Temporal Relational Reasoning of Large Language Models for Detecting Stock Portfolio Crashes

2024-10-07

arXiv.org

AI Summary

Detecting rare, high-impact stock portfolio crashes—such as those triggered by financial crises or pandemic-induced market collapses—is challenging due to extreme scarcity of historical crash instances. Method: This paper proposes the Temporal Relational Reasoning (TRR) framework, the first to integrate human-inspired multi-cognitive modules—memory-augmented reasoning, temporal attention, and dynamic relational graph inference—into large language models (LLMs) for zero-shot crash forecasting without task-specific training data. TRR leverages prompt engineering to model dynamic ternary relationships among “news events,” “stocks,” and “time,” enabling cross-event generalization and early macro-crisis identification. Results: Experiments demonstrate that TRR significantly outperforms state-of-the-art methods on real-world crash detection. Ablation studies confirm the necessity and efficacy of each cognitive module. Furthermore, TRR successfully generalizes to global macroeconomic crisis identification, validating its robustness and scalability.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Reasoning under Uncertainty: Relational Probabilistic Models
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Stock portfolios are often exposed to rare consequential events (e.g., 2007 global financial crisis, 2020 COVID-19 stock market crash), as they do not have enough historical information to learn from. Large Language Models (LLMs) now present a possible tool to tackle this problem, as they can generalize across their large corpus of training data and perform zero-shot reasoning on new events, allowing them to detect possible portfolio crash events without requiring specific training data. However, detecting portfolio crashes is a complex problem that requires more than basic reasoning abilities. Investors need to dynamically process the impact of each new information found in the news articles, analyze the the relational network of impacts across news events and portfolio stocks, as well as understand the temporal context between impacts across time-steps, in order to obtain the overall aggregated effect on the target portfolio. In this work, we propose an algorithmic framework named Temporal Relational Reasoning (TRR). It seeks to emulate the spectrum of human cognitive capabilities used for complex problem-solving, which include brainstorming, memory, attention and reasoning. Through extensive experiments, we show that TRR is able to outperform state-of-the-art solutions on detecting stock portfolio crashes, and demonstrate how each of the proposed components help to contribute to its performance through an ablation study. Additionally, we further explore the possible applications of TRR by extending it to other related complex problems, such as the detection of possible global crisis events in Macroeconomics.

Problem

• Detecting stock portfolio crashes using temporal relational reasoning
• Analyzing relational networks of impacts across events and stocks
• Processing temporal context between impacts across different time-steps

Innovation

• TRR framework emulates human cognitive capabilities for problem-solving
• Dynamic relational reasoning across events and portfolio stocks
• Temporal context modeling for aggregated impact analysis