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RiskLabs: Predicting Financial Risk Using Large Language Model based on Multimodal and Multi-Sources Data (2404.07452v2)

Published 11 Apr 2024 in q-fin.RM, cs.AI, cs.CE, cs.LG, and q-fin.PM

Abstract: The integration of AI techniques, particularly LLMs, in finance has garnered increasing academic attention. Despite progress, existing studies predominantly focus on tasks like financial text summarization, question-answering, and stock movement prediction (binary classification), the application of LLMs to financial risk prediction remains underexplored. Addressing this gap, in this paper, we introduce RiskLabs, a novel framework that leverages LLMs to analyze and predict financial risks. RiskLabs uniquely integrates multimodal financial data, including textual and vocal information from Earnings Conference Calls (ECCs), market-related time series data, and contextual news data to improve financial risk prediction. Empirical results demonstrate RiskLabs' effectiveness in forecasting both market volatility and variance. Through comparative experiments, we examine the contributions of different data sources to financial risk assessment and highlight the crucial role of LLMs in this process. We also discuss the challenges associated with using LLMs for financial risk prediction and explore the potential of combining them with multimodal data for this purpose.

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Citations (10)
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Summary

  • The paper introduces the RiskLabs framework that combines earnings calls, news data, and time-series analysis to predict market volatility and Value at Risk.
  • The paper employs innovative modules—such as the Earnings Conference Call Encoder and multi-task prediction—to achieve superior short- and medium-term forecasting compared to classical methods.
  • The paper demonstrates that leveraging LLMs and multimodal data significantly enhances financial risk prediction accuracy, highlighting new possibilities for AI in finance.

RiskLabs: LLM-Based Financial Risk Prediction

The paper "RiskLabs: Predicting Financial Risk Using LLM Based on Multi-Sources Data" (2404.07452) introduces RiskLabs, a novel framework that leverages LLMs to analyze and predict financial risks by combining textual and vocal information from ECCs, market-related time series data, and contextual news data. The framework addresses the gap in applying LLMs for financial risk prediction and demonstrates effectiveness in forecasting volatility and variance in financial markets.

RiskLabs Framework Components

The RiskLabs framework (Figure 1) comprises four key modules designed to process diverse data streams: the Earnings Conference Call Encoder, the News-Market Reactions Encoder, the Time-Series Encoder, and the Multi-Task Prediction. The Earnings Conference Call Encoder leverages LLMs to handle both audio and transcript data from earnings calls. The News-Market Reactions Encoder establishes an LLM-driven pipeline to collect and interpret news data. The Time-Series Encoder organizes and analyzes time-related data, while the Multi-Task Prediction module amalgamates outputs from the other modules for multifaceted prediction. Figure 1

Figure 1: This figure illustrates the RiskLabs Framework. The model accepts multiple-source inputs: Earnings Conference Call Audio {additional_guidance} Transcript, Daily News, and Time Series Data. The second area visualizes the model's pipeline to encode diverse sources and illustrates how LLMs are applied for data analysis. The third area describes how the model consolidates outputs from both embeddings and LLM analysis for use in subsequent stages. The model will perform multi-task learning: our RiskLabs will predict the Volatility of different terms and VaR in the meantime.

Earnings Conference Call Encoder

The Earnings Conference Call Encoder module consists of Audio Encoding, Transcript Encoding, Earnings Conference Call Analyzer, and Additive Multi-modal Fusion. Audio Encoding converts audio data into vector representations using Wav2vec2, followed by a multi-head self-attention mechanism. Transcript Encoding uses SimCSE to extract vector representations of sentences in earnings call transcripts, also followed by multi-head self-attention. The Earnings Conference Call Analyzer summarizes the earnings call and examines financial metrics using LLMs (Figure 2). Additive Multi-modal Fusion integrates the feature sets from both audio and textual data. Figure 2

Figure 2: This figure visualizes the mechanism of the earnings conference call analyzer. It takes earnings conference calls as input. There is a two-step analysis: first, it summarizes the primary ideas. Second, it examines specific financial metrics and events mentioned in the call. At the top middle section, the illustration shows the analyzer segmenting the earnings conference call into smaller parts for more accurate summarization. The middle section demonstrates how the analyzer extracts and evaluates information from the earnings conference call. After this comprehensive analysis, the model produces a detailed analysis of the earnings conference call.

News-Market Reactions Encoder

The News-Market Reactions Encoder uses an enriched news pipeline to extract attributes associated with news and attach them to news groups (Figure 3). The pipeline analyzes sentiments from news and extracts information based on a binary question bank designed for different topics. If the pipeline captures a specific topic, it provides feedback on potential market response. The module assesses similarities across news collections using attributes tagged during the enrichment pipeline (Figure 4). Figure 3

Figure 3: This figure illustrates the pipeline for enriching the news information. First, the pipeline will analyze the sentiments from the target news. Then, based on the binary questions bank designed for different topics, Pipeline can extract the information and answer these questions. Finally, if the pipeline could capture the signal of a specific topic, it would also give feedback on the potential market response.

Figure 4

Figure 4: This diagram illustrates the process by which the News Analyzer assesses similarities across various news collections. As news items pass through the enrichment pipeline, they are tagged with multiple attributes. Each circle in the figure represents one of these attributes. To identify similar news collections from historical data, the analyzer starts by comparing these attributes and filtering out certain ones. Subsequently, it evaluates the similarity among the remaining attributes to determine the connections between different news collections.

Time-Series Encoder

The Time-Series Encoder captures VIX values using a BiLSTM network and extracts relationships among multiple response variables using a VAR-based method. The paper models the time decay effect of an earnings conference call's influence over time using an exponential decay function (Figure 5). The framework also employs a rolling window methodology, systematically progressing the training set forward in alignment with time (Figure 6). Figure 5

Figure 5: This figure illustrates the methodology for modeling the impact of earnings conference calls on the stock market when earnings conference calls are not available. It uses a curve to represent the time decay effect of an earnings conference call's influence over time. When an earnings conference call is initially issued, its impact on the market is at its peak. Over time, this influence gradually diminishes.

Figure 6

Figure 6: This figure provides a visual representation of the Rolling Window methodology in action. By establishing a fixed window length, the approach systematically progresses the training set forward, day by day, in alignment with the passage of time.

Multi-Task Prediction

The Multi-Task Prediction module aggregates features from the various modules into a comprehensive feature representation, connected to a two-layer neural network for regression. It employs a joint modeling approach, concurrently modeling volatility prediction and VaR prediction using a multi-task framework. The module consists of two separate single-layer feedforward networks for predicting volatility and VaR values individually.

Experimental Results and Analysis

The paper presents experimental results comparing RiskLabs against classical methods, LSTM, MT-LSTM-ATT, HAN, MRDM, HTML, and GPT-3.5-Turbo. RiskLabs demonstrates superior performance in predicting financial risks, particularly in short-term and medium-term forecasts. The framework also shows superior performance in VaR prediction, highlighting its effectiveness in providing a more nuanced and comprehensive approach to financial risk prediction (Figure 7). The direct application of LLMs for financial risk prediction using GPT-3.5-Turbo proves ineffective, underscoring the importance of proper utilization of LLMs. Figure 7

Figure 7

Figure 7: This figure presents two plots that compare daily Value at Risk predictions (red curve) with the actual returns of the asset (black dot). It visualizes the percentage of actual returns exceeding the predicted VaR. For instance, with a predefined VaR of 0.05, observing approximately 5\% of the actual returns surpassing the predicted VaR curve indicates a high degree of prediction accuracy. The left plot showcases the VaR forecast using the historical method, illustrating how this traditional technique estimates risk in relation to the asset's actual performance. The right plot, on the other hand, using a fully connected neural network for VaR prediction, offering a modern computational approach to risk assessment.

Ablation studies reveal the relative contributions of each module in RiskLabs. Combining "Audio + Text" yields better results than the HTML model for 3-day forecasts, while RiskLabs demonstrates predictions closely aligned with HTML for longer periods. Integrating earnings call analysis text and time-series data leads to incremental improvements in RiskLab's performance.

Conclusion

The RiskLabs framework demonstrates high efficacy in predicting financial risks and the strategic application of LLMs in processing financial data enhances the predictive power of deep learning models. The framework marks a significant step forward in the application of AI in finance. The ongoing enhancements to the News-Market Reactions Encoder module and the implementation of dynamic training windows aim to further improve the model's performance and adaptability.

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