A Quantitative Framework for Portfolio Governance Using Machine Learning Techniques

Authors

  • Yashasvi Makin Research Scholar
  • Pavan K Gondhi

DOI:

https://doi.org/10.22399/ijcesen.2474

Keywords:

Machine Learning, Predictive Analytics, Investment Governance, Asset Allocation, Risk Assessment, Artificial Intelligence

Abstract

This research explores how machine learning, a data-driven technology, can transform the management of investment portfolios. The objective is to assess whether machine learning can surpass the performance of traditional approaches, such as Modern Portfolio Theory, which have been established for decades. We explored various machine learning techniques, including those that predict stock prices, group investments based on patterns, and dynamically reallocate assets. Our comprehensive analysis leveraged a robust dataset spanning stock prices, economic indicators, as well as news and social media sentiment. Rigorous data processing and rigorous testing revealed that machine learning techniques substantially outclassed traditional approaches, generating higher returns while incurring lower risk, as reflected by a Sharpe ratio of 1.9 versus 1.3 for Modern Portfolio Theory. This technique also proved more adept at navigating volatile market conditions. Although this research faces challenges such as addressing noisy data or excessively complex models, the findings indicate that machine learning could be a transformative innovation in enhancing investment management practices. While the findings show promising results, there remains scope for further improvements, particularly in devising real-time adaptation mechanisms and ensuring equitable outcomes for all investors.

The integration of machine learning into financial modeling presents a paradigm shift from traditional linear parametric methods, offering a more versatile framework for addressing complex challenges in portfolio governance (Dixon, and Halperin (2019)).

References

[1] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville. The MIT Press. https://mitpress.mit.edu/9780262035613/deep- learning/

[2] Arrow, K. J. (1964). The Role of Securities in the Optimal Allocation of Risk-bearing. The Review of Economic Studies, 31(2), 91. https://doi.org/10.2307/2296188

[3] Bekaert, G., & Harvey, C. R. (2003). Emerging markets finance. Journal of Empirical Finance, 10, 3. https://doi.org/10.1016/s0927-

5398(02)00054-3

[4] Bishop, C. (2007). Pattern Recognition and Machine Learning. Journal of Electronic Imaging, 16(4), 49901.

https://doi.org/10.1117/1.2819119

[5] Bloomberg. (2025). Bloomberg Terminal, “Historical Financial Data,” Bloomberg L.P., New York, NY, USA, accessed Mar, 2025. [Data set].

https://www.bloomberg.com/professional/insigh ts/

[6] Carvalho, R., Almeida, R., & Freitas, A. (2023). A survey on machine learning in portfolio management: Techniques and applications. IEEE Transactions on Neural Networks and Learning Systems, 34.

https://doi.org/10.1109/TNNLS.2022.3144761

[7] Cont, R. (2001). Empirical properties of asset returns: stylized facts and statistical issues. Quantitative Finance, 1(2). https://doi.org/10.1080/713665670

[8] Dixon, M., & Halperin, I. (2019). The Four Horsemen of Machine Learning in Finance. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3453564

[9] Dixon, M., Halperin, I., & Bilokon, P. (2020a). Frontiers of Machine Learning and Finance. In Springer eBooks (p. 519). Springer Nature. https://doi.org/10.1007/978-3-030-41068-1_12

[10] Dixon, M., Halperin, I., & Bilokon, P. (2020b). Machine Learning in Finance. In Springer eBooks. Springer Nature. https://doi.org/10.1007/978-3-030-41068-1

[11] Fama, E. F., & French, K. R. (1992). The Cross‐ Section of Expected Stock Returns. The Journal of Finance, 47(2), 427.

https://doi.org/10.1111/j.1540- 6261.1992.tb04398.x

[12] Federal Reserve Board. (2025). The title is:

**Federal Reserve Board Publication**. https://www.federalreserve.gov/publications/files

/2025-stress-test-scenarios-20250205.pdf

[13] Financial Accounts of the United States. (2025). https://www.federalreserve.gov/releases/z1/

[14] Fischer, M., Gupta, A., & Li, X. (2023). Ensemble machine learning and clustering techniques for portfolio diversification. Journal of Financial Data Science, 5.

https://doi.org/10.3905/jfds.2023.1.087

[15] Heaton, J. B., Polson, N., & Witte, J. H. (2016). Deep learning for finance: deep portfolios. Applied Stochastic Models in

Business and Industry, 33(1), 3.

https://doi.org/10.1002/asmb.2209

[16] Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735.

https://doi.org/10.1162/neco.1997.9.8.1735

[17] Iacurci, G. (2025). Why uncertainty makes the stock market go haywire. https://www.cnbc.com/2025/03/19/why- uncertainty-makes-the-stock-market-go- haywire.html

[18] Kingma, D. P., & Ba, J. (2014). Adam: A

Method for Stochastic Optimization. arXiv (Cornell University).

https://doi.org/10.48550/arxiv.1412.6980

[19] Kolm, P. N., Tütüncü, R., & Fabozzi, F. J. (2013). 60 Years of portfolio optimization: Practical challenges and current trends. European Journal of Operational Research, 234(2), 356. https://doi.org/10.1016/j.ejor.2013.10.060

[20] Koza, J. R. (2024). Genetic Programming. https://mitpress.mit.edu/9780262527910/

[21] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84.

https://doi.org/10.1145/3065386

[22] LeCun, Y., Bengio, Y., & Hinton, G. E. (2015). Deep learning [Review of Deep learning]. Nature, 521(7553), 436. Nature Portfolio. https://doi.org/10.1038/nature14539

[23] Li, B., & Hoi, S. C. H. (2012). Online Portfolio Selection: A Survey. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1212.2129

[24] Li, T., Londoño, J. M., & Ma, S. (2025). The

Global Transmission of Inflation Uncertainty. FEDS Notes. https://doi.org/10.17016/2380- 7172.3692

[25] Markowitz, H. M. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77.

https://doi.org/10.2307/2975974

[26] Please provide me with the text you want me to extract the title from. I need the full text to identify the title accurately. (2025). https://www.federalreserve.gov/monetarypolicy/ 2025-02-mpr-summary.htm

[27] Prado, M. L. de. (2018). Advances in Financial Machine Learning.

https://dl.mehralborz.ac.ir/handle/Hannan/3238

[28] S. Sutton, R., & G. Barto, A. (2018).

Reinforcement Learning: An Introduction. https://mitpress.mit.edu/9780262039246/reinforc ement-learning/

[29] Sato, Y., Kim, J., & Nguyen, T. (2022). Reinforcement learning for portfolio optimization: A review and practical implementation. ACM Computing Surveys (CSUR), 55. https://doi.org/10.1145/3495249

[30] Sharpe, W. F. (1966). Mutual Fund Performance. The Journal of Business, 39, 119.

https://doi.org/10.1086/294846

[31] This text does not contain a title. It is a date. (2025).

https://www.federalreserve.gov/monetarypolicy/ beigebook202501.htm

[32] Wang, S., Tan, K., & Zhao, Y. (2023).

Transformer-based sentiment analysis for financial forecasting. Expert Systems with Applications, 216.

https://doi.org/10.1016/j.eswa.2022.119503

[33] World Bank Group. (2025). World Bank, “Global Economic Indicators,” World Bank Group [Data set].

[34] Yahoo. (2025). Yahoo Finance, “Historical Stock Prices,” Yahoo, Sunnyvale, CA, USA [Data set].

Downloads

Published

2025-06-08

How to Cite

Makin , Y., & Pavan K Gondhi. (2025). A Quantitative Framework for Portfolio Governance Using Machine Learning Techniques. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.2474

Issue

Section

Research Article