Provide an improved factor pricing model using neural networks and the gray wolf optimization algorithm

Subject Areas : Economic and Financial Time Series

Reza  Tehrani ¹ (Financial Management and Insurance, Faculty of Management, University of Tehran, Tehran, Iran)
Ali  Souri ² (Faculty of Economics, University of Tehran, Tehran, Iran)
Ardeshir  Zohrabi ³ (Financial Management and Insurance, Faculty of Management, University of Tehran, Tehran, Iran)
Seyyed Jalal  Sadeghi Sharif ⁴ (Faculty of Management & Accounting, Shahid Beheshti, University, Tehran, Iran)

Received: 2021-10-13 Accepted : 2022-01-01 Published : 2023-01-01

Keywords: Asset pricing Model, Neural network, Gray Wolf Optimization, Machine Learning,

Abstract :

The issue of asset pricing in the market is one of the most important and old issues in the financial world. Factor pricing models seek to be able to determine a significant relationship between return on assets based on the risk parameters of that asset. A wide range of factors can be found in the literature that can be an element for measuring the risk of an asset, but the big question is which of these models will work better. The factors studied in this research include factors that cover market risk, valuation risk, psychological (technical) market risk, profit quality risk, profitability, investment, etc. In this study, we have tried to Using machine learning techniques and optimization tools is a way to derive adaptive-robust nonlinear models that can reduce the risk of model error as much as possible. In this research, two models have been developed. In the first model, using the feature extraction technique and optimization of models based on neural network, a non-linear and adaptable model has been developed for each asset. In the second approach, a portfolio of improved neural network-based models is used in the first stage, which can be used to minimize the risk of model error and achieve a model that is resistant to different market conditions. Finally, it can be seen that the development of these models can significantly improve the risk of error and average error of the model compared to traditional CAPM approaches and the Fama and French three-factor model.

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