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Best Links of the Week

The best quant mashup links for the week ending Saturday, 11/28 as voted by our readers:

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My fellow traders, ask not what Quantocracy can do for you, ask what you can do for Quantocracy. Vote for your favorite links on our quant mashup to encourage bloggers to write quality content. We do our part by providing this site without annoying advertising. All we ask is that you take a moment to participate in the process.

If you haven’t done so already, register to vote. Once registered, you can choose to remain logged in indefinitely, making voting as simple and painless as possible.

Read on Readers!
Mike @ Quantocracy

Quantocracy’s Daily Wrap for 11/27/2015

This is a summary of links featured on Quantocracy on Friday, 11/27/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Persistent Momentum [Factor Wave]

    Somewhat related to the idea of acceleration that I have been writing about recently, is the concept of persistent momentum. That is, do stocks that have performed well over several periods, beat those that have done well for only one period? This idea was tested by Hong-Yi Chen, Pin-Huang Chou and Chia-Hsun Hsieh in their paper Persistency of the Momentum Effect: The Role of Consistent Winners
  • Predicting volatility [EP Chan]

    Predicting volatility is a very old topic. Every finance student has been taught to use the GARCH model for that. But like most things we learned in school, we don't necessarily expect them to be useful in practice, or to work well out-of-sample. (When was the last time you need to use calculus in your job?) But out of curiosity, I did a quick investigation of its power on predicting the

Quantocracy’s Daily Wrap for 11/26/2015

This is a summary of links featured on Quantocracy on Thursday, 11/26/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • PDF: The PCA Model in the FX Market: Economic Factors and Volatility Modelling [Kevin Pei]

    The PCA Model in the FX Market: Economic Factors and Volatility Modelling
  • When Risk Goes Unrewarded [Larry Swedroe]

    Risk-based asset pricing theory suggests, simply, that assets bearing a higher risk should compensate investors with higher returns. While most papers investigating the risk-return relationship of assets are focused on equity markets, surprisingly few studies explore this phenomenon in currency markets (which are among the deepest and most liquid markets in the world). In fact, the FX markets are
  • Is Momentum Effect Result of Over- of Under-reaction? [Quantpedia]

    Several studies have attributed the high excess returns of the momentum strategy in the equity market to investor behavioral biases. However, whether momentum effects occur because of investor underreaction or because of investor overreaction remains a question. Using a simple model to illustrate the linkage between idiosyncratic volatility and investor overreaction as well as the stock turnover
  • Visualisation (Now with 3D!) – JavaScript for Financial Analysts [John Orford]

    First draft of 'JavaScript for Financial Analysts' Chapter 9. ~ The web dominates our communication. The driver of this crushing victory? The humble webpage increasingly coupled with JavaScript. Up until now we have focused on the basics of how to code JavaScript in a functional manner, now for some fun. The next chapters will explore JavaScript's rich ecosystem of libraries. The

Quantocracy’s Daily Wrap for 11/25/2015

This is a summary of links featured on Quantocracy on Wednesday, 11/25/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Is The Acceleration Factor A Better Way To Measure Momentum? [Capital Spectator]

    Momentum has received a lot of attention in the asset-pricing literature over the past several decades, and for good reason. Trending behavior is a staple in markets. In contrast with other pricing anomalies, short-term return persistencepositive and negativeis a robust factor across asset classes. The fact that momentum is deployed far and wide in the money management industry and
  • RUT Straddle – 38 DTE – Results Summary [DTR Trading]

    This is the first article in a series where we will look at the backtest results of selling at-the-money (ATM) options straddles on the Russell 2000 index (RUT). In the prior series, we looked at the performance of this same strategy on the SPX. For background on the setup for the backtests, as well as the nomenclature used in the tables below, please see the introductory article for this series:

Quantocracy’s Daily Wrap for 11/24/2015

This is a summary of links featured on Quantocracy on Tuesday, 11/24/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A framework for rapid and robust system development based on k-means clustering [Robot Wealth]

    Important preface: This post is in no way intended to showcase a particular trading strategy. It is purely to share and demonstrate the use of the framework Ive put together to speed the research and development process for a particular type of trading strategy. Comments and critiques regarding the framework and the methodology used are most welcome. Backtest results presented are for
  • International Evidence for our favorite Value metric: Enterprise Multiples [Alpha Architect]

    The enterprise multiple (EM) predicts the cross section of international returns. The return predictability of EM is similarly pronounced in developed and emerging markets and likewise strong among small and large firms. An international portfolio of low-EM firms outperforms a portfolio of high-EM firms by about 1% per month. The EM value premium is individually significant for the majority of
  • Improving A Hedge Fund Investment – Cantab Capital’s Quantitative Aristarchus Fund [Jonathan Kinlay]

    In this post I am going to take a look at what an investor can do to improve a hedge fund investment through the use of dynamic capital allocation. For the purposes of illustration I am going to use Cantab Capitals Aristarchus program a quantitative fund which has grown to over $3.5Bn in assets under management since its opening with $30M in 2007 by co-founders Dr. Ewan Kirk and Erich
  • Migration from Good Factor Exposures [Factor Wave]

    There are a number of ways to become more confident in the idea of factor investing. The simplest is to just compare the results of factor portfolios to those based on other methods. This will show outperformance but it wont give us a reason for the outperformance. So anything that can give us more reason to believe is always welcome. Eugene Fama and Kenneth French wrote a paper,
  • Visualisation Pt 1 – Javascript for the Financial Analyst Chapter 9 [John Orford]

    First draft of 'JavaScript for Financial Analysts' Chapter 9. ~ The web dominates our communication. The driver of this crushing victory? The humble webpage increasingly coupled with JavaScript. Up until now we have focused on the basics of how to code JavaScript in a functional manner, now for some fun. The next chapters will explore JavaScript's rich ecosystem of libraries. The

Quantocracy’s Daily Wrap for 11/23/2015

This is a summary of links featured on Quantocracy on Monday, 11/23/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Valuations Do Matter (Even Over Shorter Time Frames) / Momentum Driven Valuation Timing [EconomPic]

    I often read that valuations don't matter over the short-term (a case often cited against market timing). Over very short periods (hours, days, etc…) this certainly may be true, but while there can be a lot of variability around month-to-month or year-to-year performance, I completely disagree with the sentiment that it doesn't matter. That said, there are better ways than just using
  • Frog in the Pan: Identifying the Highest Quality Momentum Stocks [Alpha Architect]

    We test a frog-in-the-pan (FIP) hypothesis that predicts investors are inattentive to information arriving continuously in small amounts. Intuitively, we hypothesize that a series of frequent gradual changes attracts less attention than infrequent dramatic changes. Consistent with the FIP hypothesis, we find that continuous information induces strong persistent return continuation that does not
  • Better Tests with Oversampling [Financial Hacker]

    The more data you use for testing or training your strategy, the less bias will affect the test result and the more accurate will be the training. The problem: price data is always in short supply. Even shorter when you must put aside some part for out-of-sample tests. Extending the test or training period far into the past is not always a solution. The markets of the 1990s or 1980s were very
  • Great online courses for learning R [R for Traders]

    The last few months have seen a flurry of activity in terms of new courses being created for the R programming language. Udemyis one such online venue that provides a surprisingly broad array of topics related to the R language. These topics include statistical analysis, regression, data science, machine learning, quantitative trading, data visualization and more. As an adjunct instructor in the
  • Due Diligence: Ask This, Not That [Flirting with Models]

    Summary Due diligence is an important practice in our industry and one that should be ever-evolving. There are some questions we receive on due diligence questionnaires that are well intentioned, but we think can be improved. Finally, in doing due diligence, we think that after the question how, there should almost always be a follow-up question of why? We answer a lot of due
  • Avoiding Stock Market Crashes with the Hi-Lo Index of the S&P500 [System Trader Success]

    This daily indicator is calculated as the ratio of the number of S&P500 stocks that have reached new 3-month-highs minus those that have reached new 3-month-lows, divided 500. Exiting and entering the stock market according the indicators signals would have avoided major drawdowns of the market during the backtest period from Jan-2000 to Aug-2015. Switching according to the signals between
  • Is the Stock Market Different? [Quintuitive]

    Overall, we expect the stock market to go higher. There is a good reason for that the stock market is positive close to 54% of the days. A natural questions is whether this holds for other markets as well. There is inflation after all. Looks like the stock market is more or less unique in that regard. A little bit of R magic over the last 10 years of back-adjusted data from CSIData, and we
  • Another Look At Thanksgiving Week [Quantifiable Edges]

    Historically Thanksgiving week has shown some very strong tendencies. The table below is one I have shown a few times over the years. I decided to update it again this year. 2015-11-23 image1 Monday and Tuesday dont show anything suggesting an edge. Mondays total return was actually negative until 2008 when it posted a gain of over 6%. Wednesday and Friday, on the other hand, appear to be

Quantocracy’s Daily Wrap for 11/22/2015

This is a summary of links featured on Quantocracy on Sunday, 11/22/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Best Links of the Week [Quantocracy]

    The best quant mashup links for the week ending Saturday, 11/21 as voted by our readers: Unsupervised candlestick classification for fun and profit part 2 [Robot Wealth] Searching for an Efficient Market Regime Filter [Helix Trader] How the Number of Firms and Holding Periods Affect Momentum Funds [Alpha Architect] David Versus Goliath [Investment Idiocy] The Mean Reversion Case For (and
  • Interview with Jay Kaeppel [Better System Trader]

    Jay Kaeppel has over 25 years experience in the financial markets. He has worked as the Head Trader for a CTA and published a number of popular trading books on Futures, Options and Stock Market Seasonality. He also spent a number of years writing a weekly column titled Kaeppels Corner and publishes on his blog Jay On The Markets. He is now Portfolio Manager for Alpha Investment
  • Relationship Between Growth & Momentum [John Orford]

    Matt wrote me a while back about how thinking about Value and Growth lead you to Mean Reversion and Momentum. I like connections. Here's the line of reasoning. Value stocks are priced low by whichever definition you feel like using, but when investing in value stocks you are betting against the pessimism knocking the price down and hope for an uptick. In a sense, some form of mean reversion
  • Recent Readings and New Directions [Dekalog Blog]

    Since my last post I have been doing a fair bit of online research and fortunately I have discovered the following papers, which mesh nicely with what I am trying to do with Conditional Restricted Boltzmann Machines to model time series:- Deep Learning Architecture for Univariate Time Series Forecasting Temporal Autoencoding Restricted Boltzmann Machine Temporal Autoencoding Improves Generative

Best Links of the Week

The best quant mashup links for the week ending Saturday, 11/21 as voted by our readers:

And in case you missed it, the backstory behind our new collection of quant trading books curated by Jacques Joubert of Quants Portal.

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My fellow traders, ask not what Quantocracy can do for you, ask what you can do for Quantocracy. Vote for your favorite links on our quant mashup to encourage bloggers to write quality content. We do our part by providing this site without annoying advertising. All we ask is that you take a moment to participate in the process.

If you haven’t done so already, register to vote. Once registered, you can choose to remain logged in indefinitely, making voting as simple and painless as possible.

Read on Readers!
Mike @ Quantocracy

Quantocracy’s Daily Wrap for 11/21/2015

This is a summary of links featured on Quantocracy on Saturday, 11/21/2015. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Bring More Data [Dual Momentum]

    Several months ago we posted an article called Bring Data where we showed the importance of having abundant data for system development and validation. This was further reinforced to us recently when someone brought us some additional U.S. stock sector data. Previously, we only had sector data from Morningstar that went back to 1992. (S&P sector data also goes back to only the early