AI-Driven Multiscenario Interest Rate Forecasting: A Proof of Concept for Banking Asset Management
- URL: http://arxiv.org/abs/2608.12424v2
- Date: Fri, 14 Aug 2026 14:56:40 GMT
- Title: AI-Driven Multiscenario Interest Rate Forecasting: A Proof of Concept for Banking Asset Management
- Abstract summary: This study develops an AI-supported prototype for multiperspective interest rate forecasting.<n>Tested in a major European bank, the system enables more precise and flexible prediction of interest rate developments.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This study focuses on developing an AI-supported prototype for multiperspective interest rate forecasting that combines classical econometric models with modern artificial intel-ligence methods. Tested in a major European bank, the system enables more precise and flexible prediction of interest rate developments, supporting strategic decision-making in Asset-Liability Management (ALM). It integrates topic modeling, sentiment analysis, econometric forecasting, and market-based analyses within an interactive platform. Leveraging AI to analyze large volumes of financial documents and market data enables the identification of monetary policy trends and sentiment signals at an early stage. The core econometric model is a Bayesian vector autoregression (BVAR) that enables simulation-based scenario analyses to evaluate economic developments from multiple perspectives. The system's innovation lies in its integration of several forecasting approaches that consolidate previously separate information sources and present them transparently and interpretably. Financial analysts and risk managers thus gain a better basis for making decisions, allowing them to assess interest rate risks more accurately and manage market movements more proactively. While the prototype demonstrates how AI can transform interest rate management in banking, further development is required to optimize real-time data integration and regulatory compliance. Even at this stage, the study shows that multi-perspective, AI-driven forecasting provides substantial added value for banks by increasing transparency, strengthening evidence-based decision-making, and improving risk management.
Related papers
- MEME: Modeling the Evolutionary Modes of Financial Markets [11.120179118542518]
We introduce MEME, designed to reconstruct market dynamics through the lens of evolving logics.<n>MEME employs a multi-agent extraction module to transform noisy data into high-fidelity Investment Arguments.<n>Experiments on three heterogeneous Chinese stock pools from 2023 to 2025 demonstrate that MEME consistently outperforms seven SOTA baselines.
arXiv Detail & Related papers (2026-02-12T13:16:05Z) - Trade in Minutes! Rationality-Driven Agentic System for Quantitative Financial Trading [57.28635022507172]
TiMi is a rationality-driven multi-agent system that architecturally decouples strategy development from minute-level deployment.<n>We propose a two-tier analytical paradigm from macro patterns to micro customization, layered programming design for trading bot implementation, and closed-loop optimization driven by mathematical reflection.
arXiv Detail & Related papers (2025-10-06T13:08:55Z) - FutureX: An Advanced Live Benchmark for LLM Agents in Future Prediction [92.7392863957204]
FutureX is the largest and most diverse live benchmark for future prediction.<n>It supports real-time daily updates and eliminates data contamination through an automated pipeline for question gathering and answer collection.<n>We evaluate 25 LLM/agent models, including those with reasoning, search capabilities, and integration of external tools.
arXiv Detail & Related papers (2025-08-16T08:54:08Z) - Deriving Strategic Market Insights with Large Language Models: A Benchmark for Forward Counterfactual Generation [55.2788567621326]
We introduce a novel benchmark, FIN-FORCE-FINancial FORward Counterfactual Evaluation.<n>By curating financial news headlines, FIN-FORCE supports LLM based forward counterfactual generation.<n>This paves the way for scalable and automated solutions for exploring and anticipating future market developments.
arXiv Detail & Related papers (2025-05-26T02:41:50Z) - FinRobot: AI Agent for Equity Research and Valuation with Large Language Models [6.2474959166074955]
This paper presents FinRobot, the first AI agent framework specifically designed for equity research.
FinRobot employs a multi-agent Chain of Thought (CoT) system, integrating both quantitative and qualitative analyses to emulate the comprehensive reasoning of a human analyst.
Unlike existing automated research tools, such as CapitalCube and Wright Reports, FinRobot delivers insights comparable to those produced by major brokerage firms and fundamental research vendors.
arXiv Detail & Related papers (2024-11-13T17:38:07Z) - Data Analysis in the Era of Generative AI [56.44807642944589]
This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges.
We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow.
We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
arXiv Detail & Related papers (2024-09-27T06:31:03Z) - Developing An Attention-Based Ensemble Learning Framework for Financial Portfolio Optimisation [0.0]
We propose a multi-agent and self-adaptive portfolio optimisation framework integrated with attention mechanisms and time series, namely the MASAAT.
By reconstructing the tokens of financial data in a sequence, the attention-based cross-sectional analysis module and temporal analysis module of each agent can effectively capture the correlations between assets and the dependencies between time points.
The experimental results clearly demonstrate that the MASAAT framework achieves impressive enhancement when compared with many well-known portfolio optimsation approaches.
arXiv Detail & Related papers (2024-04-13T09:10:05Z) - Long Short-Term Memory Pattern Recognition in Currency Trading [0.0]
Wyckoff Phases is a framework devised by Richard D. Wyckoff in the early 20th century.
The research explores the phases of trading range and secondary test, elucidating their significance in understanding market dynamics.
By dissecting the intricacies of these phases, the study sheds light on the creation of liquidity through market structure.
The study highlights the transformative potential of AI-driven approaches in financial analysis and trading strategies.
arXiv Detail & Related papers (2024-02-23T12:59:49Z) - Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection [0.07499722271664146]
This paper introduces MarketSenseAI, an innovative framework leveraging GPT-4's advanced reasoning for selecting stocks in financial markets.
MarketSenseAI analyzes diverse data sources, including market trends, news, fundamentals, and macroeconomic factors, to emulate expert investment decision-making.
Our findings highlight the transformative potential of Large Language Models in financial decision-making.
arXiv Detail & Related papers (2024-01-08T08:58:46Z) - Diffusion Variational Autoencoder for Tackling Stochasticity in
Multi-Step Regression Stock Price Prediction [54.21695754082441]
Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility.
Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions.
We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction.
Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
arXiv Detail & Related papers (2023-08-18T16:21:15Z) - Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [48.87381259980254]
We document the capability of large language models (LLMs) like ChatGPT to predict stock market reactions from news headlines without direct financial training.<n>Using post-knowledge-cutoff headlines, GPT-4 captures initial market responses, achieving approximately 90% portfolio-day hit rates for the non-tradable initial reaction.
arXiv Detail & Related papers (2023-04-15T19:22:37Z) - Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics
in Limit-Order Book Markets [84.90242084523565]
Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics.
By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention.
By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
arXiv Detail & Related papers (2022-03-07T18:59:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.