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Quantocracy’s Daily Wrap for 07/22/2016

This is a summary of links featured on Quantocracy on Friday, 07/22/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • What Drives Momentum Performance? [EconomPic]

    Mar Vista Investment Partners has a really interesting research piece out The Price You Pay which has a great table outlining the benefit of an asymmetric return profile (i.e. having more market exposure during up markets than down markets). It is a mathematical truism that superior down capture in negative periods provides more capital for compounding in the ensuing positive periods. Using
  • The Arbitrage of Price-to-Book [Portfolio Perfection]

    The trending value strategy buys the top 25 stocks by their 6 month price momentum among the top decile of stocks ranked by value composite 2 (VC2), a combination of price-to-earnings ratio, price-to-sales ratio, price-to-book ratio, earnings before interest tax depreciation and amortization to enterprise value ratio (EBITDA/EV), price-to-cash flow ratio, and shareholder yield. Price-to-book was

Quantocracy’s Daily Wrap for 07/21/2016

This is a summary of links featured on Quantocracy on Thursday, 07/21/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quantitative Strategy Development Overview Brian Peterson [Open Source Quant]

    I have had the pleasure of getting to know and work with Brian Peterson of late building out the blotter::mcsim function in the blotter package. I will be writing about this function soon and where it is headed, but in this post i wanted to share a presentation Brian gave the CapeR User Group last week on Developing and Backtesting Systematic Trading Strategies with an application in R, and in
  • Risk Managing Risk Management [Flirting with Models]

    Well, despite some recent market turmoil from the Brexit, the S&P 500 is still hovering near its high from last year on a price basis. If we include the reinvestment of dividends, then we have already seen new highs in April, May, and June of this year. As we wrote about previously, a bear market might be the only way to boost the expected returns on U.S. equities. Investor who thought they
  • Stale Performance Chasing: Beware of Horizon Effects [Alpha Architect]

    Investors talk a big game when describing how they evaluate mutual funds. They say they consider things like the objectives of the fund, its size, and the longevity of its managers. But theres one factor that looms larger than all the others: Performance. We wrote here about how investors tend to chase the performance of funds, allocating to funds that have done well, and redeeming from

Quantocracy’s Daily Wrap for 07/20/2016

This is a summary of links featured on Quantocracy on Wednesday, 07/20/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Machine Learning in Algorithmic Trading Systems Presentation [Robot Wealth]

    Last night it was my pleasure to present at the Tyro Fintech Hub in Sydney on the topic of using machine learning in algorithmic trading systems. Here you can download the presentation Many thanks to all who attended and particularly for the engaging questions. I thoroughly enjoyed myself! In particular, thanks to Andrien Juric for oraganising the event and Sharon Lu from Tyro for making available
  • Unbalanced Classes in Machine Learning and the Stock Market [MKTSTK]

    Many assets exhibit bull or bear trends which persist for long periods of time. This presents an interesting problem for anyone trying to predict the future return of an asset: a lack of diversity in your training set. This problem is known as unbalanced classes in the machine learning field. The basic issue is that many classification methods work best when your training data is roughly uniform
  • Hull Moving Average Filter | Trading Strategy (Entry & Exit) [Oxford Capital]

    Developer: Alan Hull. Source: Kaufman, P. J. (2013). Trading Systems and Methods. New Jersey: John Wiley & Sons, Inc. Concept: Trend following trading strategy based on low lag moving averages. Research Goal: To verify performance of the Hull Moving Average (HMA). Specification: Table 1. Results: Figure 1-2. Trade Filter: Long Trades: Two Hull Moving Averages turn upwards. Short Trades: Two
  • Impact of 1987 Black Monday on Trading Behavior of Stock Investors [Quantpedia]

    Using a simple sign test, we report new empirical evidence, taken from both the US and the German stock markets, showing that trading behavior substantially changed around Black Monday in 1987. It turned out that before Black Monday investors behaved more as in the momentum strategy; and after Black Monday more as in the contrarian strategy. We argue that crashes, in general, themselves are merely

Quantocracy’s Daily Wrap for 07/19/2016

This is a summary of links featured on Quantocracy on Tuesday, 07/19/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Introduction to Zipline in Python [Quant Insti]

    Python has emerged as one of the most popular language for programmers in financial trading, due to its ease of availability, user-friendliness and presence of sufficient scientific libraries like Pandas, NumPy, PyAlgoTrade, Pybacktest and more. Python serves as an excellent choice for automated trading when the trading frequency is low/medium, i.e. for trades which do not last less than a few
  • Style Momentum in Australia? [Alpha Architect]

    Jegadeesh and Titman (1993) popularized a simple idea: "past winners outperform past losers." Post JT, the relative strength, or "momentum anomaly," was forever ingrained in the minds of academic researchers (which is odd, since the idea had been around 50 years prior to JT 1993, but I digress). Later studiiessee Meb Faber, Gary Antonacci, or the new Haghani and Dewey

Quantocracy’s Daily Wrap for 07/18/2016

This is a summary of links featured on Quantocracy on Monday, 07/18/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Where are the billionaire financial academics? [Mathematical Investor]

    According to the just-published 2016 Rich List of the World's Top-Earning Hedge Fund Managers by Institutional Investor's Alpha magazine, eight of the top ten earners fall into the "quant" category, and half of the 25 richest of the year are quants. The firms listed include the likes of Renaissance Technologies, D.E. Shaw, Two Sigma, Millennium, Citadel and Schonfeld, none of
  • Combining Different Momentum Factors [Systematic Relative Strength]

    Momentum can be calculated in a number of different ways. As long as you are measuring the strength of price appreciation over an intermediate time horizon most logical calculation methods will work to one degree or another. The standard, academic definition of momentum usually means taking the price appreciation of a security over a predefined time period and comparing it to all of the other

Quantocracy’s Daily Wrap for 07/17/2016

This is a summary of links featured on Quantocracy on Sunday, 07/17/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • RNeat Square Root Neural Net Trained Using Augmenting Topologies [Gekko Quant]

    A simple tutorial demonstrating how to train a neural network to square root numbers using a genetic algorithm that searches through the topological structure space. The algorithm is called NEAT (Neuro Evolution of Augmenting Topologies) available in the RNeat package (not yet on CRAN). The training is very similar to other machine learning / regression packages in R. The training function takes a

Quantocracy’s Daily Wrap for 07/12/2016

This is a summary of links featured on Quantocracy on Tuesday, 07/12/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • An Introduction to Portfolio Component Value At Risk [QuantStrat TradeR]

    This post will introduce component value at risk mechanics found in PerformanceAnalytics from a paper written by Brian Peterson, Kris Boudt, and Peter Carl. This is a mechanism that is an easy-to-call mechanism for computing component expected shortfall in asset returns as they apply to a portfolio. While the exact mechanics are fairly complex, the upside is that the running time is nearly
  • Mailbag: Can You Get A Job In HFT Without A Degree? [Quant Start]

    I was emailed yesterday with an interesting career question about working in High Frequency Trading (HFT). The question posed was "Is it possible to get a HFT-related job in a big company without a formal degree?". The short answer is that yes, it is possible. The longer answer is that it is going to be difficult and this article will explain why. I am going to make an assumption here
  • Candid Conversation with an Algorithmic Trader (Part 2) [Quant Insti]

    If you dont know who you are, the stock market is an expensive place to find out George Goodman In the previous post, I had a conversation with a few experts in the field of Algorithmic Trading to gain some insights into this seemingly black-box. That conversation not only helped me dispel some of my doubts regarding Algo Trading, but it also strengthened my desire to jump headfirst
  • The Folly of Stock Market Forecasting [Alpha Architect]

    The idea that one can predict stock market movements is somewhat insane. The major problem with stock market forecasting is the lack of evidence that it is possible. I am unaware of any market commentator that has been successfulon a consistent basisat predicting the future direction of the market. Certainly, every once in a while a pundit or luminary may get something right, but it

Quantocracy’s Daily Wrap for 07/11/2016

This is a summary of links featured on Quantocracy on Monday, 07/11/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Has Momentum Lost Its Momentum? [Quantpedia]

    We evaluate the robustness of momentum returns in the US stock market over the period 1965 to 2012. We find that momentum profits have become insignificant since the late 1990s partially driven by pronounced increase in the volatility of momentum profits in the last 14 years. Investigations of momentum profits in high and low volatility months address the concerns about unprecedented levels of
  • Multi-Factor: Mix or Integrate? [Flirting with Models]

    Recently a paper was published by AQR where the authors advocate for an integrated approach to multi-factor portfolios, preferring securities that exhibit strong characteristics across all desired factors instead of a mixed approach, where securities are selected based upon extreme exposure to a single characteristic. We believe the integrated approach fails to acknowledge the impact of the
  • Interview With Artur Sepp [Factor Wave]

    Artur Sepp is a rare example of a quant who combines excellent technical skill with a practical understanding of markets. If you can't learn from his presentations the fault is more likely to be yours rather than his. He recently agreed to do an interview for us. Here is the first part. Q: What is your educational background? A: My educational background is a bit unusual. I have a PhD in
  • An Extremely Quick Move From A 50-Day Low To A 50-Day High [Quantifiable Edges]

    Remarkable about Fridays 50-day high close is that it came just 8 trading days after SPX closed at a 50-day low. Thats quite rare to see. The study below is from this weekends Quantifiable Edges Subscriber Letter. It looks at all the instances since 1950 of a move from a 50-day closing low to a 50-day closing high that have occurred within 2 weeks.