Sentiment-driven prediction of financial returns: a Bayesian-enhanced FinBERT approach

• URL: http://arxiv.org/abs/2403.04427v1
• Date: Thu, 7 Mar 2024 11:56:36 GMT
• Title: Sentiment-driven prediction of financial returns: a Bayesian-enhanced FinBERT approach
• Authors: Raffaele Giuseppe Cestari and Simone Formentin
• Abstract summary: We showcase the efficacy of leveraging sentiment information extracted from tweets using the FinBERT large language model. This success translates into demonstrably higher cumulative profits during backtested trading.
• Score: 1.131316248570352
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Predicting financial returns accurately poses a significant challenge due to the inherent uncertainty in financial time series data. Enhancing prediction models' performance hinges on effectively capturing both social and financial sentiment. In this study, we showcase the efficacy of leveraging sentiment information extracted from tweets using the FinBERT large language model. By meticulously curating an optimal feature set through correlation analysis and employing Bayesian-optimized Recursive Feature Elimination for automatic feature selection, we surpass existing methodologies, achieving an F1-score exceeding 70% on the test set. This success translates into demonstrably higher cumulative profits during backtested trading. Our investigation focuses on real-world SPY ETF data alongside corresponding tweets sourced from the StockTwits platform.

Related papers

• Enhancing Financial Market Predictions: Causality-Driven Feature Selection [3.7426958478253254]
  FinSen dataset integrates economic and financial news articles from 197 countries with stock market data. Our study leverages causally validated sentiment scores and LSTM models to enhance market forecast accuracy and reliability.
  arXiv  Detail & Related papers  (2024-08-02T04:40:15Z)
• Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs [49.57641083688934]
  We introduce a novel approach to anomaly detection in financial data using Large Language Models (LLMs) embeddings. Our experiments demonstrate that LLMs contribute valuable information to anomaly detection as our models outperform the baselines.
  arXiv  Detail & Related papers  (2024-06-05T20:19:09Z)
• AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework [48.3060010653088]
  We release AlphaFin datasets, combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data. We then use AlphaFin datasets to benchmark a state-of-the-art method, called Stock-Chain, for effectively tackling the financial analysis task.
  arXiv  Detail & Related papers  (2024-03-19T09:45:33Z)
• A Bargaining-based Approach for Feature Trading in Vertical Federated Learning [54.51890573369637]
  We propose a bargaining-based feature trading approach in Vertical Federated Learning (VFL) to encourage economically efficient transactions. Our model incorporates performance gain-based pricing, taking into account the revenue-based optimization objectives of both parties.
  arXiv  Detail & Related papers  (2024-02-23T10:21:07Z)
• Measuring Consistency in Text-based Financial Forecasting Models [10.339586273664725]
  FinTrust is an evaluation tool that assesses logical consistency in financial text. We show that the consistency of state-of-the-art NLP models for financial forecasting is poor. Our analysis of the performance degradation caused by meaning-preserving alternations suggests that current text-based methods are not suitable for robustly predicting market information.
  arXiv  Detail & Related papers  (2023-05-15T10:32:26Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines. We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• Feature Selection with Annealing for Forecasting Financial Time Series [2.44755919161855]
  This study provides a comprehensive method for forecasting financial time series based on tactical input output feature mapping techniques using machine learning (ML) models. Experiments indicate that the FSA algorithm increased the performance of ML models, regardless of problem type.
  arXiv  Detail & Related papers  (2023-03-03T21:33:38Z)
• FinQA: A Dataset of Numerical Reasoning over Financial Data [52.7249610894623]
  We focus on answering deep questions over financial data, aiming to automate the analysis of a large corpus of financial documents. We propose a new large-scale dataset, FinQA, with Question-Answering pairs over Financial reports, written by financial experts. The results demonstrate that popular, large, pre-trained models fall far short of expert humans in acquiring finance knowledge.
  arXiv  Detail & Related papers  (2021-09-01T00:08:14Z)
• Interpretable ML-driven Strategy for Automated Trading Pattern Extraction [2.7910505923792646]
  We propose a volume-based data pre-processing method for financial time series analysis. We use a statistical approach for assessing the performance of the method. Our analysis shows that the proposed method allows successful classification of the financial time series patterns.
  arXiv  Detail & Related papers  (2021-03-23T09:55:46Z)
• Gaussian process imputation of multiple financial series [71.08576457371433]
  Multiple time series such as financial indicators, stock prices and exchange rates are strongly coupled due to their dependence on the latent state of the market. We focus on learning the relationships among financial time series by modelling them through a multi-output Gaussian process.
  arXiv  Detail & Related papers  (2020-02-11T19:18:18Z)

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.