Deep Multiple Instance Learning For Forecasting Stock Trends Using Financial News

TL;DR

This paper introduces a multi-instance learning framework that leverages financial news to improve stock trend forecasting, demonstrating superior accuracy over existing methods on S&P 500 data.

Contribution

It develops a novel adaptive multi-instance learning model that effectively captures news uncertainty for stock trend prediction without requiring instance-level annotations.

Findings

Outperforms state-of-the-art approaches in trend prediction accuracy

Treats each trading day as a bag with multiple news instances

Demonstrates robustness in directional movement forecasting

Abstract

A major source of information can be taken from financial news articles, which have some correlations about the fluctuation of stock trends. In this paper, we investigate the influences of financial news on the stock trends, from a multi-instance view. The intuition behind this is based on the news uncertainty of varying intervals of news occurrences and the lack of annotation in every single financial news. Under the scenario of Multiple Instance Learning (MIL) where training instances are arranged in bags, and a label is assigned for the entire bag instead of instances, we develop a flexible and adaptive multi-instance learning model and evaluate its ability in directional movement forecast of Standard & Poors 500 index on financial news dataset. Specifically, we treat each trading day as one bag, with certain amounts of news happening on each trading day as instances in each bag. Experiment results demonstrate that our proposed multi-instance-based framework gains outstanding results in terms of the accuracy of trend prediction, compared with other state-of-art approaches and baselines.