CLVSA: A Convolutional LSTM Based Variational Sequence-to-Sequence Model with Attention for Predicting Trends of Financial Markets

Abstract

Financial markets are a complex dynamical system. The complexity comes from the interaction between a market and its participants, in other words, the integrated outcome of activities of the entire participants determines the markets trend, while the markets trend affects activities of participants. These interwoven interactions make financial markets keep evolving. Inspired by stochastic recurrent models that successfully capture variability observed in natural sequential data such as speech and video, we propose CLVSA, a hybrid model that consists of stochastic recurrent networks, the sequence-to-sequence architecture, the self- and inter-attention mechanism, and convolutional LSTM units to capture variationally underlying features in raw financial trading data. Our model outperforms basic models, such as convolutional neural network, vanilla LSTM network, and sequence-to-sequence model with attention, based on backtesting results of six futures from January 2010 to December 2017. Our experimental results show that, by introducing an approximate posterior, CLVSA takes advantage of an extra regularizer based on the Kullback-Leibler divergence to prevent itself from overfitting traps.

arXiv comment7 pages, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19)

References in corpus (12)

• Adam: A Method for Stochastic Optimization
• Auto-Encoding Variational Bayes
• Neural Machine Translation by Jointly Learning to Align and Translate
• Sequence to Sequence Learning with Neural Networks
• Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
• Stochastic Backpropagation and Approximate Inference in Deep Generative Models
• Neural GPUs Learn Algorithms
• Learning Stochastic Recurrent Networks
• Long Short-Term Memory-Networks for Machine Reading
• Variable Rate Image Compression with Recurrent Neural Networks
• Financial Markets Prediction with Deep Learning
• Idiosyncrasies and challenges of data driven learning in electronic trading

Cited by in corpus (5)

• Applications of deep learning in stock market prediction: recent progress
• MCI-GRU: Stock Prediction Model Based on Multi-Head Cross-Attention and Improved GRU
• Explainable Tensorized Neural Ordinary Differential Equations forArbitrary-step Time Series Prediction
• Transformers with Attentive Federated Aggregation for Time Series Stock Forecasting
• Dual-CLVSA: a Novel Deep Learning Approach to Predict Financial Markets with Sentiment Measurements