Stock Price and Index Forecasting by Arbitrage Pricing Theory-Based Gaussian TFA Learning

Abstract

Viewed as a promising application of neural networks, financial time series forecasting was studied in the literature of neural nets and machine learning. The recently developed Temporal Factor Analysis (TFA) model mainly targeted at further study of the Arbitrage Pricing Theory (APT) is found to have potential application in the prediction of stock price and index. In this paper, we aim to illustrate the superiority of using the APT-based Gaussian TFA model as compared to three conventional approaches which are not financial model-based.