Optimizing Investment Strategies: AI-Based Predictive Models In Asset Management
Keywords:
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Abstract
Investment strategies in asset management can benefit immensely from AI-based predictive models. This study presents a novel AI framework that combines long short-term memory (LSTM) networks with reinforcement learning to optimize investment decisions. Bioanalyzing historical market data and learning from market trends, our model predicts asset prices and recommends investment actions. Evaluated on a historical stock market dataset, the model achieved a return on investment (ROI) 15% higher than traditional heuristic-based strategies. The application of Long Short-Term Memory (LSTM) neural networks in investment strategy optimization presents a significant advancement over traditional methods. By leveraging historical financial data and macroeconomic indicators, the LSTM model effectively captures complex market patterns and temporal dependencies, leading to superior predictive accuracy. Empirical results demonstrate that the LSTM-based approach achieves a mean annualized return of 10.07% and a Sharpe ratio of 0.98. These metrics surpass the performance of conventional models, such as the Capital Asset Pricing Model (CAPM), the Three-Factor Model (3FM), and the Equally Weighted Portfolio (EQWT), which yielded lower returns and Sharpe ratios. The integration of LSTM-based predictions with the Mean-Variance Optimization (MVO) framework enhances dynamic asset allocation, allowing for continuous adaptation to changing market conditions. This robust methodology not only improves returns but also offers enhanced risk management, making it a valuable tool for modern portfolio management.
References
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38. Deng X, Lv T. Power system planning with increasing variable renewable energy: A review of optimization models. Journal of Cleaner Production. 2020 Feb 10;246:118962.
39. Bengtsson M. Condition based maintenance systems: an investigation of technical constituents and organizational aspects. Eskilstuna, Sweden: Mälardalen University; 2004 Sep.
40. Wang P, Vachtsevanos G. Fault prognostics using dynamic wavelet neural networks. AI EDAM. 2001 Sep;15(4):349-65.
41. Traini E, Bruno G, D’antonio G, Lombardi F. Machine learning framework for predictive maintenance in milling. IFAC-PapersOnLine. 2019 Jan 1;52(13):177-82.
42. Kiangala KS, Wang Z. An effective predictive maintenance framework for conveyor motors using dual time-series imaging and convolutional neural network in an industry 4.0 environment. Ieee Access. 2020 Jul 2;8:121033-49.
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44. Peraka NS, Biligiri KP. Pavement asset management systems and technologies: A review. Automation in Construction. 2020 Nov 1;119:103336.
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55. Li J, Zhu Y, Xu Y, Guo C. A transformer fault diagnosis method based on sub-clustering reduction and multiclass multi-kernel support vector machine. In2017 IEEE Conference on Energy Internet and Energy System Integration (EI2) 2017 Nov 26 (pp. 1-6). IEEE.
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61. Gill MS, Kaur K, Vij TS, Mohideen AS, Lakshmi MR. Green marketing: a study of consumer perception and preferences in India. Journal of Survey in Fisheries Sciences. 2023 Jun 17;10(3S):6612-9.
62. Umamaheswari S, Valarmathi A. Role of artificial intelligence in the banking sector. Journal of Survey in Fisheries Sciences. 2023 Apr 21;10(4S):2841-9.
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2. Haes Alhelou H, Hamedani-Golshan ME, Njenda TC, Siano P. A survey on power system blackout and cascading events: Research motivations and challenges. Energies. 2019 Feb 20;12(4):682.
3. Heggarty T, Bourmaud JY, Girard R, Kariniotakis G. Quantifying power system flexibility provision. Applied energy. 2020 Dec 1;279:115852.
4. Perico P, Arica E, Powell DJ, Gaiardelli P. MES as an Enabler of Lean Manufacturing. IFAC-PapersOnLine. 2019 Jan 1;52(13):48-53.
5. Davis R. An introduction to asset management. Retrieved November. 2016;20:2016.
6. Hajek P, Novotny J. Fuzzy rule-based prediction of gold prices using news affect. Expert Systems with Applications. 2022 May 1;193:116487.
7. Livieris IE, Pintelas E, Pintelas P. A CNN–LSTM model for gold price time-series forecasting. Neural computing and applications. 2020 Dec;32:17351-60.
8. Kilimci ZH. Ensemble regression-based gold price (XAU/USD) prediction. Journal of Emerging Computer Technologies. 2022 Jul;2(1):7-12.
9. Shapiro D, Qassoud H, Lemay M, Bolic M. Unsupervised deep learning recommender system for personal computer users. INTELLI 2017. 2017 Jul 23:31.
10. Metz C. The rise of the artificially intelligent hedge fund. InWired 2016 Jan 25.
11. Obeidat S, Shapiro D, Lemay M, MacPherson MK, Bolic M. Adaptive portfolio asset allocation optimization with deep learning. International Journal on Advances in Intelligent Systems. 2018;11(1):25-34.
12. Elluru S, Gupta H, Kaur H, Singh SP. Proactive and reactive models for disaster resilient supply chain. Annals of Operations Research. 2019 Dec;283:199-224.
13. von Winterfeldt D. User-model interaction in decision support systems for risk management. Annals of Operations Research. 1988 Dec;16:185-97.
14. Kilpatrick J, Barter L. COVID-19: managing supply chain risk and disruption. Deloitte: Toronto, ON, Canada. 2020.
15. Akeem LB. Effect of cost control and cost reduction techniques in organizational performance. International business and management. 2017;14(3):19-26.
16. Piryonesi SM, El-Diraby TE. Role of data analytics in infrastructure asset management: Overcoming data size and quality problems. Journal of Transportation Engineering, Part B: Pavements. 2020 Jun 1;146(2):04020022.
17. Idrees M, Riaz MT, Waleed A, Paracha ZJ, Raza HA, Khan MA, Hashmi WS. Fuzzy logic based calculation and analysis of health index for power transformer installed in grid stations. In2019 international symposium on recent advances in electrical engineering (RAEE) 2019 Aug 28 (Vol. 4, pp. 1-6). IEEE.
18. Devan M, Prakash S, Jangoan S. Predictive maintenance in banking: leveraging AI for real-time data analytics. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online). 2023;2(2):483-90.
19. Subramanian M, Malathi S, Shajahan US, Dhinakaran DP, Kaur K, Lakshmi MR. Fintech and the Future of Financial Services. Journal of Survey in Fisheries Sciences. 2023 Jun 17;10(3S):6620-7.
20. Gill MS, Kaur K, Vij TS, Mohideen AS, Lakshmi MR. Green marketing: a study of consumer perception and preferences in India. Journal of Survey in Fisheries Sciences. 2023 Jun 17;10(3S):6612-9.
21. Dhinakaran DP, Rajarajan M. Employees Satisfaction towards Labour welfare Measures in Tamil Nadu State Transport Corporation Limited, Kumbakonam. Asian journal of Management. 2012;3(3):163-8.
22. Subhadra PS, Kalaivani A, Markan R, Kumar R, Natarajan S, Rajalakshmi M. Rise of Artificial Intelligence in Business and Industry. Journal of Informatics Education and Research. 2024 May 1;4(2).
23. Aleixo JE, Reis JL, Teixeira SF, de Lima AP. Artificial intelligence applied to digital marketing. InThe Use of Artificial Intelligence in Digital Marketing: Competitive Strategies and Tactics 2024 (pp. 21-72). IGI Global.
24. Song T, Zhang Q, Cai G, Cai M, Qian J. Development of Machine Learning and Artificial Intelligence in Toxic Pathology. Frontiers in Computing and Intelligent Systems. 2023;6(3):137-41.
25. Zhang Q, Cai G, Cai M, Qian J, Song T. Deep Learning Model Aids Breast Cancer Detection. Frontiers in Computing and Intelligent Systems. 2023 Dec 1;6(1):99-102.
26. Qian J, Song T, Zhang Q, Cai G, Cai M. Analysis and Diagnosis of Hemolytic Specimens by AU5800 Biochemical Analyzer Combined With AI Technology. Frontiers in Computing and Intelligent Systems. 2023;6(3):100-3.
27. Xu J, Pan L, Zeng Q, Sun W, Wan W. Based on TPUGRAPHS Predicting Model Runtimes Using Graph Neural Networks. Frontiers in Computing and Intelligent Systems. 2023 Nov 26;6(1):66-9.
28. He Z, Chen W, Zhou Y, Weng H, Shen X. The Importance of AI Algorithm Combined With Tunable LCST Smart Polymers in Biomedical Applications. Frontiers in Computing and Intelligent Systems. 2023;6(3):92-5.
29. Shobana G, Umamaheswari K. Forecasting by machine learning techniques and econometrics: A review. In2021 6th international conference on inventive computation technologies (ICICT) 2021 Jan 20 (pp. 1010-1016). IEEE.
30. Mishev K, Gjorgjevikj A, Vodenska I, Chitkushev LT, Trajanov D. Evaluation of sentiment analysis in finance: from lexicons to transformers. IEEE access. 2020 Jul 16;8:131662-82.
31. Sun Y, Peng H. A Quantum Evolutionary Algorithm and Its Application to Optimal Dynamic Investment in Market Microstructure Model. InCCF Conference on Computer Supported Cooperative Work and Social Computing 2021 Nov 26 (pp. 386-396). Singapore: Springer Nature Singapore.
32. Liang Y, Lin Y, Lu Q. Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM. Expert Systems with Applications. 2022 Nov 15;206:117847.
33. Rundo F, Trenta F, Di Stallo AL, Battiato S. Machine learning for quantitative finance applications: A survey. Applied Sciences. 2019 Dec 17;9(24):5574.
34. Hatziargyriou N, Milanovic J, Rahmann C, Ajjarapu V, Canizares C, Erlich I, Hill D, Hiskens I, Kamwa I, Pal B, Pourbeik P. Definition and classification of power system stability–revisited & extended. IEEE Transactions on Power Systems. 2020 Dec 8;36(4):3271-81.
35. Ashraf S, Alfandi O, Ahmad A, Khattak AM, Hayat B, Kim KH, Ullah A. Bodacious‐Instance Coverage Mechanism for Wireless Sensor Network. Wireless Communications and Mobile Computing. 2020;2020(1):8833767.
36. Zhao J, Gómez-Expósito A, Netto M, Mili L, Abur A, Terzija V, Kamwa I, Pal B, Singh AK, Qi J, Huang Z. Power system dynamic state estimation: Motivations, definitions, methodologies, and future work. IEEE Transactions on Power Systems. 2019 Jan 23;34(4):3188-98.
37. Heggarty T, Bourmaud JY, Girard R, Kariniotakis G. Quantifying power system flexibility provision. Applied energy. 2020 Dec 1;279:115852.
38. Deng X, Lv T. Power system planning with increasing variable renewable energy: A review of optimization models. Journal of Cleaner Production. 2020 Feb 10;246:118962.
39. Bengtsson M. Condition based maintenance systems: an investigation of technical constituents and organizational aspects. Eskilstuna, Sweden: Mälardalen University; 2004 Sep.
40. Wang P, Vachtsevanos G. Fault prognostics using dynamic wavelet neural networks. AI EDAM. 2001 Sep;15(4):349-65.
41. Traini E, Bruno G, D’antonio G, Lombardi F. Machine learning framework for predictive maintenance in milling. IFAC-PapersOnLine. 2019 Jan 1;52(13):177-82.
42. Kiangala KS, Wang Z. An effective predictive maintenance framework for conveyor motors using dual time-series imaging and convolutional neural network in an industry 4.0 environment. Ieee Access. 2020 Jul 2;8:121033-49.
43. Petchrompo S, Parlikad AK. A review of asset management literature on multi-asset systems. Reliability Engineering & System Safety. 2019 Jan 1;181:181-201.
44. Peraka NS, Biligiri KP. Pavement asset management systems and technologies: A review. Automation in Construction. 2020 Nov 1;119:103336.
45. Ibrahim MS, Dong W, Yang Q. Machine learning driven smart electric power systems: Current trends and new perspectives. Applied Energy. 2020 Aug 15;272:115237.
46. Koksal A, Ozdemir A. Composite system reliability assessment to reduce the curtailment risk using the storage systems. In2022 IEEE 7th International Energy Conference (ENERGYCON) 2022 May 9 (pp. 1-6). IEEE.
47. Bouabdallaoui Y, Lafhaj Z, Yim P, Ducoulombier L, Bennadji B. Predictive maintenance in building facilities: A machine learning-based approach. Sensors. 2021 Feb 3;21(4):1044.
48. DE FREITAS FD, DE ALMEIDA AR. Portfolio selection with predicted returns using neural networks. InIASTED International Conference on Artificial Intelligence and Applications 2001 (pp. 99-103).
49. Jang G, Lai F. Intelligent trading of an emerging market. Trading on the Edge. 1994:80-101.
50. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research. 2002 Jun 1;16:321-57.
51. Cristianini N, Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods. Cambridge university press; 2000 Mar 23.
52. Dunis CL. Artifi cial Intelligence in Financial Markets. Palgrave Macmillan; 2016.
53. Aboelenein AA. Technology Effects on Emerging Economies’ Financial Services and Economic Development: The case of Egypt and India.
54. Vince R. The handbook of portfolio mathematics: formulas for optimal allocation and leverage. John Wiley & Sons; 2007 May 25.
55. Li J, Zhu Y, Xu Y, Guo C. A transformer fault diagnosis method based on sub-clustering reduction and multiclass multi-kernel support vector machine. In2017 IEEE Conference on Energy Internet and Energy System Integration (EI2) 2017 Nov 26 (pp. 1-6). IEEE.
56. Ghosh D, Ghose T, Mohanta DK. Reliability analysis of a geographic information system-aided optimal phasor measurement unit location for smart grid operation. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability. 2013 Aug;227(4):450-8.
57. Surekha M, Dhinakaran DP. IMPACT OF HUMAN RESOURCE MANAGEMENT (HRM) PRACTICES ON EMPLOYEE PERFORMANCE. International Journal of Early Childhood Special Education. 2022 May 1;14(3).
58. Dhanasekaran B, Marishkumar P, Sunil S, Subhashini S. A study on service quality in higher education institutions with special reference to western Tamilnadu. JETT. 2023;14(5):60-7.
59. Maneesh P. Barriers to Healthcare for Sri Lankan Tamil Refugees in Tamil Nadu, India. Turkish Journal of Computer and Mathematics Education (TURCOMAT). 2021 May 25;12(12):4075-83.
60. Lakshmi B. Rural Entrepreneurship in India: An Overview. Eur. Chem. Bull. 2023;12:1180-7.
61. Gill MS, Kaur K, Vij TS, Mohideen AS, Lakshmi MR. Green marketing: a study of consumer perception and preferences in India. Journal of Survey in Fisheries Sciences. 2023 Jun 17;10(3S):6612-9.
62. Umamaheswari S, Valarmathi A. Role of artificial intelligence in the banking sector. Journal of Survey in Fisheries Sciences. 2023 Apr 21;10(4S):2841-9.
63. Kalaiselvi S, Dhinakaran DP. Green Marketing: A Study of Consumers Attitude towards Eco-Friendly Products in Thiruvallur District. Annals of the Romanian Society for Cell Biology. 2021 Apr 15:6026.
Published
2022-07-25
How to Cite
Vinay Banda. (2022). Optimizing Investment Strategies: AI-Based Predictive Models In Asset Management. Revista Electronica De Veterinaria, 23(4), 131-143. https://doi.org/10.69980/redvet.v23i4.870
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