Impact of Using Short-Term Trading Strategies on Securities’ Returns: Evidence from Djia Securities Market

The purpose of this study was to analyze short-term trend following trading strategies to understand their impact on returns of securities trading at the Dow Jones Industrial Average (DJIA). It achieved the objectives by analyzing the impact of momentum and moving average strategies, as well as a combined alpha strategy, and then comparing their returns with the market returns and buy-and-hold returns. Recently, scholars have questioned the consistency of the efficient market hypothesis after the claims of superior returns emerged. Publications have shown both agreement and disagreement with the concept of delivering abnormal returns by traders and other participants. While the Efficient Market Hypothesis asserts no one can beat the market by utilizing technical or fundamental analysis, empirical results of technical traders have shown it is possible to deliver superior returns. In view of these conflicting claims, empirical research in a well-developed securities market like the Dow Jones Industrial Average (DJIA) was useful in assessing the returns of securities. A quantitative research methodology was adopted in this study, together with an experimental research design. Historical price and volume data from Yahoo Finance for the Dow Jones Industrial Average as the market index, and its constituent stocks as equity data were used. One stock was randomly picked from each strata (high vs low performing stocks), making a total of 2 stocks selected to be studied. Specifically, Cisco (CSCO) (high performing) and Walgreens Boots Alliance Inc (WBA) (low performing) data for a period of two years was studied. The beginning period was set 2 years from the date of the analysis (August 2023 to August 2025). The downloaded data was analyzed using Python 3.12.7 using Jupyter Notebook. From the empirical results, the momentum strategy can consistently deliver better returns than the market (CSCO 32.46% and WBA 49.95% vs the DJIA Market 12.32%). Moving averages did not deliver better returns than the market (CSCO 5.07% and WBA -1.10%, vs DJIA Market 12.32%). Combined strategy showed mixed results (CSCO 9.56%, 42.75% vs DJIA Market 12.32%). The Efficiency Market Hypothesis does not hold true for all the securities tested. The DJIA market exhibited a weak form of efficiency. Trend following strategies have been shown to have the power to assist the trader in entering and exiting trades at the right time and generate superior returns. Combining these two strategies gives the trader a chance to choose the best-performing one at that particular time. Using the right signals in short-term trading can be profitable both in the bullish and the bearish markets. The researcher recommended the use of active portfolio management strategies such as technical analysis to maximize returns for investors and create more wealth. Use of predictive computing techniques such as machine learning is highly recommended to consistently beat the market as trading data and methods become more complex.

Trading strategies, Momentum, Moving averages, Market returns, Superior returns, Efficient Market Hypothesis.

1. Abuselidze, G. D., & Slobodianyk, A. N. (2021). Value assessment of shares of corporate issuers by applying the methods of fundamental analysis in the stock exchange market. In The Challenge of Sustainability in Agricultural Systems: Volume 2 (pp. 25-39). Cham: Springer International Publishing. [Google Scholar] [Crossref]

2. Agarwal, V., & Ren, H. (2023). Hedge funds: performance, risk management, and impact on asset markets. Risk Management, and Impact on Asset Markets (February 22, 2023). Oxford Research Encyclopedia of Economics and Finance. [Google Scholar] [Crossref]

3. Alhashel, B. S., Almudhaf, F. W., & Hansz, J. A. (2018). Can technical analysis generate superior returns in securitized property markets? Evidence from East Asia markets. Pacific-Basin Finance Journal, 47, 92108. [Google Scholar] [Crossref]

4. Amiri, A., Ravanpaknodezh, H., & Jelodari, A. (2016). Comparison of stock valuation models with their intrinsic value in Tehran Stock Exchange. Marketing and Branding Research, 3, 24-40. [Google Scholar] [Crossref]

5. Baumann, M. H. (2022). Beating the market? A mathematical puzzle for market efficiency. Decisions in Economics and Finance, 45(1), 279-325. [Google Scholar] [Crossref]

6. Charoenwong, B. (2012). An Exploration of Simple Optimized Technical Trading Strategies (Doctoral dissertation). [Google Scholar] [Crossref]

7. Chourmouziadis, K., & Chatzoglou, P. D. (2016). An intelligent short term stock trading fuzzy system for assisting investors in portfolio management. Expert Systems with Applications, 43, 298-311. [Google Scholar] [Crossref]

8. Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches. (5th ed.). Sage Publications. [Google Scholar] [Crossref]

9. Edwards, R. D., Magee, J., & Bassetti, W. C. (2018). Technical analysis of stock trends. CRC press. [Google Scholar] [Crossref]

10. Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654-669. [Google Scholar] [Crossref]

11. Gârleanu, N., & Pedersen, L. H. (2013). Dynamic trading with predictable returns and transaction costs. The Journal of Finance, 68(6), 2309-2340. [Google Scholar] [Crossref]

12. Ghouse, S. H. N. M., Ahmad, N., & Salamudin, N. (2018). Contrarian Strategies in Developing Asian Countries: Dogs of the Dow Theory (DoD) versus Puppies of the Dow Theory (PoD). International Journal of Academic Research in Business and Social Sciences, 8(12), 2003–2012. [Google Scholar] [Crossref]

13. Gunawan, T. I. (2024). Understanding investment decision-making: A qualitative inquiry into high-frequency trading, investment strategies, and portfolio performance in the financial market. Golden Ratio of Finance Management, 4(2), 131-141. [Google Scholar] [Crossref]

14. Hamid, K., Suleman, M. T., Ali Shah, S. Z., Akash, I., & Shahid, R. (2017). Testing the weak form of efficient market hypothesis: Empirical evidence from Asia-Pacific markets. Available at SSRN 2912908. [Google Scholar] [Crossref]

15. Hilber, N., Reichmann, O., Schwab, C., & Winter, C. (2013). Computational methods for quantitative finance: [Google Scholar] [Crossref]

16. Finite element methods for derivative pricing. Springer Science & Business Media. [Google Scholar] [Crossref]

17. Jogani, A. (2024). The basics of technical analysis. Available at SSRN 4870943. [Google Scholar] [Crossref]

18. Khuntia, S., & Pattanayak, J. K. (2018). Adaptive market hypothesis and evolving predictability of bitcoin. Economics Letters, 167, 26-28. [Google Scholar] [Crossref]

19. Lee, M. C., Chang, J. W., Hung, J. C., & Chen, B. L. (2021). Exploring the effectiveness of deep neural networks with technical analysis applied to stock market prediction. Computer Science and Information Systems, 18(2), 401-418. [Google Scholar] [Crossref]

20. Lo, A. W. (2004). The adaptive markets hypothesis. The Journal of Portfolio Management, 30(5), 15-29. [Google Scholar] [Crossref]

21. Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of economic perspectives, 17(1), 59-82. [Google Scholar] [Crossref]

22. Marwala, T., & Hurwitz, E. (2017). Efficient Market Hypothesis. In Artificial Intelligence and Economic Theory: Skynet in the Market (pp. 101-110). Springer, Cham. [Google Scholar] [Crossref]

23. Murphy, J. J. (2009). The visual investor: how to spot market trends (Vol. 443). John Wiley & Sons. [Google Scholar] [Crossref]

24. Nardi, P. M. (2018). Doing survey research: A guide to quantitative methods. Routledge. [Google Scholar] [Crossref]

25. Nazário, R. T. F., e Silva, J. L., Sobreiro, V. A., & Kimura, H. (2017). A literature review of technical analysis on stock markets. The Quarterly Review of Economics and Finance, 66, 115-126. [Google Scholar] [Crossref]

26. Omar, M. H. A. E. R., Hammad, J. A. E. D. S., El-Behairy, O. H., El-Tohami, S. A., & Mohamed, T. I. (2025). Behavioral dynamics and market adaptation: Cross-country empirical evidence of the adaptive market hypothesis. Academic Journal of Social Sciences, 1(2), 18-65. [Google Scholar] [Crossref]

27. Pathak, A., & Shetty, N. P. (2019). Indian Stock Market Prediction Using Machine Learning and Sentiment Analysis. In Computational Intelligence in Data Mining (pp. 595-603). Springer, Singapore. [Google Scholar] [Crossref]

28. Prabakaran, V., & Krishnaveni, P. (2016). DOW Theory in Assessing Equity Share Price Movement. Asian Journal of Research in Social Sciences and Humanities, 6(10), 1326-1336. [Google Scholar] [Crossref]

29. Precious, E. O., & Marwa, N. (2023, June). Comparative analysis of moving average and bollinger bands as an investment strategy in a select Crypto Asset. In ICABR Conference (pp. 53-70). Cham: Springer Nature Switzerland. [Google Scholar] [Crossref]

30. Stylios, C. D., & Kreinovich, V. (2018). Dow Theory’s Peak-and-Trough Analysis Justified. In Constraint Programming and Decision Making: Theory and Applications (pp. 123-128). Springer, Cham. [Google Scholar] [Crossref]

31. Timmermann, A., & Granger, C. W. (2004). Efficient market hypothesis and forecasting. International Journal of forecasting, 20(1), 15-27. [Google Scholar] [Crossref]

32. Wattana torn, W., & Nathaphan, S. (2017). The Predictable Market and Mutual Fund's Superior Performance. The Evidence from the Higher Moment Method. Journal of Applied Economic Sciences, 12(5). [Google Scholar] [Crossref]

33. Yadav, S. (2017). Implications of Dow Theory in Indian Stock Market. Research Journal of Social and Management, 7(1), 98-103. [Google Scholar] [Crossref]

34. Zhao, Y., & Yang, G. (2023). Deep learning-based integrated framework for stock price movement prediction. Applied Soft Computing, 133, 109921. [Google Scholar] [Crossref]

35. Zhou, J., & Lee, J. M. (2013). Adaptive market hypothesis: evidence from the REIT market. Applied Financial Economics, 23(21), 1649-1662. [Google Scholar] [Crossref]

• The Correlation of Marketing Strategies and Financial Performance of Motor Parts Businesses in the City of Mati
• The Role of Blockchain-Based Smart Contracts in Enhancing Financial Transparency and Efficiency in the Emerging Market
• Exploring Intention among Universiti Teknologi Mara Terengganu Community to Donate into Education Waqf Fund
• Utilizing AHP Method to Rank Preference of Waqf Fund Usage for UiTM Terengganu, Malaysia
• The Influence of Portfolio Diversification on Financial Performance: Evidence from Listed Banks on the Ghana Stock Exchange