Stock Movement Prediction and Portfolio Management via Multimodal Learning with Transformer

Divyanshu Daiya · Che Lin

36 citations0/0 files storedIEEE International Conference on Acoustics Speech and Signal Processing

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Time Series Analysis and Forecasting

Abstract

This paper introduces a novel high performing multimodal deep learning architecture(Trans-DiCE) for stock movement prediction utilizing financial indicators and news data. Our multimodal architecture uses dilated causal convolutions and Transformer blocks for feature extraction from both data sources. The masked multi-head self-attention layers inside Transformers preserve causality and improve features based on contextual information. To integrate the derived multimodal model representations, we use stacked Transformer blocks. We show empirically that our model performs best compared to state-of-the-art baseline methods for S&P 500 index and individual stock prediction and provides a significant 3.45% improvement from 74.29% to 77.74%. We also demonstrate our model’s utility for the Portfolio Management task. We propose a Deep Reinforcement Learning Framework utilizing Trans-DiCE for Portfolio Optimization, providing noticeable gain on Sharpe Ratio and 7.9% increase in Portfolio Value over the existing state of the art Models.

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References

2017A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction 2015A LSTM-based method for stock returns prediction: A case study of China stock market 2015Deep learning for event-driven stock prediction 2018Deep learning-based feature engineering for stock price movement prediction 2016Deep learning for stock prediction using numerical and textual information 2017A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem 2014Using Structured Events to Predict Stock Price Movement: An Empirical Investigation 2018Listening to Chaotic Whispers

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2024Stock trend prediction based on dynamic hypergraph spatio-temporal network 2024Deep Reinforcement Learning Model for Stock Portfolio Management Based on Data Fusion

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