Ensemble Application of Transfer Learning and Sample Weighting for Stock Market Prediction

Abstract

Forecasting stock market behavior is an interesting and challenging problem. Regression of prices and classification of daily returns have been widely studied with the main goal of supplying forecasts useful in real trading scenarios. Unfortunately, the outcomes are not directly related with the maximization of the financial gain. Firstly, the optimal strategy requires to invest on the most performing asset every period and trading accordingly is not trivial given the predictions. Secondly, price fluctuations of different magnitude are often treated as equals even if during market trading losses or gains of different intensities are derived. In this paper, the problem of stock market forecasting is formulated as regression of market returns. This approach is able to estimate the amount of price change and thus the most performing assets. Price fluctuations of different magnitude are treated differently through the application of different weights on samples and the scarcity of data is addressed using transfer learning. Results on a real simulation of trading show how, given a finite amount of capital, the predictions can be used to invest in high performing stocks and, hence, achieve higher profits with less trades.