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Benzinga Multi-Factor News Sentiment Model: The model uses derived features related to news sentiment, news volume, change in news sentiment, and change in news volume in conjunction with additional factors to identify promising long positions from the Russell 1000 universe. The model retrains dynamically on a roll forward basis to consider most relevant features and account for changes in market regime. The goal of the strategy is to outperform the Russell 1000 index in risk-adjusted returns measured via Sharpe ratio.
This is a long-only model for investing in stocks from the Russell 1000 index based on features derived from Benzinga’s news dataset, which aggregates from thousands of sources.
The model retrains every two months and is tested out-of-sample on data from January 2016 to September 2020.
The backtest considers transaction costs and slippage. See report for additional details.