Implications of Artificial Intelligence (AI) and machine learning-based fintech for the financial assets related traditional investment theories

Rubaiyat Shaimom Chowdhury, Md. Aminul Islam, Dayang Hasliza Binti Muhd Yuauf, Mohammad Bin Amin, Md. Sharif Hassan, Suborna Barua, Masuk Abdullah

Article ID: 7415
Vol 8, Issue 12, 2024

VIEWS - 83 (Abstract) 26 (PDF)

Abstract


New technologies always have an impact on traditional theories. Finance theories are no exception to that. In this paper, we have concentrated on the traditional investment theories in finance. The study examined five investment theories, their assumptions, and their limitation from different works of literature. The study considered Artificial Intelligence (AI) and Machine Learning (ML) as representative of financial technology (fintech) and tried to find out from the literature how these new technologies help to reduce the limitations of traditional theories. We have found that fintech does not have an equal impact on every conventional finance theory. Fintech outperforms all five traditional theories but on a different scale.


Keywords


artificial intelligence; machine learning; fintech; traditional investment theories; financial assets

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References


Agrawal, M., Khan, A. U., & Shukla, P. K. (2019). Stock price prediction using technical indicators: A predictive model using optimal deep learning. International Journal of Recent Technology and Engineering (IJRTE), 8(2), 2297–2305.

Al-Baity, H. H. (2023). The artificial intelligence revolution in digital finance in Saudi Arabia: a comprehensive review and proposed framework. Sustainability, 15(18), 13725. https://doi.org/10.3390/su151813725

Amin, M. B., & Oláh, J. (2024). Effects of green HRM practices on circular economy-based performance of banking organizations in an emerging nation. Banks and Bank Systems, 19 (2), 75-87. http://dx.doi.org/10.21511/bbs.19(2).2024.06

Amin, M. B., Asaduzzaman, M., Debnath, G. C., et al. (2024). Effects of circular economy practices on sustainable firm performance of green garments. Oeconomia Copernicana, 15(2), 637-682. https://doi.org/10.24136/oc.2795

Anderson, A. S. (1978). A multi-period capital asset pricing model [PhD thesis]. University of Arkansas.

Basu, S. (1977). Investment Performance of Common Stocks in Relation to Their Price-Earnings Ratios: A Test of the Efficient Market Hypothesis. Journal of Finance, 32(3), 663–682. https://doi.org/10.1111/j.1540-6261.1977.tb01979.x

Beketov, M., Lehmann, K., & Wittke, M. (2018). Robo Advisors: quantitative methods inside the robots. Journal of Asset Management, 19(6), 363–370.

Bhat, H. S., Zaelit, D. (2011). Predicting private company exits using qualitative data. In: Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining; 24–27 May 2011; Shenzhen, China. pp. 399–410.

Bhatia, A., Chandani, A., Atiq, R., et al. (2021), Artificial intelligence in financial services: A Qualitative Research to discover Robo-Advisory services. Qualitative Research in Financial Markets, 13(5), 632–654.

Bielinski, A., & Broby, D. (2021). Machine Learning Methods in Asset Pricing. Princeton University Press.

Blomeyer, E. C. (1980). Tests of market efficiency for American call options. Indiana University Publishing.

Budiartha, I., & Kusuma, N. P. N. (2022). The Capital Asset Pricing Model Forecast Using Artificial Intelligence. Budapest International Research and Critics Institute (BIRCI-Journal) Humanities and Social Sciences, 5(1), 808–819. https://doi.org/10.33258/birci.v5i1.3677

Butaru, F., Chen, Q., Clark, B., et al. (2016). Risk and risk management in the credit card industry. Journal of Banking & Finance, 72, 218–239. https://doi.org/10.1016/j.jbankfin.2016.07.015

Chen, L., Pelger, M., & Zhu, J. (2023). Deep learning in asset pricing. Management Science, 70(2). http://dx.doi.org/10.2139/ssrn.3350138

Chopra, R., & Sharma, G. D. (2021). Application of artificial intelligence in stock market forecasting: a critique, review, and research agenda. Journal of risk and financial management, 14(11), 526. https://doi.org/10.3390/jrfm14110526

Chu, A. B. (2018). Mobile Technology and financial inclusion. Handbook of Blockchain, Digital Finance, and Inclusion, 1, 131–144. https://doi.org/10.1016/B978-0-12-810441-5.00006-3

Clark, J. J, Hindelang, T. J., & Pritchard, R. E. (1979). Capital budgeting-planning and control of capital expenditure. Prentice-Hall Publishing.

Correia, C., Flynn, D, Uliana, E., et al. (1993). Financial Management. Juta And Company Publishing.

Dimson, E. (1979). Risk Measurement when shares are subject to infrequent trading. Journal of Financial Economics, 7(2), 197–226. https://doi.org/10.1016/0304-405X(79)90013-8

Ding, Z. G., Jin, B., & Xu, D. (2017). Test of Efficient Market: Criticism of Behavioral Finance to EMH Theory. Contemporary Economic Research, 3, 51–59.

Dixon, M., & Chong, J. (2014). A Bayesian approach to ranking private companies based on predictive indicators [PhD thesis]. The University of San Francisco.

Dobbins, R., Witt, S., & Fielding, J. (1994). Portfolio theory and investment management. Wiley-Blackwell Publishing.

Dubinskas, P., & Urbšienė, L. (2017). Investment portfolio optimization by applying a genetic algorithm-based approach. Ekonomika, 96(2), 66–78.

Dugas, C., Bengio, Y., Bélisle, F., et al. (2000). Incorporating Second-Order Functional Knowledge for Better Option Pricing, Working Paper 2002s-46. CIRANO, Montréal Publishing.

Elton, E. J., & Gruber, M. J. (1995). Modern portfolio theory and investment analysis. 5th ed. Wiley Publishing

Firth, M. (1977). The valuation of shares and the efficient-markets theory. MacMillan Publishing

French, K. R. (1980). Stock returns and the weekend effect. Journal of Financial Economics, 8(1), 55–69. https://doi.org/10.1016/0304-405X(80)90021-5.

Friedman, M. (1953). The methodology of positive economics Essays in Positive Economics. Chicago University Press.

Galai, D. (1982). A survey of empirical tests of option pricing models in Menachem Brenner. Lexington Books.

Garcia, R., & Gençay, R. (2000). Pricing and hedging derivative securities with neural networks and a homogeneity hint. Journal of Econometrics, 94(1), 93–115. https://doi.org/10.1016/S0304-4076(99)00018-4

Gazi, M. A. I., Rahman, M. K. H., Masud, A. A., et al. (2024a). AI Capability and Sustainable Performance: Unveiling the Mediating Effects of Organizational Creativity and Green Innovation with Knowledge Sharing Culture as a Moderator. Sustainability, 16(17), 7466. https://doi.org/10.3390/su16177466

Gençay, R., & Salih, A. (2001). Degree of Mispricing with the Black-Scholes Model and Nonparametric Cures [PhD thesis]. University of Windsor.

Gencay, R., Qi, M. (2001). Pricing and Hedging Derivative Securities with Neural Networks: Bayesian Regularization, Early Stopping and Bagging. IEEE Transactions on Neural Networks, 12(4), 726–734. https://doi.org/10.1109/72.93508

Ghaziri, H., Elfakhani, S., & Assi, J. (2000). Neural Networks Approach to Pricing Options. Neural Network World, 10(2), 271–277.

Graham, B., & Dodd, D. (1951) Security Analysis Principles and Technique. McGraw-Hill Publishing.

Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223–2273. https://doi.org/10.1093/rfs/hhaa009

Harrington, D. R. (1987). Modern portfolio theory, the capital asset pricing model and arbitrage pricing theory: a user’s guide. 2nd ed. Prentice-Hall Publishing.

Harrington, D. R., Korajczyk, & R. A. (1993). The CAPM controversy: an Overview. In: Diana, R. H., Robert, A. K. (editors). AIMR Publishing. pp. 1–4.

Harris, M. D. (1992). Natural Language in Banking. Intelligent Systems in Accounting, Finance and Management, 1(1), 65–73.

Henderson, S., Peirson, G., & Brown, R. (1992). Financial accounting theory - its nature and development. Longman Cheshire.

Hendriksen, E. S., & Van Breda, M. F. (1992). Accounting theory. Irwin. https://doi.org/10.1002/j.1099-1174.1992.tb00008.x

Hutchinson, J. M., Lo, A. W., and Poggio, T. (1994) A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks. Journal of Finance, 49(3), 851–889.

Islam, K. A., Amin, M. B., Hossain, S. A., et al. (2023). Critical success factors of the financial performance of commercial private banks: A study in a developing nation. Banks and Bank Systems, 18(4), 129. http://dx.doi.org/10.21511/bbs.18(4).2023.12

Jones, C. P. (1998). Investments: analysis and management. Wiley Publishing.

Karim, M. R., Nordin, N., Yusof, M. F., et al. (2023). Does ERP implementation mediate the relationship between knowledge management and the perceived organizational performance of the healthcare sector? Evidence from a developing country. Cogent Business & Management, 10(3), 2275869. https://doi.org/10.1080/23311975.2023.2275869

Keane, S. M. (1983). Stock market efficiency: theory, evidence and implications Deddington. P. Allan, Deddington, Oxford Publishing.

Kelly, D. L. (1994) Valuing and Hedging American Put Options Using Neural Networks [PhD thesis]. University of California.

Keogh, W. J. (1994). The stability of beta and the usability of the capital asset pricing model in the South African context [PhD thesis]. University of Orange Free State.

Killeen, A., Chan, R. (2018). Global financial institutions 2.0 in Handbook of Blockchain, Digital Finance, and Inclusion. 2nd ed. Elsevier Inc Publishing. pp. 213–242.

Korinek, A., Stiglitz, J. E. (2017). Artificial intelligence and its implications for income distribution and unemployment. NBER Publishing.

Kourentzes, N., Barrow, D. K., & Crone, S. F. (2014). Neural Network Ensemble Operators for Time Series Forecasting. Expert Systems with Applications, 41, 4235–4244. https://doi.org/10.1016/j.eswa.2013.12.011

Kyriakou, I., Mousavi, P., Nielsen, J. P., et al. (2019). Machine Learning for Forecasting Excess Stock Returns–The Five-Year-View. EconPapers.

Laing, T A (1988). Abnormal return measurement in event studies - the arbitrage pricing theory contrasted with methods used in previous studies. MBA research paper [Master’s thesis]. University of Cape Town.

Levy, H., & Sarnat, M. (1994). Capital investment and financial decisions. 5th edition. In: Hemel Hempstead, Hertfordshire. Prentice-Hall Publishing.

Linley, P. M. (1992). An evaluation of the capital asset pricing model and arbitrage pricing theory in the pricing of assets. University of Cape Town.

Lintner, J. (1965). The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets. The Review of Economics and Statistics, 47(1), 13–37. https://doi.org/10.2307/1924119

Liu, M. (1996). Option Pricing with Neural Networks. Journals A-Z, 2, 760–765.

Mankiw, N. G. (2014). Principles of economics. Cengage Learning. Cengage Learning.

Mashrur, A., Luo, W., Zaidi, N. A., et al. (2020) Machine learning for financial risk management: A survey. IEEE Access, 8: 203203–203223. https://doi.org/10.1109/ACCESS.2020.3036322

Méndez-Suárez, M., García-Fernández, F., & Gallardo, F. (2019). Artificial intelligence modelling framework for financial automated advising in the copper market. Journal of Open Innovation: Technology, Market, and Complexity, 5(4), 81.

Mollah, M. A., Amin, M. B., Debnath, G. C., et al. (2024). Nexus among Digital Leadership, Digital Transformation, and Digital Innovation for Sustainable Financial Performance: Revealing the Influence of Environmental Dynamism. Sustainability, 16(18), 8023. https://doi.org/10.3390/su16188023

Mossin, J. (1966) Equilibrium in a Capital Asset Market. Econometrica, 34, 768–783. http://dx.doi.org/10.2307/1910098

Mosteanu, N. R., Faccia, A. (2021). Fintech Frontiers in Quantum Computing, Fractals, and Blockchain Distributed Ledger: Paradigm Shifts and Open Innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 19. https://doi.org/10.3390/joitmc7010019

Mustafi, M. A. A., Dong, Y. J., Hosain, M. S., et al. (2024). Green Supply Chain Management Practices and Organizational Performance: A Mediated Moderation Model with Second-Order Constructs. Sustainability, 16(16), 6843. https://doi.org/10.3390/su16166843

Neely, A. (2005). The Evolution of Performance Measurement Research–Developments in the Last Decade and a Research Agenda for the Next. International Journal of Operations & Production Management, 25, 1264–1277. https://doi.org/10.1108/01443570510633648

O’ Brien, J., & Srivastava, S. (1995). Investments: a visual approach-modem portfolio theory and CAPM tutor. Cengage Learning Publishing.

Oosthuizen, C. P. (1992). Measuring share market volatility within the framework of the capital asset pricing model. University of Stellenbosch.

P ́astor, L., Stambaugh, R. F. (2000) Comparing asset pricing models: an investment perspective. Journal of Financial Economics, 56(3), 335–381. https://doi.org/10.1016/S0304-405X(00)00044-1

Patel, C. I., Labana, D., Pandya, S., et al. (2020). Histogram of oriented gradient-based fusion of features for human action recognition in action video sequences. Sensors,20(24), 7299. https://doi.org/10.3390/s20247299

Patel, J., Shah, S., Thakkar, P., et al. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert systems with applications, 42(1), 259–268. https://doi.org/10.1016/j.eswa.2014.07.040

Philip, N. (2020). Machine Learning Algorithms for Financial Asset Price Forecasting [Master’s thesis]. University of Oxford.

Pike, R., & Neale, B. (1996). Corporate finance and investment decisions and strategies. Prentice-Hall Publishing.

Qi, M., and Maddala, G. S. (1996) Option Pricing Using Artificial Neural Networks. Statistics and Application, 2(4), 78–91.

Qing, W., Amin, M. B., Gazi, M. A. I., et al. (2023). Mediation effect of technology adaptation capabilities between the relationship of service quality attributes and customer satisfaction: an investigation on young customers perceptions toward e-commerce in China. IEEE Access, 11, 123904-123923. https://doi.org/10.1109/ACCESS.2023.3328775

Rabbi, M. F., Amin, M. B., Al-Dalahmeh, M., & Abdullah, M. (2024). Assessing the role of information technology in promoting environmental sustainability and preventing crime in E-commerce. International Review of Applied Sciences and Engineering. https://doi.org/10.1556/1848.2024.00834

Rahman, M. H., Amin, M. B., Yusof, M. F., et al. (2024). Influence of teachers’ emotional intelligence on students’ motivation for academic learning: an empirical study on university students of Bangladesh. Cogent Education, 11(1), 2327752. https://doi.org/10.1080/2331186X.2024.2327752

Ran, T., Su, C. W., Yidong, X., et al. (2021). Robo advisors, algorithmic trading and investment management: Wonders of fourth industrial revolution in financial markets. Technological Forecasting and Social Change, 163. https://doi.org/10.1016/j.techfore.2020.120421

Rees, B. (1995). Financial analysis. Prentice-Hall Publishing.

Regona, M., Yigitcanlar, T., Xia, B., et al. (2022). Opportunities and adoption challenges of AI in the construction industry: a PRISMA review. Journal of Open Innovation: Technology, Market, and Complexity, 8(1), 45.

Roll, R. (1981). A Possible Explanation of the Small Firm Effect. Journal of Finance, 36, 879–4888. http://dx.doi.org/10.1111/j.1540-6261.1981.tb04890.x

Ross, S. A., Westerfield, R. W., & Jaffe, J. F. (1990). Corporate finance. Irwin.

Ross, S. A., Westerfield, R. W., Jordan, B. D., et al. (1996). Fundamentals of corporate finance, 1st ed. McGraw-Hill Education Publishing.

Saito, S. and Jun, L. (2000). Neural Network Option Pricing in Connection with the Black and Scholes Model. In: Proceedings of the Fifth Conference of the Asian Pacific Operations Research Society; 5–7 July 2000; Singapore.

Seneque, P. J. C. (1987). Recent developments in the pricing of financial assets. De Ra-Tione, 1(2), 28–40.

Sharpe, W. F. (1985). Investments. ‎Prentice Hall Publishing.

Shin-Yuan, H., Ting-Peng, L., & Wei-Chi, L. V. (1996). Integrating arbitrage pricing theory and artificial neural networks to support portfolio management. Decision support systems, 18, 301–316.

Simister, G. (1988). Practical issues in options trading. In: Stewart, H. (editor). in Options: recent advances. Manchester University Press. pp. 10–21.

Treynor, J. L., & Black, F. (1973). How to use security analysis to improve portfolio selection, in Capital market. In : edited by James, L. B. Lexington Books Publishing. pp. 581–603.

Van Horne, J. C. (1992). Financial management and policy. Prentice-Hall Publishing.

Van Rhijn, H. J. P. (1994). The capital asset pricing model for financial decision-making under South African. South African Actuarial Journal, 11. https://doi.org/10.4314/saaj.v11i1.2

Vargas, M. R., dos Anjos, C. E., Bichara, G. L., et al. (2018). Deep learning for stock market prediction using technical indicators and financial news articles. In: Proceedings of the international joint conference on neural networks (IJCNN 2018); 18–23 June 2018; Queensland, Australia. pp. 1–8.

Viljoen, B. (1996). Option dealing strategies and the related risk management PROCE-DURES. Raurek Bulletin, 5(6), 40–46.

Viljoen, T. (1989). A critical analysis of the performance of various risk measures during and after the stock market crash of 1987 MCom dissertation. University of Witwatersrand Publishing.

Viscione, J. A., & Roberts, G. S. (1987). Contemporary financial management. Merrill Pub Co Publishing.

Weston, J. F., & Copeland, T. E. (1992). Managerial finance, 9th ed. ‎Dryden Pr Publishing.

Yao, J., Li, Y., & Tan, L. (2000). Option Price Forecasting Using Neural Networks. Omega, 28(4), 455–466.

Yu, L., Wang, S., & Lai, K. K. (2008). Neural network-based mean–variance–skewness model for portfolio selection. Computers & Operations Research, 35(1), 34–46.

Yu, M. (2019). Research on the current situation and development trend of marketing industry under the background of artificial intelligence. Wealth Life, 82–83.

Yu, X. J., & Peng, Y. Y. (2017). The Application and Challenges of Artificial Intelligence in the Field of Financial Risk Management. Southern Finance, 9, 70–74.

Zhang, Y. P. (2015). Are Investors Really Rational: The Challenge of Behavioral Finance to Fama’s EMH. Academics, 1, 116–125.




DOI: https://doi.org/10.24294/jipd.v8i12.7415

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