Can ChatGPT Compute Trustworthy Sentiment Scores from Bloomberg Market
Wraps?
- URL: http://arxiv.org/abs/2401.05447v1
- Date: Tue, 9 Jan 2024 10:34:19 GMT
- Title: Can ChatGPT Compute Trustworthy Sentiment Scores from Bloomberg Market
Wraps?
- Authors: Baptiste Lefort, Eric Benhamou, Jean-Jacques Ohana, David Saltiel,
Beatrice Guez, Damien Challet
- Abstract summary: We study how global news headlines may affect stock market movements using ChatGPT and a two-stage prompt approach.
We document a statistically significant positive correlation between the sentiment score and future equity market returns over short to medium term, which reverts to a negative correlation over longer horizons.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We used a dataset of daily Bloomberg Financial Market Summaries from 2010 to
2023, reposted on large financial media, to determine how global news headlines
may affect stock market movements using ChatGPT and a two-stage prompt
approach. We document a statistically significant positive correlation between
the sentiment score and future equity market returns over short to medium term,
which reverts to a negative correlation over longer horizons. Validation of
this correlation pattern across multiple equity markets indicates its
robustness across equity regions and resilience to non-linearity, evidenced by
comparison of Pearson and Spearman correlations. Finally, we provide an
estimate of the optimal horizon that strikes a balance between reactivity to
new information and correlation.
Related papers
- Leveraging RNNs and LSTMs for Synchronization Analysis in the Indian Stock Market: A Threshold-Based Classification Approach [0.0]
Our research presents a new approach for forecasting the synchronization of stock prices using machine learning and non-linear time-series analysis.
We apply this methodology to a dataset of 20 highly capitalized stocks from the Indian market over a 21-year period.
The findings reveal that our approach can predict stock price synchronization, with an accuracy of 0.98 and F1 score of 0.83.
arXiv Detail & Related papers (2024-08-27T11:08:37Z) - Learning Graph Structures and Uncertainty for Accurate and Calibrated Time-series Forecasting [65.40983982856056]
We introduce STOIC, that leverages correlations between time-series to learn underlying structure between time-series and to provide well-calibrated and accurate forecasts.
Over a wide-range of benchmark datasets STOIC provides 16% more accurate and better-calibrated forecasts.
arXiv Detail & Related papers (2024-07-02T20:14:32Z) - Joint Latent Topic Discovery and Expectation Modeling for Financial
Markets [45.758436505779386]
We present a groundbreaking framework for financial market analysis.
This approach is the first to jointly model investor expectations and automatically mine latent stock relationships.
Our model consistently achieves an annual return exceeding 10%.
arXiv Detail & Related papers (2023-06-01T01:36:51Z) - Dual-CLVSA: a Novel Deep Learning Approach to Predict Financial Markets
with Sentiment Measurements [11.97251638872227]
We propose a novel deep learning approach, named dual-CLVSA, to predict financial market movement with both trading data and the corresponding social sentiment measurements, each through a separate sequence-to-sequence channel.
The experiment results show that dual-CLVSA can effectively fuse the two types of data, and verify that sentiment measurements are not only informative for financial market predictions, but they also contain extra profitable features to boost the performance of our predicting system.
arXiv Detail & Related papers (2022-01-27T20:32:46Z) - Stock Price Prediction Under Anomalous Circumstances [81.37657557441649]
This paper aims to capture the movement pattern of stock prices under anomalous circumstances.
We train ARIMA and LSTM models at the single-stock level, industry level, and general market level.
Based on 100 companies' stock prices in the period of 2016 to 2020, the models achieve an average prediction accuracy of 98%.
arXiv Detail & Related papers (2021-09-14T18:50:38Z) - Long-term, Short-term and Sudden Event: Trading Volume Movement
Prediction with Graph-based Multi-view Modeling [21.72694417816051]
We propose a graphbased approach that can incorporate multi-view information, i.e., long-term stock trend, short-term fluctuation and sudden events information jointly into a temporal heterogeneous graph.
Our method is equipped with deep canonical analysis to highlight the correlations between different perspectives of fluctuation for better prediction.
arXiv Detail & Related papers (2021-08-23T03:06:04Z) - REST: Relational Event-driven Stock Trend Forecasting [76.08435590771357]
We propose a relational event-driven stock trend forecasting (REST) framework, which can address the shortcoming of existing methods.
To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts.
To address the second shortcoming, we construct a stock graph and design a new propagation layer to propagate the effect of event information from related stocks.
arXiv Detail & Related papers (2021-02-15T07:22:09Z) - A Sentiment Analysis Approach to the Prediction of Market Volatility [62.997667081978825]
We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements.
The sentiment captured from news headlines could be used as a signal to predict market returns; the same does not apply for volatility.
We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information.
arXiv Detail & Related papers (2020-12-10T01:15:48Z) - Predictive intraday correlations in stable and volatile market
environments: Evidence from deep learning [2.741266294612776]
We apply deep learning to learn and exploit lagged correlations among S&P 500 stocks to compare model behaviour in stable and volatile markets.
Our findings show that accuracies, while remaining significant, decrease with shorter prediction horizons.
We discuss implications for modern finance theory and our work's applicability as an investigative tool for portfolio managers.
arXiv Detail & Related papers (2020-02-24T17:19:54Z) - 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.