arXiv · arXiv · 2026
Multimodal time-to-event prediction often requires integrating sensitive data distributed across multiple parties, making centralized model training impractical due to privacy constraints. At the same time, most existing multimodal survival models produce single deterministic predictions without indicating how confident the model is in its estimates, which can limit their reliability in real-world decision making. To…
Abhilash Kar, Basisth Saha, Tanmay Sen, Biswabrata Pradhan
arXiv · arXiv · 2026
Credit risk assessment increasingly relies on diverse sources of information beyond traditional structured financial data, particularly for micro and small enterprises (mSEs) with limited financial histories. This study proposes a multimodal framework that integrates structured credit variables, climate panel data, and unstructured textual narratives within a unified learning architecture. Specifically, we use long s…
Zongxiao Wu, Ran Liu, Jiang Dai, Dan Luo
arXiv · arXiv · 2025
Financial trading systems must convert multimodal market history into executable positions while limiting overfitting from repeated strategy search. We introduce MM-ARC (MultiModal Adaptive Routing of Capital), which routes capital across trend, reversal, breakout, and exposure-control experts using aligned chart, numerical, and technical-text views. Within each market, regime-conditioned strategy pools are shared wi…
Yang Chen, Yuchen Cao, Jacky Keung, Leilei Gan, Kun Kuang
arXiv · arXiv · 2024
We propose a reinforcement learning (RL) framework that leverages multimodal data including historical stock prices, sentiment analysis, and topic embeddings from news articles, to optimize trading strategies for SP100 stocks. Building upon recent advancements in financial reinforcement learning, we aim to enhance the state space representation by integrating financial sentiment data from SEC filings and news headlin…
Sumit Nawathe, Ravi Panguluri, James Zhang, Sashwat Venkatesh
arXiv · arXiv · 2024
In this paper, we tackle the challenge of predicting stock movements in financial markets by introducing Higher Order Transformers, a novel architecture designed for processing multivariate time-series data. We extend the self-attention mechanism and the transformer architecture to a higher order, effectively capturing complex market dynamics across time and variables. To manage computational complexity, we propose a…
Soroush Omranpour, Guillaume Rabusseau, Reihaneh Rabbany
arXiv · arXiv · 2024
Financial LLMs hold promise for advancing financial tasks and domain-specific applications. However, they are limited by scarce corpora, weak multimodal capabilities, and narrow evaluations, making them less suited for real-world application. To address this, we introduce \textit{Open-FinLLMs}, the first open-source multimodal financial LLMs designed to handle diverse tasks across text, tabular, time-series, and char…
Jimin Huang, Mengxi Xiao, Dong Li, Zihao Jiang, Yuzhe Yang
arXiv · arXiv · 2024
Financial trading is a crucial component of the markets, informed by a multimodal information landscape encompassing news, prices, and Kline charts, and encompasses diverse tasks such as quantitative trading and high-frequency trading with various assets. While advanced AI techniques like deep learning and reinforcement learning are extensively utilized in finance, their application in financial trading tasks often f…
Wentao Zhang, Lingxuan Zhao, Haochong Xia, Shuo Sun, Jiaze Sun
arXiv · arXiv · 2024
Quantum theory provides a comprehensive framework for quantifying uncertainty, often applied in quantum finance to explore the stochastic nature of asset returns. This perspective likens returns to microscopic particle motion, governed by quantum probabilities akin to physical laws. However, such approaches presuppose specific microscopic quantum effects in return changes, a premise criticized for lack of guarantee. …
Li Lin
arXiv · arXiv · 2022
Bitcoin, with its ever-growing popularity, has demonstrated extreme price volatility since its origin. This volatility, together with its decentralised nature, make Bitcoin highly subjective to speculative trading as compared to more traditional assets. In this paper, we propose a multimodal model for predicting extreme price fluctuations. This model takes as input a variety of correlated assets, technical indicators…
Yanzhao Zou, Dorien Herremans
arXiv · arXiv · 2019
In today's increasingly international economy, return and volatility spillover effects across international equity markets are major macroeconomic drivers of stock dynamics. Thus, information regarding foreign markets is one of the most important factors in forecasting domestic stock prices. However, the cross-correlation between domestic and foreign markets is highly complex. Hence, it is extremely difficult to expl…
Sang Il Lee, Seong Joon Yoo
arXiv · arXiv · 2025
Small and Medium-sized Enterprises (SMEs) are known to play a vital role in economic growth, employment, and innovation. However, they tend to face significant challenges in accessing credit due to limited financial histories, collateral constraints, and exposure to macroeconomic shocks. These challenges make an accurate credit risk assessment by lenders crucial, particularly since SMEs frequently operate within inte…
Sahab Zandi, Kamesh Korangi, Juan C. Moreno-Paredes, María Óskarsdóttir, Christophe Mues
arXiv · arXiv · 2026
In financial markets, a sequential policy that reacts systematically to price movements may become predictable to other market participants. This paper studies whether large language model (LLM) agents exhibit such directional structure through RetailAgent, an experimental framework in which an LLM observes anonymized intraday equity price histories and permitted state, then repeatedly chooses long (hold the stock) o…
Yupeng Zhang, Liuyuan Jiang, Hongyi Huang, Bingheng Li, Lisha Chen
arXiv · arXiv · 2023
This report outlines a transformative initiative in the financial investment industry, where the conventional decision-making process, laden with labor-intensive tasks such as sifting through voluminous documents, is being reimagined. Leveraging language models, our experiments aim to automate information summarization and investment idea generation. We seek to evaluate the effectiveness of fine-tuning methods on a b…
Lezhi Li, Ting-Yu Chang, Hai Wang
arXiv · arXiv · 2013
We investigate a statistical-static hedging technique for pricing assets considered as single-step stochastic cash flows. The valuation is based on constructing in a canonical way a European style derivative on a benchmark security such that the physical payoff distribution coincides with the (corrected) physical asset price distribution. It turns out that this pricing technique is economically viable under some natu…
Jarno Talponen