A Model with Evolutionary Covariance-based Learning for High-Frequency Financial Forecasting

Abstract

Several approaches have been investigated to develop models able to solve forecasting problems. However, a limitation arises in the particular case of daily-frequency financial forecasting and is called the random walk dilemma (RWD). In this context, the concept of time phase adjustment can be included in forecasting models to overcome such a drawback. But the evolution of trading systems has increased the frequency for performing operations in the stock market for fractions of seconds, which requires the analysis of high-frequency financial time series. Thus, this work proposes a model, called the increasing decreasing linear neuron (IDLN), to forecast high-frequency financial time series from the Brazilian stock market. Furthermore, an evolutionary covariance-based method with automatic time phase adjustment is presented for the design of the proposed model, and the obtained results overcame those obtained by classical forecasting models in the literature.