Summary of Enhancing Financial Market Predictions: Causality-driven Feature Selection, by Wenhao Liang et al.
Enhancing Financial Market Predictions: Causality-Driven Feature Selection
by Wenhao Liang, Zhengyang Li, Weitong Chen
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Computation and Language (cs.CL); Databases (cs.DB)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed FinSen dataset is a game-changer in financial market analysis, combining economic and financial news articles from 197 countries with stock market data to provide a comprehensive, global perspective. The dataset covers 15 years (2007-2023) and includes temporal information, yielding 160,000 records on financial market news. To enhance market forecast accuracy and reliability, the study leverages causally validated sentiment scores and LSTM models. An innovative Focal Calibration Loss is introduced to reduce Expected Calibration Error (ECE) to 3.34% with the DAN 3 model, demonstrating improved prediction accuracy and alignment of probabilistic forecasts with real outcomes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The FinSen dataset helps predict financial markets more accurately by combining news articles from around the world with stock market data. The study uses special models that understand how people feel about financial events and combines this information with precise forecasting techniques to create trustworthy predictions. This is important for the financial sector because accurate forecasts can help make better decisions. |
Keywords
» Artificial intelligence » Alignment » Lstm
