Do the Fama and French Five-Factor Model Forecast Well Using Ann?

Abstract

Forecasting the stock returns in the emerging markets is challenging due to their peculiar characteristics. These markets exhibit linear as well as nonlinear features and Conventional forecasting methods partially succeed in dealing with the nonlinear nature of stock returns. Contrarily, Artificial Neural Networks (ANN) is a flexible machine learning tool which caters both the linear and nonlinear markets. This paper investigates the forecasting ability of ANN by using Fama and French five-factor model. We construct ANN’s based on the composite factors of the FF5F model to predict portfolio returns in two stages; in stage one, the study identifies the best-fit combination of training, testing, and validation along with the number of neurons full sample period. In stage two, the study uses this best combination to forecast the model under 48-months rolling window analysis. In-sample and out-sample comparisons, regression, and goodness of fit test and actual and predicted values of the stock returns of our ANN model reveal that the proposed model accurately predicts the one-month ahead returns. Our findings reinforce the investment concept that the markets compensate the high-risk portfolios more than mid and low beta portfolios and the methodology will significantly improve the return on investment of the investors.

Effect of Health System Performance on Volatility During the Covid-19 Pandemic: A Neural Network Approach

“…The choice of the method was based on the advantages it confers compared to other procedures. Thus, it was observed that NNs contribute to an increase in the predictability of stock prices compared to conventional methods (Sahiner et al, 2021), capture information in a more comprehensive manner (Chang et al, 2022), have a high error tolerance (Mijwel, 2018), accurately process sets of homogeneous data (Castello & Resta, 2022;Tripathi et al, 2022), demonstrate a better long-term predictive power compared to statistical methods (Jan & Ayub, 2019), and are superior to regression models or those based on the approach technique (Ozdemir & Tokmakcioglu, 2022). Compared to linear models, NNs can provide solutions to complex relationships without being reprogrammed (Caliskan Cavdar & Aydin, 2020;Talwar et al, 2022).…”

“…The methods recently used in the study of the volatility of financial assets identified by the authors included logistic regression (Chang et al, 2022), stochastic dominance of the second order (Ozdemir & Tokmakcioglu, 2022), multivariate regression based on a deep neural network with backpropagation algorithm and Bayesian network (Naveed et al, 2023), traditional econometric models Neuro Fuzzy, ANFIS and CANFIS, EGARCH and VaR (Sahiner et al, 2021), Analytic Hierarchy Process (AHP) method, ANN based on FF5F model factors (Jan & Ayub, 2019), and ANN based on the multilayer perceptron model as a machine learning algorithm (Khansari et al, 2022).…”

“…The use of variables in ANN processing by various researchers includes historical values (Chang et al, 2022), stock returns (Jan & Ayub, 2019), stock selection methods and portfolio optimisation (Ozdemir & Tokmakcioglu, 2022), traded derivative contracts (TFDCs), exchange rate, daily COVID-19 cases and deaths (Naveed et al, 2023), bond markets of BRICS countries (Castello & Resta, 2022), and capital markets indices (Sahiner et al, 2021). ANN modelling is preferred over GARCH and EGARCH models as neural network prediction models demonstrate improved forecast accuracy (Sahiner et al, 2021).…”

Artificial Intelligence in Business and Economics Research: Trends and Future

“…Relevant examples of this are the works by Trafalis and Ince (2000), or Wei-Yang, Ya-Han, and Chih-Fong (2012), or Wong, Fortino, and Abbott (2020), where models based on this technology are presented, providing a framework compatible with the prediction of the Great Depression of 2008 (Yu et al, 2014), and a useful tool for the prediction of crisis situations in financial institutions. Financial activity has been one of the most relevant fields of application of this new AI (Jan & Ayub, 2019;Weng et al, 2018).…”