Designing an Intelligent Financial Asset Pricing Model by Integrating Neural Networks, Behavioral Data, and Macroeconomic Indicators
Keywords:
Asset pricing, neural networks, behavioral data, macroeconomic indicators, capital market, deep learning, thematic analysisAbstract
This study aimed to design an intelligent conceptual model for financial asset pricing by integrating neural networks, investor behavioral data, and macroeconomic indicators. This qualitative study was conducted using thematic analysis. The participants included 18 experts in capital markets, quantitative finance, financial data analytics, academic finance, and investment management in Tehran, selected through purposive and snowball sampling. Data were collected through semi-structured interviews and continued until theoretical saturation was achieved. Saturation was reached in the sixteenth interview, while two additional interviews were conducted to confirm thematic stability. All interviews were audio-recorded, transcribed verbatim, and analyzed using NVivo software. Data analysis followed open, axial, and selective coding procedures. Trustworthiness was ensured through member checking, peer debriefing, audit trail, and maximum variation in participant selection. The analysis produced four main categories: data-driven architecture of intelligent asset pricing, the role of behavioral data in adjusting expectations and perceived risk, integration of macroeconomic indicators into dynamic asset valuation, and requirements for governance, explainability, and model risk control. Participants emphasized that traditional asset pricing models are often unable to capture nonlinear interactions among investor behavior, macroeconomic conditions, and asset-specific characteristics, particularly during periods of market volatility. Neural networks were perceived as useful tools for learning hidden patterns and improving return prediction; however, participants also stressed that model accuracy without explainability, data quality, and institutional oversight may increase systemic error and false confidence. The findings suggest that intelligent financial asset pricing should be designed as a multilayered system that simultaneously integrates market data, macroeconomic indicators, behavioral signals, and explainability mechanisms. Such a model can improve investment decision-making, risk management, and the detection of behavioral distortions in capital markets.
Downloads
References
Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. The Journal of Finance, 61(4), 1645–1680. https://doi.org/10.1111/j.1540-6261.2006.00885.x
Bali, T. G., Engle, R. F., & Murray, S. (2016). Empirical asset pricing: The cross section of stock returns. Wiley.
Barberis, N., Shleifer, A., & Vishny, R. (1998). A model of investor sentiment. Journal of Financial Economics, 49(3), 307–343. https://doi.org/10.1016/S0304-405X(98)00027-0
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Chen, L., Pelger, M., & Zhu, J. (2024). Deep learning in asset pricing. Management Science, 70(2), 714–750. https://doi.org/10.1287/mnsc.2023.4695
Chen, N. F., Roll, R., & Ross, S. A. (1986). Economic forces and the stock market. The Journal of Business, 59(3), 383–403.
Cochrane, J. H. (2005). Asset pricing (Rev. ed.). Princeton University Press.
Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). SAGE Publications.
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417. https://doi.org/10.2307/2325486
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3–56. https://doi.org/10.1016/0304-405X(93)90023-5
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
Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59–82. https://doi.org/10.1177/1525822X05279903
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185
Kelly, B. T., Pruitt, S., & Su, Y. (2019). Characteristics are covariances: A unified model of risk and return. Journal of Financial Economics, 134(3), 501–524. https://doi.org/10.1016/j.jfineco.2019.05.001
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. SAGE Publications.
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
Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30.
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91. https://doi.org/10.2307/2975974
Merton, R. C. (1973). An intertemporal capital asset pricing model. Econometrica, 41(5), 867–887. https://doi.org/10.2307/1913811
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?” Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135–1144). https://doi.org/10.1145/2939672.2939778
Ross, S. A. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13(3), 341–360. https://doi.org/10.1016/0022-0531(76)90046-6
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425–442. https://doi.org/10.1111/j.1540-6261.1964.tb02865.x
Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance, 62(3), 1139–1168. https://doi.org/10.1111/j.1540-6261.2007.01232.x
