In their whitepaper, "News-Driven Return Reversals," Eric C. So of MIT and Sean Wang of UNC show that abnormal short-term returns reversals take place during the period immediately surrounding earnings announcements. They surmise that this reversal results from market makers' response to a temporary demand imbalance, as they temporarily shift the stock's price to ride out the imbalance.
Our friends at Quantpedia summarize the reasons behind this phenomenon in the following abstract:
In general, reversal in price of an asset occurs due to investors' overreaction to asset-related news and the subsequent price correction. In this case, the most probable reason for the phenomenon, according to the authors, is the market makers' aversion to inventory risks that tend to increase dramatically in the pre-announcement period. Consequently, the market makers demand higher compensation for providing liquidity due to higher risk and therefore raise prices, which are expected to reverse after the earnings announcement.
The authors reached their conclusion based on data from 1996 to 2011; I endeavored to replicate their results using our pricing data and EventVestor's Earnings Calendar dataset, which spans from 2007 to the present day. In my analysis I was able to confirm the reversal that the authors observed. I also studied the reversal's robustness, and determined parameters and stock universes that improve the returns of the strategy that So and Wang proposed.
OOS Study Results
As the paper does, I find evidence of returns reversal during earnings announcements; while the paper tested using data from 1996 to 2011, I used data from 2007 to 2016. The average reversal among all stocks in my data is 0.449%, compared to a result of 1.448% in the paper. I found that we can reasonably increase the reversal to 0.6% by selecting firms based on a minimum average dollar volume percentile, or based on a minimum market cap.
Trading Strategy Details
In order to ensure liquidity, the strategy limits its universe to those stocks in the Q500US which are at or above the 95th percentile of average dollar volume among all equities on the platform. One day before each earnings announcement for each company, the algorithm determines the stock's 5-day returns quintile among all equities. Since reversal is expected, the algorithm goes short on the stock if it's in the highest quintile and long if it's in the lowest quintile. Positions are usually held for one day.
- View Whitepaper Walkthrough and OOS Validation
- View Example Trading Strategy*
- View Pyfolio Analysis of Example Strategy
N.B.: As a result of parameter optimization, this strategy may be overfit.
Attached is the whitepaper walkthrough and OOS validation of the original study (Hit "Clone Notebook" to see a complete analysis).
FAQ
What is the Quantpedia Trading Strategy Series?
Quantpedia is an online resource for discovering trading strategies and we’ve teamed up with them to bring you interactive and high quality trading strategy examples based off financial research. Our goal is that you’re able to replicate the process we’ve used here for your own research and backtesting.
Where can I find more trading strategy ideas?
You can find the full Quantpedia Series here along with other research. Other than that, you can browse Quantpedia’s strategies or look through our forums for ideas posted by community members. Want to feature your own? Submit your proposal to SLEE @ quantopian.com
I can only run the backtest till 2014, why is that?
This algorithm uses EventVestor's Earnings Calendar dataset to time earnings announcements. It's a premium dataset that's available for $5/month which gives you full access to the data for backtesting, live trading, and contest submissions.
