This is a simple long/short trading strategy that attempts to profit off the drift following a positive or negative news sentiment release. Accern analyzes millions of news and blog articles through their multi-layer deep learning approach to provide two scores for their Alphaone datafeed:
article_sentiment- a score in [-1,1] reflecting the sentiment of articles written about the company in the last day. The higher score, the more positive the outlook
impact_score- on [0,100], this is the probability that the stock price will change by more than 1% (given by: close - open / open) on the next trading day
In this strategy, I use the sentiment to rank each security within the Health Care & Energy Sector (found through the Factor Tearsheet). My economic hypothesis being that overall news & blog sentiment may have a following drift, similar to the one found for earnings announcements.
- Data set: The full dataset used is Accern's Alphaone Dataset
- Weights: The weight for each security is determined by the total number of longs and shorts we have in that current day. This number as well as the rebalance is done on a weekly basis.
- Days held: Positions are currently held for 7 days but are easily changeable by modifying the
- Article Sentiment: Look at the 40 day article sentiment average
- Sectors: Using the Factor Tearsheet I found that my factor held the highest IC for stocks in the Energy & Healthcare. This may change with liquidity constraints, time periods, and even the factor that you are using.
- The full Accern Alphaone datafeed includes availability for live trading.
For out-of-sample results, please view the first reply to this thread.