Multimodal Representation Learning for High-Stakes Investment Decisions: A Systematic Review and the Multimodal Investment Readiness (MIR) Framework

Abstract

Investment decisions in institutional settings increasingly depend on data that come from different sources and are not always easy to combine reliably. In this paper, we use the MIR framework to evaluate multimodal representation learning methods, with a focus on how well they translate from model development into practical institutional use. More and more, financial decision-making systems use different types of data, such as market time series, text, audio-derived signals, satellite images, and graph-structured linkages. A common problem with many old methods is that they analyze various inputs individually. This makes it hard to see how signals interact across modalities and can leave the system open to attack if one source is absent or inaccurate. Multimodal representation learning provides an effective alternative by acquiring common representations across diverse data sources instead of analyzing each input independently. This study analyzes the current research from five pragmatic viewpoints: the methodologies employed in finance, the predominant combinatorial strategies identified in prior studies, the actual outcomes in financial applications, the challenges of implementing these methodologies within institutions, and the approaches to evaluate these systems. Based on this research, I propose the MIR framework. MIR includes three useful parts that are typically missing from other projects. The first is modality trust scoring, which lets the model change how much weight it gives to each input based on how reliable it is in a certain situation. The second kind is event-focused alignment contracts. These contracts spell out how modality pairings should match up across time and can also help with model-risk controls. The third kind is uncertainty-to-action layers. These layers link model uncertainty to actions that affect the portfolio, such as sizing, hedging, or deferring execution. The framework is meant to bring the goals of machine learning research and the needs of institutional investment closer together. In institutional investing, governance, auditability, and risk oversight are just as essential as predictive performance.