On-Line Portfolio Selection Using Recurrent Neural Network Based on Moving Average Reversion
Date Issued
2016
Date
2016
Author(s)
Lee, Chi-Wei
Abstract
As the development of computing power and the neural network methods become more and more mature, the prediction on financial instruments using artificial related methods becomes more and more viable. In this thesis, we combined the moving average reversion and recurrent neural network on online portfolio selection, which is one of the most popular topics in the area of financial industry. Moving average reversion technique has been proved powerful on online portfolio selection. On-line portfolio selection, in short, is to predict the stocks’prices of the next trading day and adjust the previous portfolio accordingly, while moving average reversion exploits the mean reversion characteristic of stock to better maximize the total wealth in the final trading day. Recurrent neural network (RNN) has been attracting increasing interest in time series prediction like handwriting recognition and speech recognition. RNNs use their internal memory to better utilize the historical sequences, which follows other time series prediction methods. Combining RNN and moving average reversion method, we’ve shown some improvement on popular datasets.
Subjects
Recurrent neural network
Moving average reversion
On-line learning
Portfolio selection
Type
thesis
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