2017-03-21 (火) 11:23:32 (1508d) | Topic path: Top / SIG-FIN-018-15


Predicting stock fluctuations using Two-level Mapping and SCW


Muhtar Fukuda (Faculty of Environmental and Information Studies, Nagoya Sangyo University)


Due to high uncertainty in the stock market, it is difficult to predict the future fluctuations of stock prices even if we use the state-of-the-art techniques of machine learning, such as Deep Learning. However, in the some cases, choosing an appropriate learning model, feature values and outputs, we can have desirable results, especially on short-term stock fluctuations about some market indices. Some initial reliable results have been achieved in our related work, by using Soft Confidence-Weighted (SCW) Leaning, 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.

Key Words

Stock Fluctuation Prediction, Fluctuation Strength, Feature Transformation, Two-level Mapping, Soft Confidence-Weighted Learning



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