Neural Network vs. Linear Models for Stock Market Sectors Forecasting

Abstract

The majority of work on forecasting the stock market has focused on individual stocks or stock indexes. In this study we consider the problem of forecasting stock sectors (or industries). We have found no study that considers this problem. Stock sectors are indexes that group several stocks covering a specific sector in the economy, for example the banking sector, the retail sector, etc. It is important for investment allocation purposes to know where each sector is going. In this study we apply linear models, such as Box-Jenkins methodology and multiple regression, as well as neural networks on the sector forecasting problem. As it turns out neural networks yielded the best forecasting performance