stock Exchange of India, 2023) and considering 43% of Indian traders possess these high-risk instruments (Zerodha, 2024).
Methodology : Four quantitative parameters––Bollinger Bands, intramonthly returns, frequency of intramonthly returns, and beta values––were examined for four actively traded Indian penny stocks (Suzlon Energy Ltd., Comfort Intech Ltd., Vivanta Industries Ltd., and Veeram Securities Ltd.) from 2004 to 2024. The measurements were incorporated into a new combined volatility factor index (CVFI) to systematically evaluate risk–return profiles.
Findings : Comfort Intech Ltd. was identified as the most appropriate choice, demonstrating the highest risk and return (CVFI = 4), subsequently followed by Suzlon Energy Ltd. (CVFI = 3) and Vivanta Industries Ltd. (CVFI = 3). Strategic trading opportunities emerged at points where the correlation between price and return diminished, as revealed by Suzlon’s closing price of ₹ 394.71, which resulted in a return of 0.36%.
Practical Implications : The CVFI framework equipped investors with a data-driven tool for penny stock evaluation while assisting regulators in monitoring market volatility. Brokerages could use these insights to educate retail investors about risk management in high-volatility segments.
Originality : The results of the study reflected the potential of the newly designed robust platform for multidimensional volatility assessment for Indian penny stocks, mitigating a significant gap in emerging market research. CVFI is based on the integration of both technical and fundamental indicators for a comprehensive analysis of candidates for a penny stock portfolio.
Downloads
Published
How to Cite
Issue
Section
References
1) Arora, L., Kumar, S., & Verma, P. (2016). Growth measures and stock returns. Indian Journal of Finance, 10(11), 43–53. https://doi.org/10.17010/ijf/2016/v10i11/104916
2) Banerjee, A. K., Chatterjee, S., & Dutta, A. (2024). Market power, industry concentration and earnings management: Does corporate governance matter? The Journal of Risk Finance, 25(1), 1–18. https://doi.org/10.1108/jrf-05-2023-0116
3) Basvi, B. (2024). A model on a comparative cross-regime analysis on modelling and forecasting of the Zimbabwe stock market volatility using ARCH (1), GARCH (1, 1) and EGARCH (1, 2) as the extension to account for leverage effects from April 2012 to April 2024. International Journal of Research and Scientific Innovation, 11(5), 745–760. https://doi.org/10.51244/ijrsi.2024.1105047
4) Bodie, Z., Kane, A., & Marcus, A. (2014). Investments (10th ed.). McGraw-Hill Education. https://www.scirp.org/reference/referencespapers?referenceid=2653733
5) Bollinger, J. (2002). Bollinger on Bollinger bands. McGraw-Hill.
6) Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal of Finance, 52(1), 57–82. https://doi.org/10.1111/j.1540-6261.1997.tb03808.x
7) Chen, J., & Yang, L. (2021). A bibliometric review of volatility spillovers in financial markets: Knowledge bases and research fronts. Emerging Markets Finance and Trade, 57(5), 1358–1379. https://doi.org/10.1080/1540496x.2019.1695119
8) Christopher, M., & Holweg, M. (2017). Supply chain 2.0 revisited: A framework for managing volatility-induced risk in the supply chain. International Journal of Physical Distribution & Logistics Management, 47(1), 2–17. https://doi.org/10.1108/IJPDLM-09-2016-0245
9) Dang, V. C., & Nguyen, Q. K. (2024). Internal corporate governance and stock price crash risk: Evidence from Vietnam. Journal of Sustainable Finance & Investment, 14(1), 24–41. https://doi.org/10.1080/20430795.2021.2006128
10) Dhingra, B., Batra, S., Aggarwal, V., Yadav, M., & Kumar, P. (2024). Stock market volatility: A systematic review. Journal of Modelling in Management, 19(3), 925–952. https://doi.org/10.1108/jm2-04-2023-0080
11) Doley, S. R. (2022). Hypothesis test of weak form of market efficiency in some selected stocks. Indian Journal of Research in Capital Markets, 8(4), 38–47. https://doi.org/10.17010/ijrcm/2021/v8i4/168585
12) Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3–56. https://doi.org/10.1016/0304-405x(93)90023-5
13) Farhat, A., & Hili, A. (2024). The performance of compliant stocks during the Covid-19 crisis. Journal of Asset Management, 25(1), 70–95. https://doi.org/10.1057/s41260-023-00331-2
14) Gottesman, A., & Morey, M. (2024). Investor confidence and reaction to a stock market crash. Journal of Behavioral Finance, 25(3), 374–388. http://ndl.ethernet.edu.et/bitstream/123456789/29095/1/Frederick%20J%20Gravetter_2017.pdf
15) Gravetter, F. J., & Wallnau, L. B. (2017). Statistics for the behavioral sciences: A life-span view (10th ed.). Cengage Learning. https://doi.org/10.1136/bmj.1.6160.1470
16) Harshita, Singh, S., & Yadav, S. S. (2018). Calendar anomaly: Unique evidence from the Indian stock market. Journal of Advances in Management Research, 15(1), 87–108. https://doi.org/10.1108/JAMR-11-2016-0096
17) Kakran, S., Sidhu, A., Bajaj, P. K., & Dagar, V. (2023). Novel evidence from APEC countries on stock market integration and volatility spillover: A Diebold and Yilmaz approach. Cogent Economics & Finance, 11(2), Article ID 2254560. https://doi.org/10.1080/23322039.2023.2254560
18) Lakshmanasamy, T. (2021). The causal relationship between volatility in crude oil price, exchange rate, and stock price in India: GARCH estimation of spillover effects. Indian Journal of Research in Capital Markets, 8(3), 8–21. https://doi.org/10.17010/ijrcm/2021/v8i3/167954
19) Li, X., Sullivan, R. N., & Garcia-Feijóo, L. (2014). The limits to arbitrage and the low-volatility anomaly. Financial Analysts Journal, 70(1), 52–63. https://doi.org/10.2469/faj.v70.n1.3
20) Mangala, D., & Lohia, V. (2017). Semi-monthly and turn-of-the-month seasonality in stock returns: Evidence from emerging markets. Indian Journal of Finance, 11(4), 39–52. https://doi.org/10.17010/ijf/2017/v11i4/112629
21) Melas, K. D., Faitatzoglou, A., Michail, N. A., & Artemiou, A. (2024). Volatility spillovers among the major commodities: A review. Journal of Risk and Financial Management, 17(8), 365. https://doi.org/10.3390/jrfm17080365
22) National Stock Exchange of India. (2023). Indian retail investor survey 2023. NSE India. https://nsearchives.nseindia.com/web/sites/default/files/inline-files/NSE%20Annual%20Report%202022-23_0_0.pdf
23) Nayak, D., & Barodawala, R. (2021). The impact of macroeconomic factors on the Indian stock market: An assessment. Arthshastra Indian Journal of Economics & Research, 10(2–3), 27–40. https://doi.org/10.17010/aijer/2021/v10i2-3/167172
24) Nofsinger, J. R., & Varma, A. (2014). Pound wise and penny foolish? OTC stock investor behavior. Review of Behavioral Finance, 6(1), 2–25. https://doi.org/10.1108/rbf-12-04-02
25) Panandikar, S. C. (2007). A model to predict the BSE index and its volatility. Indian Journal of Finance, 1(3), 3–7. https://www.indianjournaloffinance.co.in/index.php/IJF/article/view/72002
26) Perera, K., Kuruppuarachchi, D., Kumarasinghe, S., & Suleman, M. T. (2025). Climate risk and the idiosyncratic volatility puzzle. Applied Economics, 57(27), 3689–3708. https://doi.org/10.1080/00036846.2024.2337816
27) Rahmadhisa, N. P., Susanti, D., & Subartini, B. (2025). IDX30 stock portfolio optimization using genetic algorithm based on capital asset pricing Model. International Journal of Quantitative Research and Modeling, 6(2), 200–207. https://doi.org/10.46336/ijqrm.v6i2.981
28) Rupande, L., Muguto, H. T., & Muzindutsi, P.-F. (2019). Investor sentiment and stock return volatility: Evidence from the Johannesburg Stock Exchange. Cogent Economics & Finance, 7(1), Article ID 1600233. https://doi.org/10.1080/23322039.2019.1600233
29) Santur, Y. (2020). Deep learning based regression approach for algorithmic stock trading: A case study of the Bist30. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 10(4), 1195–1211. https://doi.org/10.17714/gumusfenbil.707088
30) Sehgal, S., Vasishth, V., & Deisting, F. (2024). Lottery factor and stock returns: Evidence from India. Borsa Istanbul Review, 24(3), 449–459. https://doi.org/10.1016/j.bir.2024.02.006
31) Song, I., & Park, Y. K. (2019). Penny stocks: A handy tool for enhancing fund performance? Asia-Pacific Journal of Financial Studies, 48(4), 445–475. https://doi.org/10.1111/ajfs.12345/10.1111/ajfs.12345
32) Stereńczak, S. (2024). Illiquidity and stock returns: The moderating role of investors' holding period in Central and Eastern European markets. International Journal of Emerging Markets, 19(7), 2025–2045. https://doi.org/10.1108/IJOEM-01-2022-0125
33) Tsay, R. S. (2010). Analysis of financial time series (3rd ed.). Wiley. https://www.amazon.com/Analysis-Financial-Time-Ruey-Tsay/dp/0470414359
34) Varghese, P. A., & Anthony, R. (2016). Is smart beta strategy a successful guide for creation of superior portfolios? Indian Journal of Research in Capital Markets, 3(3), 42–48. https://www.indianjournalofmarketing.com/index.php/ijrcm/article/view/103701
35) Wang, W., Wang, H., & Wu, J. (2021). Mixed ownership reform and corporate tax avoidance: Evidence of Chinese listed firms. Pacific-Basin Finance Journal, 69, Article ID 101648. https://doi.org/10.1016/j.pacfin.2021.101648
36) Yu, X., Huang, Y., & Xiao, K. (2021). Global economic policy uncertainty and stock volatility: Evidence from emerging economies. Journal of Applied Economics, 24(1), 416–440. https://doi.org/10.1080/15140326.2021.1953913
37) Zerodha. (2024). Annual retail trading survey: Investor behavior patterns in 2024. Zerodha Research. https://zerodha.com/research/
