Quantocracy

Quant Blog Mashup

Quantocracy’s Daily Wrap for 01/03/2017

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

  • Demystifying the Hurst Exponent Part 2 [Robot Wealth]

    What if you had a tool that could help you decide when to apply mean reversion strategies and when to apply momentum to a particular time series? Thats the promise of the Hurst exponent, which helps characterise a time series as mean reverting, trending, or a random walk. For a brief introduction to Hurst, including some Python code for its calculation, check out our previous post. Even if you
  • Tactical Asset Allocation in December [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
  • Testing Momentum s Robustness [Sharpe Returns]

    Happy new year! I have noticed that my quantitative posts get the most readership and discussion. So this year, Ill be posting a lot more research and will start the year off by exploring momentums robustness. There are two good ways to test the robustness of a rules-based trading strategy: The test of time – how does the strategy behave in different market regimes? Parameter sensitivity
  • Are Commodities Still a Good Portfolio Diversifier? [Dual Momentum]

    Overfitting the data is a serious problem when constructing financial models. One way to guard against this is to have lots of data. This helps you determine if your results are robust by seeing how they hold up over different time periods. But this assumes the underlying market dynamics remain stable over time. That is not always the case. Gogi Gerwal gives a good example of how you may be misled
  • Wakey, Wakey: Trends in Active Fund Pre-Fee Excess Returns [Basis Pointing]

    In a recent posting, I compared the prices of US active mutual fund to estimates of future pre-fee excess returns. In summary, I found that the annual expenses of most active funds met or exceeded a generous estimate of their potential before-fee excess returns. That is, many funds look like theyre priced to fail. What I didnt include in that posting, though, was detail on how I derived

Quantocracy’s Daily Wrap for 01/02/2017

This is a summary of links featured on Quantocracy on Monday, 01/02/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Statistical Arbitrage: Finding Correlated Stock Pairs (h/t Algotrading Reddit) [Above Index]

    Statistical Arbitrage , A.K.A StatArb is a pair trading strategy that invloves buying and selling a pair of stocks based on a underlying correlation between them. This correlation usually exist in a given sector or competitors, for example Pepsi (PEP) and Coca-Cola (KO) is a pretty popular pair. The logic behind the strategy is that pair stocks tend to follow one another, so when they fall outta
  • “Matt s Breadth Indicator” Update [Throwing Good Money]

    Happy new year! Its that time again, when everyone with a blog does a wrap up of the previous year. Heres my look-back. Many of you follow along with the +/-30% per quarter wider-market breadth indicator. Which is too much of a mouthful, so Ive humbly named it after myself instead. I wanted to provide an update since Ive been tracking it for awhile. The premise is this: breadth

Quantocracy’s Daily Wrap for 01/01/2017

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

  • Genetic algorithm for trading in Cpp [Imanol Perez]

    This code tries to show how to use genetic algorithms to create a simple trading strategy. It is intended as a proof of concept, rather than trying to provide a ready-to-use strategy. The historical data used is the following: SP500.dat. It is the closing prices of S&P 500 from 2006/12/18 to 2016/06/15, so almost 10 years. Genetic algorithms Genetic algorithms simulate the evolution of a

Quantocracy’s Daily Wrap for 12/31/2016

This is a summary of links featured on Quantocracy on Saturday, 12/31/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Relationship Between the VIX and SP500 Revisited [Relative Value Arbitrage]

    A recent post on Bloomberg website entitled Rising VIX Paints Doubt on S&P 500 Rally pointed out an interesting observation: While the S&P 500 Index rose to an all-time high for a second day, the advance was accompanied by a gain in an options-derived gauge of trader stress that usually moves in the opposite direction The article refers to a well-known phenomenon that under normal market
  • Over 300 quant links from #QuantLinkADay [Cuemacro]

    Ive been tweeting regularly over the past few years, usually around quant finance, coding and also a bit on burgers. Last December I decided to regularly start tweeting a quant link every day, for which I used the imaginative hashtag #QuantLinkADay (yes, that hashtag took a lot of thought!), to flag interesting quant papers (generally focusing on newly published material), quant tutorials,
  • Backtesting the Implied Volatility Long/Short Strategy [Black Arbs]

    This is a stylized implementation of the strategy described in the research paper titled "What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns?" by Yuhang Xing, Xiaoyan Zhang and Rui Zhao. The authors show that their SKEW factor predicts individual equity returns up to 6 months! ABSTRACT The shape of the volatility smirk has significant cross-sectional
  • Predictive Nature Of Valuations [Larry Swedroe]

    As we approach the end of 2016, the Shiller CAPE 10 stands at about 28, a level rarely exceeded (with the exception of in the late-1990s technology-driven bull market). Such heights cause many investors to worry about what current valuations may mean for future expected returns. Ill try to provide some insights by reviewing the literature, which demonstrates a link between current valuations
  • Divide By 20: One Year later [Throwing Good Money]

    Happy New Year, one day early. Heres wishing 2017 is successful for you in whichever way you define success. Arent calendars wonderful? A couple of days ago, up pops a reminder on my calendar to revisit a post I did a year ago. At the very beginning of 2016, I wrote a post on whether yearly performance was mean-reverting, and found some interesting things. You might want to go back and take

Quantocracy’s Daily Wrap for 12/30/2016

This is a summary of links featured on Quantocracy on Friday, 12/30/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • PortfolioCharts’ Golden Butterfly [Allocate Smartly]

    This is a test of the Golden Butterfly, the homegrown buy & hold strategy from PortfolioCharts.com. PortfolioCharts is to buy & hold what AllocateSmartly is to tactical asset allocation, an independent and unbiased catalog of strategy performance, so when they put their stamp of approval on a portfolio, it deserves consideration. This strategy is similar in concept to Brownes

Quantocracy’s Daily Wrap for 12/28/2016

This is a summary of links featured on Quantocracy on Wednesday, 12/28/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Most popular posts – 2016 [Eran Raviv]

    Another year. Looking at my google analytics reports I cant help but wonder how is it that I am so bad in predicting which posts would catch audience attention. Anyhow, top three for 2016 are: On the 60/40 portfolio mix The case for Regime-Switching GARCH Most popular machine learning R packages And my personal favorites: ASA statement on p-values Why bad trading strategies may perform well?

Quantocracy’s Daily Wrap for 12/27/2016

This is a summary of links featured on Quantocracy on Tuesday, 12/27/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Reflecting on Research in 2016 [Flirting with Models]

    On behalf of the entire Newfound Research team, we would like to wish you and yours a happy holiday season. We treat this weekly research commentary as a sacred part of our investment process. We continue to be honored and humbled by the vast and growing number of readers it reaches, a sign of the trust and confidence you place in our work. To remain transparent can often be uncomfortable, as new
  • Recommended Quant Readings for you Best of 2016! [Quant Insti]

    As 2016 nears its finish line, here we are with the list of recommended reading on our blog with the top-rated blog posts, as voted by you! Enjoy the last few days doing what you love most! Read on. System Architecture of Algorithmic Trading This one is straight out of a lecture in the curriculum of QuantInstis Executive Programme in Algorithmic Trading (EPAT). It compares the traditional

Quantocracy’s Daily Wrap for 12/24/2016

This is a summary of links featured on Quantocracy on Saturday, 12/24/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mean Reversion Volatility Strategy [Milton FMR]

    Ever wondered if you can design a profitable trading strategy by trading volatility ETFs ? Well, yes you can. Those ETFs are highly ineffective vehicles on a long term investment horizon. However short term strategies have shown to be a rewarding way to trade these ETFs. Before we move onto strategy design we have to choose two volatility ETFs for backtesting. We will backtest our strategies with

Quantocracy’s Daily Wrap for 12/23/2016

This is a summary of links featured on Quantocracy on Friday, 12/23/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Using Absolute Momentum to Positively Skew Calendar Year Returns [EconomPic]

    There are instances where I "borrow" an idea from someone (actually… most of my posts were at a minimum inspired by someone else). In this case, I am stealing the initial concept from Ryan Detrick who posted the following chart of annual U.S. stock returns going back ~200 years as there is a lot of interesting information in his chart. As Ryan pointed out in a supporting post most

Quantocracy’s Daily Wrap for 12/22/2016

This is a summary of links featured on Quantocracy on Thursday, 12/22/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Time Series Momentum, Volatility Scaling, and Crisis Alpha [Alpha Architect]

    If you couldnt tell from our recent monster commodity futures post, weve been thinking a lot about futures recently. The futures research area is relatively fresh, and a lot more exciting than hacking through equity stock selection research where we already understand the basic answer buy cheap/quality, buy strength, and embrace relative performance pain. As part of our research
  • Applying Genetic Algorithms to define a Trading System [Quant Dare]

    When talking about quantitative trading, there are a large number of indicators and operators we can use as a buy/sell rule. But apart from deciding what indicator we will follow, the most important part would be setting the correct parameters. So, one method we can use to find adequate parameters without spending a lot of time in the simulation of a lot of combinations would be using a genetic
  • An Effect of Monetary Conditions on Carry Trades [Quantpedia]

    This paper investigates the relation between monetary conditions and the excess returns arising from an investment strategy that consists of borrowing low-interest rate currencies and investing in currencies with high interest rates, so-called "carry trade". The results indicate that carry trade average excess return, Sharpe ratio and 5% quantile differ substantially across expansive and
  • Sorting Through The Factor Zoo [Larry Swedroe]

    As Professor John Cochrane observed, the literature on investment factors now fills a veritable factor zoo with hundreds of options. How do investors select from among this huge array of possibilities? Noah Beck, Jason Hsu, Vitali Kalesnik and Helge Kostka, authors of the paper Will Your Factor Deliver? An Examination of Factor Robustness and Implementation Costs, which appears in the
  • 38 DTE Iron Condor Results Summary [DTR Trading]

    The introduction to this series, here, described the different variations of SPX iron condors (IC) and exits that were tested at 38 days to expiration (DTE). Recall, the tests covered 9 IC variations, with short strike deltas at four locations, utilizing 12 exits. In all, there were 432 test runs (9 variations x 4 deltas x 12 exits). Each test run executed more than 200 SPX IC trades between the