The Efficacy of Neural Networks in Predicting Returns on Stock and Bond Indices*

Abstract

This paper uses two recently developed tests to identify neglected nonlinearity in the relationship between excess returns on four asset classes and several economic and financial variables. Having found some evidence of possible nonlinearity, it was then investigated whether the predictive power of these variables could be enhanced by using neural network models instead of linear regression or GARCH models.Some evidence of nonlinearity in the relationships between the explanatory variables and large stocks and corporate bonds was found. It was also found that the GARCH models are conditionally efficient with respect to neural network models, but the neural network models outperform GARCH models if financial performance measures are used. In resonance with the results reported for the tests for neglected nonlinearity, it was found that the neural network forecasts are conditionally efficient with respect to linear regression models for large stocks and corporate bonds, whereas the evidence is not statistically significant for small stocks and intermediate-term government bonds. This difference persists even when financial performance measures for individual asset classes are used for comparison.

A comparison between Fama and French's model and artificial neural networks in predicting the Chinese stock market

“…In one comparative analysis study after another (see [38][39][40]) ANN consistently outperformed or is more accurate at predicting or forecasting than other more traditional quantitative methods. In most of these applications, neural networks outperformed traditional statistical models, such as discriminant and regression analysis [41,42].…”

The use of data mining and neural networks for forecasting stock market returns

“…As a result, the discovery and use of non-linearity in financial market movements and analysis to produce better predictions of future stock returns or indices has been greatly emphasized by various researchers and financial analysts during the last few years (see Abhyankar, Copeland, and Wong, 1997). Current studies that reflect an interest in applying neural networks to answer future stock behaviors include Chenoweth and Obradovic (1996), Desai and Bharati (1998), Gencay (1998), Leung, Daouk, and Chen (2000), Motiwalla and Wahab (2000), Pantazopoulos et al (1998), Qi and Maddala (1999), and Wood and Dasgupta (1996).…”

The three-factor model and artificial neural networks: predicting stock price movement in China

“…Popular applications include a wide range of forecasting tasks, and the literature in this area is growing (see Zhang et al 1998). In one comparative analysis study after another (e.g., Desai and Bharati 1998;Bhattacharyya and Pendharkar 1998;Jiang et al 2000), ANN models have consistently outperformed other, more traditional quantitative forecasting methods.…”