April 5, 2024
Stay up to speed on the latest insights from the RavenPack data science team.
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During the first quarter, our data science team published new research on the performance of finely-tuned large language models for sentiment classification, the link between media coverage on innovation and stock performance, enhancing risk-based multi-asset allocations in various inflationary regimes, and a deeper dive into earnings intelligence. Check it out:
Our research demonstrates that optimizing language models for sentiment analysis can significantly improve their performance. Even FinBERT, a model pre-trained on financial data, showed a 48% increase in Information Ratio (IR) when fine-tuned with sentiment-enriched annotations.
By analyzing historical news articles and patent filings, we discovered a correlation between media coverage of innovation and a company's future stock performance. This finding can be a valuable tool for identifying potential market leaders.
Our research explored how AI could enhance risk-based multi-asset strategies to adapt to various inflationary environments in real-time. Backtesting demonstrated that this strategy consistently outperformed the S&P 500 MARC by 5%, with a Sharpe Ratio of 1.29.
Combining earnings-related signals from various alternative datasets can deliver Information Ratios of up to 2.6. RavenPack's Earnings Intelligence simplifies complex earnings data (news, transcripts, and announcement dates) and provides these valuable signals as off-the-shelf factors for investors.
For more data-driven insights from RavenPack, visit our resources here.
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