Predicting stock uctuations using Two-level Mapping and SCW

Abstract

Due to high uncertainty in the stock market, it is difficult to predict the future uctuations of stock prices even if we use the state-of-the-art techniques of machine learning, such as Deep Learning. However, in some cases with choosing an appropriate machine learning algorithm, feature values and outputs for the prediction, we can have desirable predicted results, especially on short-term stock uctuations about some market indices. Some initial reliable results have been achieved in our related work, by using Soft Confidence-Weighted (SCW) Leaning, which is one of online learning. In this paper, we propose a predicting method using two-level mapping and SCW. We will focus on feature transformations using the two-level mapping. The first one is based on the mathematical concept of the Singular Value Decomposition (SVD), to get strong convergence and higher accuracy. The second one is to make the predicted Fluctuation Strength (FS) more precisely, in which we use pre-learned outputs and do relearning.