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Quantocracy’s Daily Wrap for 03/05/2017

This is a summary of links featured on Quantocracy on Sunday, 03/05/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Strategy Validation with Dave Bergstrom (@DBurgh) [Better System Trader]

    With the toolsets we have available to us today its really quite easy to create a trading strategy by just mining market data. As weve just heard in that opening bit of audio and also from previous podcast guests too, if you try enough combinations you can find something that appears to work purely by chance or by luck. The challenge however is trying to identify something that could be

Quantocracy’s Daily Wrap for 03/03/2017

This is a summary of links featured on Quantocracy on Friday, 03/03/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • More Data or Fewer Predictors: Which is a Better Cure for Overfitting? [EP Chan]

    One of the perennial problems in building trading models is the spareness of data and the attendant danger of overfitting. Fortunately, there are systematic methods of dealing with both ends of the problem. These methods are well-known in machine learning, though most traditional machine learning applications have a lot more data than we traders are used to. (E.g. Google used 10 million YouTube
  • Evidence-Based Investing? Take that Alpha and Shove It. [Alpha Architect]

    Johnny Paycheck has a great country song centered around the following lyric: Take this job and shove itI aint working here no more Campell Harvey, in the 2017 AFA Presidential Address, elaborates an analogous comment on the current state of the financial economics field: Take this alpha and shove itI aint publishing this research no more Prof. Harvey is rightly concerned that
  • Using Time-Series Momentum to Intentionally Miss the Best Months. Yes, Really. [Invest Resolve]

    The buy-and-hold crowd, including many mutual fund companies and a large cross-section of vocal pundits, like to talk about how missing the N best days/months in the market causes a serious impairment to long-term investor returns. What they fail to mention is that, because stock market volatility clusters during periods of market crisis, the best daily and monthly stock market returns are

Quantocracy’s Daily Wrap for 03/02/2017

This is a summary of links featured on Quantocracy on Thursday, 03/02/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Check Out Our Awesome New Book Library [Quantocracy]

    Check out our awesome new book library curated by four of the top rated authors in our community: Investment Idiocy (Rob Carver): General Quantitative Finance, Market History, Hedge Funds, General Programming Quant Start (Michael Halls-Moore): Python, C++, Financial Math, Quant Jobs & Interviews QuantStrat TradeR (Ilya Kipnis): R Programming Robot Wealth (Kris Longmore): Quant Trading, Machine
  • The Downside Of Momentum [Larry Swedroe]

    Momentum has been found to be a persistent and pervasive factor in the returns not only of equities, but in other asset classes (including bonds, commodities and currencies). With equities (compared to the market, value, size, profitability and quality factors), during the period 1927 through 2015, momentum has earned both the highest premium (9.6%) and the highest Sharpe ratio (0.61). However,
  • Prices Transformation Cheat Sheet [Quant Dare]

    In this entry, we discover the secrets behind prices transformation in financial series. Do you use price series in things such as technical analysis visualisation? Do you use return series in things such as volatility calculations? Do you use equity series in things such as comparing products with prices on different scales? If you answered yes to at least two of these questions, look at

Check Out Our Awesome New Book Library

Check out our awesome new book library curated by four of the top rated authors in our community:

  • Investment Idiocy (Rob Carver):
    General Quantitative Finance, Market History, Hedge Funds, General Programming
  • Quant Start (Michael Halls-Moore):
    Python, C++, Financial Math, Quant Jobs & Interviews
  • QuantStrat TradeR (Ilya Kipnis):
    R Programming
  • Robot Wealth (Kris Longmore):
    Quant Trading, Machine Learning

The General Programming and Market History categories are brand new, and a number of categories have been entirely reworked, particularly General Quant Finance, Python and Financial Math.

I’m very excited to have the subject matter expert in each category doing the curation. Who’s better than Rob on hedge funds, Mike on Python, Ilya on R, or Kris on machine learning? Exactly. A big mahalo gentlemen for your contribution to the Quantocracy community.

[check out the new book library now]

Why the change?

Unfortunately, Jacques Joubert was unable to continue curating the books section due to compliance issues with his new firm. Jacques has made a number of important contributions to this community over the years, and did a yeoman job with the very difficult task of trying to curate such a broad array of books all by himself.

Jacques asked me to pass along this note. Your presence will be sorely missed sir.

I wanted to say a special thank you to Mike for letting me take part in curating the books list on Quantocracy. I have learned a lot whilst putting it together and made many friends along way. Going forward the books list will be curated by several members of our community, many from the “A-team” and I look forward to reading their recommendations.

P.S. For those of you who are left curious, I decided that I would like to lower my overall online presence, and free up some more time to dedicate to my career objectives.

Best Regards
Jacques Joubert

Quantocracy’s Daily Wrap for 03/01/2017

This is a summary of links featured on Quantocracy on Wednesday, 03/01/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation in February [Allocate Smartly]

    This is a summary of the recent performance of a number of excellent tactical asset allocation strategies. These strategies are sourced from books, academic papers, and other publications. While we dont (yet) include every published TAA model, these strategies are broadly representative of the TAA space. Read more about our backtests or let AllocateSmartly help you follow these strategies in
  • Active Managers Should Love Passive Investing – It Makes Them Better! [Alpha Architect]

    In a recent letter to its investors, Crispin Odey commented as follows:(1) Money managers specializing in picking stocks and bonds are being driven out by mindless passive investing. Odey is a London based hedge fund manager, whose flagship fund lost almost 50% in 2016.(2) Photo courtesy of Wes. All complaints can be directed towards him. Photo courtesy of Wes. All complaints can be directed

Quantocracy’s Daily Wrap for 02/27/2017

This is a summary of links featured on Quantocracy on Monday, 02/27/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Misattributing Bad Behavior [Flirting with Models]

    The behavior gap is the difference between the returns on an investment and the returns that an investor realizes in that investment. Behavioral biases ingrained in human nature, such as anchoring, hindsight, and overconfidence drive emotional decisions that can lead to a behavior gap, but quantitative assessments of investor underperformance is often misleading, especially on an aggregated basis.

Quantocracy’s Daily Wrap for 02/26/2017

This is a summary of links featured on Quantocracy on Sunday, 02/26/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Introduction to Hidden Markov Models with Python Networkx and Sklearn [Black Arbs]

    Who is Andrey Markov? What is the Markov Property? What is a Markov Model? What makes a Markov Model Hidden? A Hidden Markov Model for Regime Detection Conclusion References Who is Andrey Markov? Markov was a Russian mathematician best known for his work on stochastic processes. The focus of his early work was number theory but after 1900 he focused on probability theory, so much so that he taught

Quantocracy’s Daily Wrap for 02/23/2017

This is a summary of links featured on Quantocracy on Thursday, 02/23/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Potential Return-Free Risk of Bonds [EconomPic]

    I've read too many posts / articles that outline why a rise in rates is good for long-term bond investors (as that would allow reinvestment at higher rates). While this can be true depending on the duration of bonds owned and/or for nominal returns over an extended period of time, it is certainly not true over shorter periods of time and absolutely not true for an investor in most real return
  • How Short Positions Affect Factor Investing? [Quantpedia]

    The performances of factor investing rely heavily on short sales, not only for building the initial long-short strategy, but also for regularly rebalancing the positions. Since short selling is subject to both legal restrictions and substantial costs, this paper examines how severely restrictions on short positions affect the financial attractiveness of factor investing. To fill the gap between
  • Dual Momentum Analysis [Quant Dare]

    Why dual momentum? Because strategies based on highest relative momentum show great results in the long run, but can experience deep falls and have little participation in the posterior rebounds after large market falls. To sidestep these drawbacks, here it is laid out a strategy based on Gary Antonaccis studies about Dual Momentum and Absolute Momentum, with the difference that, while he used

Quantocracy’s Daily Wrap for 02/22/2017

This is a summary of links featured on Quantocracy on Wednesday, 02/22/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Factor Zoo or Unicorn Ranch? [Dual Momentum]

    According to Morningstar, as of June 2016, the assets in smart beta exchange traded products totaled $490 billion. BlackRock forecasts smart beta using size, value, quality, momentum, and low-volatility will reach $1 trillion by 2020 and $2.4 trillion by 2025. This annual growth rate of 19% is double the growth rate of the entire ETF market. Are factors the cure-all for our investment needs? Or
  • Explaining the Low Risk Effect with @LarrySwedroe [Alpha Architect]

    As my co-author, Andrew Berkin, and I(1) explain in our new book, Your Complete Guide to Factor-Based Investing,(2) one of the big problems for the first formal asset pricing model developed by financial economists, the CAPM, was that it predicts a positive relation between risk and return. But empirical studies have found the actual relation to be flat, or even negative. Over the last 50
  • Country ETF Rotation Reader s Suggestions [Alvarez Quant Trading]

    My last post on Country ETF Rotation generated several ideas of what to test to improve the results. See the original post for the list ETFs being traded. One important test I left out from the original post was a baseline case. An idea applied to all the tests was trading more ETFS. For all tests, I will be showing results of trading (2,5,8) ETFs in the spreadsheet. Testing is from 1/1/2007 to

Quantocracy’s Daily Wrap for 02/21/2017

This is a summary of links featured on Quantocracy on Tuesday, 02/21/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Crisis Alpha: A Simple ETF Approach [Flirting with Models]

    Trend-following strategies such as managed futures and tactical equity have historically provided crisis alpha against sustained drawdowns. For short-horizon events (e.g. single day, week, or month events), the effectiveness of these approaches in managing risk is largely based on the luck of prior positioning. For more constant protection, option-based strategies can be applied by
  • Market Regime Detection using Hidden Markov Models in QSTrader [Quant Start]

    In the previous article on Hidden Markov Models it was shown how their application to index returns data could be used as a mechanism for discovering latent "market regimes". The returns of the S&P500 were analysed using the R statistical programming environment. It was seen that periods of differing volatility were detected, using both two-state and three-state models. In this
  • Modeling Risk With Bootstrapping Techniques In R [Capital Spectator]

    Limited data is the financial modelers biggest challenge. Making assumptions about risk is tough enough under the best of circumstances. All too often its even tougher when the historical record is thin. There are several ways to manage this challenge, including bootstrapping, aka resampling the available data to create historical records that might have occurred. Nothings perfect, of
  • New Feature: Historical Allocation Analysis [Allocate Smartly]

    Weve added a major new feature to our members area: historical allocation analysis. Every strategy that we track now includes a brand new subpage, which is updated daily and devoted to helping members better understand how each asset class has contributed to the strategys performance. In this blog post, we discuss this new feature. Note that all of the charts in this post require JavaScript,
  • Spx 1% low volatility range streaks [Voodoo Markets]

    Spx is on a low volatility streak, taking a look at how long the streaks usually last and how the current streak relates to past instances. Also looking at Spx returns once the spell breaks as do probably most others, i expected volatility to pick up, that does not seem to be the case. Bill Luby of Vix And More had a recent post supporting the case for low volatility feeding low volatility on