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Quantpedia Trading Strategy Series: An Analysis on Cross-Sectional Mean Reversion Strategies

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

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2 responses

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.

Clone Algorithm
126
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Backtest from to with initial capital
Total Returns
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Alpha
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Beta
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Sharpe
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Sortino
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Max Drawdown
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Benchmark Returns
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Volatility
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Returns 1 Month 3 Month 6 Month 12 Month
Alpha 1 Month 3 Month 6 Month 12 Month
Beta 1 Month 3 Month 6 Month 12 Month
Sharpe 1 Month 3 Month 6 Month 12 Month
Sortino 1 Month 3 Month 6 Month 12 Month
Volatility 1 Month 3 Month 6 Month 12 Month
Max Drawdown 1 Month 3 Month 6 Month 12 Month
# Backtest ID: 585447d164337e62585044a1
There was a runtime error.

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).