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Q Algorithm and Research Idea Library

This is a curated library of trading strategies and research from the Quantopian community. To learn more about quant finance or the Quantopian platform, view our free tutorials and quantitative finance lectures.

Algorithm & Research Library

  1. Reversals During Earnings Announcements by Nathan Wolfe - 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. (Algorithm + Notebook + Tearsheet)
  2. Optimizing Sharpe Ratio under Varying Capital Levels by Thomas Wiecki - With interest rates at all-time lows, the pressures on institutional and private investors to seek new harbors for vast sums of capital are high. Quantitative hedge funds are thus racing to increase capacity of their portfolio of trading algorithms. Despite these market forces, surprisingly little public information is available on estimating and maximizing capacity. In this post we will take a look at the problem of maximizing the Sharpe Ratio of a portfolio of uncorrelated trading algorithms under different capital bases. (Notebook)
  3. Are Earnings Predictable with Buyback Announcements? by Seong Lee - The announcement of stock repurchase or secondary equity offering is voluntary and can be easily moved by a few weeks or months. Therefore the timing of SEO or repurchase announcement before earnings announcement could be perceived as important information about future performance of stock during earnings announcement period. (Algorithm + Notebook)
  4. An Empirical Algorithmic Evaluation of Technical Analysis by Andrew Campbell - In this paper, the authors utilize non-parametric kernel regression to smooth a stock's daily price time series to a point where the local minima and maxima that a human technical analyst would find relevant can be separated from noisier short-term price fluctuations. The authors then search these denoised local minima and maxima for the patterns commonly pursued by technical analysts. Once they've identified occurrences of particular patterns, the authors test their predictive power by observing the subsequent forward return on the stock. (Notebook)
  5. Reversals in the PEAD by Matthew Lee - In his white paper "Overreacting to a History of Underreaction", Milian explores the possibility that well known cross sectional anomalies can reverse over time. Specifically, he investigates the reversal of the PEAD effect. He finds that contrary to previous research, stocks with the most negative previous earnings surprise actually exhibit the most positive returns following the subsequent earnings announcement. (Algorithm + Notebook)
  6. Driven to Distraction by Rob Reider - In the paper, "Driven to Distraction: Extraneous Events and Underreaction to Earnings News" (Hirshleifer, Lim, Teoh, 2009, Journal of Finance 64, 2289-2325), the authors compare Post Earnings Announcement Drift for stocks that announce earnings during peak earnings season and stocks that announce earnings when there are fewer other competing announcements. (Algorithm + Notebook)
  7. An Analysis on Cross-Sectional Mean Reversion Strategies by Matthew Lee - 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. (Algorithm + Notebook)
  8. Machine Learning on Quantopian Series by Thomas Wiecki - In this series, the author demonstrates the use of machine learning on Quantopian, starting from blank slate research all the way till algorithmic implementation. (Algorithm + Notebooks)
  9. Trading Expected Factor Flows by Rob Reider - Quants typically spend a great deal of time searching for new factors. But here’s another approach: for these standard factors, trade stocks as soon as they migrate into or out of an extreme quantile in anticipation of fund flows from rule-based smart beta ETF’s and other similar money managers. For example, as soon as a stock migrates into a low volatility quantile, buy the stock with the expectation that many other funds tracking this factor will have to eventually buy it too. And the same idea applies to shorting stocks that move out of an extreme quantile. (Algorithm) A related notebook can be found at Rob Reider's Quality Factors.

Our Methodology

Posts are rigorously filtered through a series of questions before being featured here:

  1. Is the algorithm/backtest based of an economic hypothesis or published financial/academic whitepaper?
  2. If it is an algorithm, is there substantial work (aka a research notebook) done to validate the trading strategy before backtesting?
  3. Is the code in the algorithm and notebook well-documented, clean, and easy-to-follow?

Feedback or Questions?

If you want something featured here or have general improvements on how we can improve this page, either post in the comments below or shoot me an email at SLEE@quantopian.com

Thanks

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. 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. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.

7 responses

Let's rock the boat!

9/15/2016

Updated with:

10/27/2016

Updated with:

12/5/2016

Updated with Rob Reider's Driven to Distraction research.

Updated with:

  • An Analysis on Cross-Sectional Mean Reversion Strategies by Matthew Lee - 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. (Algorithm + Notebook)
  • Machine Learning on Quantopian Series by Thomas Wiecki - In this series, the author demonstrates the use of machine learning on Quantopian, starting from blank slate research all the way till algorithmic implementation. (Algorithm + Notebooks)

2/2/2017 Updated with:

  • Trading Expected Factor Flows by Rob Reider - Quants typically spend a great deal of time searching for new factors. But here’s another approach: for these standard factors, trade stocks as soon as they migrate into or out of an extreme quantile in anticipation of fund flows from rule-based smart beta ETF’s and other similar money managers. For example, as soon as a stock migrates into a low volatility quantile, buy the stock with the expectation that many other funds tracking this factor will have to eventually buy it too. And the same idea applies to shorting stocks that move out of an extreme quantile. (Algorithm)

So, maybe we could do the pentesting on a forum thread that 200 people aren't listening to?