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Quantpedia Trading Strategy Series: An Analysis on Cross-Sectional Mean Reversion Strategies
Matthew Lee
posted Dec 16, 2016
Papers Mean Reversion Show Q Research Market Neutral

The existence of short term return reversals for equities has captured the attention of many financial researchers.

In 1990, Bruce Lehmann found that over the period of 1962 - 1986 stocks in the highest returns of the prior week typically had negative returns in the following week. In his study, he found that contrarian strategies (picking past losers and winners) generated abnormal returns of over 2% each month. In the same year, Jegadeesh found that short-term reversals exist over the 1 month horizon. These 1 month short-term reversals are why many academic researchers generally use a 2-12 momentum measurement (returns over the past 12 months, excluding the previous one) when examining momentum.

Researchers have put forth a number of theories to try and explain short-term reversals. Lehmann attributed the phenomemon to cognitive bias leading to market inefficiency while another series of studies cited market-microstructure frictions (bid-ask bounce) as the cause.

Our Study

This notebook serves to analyze the findings on cross-sectional mean reversion strategies covered in various papers, during an out of sample period from 12-01-2011 to 12-01-2016. The study is done in two parts.

Part 1 specifically covers a review of the general contrarian strategies highlighted by Lehmann and Jegadeesh. Part 2 will cover more advanced and recently discovered contrarian strategies given to us by Quantpedia.

Our universe is defined as stocks in the Q1500 - I use the Q1500 as a proxy due to the liquidity and high market cap of most stocks in the universe.

Results Overview

In my notebook, I find that utilizing a decile grouping based on a returns lookback of 13 days is correlated with 13 day average returns, with the lowest/highest decile performing slightly over/under our SPY and Q1500 market benchmark with a 1.6% average spread per quarter. The actual implementation of short term reversal strategies suffers due to the high trading activity required to rebalance the portfolio. Due to this, the findings in many research papers fail to reflect the true profitability of short-term reversal strategies.

And while the performance is below the market, it's to be noted that this strategy is long/short compared to just long-only (which is the SPY). So if we want to compare total performance of that strategy, we should compare long only reversal of the "loser stocks decile". With that being said, this long/short strategy has a sharpe of .84 and relatively low vol.

Author's Notes

This two part series serves as a glance into the performance of cross-sectional mean reversion strategies in recent years. I encourage readers interested in these strategies to expand my analysis on the data generated in this notebook and experiment with optimizing portfolio construction for these strategies. Historically, most short term reversal strategies explored by researchers fail to reproduce the same performance found in studies during live trading, due to the substantial volume of stocks traded. More investigation in constructing a strategy to reduce portfolio turnover could substantially enhance the performance of various contrarian strategies.

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

Disclaimer

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.

2 responses
Matthew Lee
Dec 16, 2016

Simple Contrarian Trading Strategy

  1. For all stocks in the Q1500US, generate a factor representing the prior returns for a period of 13 trading days.
  2. Use the Portfolio Optimization API to construct a portfolio based off our factor, subject to our defined constraints.
  3. Rebalance on a weekly basis

Notes
This algorithm utilizes the Portfolio Optimization API with some simple constraints.
The code in the algorithm is commented so you can see each constraint that we place for portfolio construction.

Burrito Dan
Dec 20, 2016

Nice work. One thing to try is to focus on saving transaction costs by a) limiting focus to top 100 most liquid/largest companies, b) including fees as a cost in the optimisation (for example maximise alpha after an estimate of fees).

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The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian.

In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.