ISSN: 2225-8329
Open access
Purpose This research investigates the impact of integrating Artificial Intelligence (AI) with traditional fundamental and technical analysis to enhance stock return predictions and manage investment risks in Malaysia's food manufacturing sector from 2013 to 2023. Design/Methodology/Approach Employing structured data (financial indicators, technical signals) and unstructured data (news sentiment, social media, satellite imagery), this study analyses 80 food manufacturing companies listed on Bursa Malaysia. Hybrid AI models using machine learning techniques, including neural networks and decision trees, are compared to traditional analytical methods through back testing and simulation. Findings Hybrid AI models substantially outperformed standalone methods, delivering an annualized return of 17.4% compared to 13.8% for fundamental analysis alone. Moreover, AI integration notably reduced portfolio drawdowns by up to 30% during market volatility and minimized emotional trading behaviors among retail investors by nearly 40%. Practical Implications Investors and asset managers in Malaysia and similar markets can significantly enhance portfolio performance and resilience by adopting AI-driven hybrid analytical techniques. Regulatory authorities must prioritize transparency and ethical considerations associated with AI usage. This study uniquely provides empirical validation of hybrid AI model superiority within an emerging Asian market, considering extended market conditions, cultural influences, and regulatory aspects.
Al-AbdulQader, K., Al-Ajmi, J., & Hussain, M. (2007). The use of fundamental and technical analyses by individual investors in Saudi Arabia. Journal of Economic and Administrative Sciences, 23(2), 73–94.
Bae, K.-H., Ozoguz, A., Tan, H., & Wirjanto, T. S. (2020). Insider trading, governance mechanisms, and market efficiency. Journal of Corporate Finance, 65, 101764. https://doi.org/10.1016/j.jcorpfin.2020.101764
Bhabra, H. S., Hossain, M., & Tan, C. S. (2022). Corporate governance and insider trading: Evidence from Malaysia. Asian Journal of Business and Accounting, 15(1), 1–24. https://doi.org/10.22452/ajba.vol15no1.1
Chen, Y., & Wei, Y. (2021). Sentiment analysis for stock market prediction: A survey. Expert Systems with Applications, 165, 113816. https://doi.org/10.1016/j.eswa.2020.113816
Cheung, Y.-W., & Chinn, M. D. (2001). Currency traders and exchange rate dynamics: A survey of the U.S. market. Journal of International Money and Finance, 20(4), 439–471. https://doi.org/10.1016/S0261-5606(01)00003-8
Chern, C. C., Tandon, K., & Yu, S. (2008). The information content of stock splits and the regulatory environment: Evidence from China. Review of Quantitative Finance and Accounting, 31(2), 155–170. https://doi.org/10.1007/s11156-007-0064-4
Dixon, M. F., Klabjan, D., & Bang, J. H. (2020). Machine learning for market microstructure and high-frequency trading. In C.-F. Lee, J. Lee, & A. C. Lee (Eds.), Handbook of financial econometrics, mathematics, statistics, and machine learning (Vol. 2, pp. 1269–1296). World Scientific. https://doi.org/10.1142/9789811202197_0046
Drakopoulou, V. (2015). The use of fundamental and technical analysis in the stock market in emerging and developed economies. Journal of Business and Economic Policy, 2(1), 69–82.
Feng, F., He, X., & Liu, T.-Y. (2018). Deep learning for financial applications: A survey. Big Data Mining and Analytics, 1(1), 1–14. https://doi.org/10.26599/BDMA.2017.9020001
Hu, Z., Liu, W., Bian, J., Liu, X., & Liu, T.-Y. (2015). Listening to chaotic whispers: A deep learning approach to news-oriented stock market prediction. In Proceedings of the 24th International Conference on World Wide Web (pp. 261–270). ACM. https://doi.org/10.1145/2736277.2741125
Ikenberry, D. L., & Ramnath, S. (2002). Underreaction to self-selected news events: The case of stock splits. The Review of Financial Studies, 15(2), 489–526. https://doi.org/10.1093/rfs/15.2.489
Jakpar, S., Tinggi, M., Yu, T. Z., & Samsudin, N. (2020). Stock split in insider trading: Lessons from Malaysia. International Journal of Academic Research in Accounting, Finance and Management Sciences, 10(1), 1–17.
Jakpar, S., Tinggi, M., Tak, A. H., & Chong, W. Y. (2018). Fundamental analysis vs technical analysis: The comparison of two analysis in Malaysia stock market. UNIMAS Review of Accounting and Finance, 2(1), 1–15.
Krauss, C., Do, X. A., & Huck, N. (2017). Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500. European Journal of Operational Research, 259(2), 689–702. https://doi.org/10.1016/j.ejor.2016.12.019
Lim, C. Y., & Zainudin, R. (2020). Robo-advisors and retail investor behavior in Malaysia. Asian Journal of Business Research, 10(1), 112–124. https://doi.org/10.14707/ajbr.200074
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. The Journal of Finance, 55(4), 1705–1765. https://doi.org/10.1111/0022-1082.00263
Mitra, S. K. (2011). Usefulness of moving average in technical analysis. International Journal of Business and Management, 6(7), 208–215. https://doi.org/10.5539/ijbm.v6n7p208
Nadeem, M., & Muhammad, S. (2013). Fundamental analysis and stock returns: Evidence from Pakistan. Journal of Business & Economics, 5(2), 45–62.
Park, C. H., & Irwin, S. H. (2004). The profitability of technical analysis: A review (AgMAS Project Research Report). University of Illinois. https://agmas.org/wp-content/uploads/2016/01/AgMAS_TechAnalysisReview.pdf
Petrusheva, N., & Jordanoski, M. (2016). The role of fundamental and technical analysis in stock market in Republic of Macedonia. Economic Development, 18(1–2), 133–144.
Rahim, N. F. A., Ismail, S., & Ahmad, N. (2019). Artificial intelligence trading systems in Bursa Malaysia: A performance evaluation. Malaysian Journal of Economic Studies, 56(2), 233–250.
Sarli, M., Tan, H. B., Aghashahi, F., & Sarli, S. (2014). Insider trading and market efficiency: Evidence from Malaysia. Asian Academy of Management Journal of Accounting and Finance, 10(2), 1–22.
Spence, M., & Zeckhauser, R. (1971). Insurance, information, and individual action. American Economic Review, 61(2), 380–387.
Wong, W. K., Manzur, M., & Chew, B. K. (2003). How rewarding is technical analysis? Evidence from Singapore stock market. Applied Financial Economics, 13(7), 543–551. https://doi.org/10.1080/09603100210139044
Wong, W., Lim, C., & Lee, S. (2022). Regulatory enforcement and insider trading in Malaysia. Capital Markets Review, 30(1), 22–44.
Zhang, L., & Zhang, X. (2023). Board independence and insider trading: Evidence from Asian markets. Asia-Pacific Journal of Accounting & Economics, 30(2), 99–118. https://doi.org/10.1080/16081625.2022.2061234
Jakpar, S., & Tinggi, M. (2025). Artificial Intelligence-Augmented Stock Analysis: Advancing Investment Strategies in the Malaysian Market. International Journal of Academic Research in Accounting, Finance and Management Sciences, 15(3), 87–97.
Copyright: © 2025 The Author(s)
Published by HRMARS (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode