Publications tagged: #finance
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Multimodal Language Models with Modality-Specific Experts for Financial Forecasting from Interleaved Sequences of Text and Time Series
Text and time series data offer complementary views of financial markets: news articles provide narrative context about company events, while stock prices reflect how markets react to those events. However, despite their complementary nature, effectively integrating these interleaved modalities for improved forecasting remains challenging. In this work, we propose a unified neural architecture that models these interleaved sequences using modality-specific experts, allowing the model to learn unique time series patterns, while still enabling joint reasoning across modalities and preserving pretrained language understanding capabilities. To further improve multimodal understanding, we introduce a cross-modal alignment framework with a salient token weighting mechanism that learns to align representations across modalities with a focus on the most informative tokens. We demonstrate the effectiveness of our approach on a large-scale financial forecasting task, achieving state-of-the-art performance across a wide variety of strong unimodal and multimodal baselines. We develop an interpretability method that reveals insights into the value of time series-context and reinforces the design of our cross-modal alignment objective. Finally, we demonstrate that these improvements translate to meaningful economic gains in investment simulations.
Ross Koval , Nicholas Andrews , Xifeng Yan
arXiv preprint arXiv:2509.19628, 2025
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Context-Aware Language Models for Forecasting Market Impact from Sequences of Financial News
Financial news plays a critical role in the information diffusion process in financial markets and is a known driver of stock prices. However, the information in each news article is not necessarily self-contained, often requiring a broader understanding of the historical news coverage for accurate interpretation. Further, identifying and incorporating the most relevant contextual information presents significant challenges. In this work, we explore the value of historical context in the ability of large language models to understand the market impact of financial news. We find that historical context provides a consistent and significant improvement in performance across methods and time horizons. To this end, we propose an efficient and effective contextualization method that uses a large LM to process the main article, while a small LM encodes the historical context into concise summary embeddings that are then aligned with the large model's representation space. We explore the behavior of the model through multiple qualitative and quantitative interpretability tests and reveal insights into the value of contextualization. Finally, we demonstrate that the value of historical context in model predictions has real-world applications, translating to substantial improvements in simulated investment performance.
Ross Koval , Nicholas Andrews , Xifeng Yan
arXiv preprint arXiv:2509.12519, 2025
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Financial Forecasting from Textual and Tabular Time Series
There is a variety of multimodal data pertinent to public companies, spanning from accounting statements, macroeconomic statistics, earnings conference calls, and financial reports. These diverse modalities capture the state of firms from a variety of different perspectives but requires complex interactions to reconcile in the formation of accurate financial predictions. The commonality between these different modalities is that they all represent a time series, typically observed for a particular firm at a quarterly horizon, providing the ability to model trends and variations of company data over time. However, the time series of these diverse modalities contains varying temporal and cross-channel patterns that are challenging to model without the appropriate inductive biases. In this work, we design a novel multimodal time series prediction task that includes numerical financial results, macroeconomic states, and long financial documents to predict next quarter's company earnings relative to analyst expectations. We explore a variety of approaches for this novel setting, establish strong unimodal baselines, and propose a multimodal model that exhibits state-of-the-art performance on this unique task. We demonstrate that each modality contains unique information and that the best performing model requires careful fusion of the different modalities in a multi-stage training approach. To better understand model behavior, we conduct a variety of probing experiments, reveal insights into the value of different modalities, and demonstrate the practical utility of our proposed method in a simulated trading setting.
Ross Koval , Nicholas Andrews , Xifeng Yan
Findings of the Association for Computational Linguistics: EMNLP 2024, 2024
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Learning to Compare Financial Reports for Financial Forecasting
Public companies in the US are required to publish annual reports that detail their recent financial performance, present the current state of ongoing business operations, and discuss future prospects. However, they typically contain over 25,000 words across all sections, large amounts of industry and legal jargon, and a high percentage of boilerplate content that does not change much year-to-year. These unique characteristics present challenges for many generic pretrained language models because it is likely that only a small percentage of the long report that reflects salient information contains meaningful signal about the future prospects of the company. In this work, we curate a large-scale dataset of paired financial reports and introduce two novel, challenging tasks of predicting long-horizon company risk and correlation that evaluate the ability of the model to recognize cross-document relationships with complex, nuanced signals. We explore and present a comprehensive set of methods and experiments, and establish strong baselines designed to learn to identify subtle similarities and differences between long documents. Furthermore, we demonstrate that it is possible to predict company risk and correlation solely from the text of their financial reports and further that modeling the cross-document interactions at a fine-grained level provides significant benefit. Finally, we probe the best performing model through quantitative and qualitative interpretability methods to reveal some insight into the underlying task signal.
Ross Koval , Nicholas Andrews , Xifeng Yan
Findings of the Association for Computational Linguistics: EACL, 2024
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Forecasting Earnings Surprises from Conference Call Transcripts
There is a multitude of textual data relevant to the financial markets, spanning genres such as financial news, earnings conference calls, and social media posts. Earnings conference calls are one of the most important to information flow as they reflect a direct communication between company executives, financial analysts, and large shareholders. Since these calls contain content that is forward-looking in nature, they can be used to forecast the future performance of the company relative to market expectations. However, they typically contain over 5,000 words of text and large amounts of industry jargon. This length and domain-specific language present problems for many generic pretrained language models. In this work, we introduce a novel task of predicting earnings surprises from earnings call transcripts and contribute a new long document dataset that tests financial understanding with complex signals. We explore a variety of approaches for this long document classification task and establish some strong baselines. Furthermore, we demonstrate that it is possible to predict companies’ future earnings surprises from solely the text of their conference calls with reasonable accuracy. Finally, we probe the models through different interpretability methods and reveal some intuitive explanations of the linguistic features captured that go beyond traditional sentiment analysis.
Ross Koval , Nicholas Andrews , Xifeng Yan
Findings of the Association for Computational Linguistics: ACL 2023, 2023