Predicting Stock Fluctuations using Soft Confidence-Weighted Learning

Abstract

Stock price prediction is a long-time and challenging topic in financial forecasts. Although stock markets are affected by many uncertain factors, numerous effectual approaches have been proposed to predict financial market trends using machine learning algorithms, such as Support Vector Machine (SVM) and Deep Belief Network (DBN). In this research, we propose a new approach to predict short-term stock uctuations using Soft Confidence-Weighted (SCW) Learning. The proposed method not only predicts stock trends, but also gives a quantitative measure for the stock uctuations. We consider RoC time series of a related stock class as inputs, uctuate up and down of a target stock as outputs, to train the prediction model with SCW. Some experimental results show that the approach is useful for practical purposes.