Work2015

A LSTM-based method for stock returns prediction: A case study of China stock market

734 citations0/0 files storedIEEE International Conference on Big Data (BigData)

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Hydrological Forecasting Using AI

Abstract

The presented paper modeled and predicted China stock returns using LSTM. The historical data of China stock market were transformed into 30-days-long sequences with 10 learning features and 3-day earning rate labeling. The model was fitted by training on 900000 sequences and tested using the other 311361 sequences. Compared with random prediction method, our LSTM model improved the accuracy of stock returns prediction from 14.3% to 27.2%. The efforts demonstrated the power of LSTM in stock market prediction in China, which is mechanical yet much more unpredictable.

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References

2011Twitter mood predicts the stock market 2009Textual analysis of stock market prediction using breaking financial news

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