Equity Factor Investing Enhanced with News & Earnings Call Transcripts

February 7, 2022

The performance of traditional equity factor strategies have been rather disappointing in recent years. In our latest research, we consider incorporating sentiment signals from news, earnings call transcripts, and insider transactions to boost the risk-adjusted returns, and revive factor performance.

In this whitepaper, we analyze how the performance of monthly-rebalanced equity factor investing strategies can be significantly enhanced by incorporating real-time sentiment from RavenPack’s news analytics and earnings transcripts datasets. We construct cross-sectional, dollar-neutral long-short portfolios as our benchmarks for nine traditional style factors based on the small, mid and large-cap U.S. stock universes over 2007-2021.

Watch the Video Summary

Cumulative log-returns of earnings-related sentiment-tilted multifactor strategies
allocated using inverse-volatility weighting at a monthly rebalancing frequency

  • Tilting the weights of a monthly-rebalanced benchmark factor portfolio using sentiment enhances strategy performance across all equity factors, while maintaining stable portfolio turnovers.

  • Tilting towards earnings-related news sentiment increases Information Ratios for yield (from -0.21 to 0.28) and investment factors (from 0.30 to 0.61), while portfolio turnover marginally increases from 3.2% to 5.7% and from 4.2% to 6.4%, respectively.

  • Statistical significance tests confirm that sentiment-tilted factors meaningfully outperform those with random tilts, indicating that the information contained in news and transcripts adds considerable value in the signal construction process.

Get the Whitepaper to review the performance for each factor and our proposed models for sentiment-tilted multifactor strategies.

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Earnings Intelligence series

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RavenPack Earnings Intelligence combines signals from separate sources augmented with sentiment and thematic analytics for a powerful alternative that removes the limits of isolated vendor data.