Abstract
This study investigates the effectiveness of three technical indicators, namely Simple moving average (SMA), On-balance volume (OBV), and Commodity channel index (CCI), in identifying profitable trading opportunities. Drawing inspiration from the work of Naved and Srivastava (2015b), this research uses historical price data from 50 undervalued companies in comparison with the returns of NIFTY 50 companies. To assess the long-term feasibility of these indicator combinations, a performance analysis is carried out over 10 years, encompassing a sizable 8,50,209 trades. The analysis focuses on trade count, total return percentage, average profit per trade, and the Sharpe Ratio. The results highlight five indicator combinations that consistently generate more positive returns than negative returns, with fewer trades. The results highlight five indicator combinations consistently generating more positive returns than negative returns, with the best strategy achieving an average return per trade distributed between 0 to 30 percent (50 percent of trades), 30 to 70 percent (25 percent of trades), and less than 25 percent of trades incurring negative returns of up to -10 percent. CCI emerges as the most effective indicator for profitability, followed by OBV and SMA. This research equips market participants with valuable insights for well-informed investment decisions, emphasizing both potential returns and risk management.
Reference21 articles.
1. Ali, I., Mahfooz, S. Z., Mehmood, N. Q., & Mehmood, M. N. (2023). Deployment of a smart trading system for intelligent stock trading. The Nucleus, 60(1), 1–8. http://www.thenucleuspak.org.pk/index.php/Nucleus/article/view/1254
2. Bashir, B., & Aslam, F. (2022). Technical indicator selection and trading signal forecasting: Varying input window length and forecast horizon for the Pakistan Stock Exchange. The Journal of Network Theory in Finance, 3(5). https://doi.org/10.21314/jntf.2021.005
3. Chandar, S. K. (2022). Convolutional neural network for stock trading using technical indicators. Automated Software Engineering, 29(1), 1–14. https://doi.org/10.1007/s10515-021-00303-z
4. Chio, P. T. (2022). A comparative study of the MACD-base trading strategies: Evidence from the US stock market. arXiv. https://doi.org/10.48550/arxiv.2206.12282
5. Di Graziano, G. (2014). Optimal trading stops and algorithmic trading. SSRN Electronic Journal, 8(4). https://doi.org/10.2139/ssrn.2381830