Multimodal Deep Learning for Enhanced Stock Market Trend Prediction

Basma Hussein Hameed

Basma Hussein Hameed Ministry of Finance

Keywords: Multimodal Deep Learning, Stock Market Prediction, Social Media Sentiment, Economic Indicators, Transformer Models, LSTM, Financial Forecasting.

Abstract

Stock market prediction is an important tool in investment decision, optimization of portfolio, and management of risks. The historical price and economic indicator paradigm of traditional forecasting approaches would fail to include the sweeping and quick movements of behaviors in a volatile market, thus lacking predictive capability . The social media, including Twitter and Reddit, have proved to be a rich source of investor sentiment, with real-time accounts of what the public expects and how the market is being shaped by psychological drivers . Yet, the majority of the available literature relies on a single dimensional research design, i.e., investigating either sentiments in a text or economic measures, therefore, omitting the complementary relationship between the two dimensions. The purpose of the current study is to build a strong multimodal deep learning model that would incorporate the investor sentiment in the social media with other aspects of economic indicators. The goal is to provide more trustworthy, consistent, and interpretable predictions concerning the stock market trend and allow investors and financial institutions to make more informed decisions. The hypothesized framework comprises two major branches: (1) a Transformer-based model of sentiment analysis (FinBERT and RoBERTa) to make context-informed embeddings out of social media posts and (2) an LSTM-based branch to infer sequential implications of economic factors such as interest rates, trading volumes, and inflation. A late-fusion approach combines and trained to discover cross modal connections across both branches before being classified as either an upward, downward or neutral tendency. The Twitter, Reddit and StockTwits Twitter data were used with the economic data of Yahoo Finance and FRED over 2023-2025. The performance was measured in Accuracy, Precision, Recall, F1-score, and ROC-AUC.The multimodal model fared much better than its unimodal counterparts with an Accuracy of 91.2%, F1-score of 90.7 and ROC-AUC of 0.94. To ensure that such improvements are not random, paired t-tests and ANOVA was used to prove that any such improvements were verified as significant statistically (p < 0.05). The sentiment data had a larger effect on the short-term forecasts, whereas the economic indicators aided in the long-term stability, which proves the complementarity of the behavioral and the fundamental data.

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