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Price Forecast with High-Frequency Finance Data: An Autoregressive Recurrent Neural Network Model with Technical Indicators

Authors:

Yuechun Gu,

Da Yan,

Sibo Yan,

Zhe JiangAuthors Info & Claims

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

Pages 2485 - 2492

https://doi.org/10.1145/3340531.3412738

Published: 19 October 2020 Publication History

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Abstract

The availability of high-frequency trade data has made it possible for the intraday forecast of price patterns. With the help of technical indicators, recent studies have shown that LSTM based deep learning models are able to predict price directions (a binary classification problem) with performance better than a random guess. However, only naive recurrent networks were adopted, and these works did not compare with the tools used by finance practitioners. Our experiments show that GARCH beats their LSTM models by a large margin.

We propose to adopt an autoregressive recurrent network instead so that the loss of the prediction at every time step contributes to the model training; we also treat a rich set of technical indicators at each time step as covariates to enhance the model input. Finally, we treat the problem of price pattern forecast as a regression problem on the price itself; even for price direction prediction, we show that our performance is much better than if we model the problem as binary classification. We show that only when all these designs are adopted, an LSTM model can beat GARCH (and by a large margin).

This work corrects the poor use of LSTM networks in recent studies, and provides "the" baseline that is able to fully unleash the power of LSTM for future work to compare with. Moreover, since our model is a price regressor with very good prediction performance, it can serve as a valuable tool for designing trading strategies (including day trading). Our model has been used by quantitative analysts in Freddie Mac for over one quarter, and is found to be more effective than traditional GARCH variants in market prediction.

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References

[1]

Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. Layer normalization. CoRR, abs/1607.06450, 2016.

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