Quantocracy

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Quantocracy’s Daily Wrap for 07/03/2016

This is a summary of links featured on Quantocracy on Sunday, 07/03/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Human significance, economic significance and statistical significance [Eran Raviv]

    We are now collecting a lot of data. This is a good thing in general. But data collection and data storage capabilities have evolved fast. Much faster than statistical methods to go along with those voluminous numbers. We are still using good ole fashioned Fisherian statistics. Back then, when you had not too many observations, statistical significance actually meant something. It does not

Quantocracy’s Daily Wrap for 07/01/2016

This is a summary of links featured on Quantocracy on Friday, 07/01/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Video: Factor Models for Traders by EP Chan (h/t Quant News)

    Factor models are not just for long-term investors. They can help traders find out why their strategies are suffering. This talk highlights the difference between factor and "alpha" models, and what short-term factors traders can use.
  • Podcast with Wes Gray of Alpha Architect (h/t Abnormal Returns) [Big Picture]

    This week on our Masters in Business podcast, we speak with Wes Gray, former Captain in U.S. Marines, and founder of Alpha Architect. He studied economics at Wharton, graduated with honors before getting his MBA and PhD at University of Chicago. Instead of heading to Wall Street like so many MBAs, however, he joined the Marines, went to Iraq, where he embedded with the Iraqi Army as a U.S. Marines
  • Quantified News Analytics: Profitability vs Pitfalls [Quant Insti]

    As sources and volumes of news have grown, so has the techniques to gather, extract, aggregate and categorise them. Important news can result in large positive or negative returns. However, owing to many news sources, we need to ask a fundamental question: Is news analytics profitable in every situation or are there some pitfalls that needs to be avoided? Information flow in News Analytics news
  • Taxonomy of CTAs [Quantpedia]

    Recently a range of alternative risk premia products have been developed promising investors hedge fund/CTA like returns with higher liquidity, transparency and relatively low fees. The attractiveness of these products rests on the assumption that they can deliver similar returns. Using a novel reporting bias free sample of 3,419 CTA funds as a testing ground, our results suggest this assumption

Quantocracy’s Daily Wrap for 06/30/2016

This is a summary of links featured on Quantocracy on Thursday, 06/30/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Can a simple Market Internals technique actually improve trading strategy results? [Better System Trader]

    In my 10+ years full-time trading career, I have found very few tools and tactics that would get my attention so deeply as Market Internals. In 2014, I spent about 6 months in a row with this unique traders tool, exploring its possibilities every single day, searching for new and creative implementation ideas for my own automated trading systems (ATSs). With a real obsession with this concept,
  • The Case for Momentum in Expensive Markets [EconomPic]

    Charlie Bilello, one of my favorite follows on Twitter, analyzed the relationship between market valuation and future returns (over various time horizons) in a recent post Valuation, Timing, and a Range of Outcomes. The post contained some very insightful tables, such as the one below, where he shows that valuations matter… if you pay less for stocks, you will generally be provided with higher
  • Questioning Everything You Knew about Asset Allocation [Alpha Architect]

    Is a 100% stock allocation crazy? As long as one addresses their needs for liquidity (as to avoid extracting capital from the markets at bad times) and can tolerate the market price volatility, a 100% or near-100% allocation to equities is not as outlandish as one might suspect. Focusing on fundamentals and valuations instead of market prices should alleviate much of the unnecessary concern with

Quantocracy’s Daily Wrap for 06/29/2016

This is a summary of links featured on Quantocracy on Wednesday, 06/29/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Deciphering Correlation Hedged Momentum [TrendXplorer]

    In a new SeekingAlpha contribution (pending approval) we combine PAAs protective multi-market breadth approach with a generalized momentum metric based on correlation hedged returns. The resulting model is called Generalized Protective Momentum (GPM). In this blogpost the correlation hedge is deciphered. The correlation hedge is a simplified version of Keller and Butlers EAA-formula (see
  • Pruitt, The Ultimate Algorithmic Trading System Toolbox [Reading the Markets]

    I am in the process of learning to code in Python and am, I must admit, no programming genius. So I was delighted to see that George Pruitt, best known for his book on TradeStations EasyLanguage (Building Winning Trading Systems with TradeStation) had written a new book that covered not only the TradeStation platform but also AmiBroker, Excel (with VBA), and Python. The Ultimate Algorithmic

Quantocracy’s Daily Wrap for 06/28/2016

This is a summary of links featured on Quantocracy on Tuesday, 06/28/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Backtesting Based on Multiple Signals – Beware of Overfitting [Alpha Architect]

    One of the dangers of being a quantitative investor is that when you see patterns in historical data you might wrongly assume they will repeat. Put another way, you might believe an effect is driven by a genuine relationship, when in reality the results are spurious and the result of luck. We wrote here about "anomaly chasing" and the risks of data mining in backtests. A responsible
  • Loading Data with Pandas [Quintuitive]

    On at least a couple of occasions lately, I realized that I may need Python in the near future. While I have amassed some limited experience with the language over the years, I never spent the time to understand Pandas, its de-facto standard data-frame library. Where does one start? For me its usually with the data. Simple stuff, loading, wrangling, etc. Re-writing my little R6 helper class to

Quantocracy’s Daily Wrap for 06/27/2016

This is a summary of links featured on Quantocracy on Monday, 06/27/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Volatility and measures of risk-adjusted return with Python [Quant Insti]

    In this post we see how to compute historical volatility in python, and the different measures of risk-adjusted return based on it. We have also provided the python codes for these measures which might be of help to the readers. Introduction Volatility measures the dispersion of returns for a given security. Volatility can be measured by the standard deviation of returns for a security over a
  • Stock Market Anomalies and Baseball Cards [Alpha Architect]

    I still have a Ken Griffey Jr. Rookie Card. To be honest, I dont even know where the thing is, but I hope it is it worth a ton of money at this point (although I doubt it). So disclaimer up front: I dabbled in baseball card trading back in the day. And for all of you out there who used to trade baseball cards, youll enjoy this recent research paper from Joey Engelberg, Linh Le, and Jared
  • 6 Reasons Why Your Fund Checklist is Hurting Performance [Flirting with Models]

    Summary Most advisors have a fund checklist or screen: a list of selection criteria they employ to help determine whether a fund is worthy of further evaluation. The vast majority of checklists we see employ a performance screen based on a 3- or 5-year period. We believe that employing such a performance screen not only misleads selection efforts, but also can be harmful to portfolio performance.
  • The Trouble with Alpha: Part I (h/t @AbnormalReturns) [Dynamic Beta]

    Investors equate alpha to outperformance. A high alpha fund presumably delivers substantial excess returns relative to its benchmark. True alpha is short-hand for manager skill. Statistically, alpha simply is the result of a linear regression between two return streams. The regression finds the straight line (ordinary least squares) that best fits the time series. Visually, beta is the slope
  • Consider Factors In Fixed Income [Larry Swedroe]

    Its been well-documented that, in equity investing, assets have earned premiums because they are exposed to the risks of a certain factor. Given that the literature provides us with a veritable factor zoo (there are more than 300), for investors to consider adding exposure to a factor, it should meet the following criteria: Persistent: It holds across long periods of time and various

Quantocracy’s Daily Wrap for 06/26/2016

This is a summary of links featured on Quantocracy on Sunday, 06/26/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Best Links of the Last Two Weeks [Quantocracy]

    The best quant mashup links for the two weeks ending Saturday, 06/25 as voted by our readers: Recommended Reading [Robot Wealth] Binary Options: Scam or Opportunity? [Financial Hacker] Some harmless data-mining: Testing individual words in EDGAR filings [Greg Harris] Simple Machine Learning Model to Trade SPY [Alpha Plot] Want to Know the Secret to Inefficient Prices? Lazy Prices. [Alpha
  • Momentum Anomaly and Baseball Cards [Quantpedia]

    We show that the market for baseball cards exhibits anomalies that are analogous to those that have been documented in financial markets, namely, momentum, price drift in the direction of past fundamental performance, and IPO under performance. Momentum profits are higher among active players than retired players, and among newer sets than older sets. Regarding IPO under performance, we find that