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Quantocracy’s Daily Wrap for 11/28/2016

This is a summary of links featured on Quantocracy on Monday, 11/28/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Very Different Kind of Trend Model [Following the Trend]

    Trend following is all about following the price. Typically the only input we need for a trend following model is the price. But what if I told that we could make a kind of trend following model which does not use the price direction as an input at all? It also has no stops and no targets. In this article, Id like to introduce you to a different way of thinking about futures trading models. It
  • Should we celebrate rising rates? [Flirting with Models]

    With 10-year rates jumping over 40bp in November, investors are beginning to talk about rising rates again. While rising rates may cause short-term volatility, coupon yield is a much more significant contributor to portfolio return over the long run. Increasing rates actually allow us to reinvest at higher coupon rates, helping offset the short-term losses. The bigger risk for asset allocators may
  • Trading Market Sentiment [Jonathan Kinlay]

    Text and sentiment analysis has become a very popular topic in quantitative research over the last decade, with applications ranging from market research and political science, to e-commerce. In this post I am going to outline an approach to the subject, together with some core techniques, that have applications in investment strategy. In the early days of the developing field of market sentiment
  • Bootstrap Aggregation, Random Forests and Boosted Trees [Quant Start]

    In a previous article the decision tree (DT) was introduced as a supervised learning method. In the article it was mentioned that the real power of DTs lies in their ability to perform extremely well as predictors when utilised in a statistical ensemble. In this article it will be shown how combining multiple DTs in a statistical ensemble will vastly improve the predictive performance on the
  • FX Market Pairs Trading Strategy [Quant Insti]

    This article is the final project submitted by the author as a part of his coursework in Executive Programme in Algorithmic Trading (EPAT) at QuantInsti. Do check our Projects page and have a look at what our students are building. About the Author Harish Maranani did his Bachelors in Technology from Acharya Nagarjuna University Electronics and Communication Engineering, and Master of Science from

Quantocracy’s Daily Wrap for 11/27/2016

This is a summary of links featured on Quantocracy on Sunday, 11/27/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Market Leverage as an Explanation of Low Volatility Anomaly [Quantpedia]

    The 'low-beta' or 'low-volatility anomaly' is one of the most researched in the field of 'alternative beta'. Despite strong published evidence going back to the 1970s that high beta/volatility stocks underperform relative to expectations generated by the Capital Asset Pricing Model (CAPM), the anomaly still persists. The explanations given for this are all
  • Podcast: Market Regimes with @HelixTrader [Better System Trader]

    Most trading strategies have an optimal type of market condition where they work at their absolute best, so having an understanding of market conditions and being able to detect and adapt to them can really have a huge impact on trading performance. But how can we measure market regimes properly? What techniques can we use to find that delicate balance between stability and reactivity so that it

Quantocracy’s Daily Wrap for 11/22/2016

This is a summary of links featured on Quantocracy on Tuesday, 11/22/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book Added: Complete Guide to Capital Markets for Quantitative Professionals [Amazon]

    The Complete Guide to Capital Markets for Quantitative Professionals is a comprehensive resource for readers with a background in science and technology who want to transfer their skills to the financial industry. It is written in a clear, conversational style and requires no prior knowledge of either finance or financial analytics. The book begins by discussing the operation of the financial
  • An EMA Trading Strategy for a Low Volatility Portfolio [Propfolio Management]

    The process Im going to follow is based on content from the University of Washingtons CFRM561 course Advanced Trading System Design. Hypothesis driven development is the core principle of this course, where each step in the development process involves hypothesizing testable ideas, and verifying these ideas before proceeding to the next stage. The stages involve identifying one or more
  • Great Minds Agree to Disagree on the Source of the Value Investing Premium [Alpha Architect]

    Active investing sounds so easy. But we all know it is extremely difficult. Ask any deep value investor how they have felt over the past few years (although, they are feeling a lot better recently). Certainly, any credible active investor should be able to answer 2 questions: 1) What is the source of their excess returns, or active premium? and 2) why is the premium is sustainable in the

Quantocracy’s Daily Wrap for 11/20/2016

This is a summary of links featured on Quantocracy on Sunday, 11/20/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Testing the Random Walk Hypothesis with R, Part One [Turing Finance]

    Whilst working on some code for my Masters I kept thinking, "it would be really awesome if there was an R package which just consumed a price series and produced a data.frame of results from multiple randomness tests at multiple frequencies". So I decided to write one and it's named emh after the Efficient Market Hypothesis. The emh package is extremely simple. You download a price
  • The Perils Of Bargain Hunting [Larry Swedroe]

    As I have been discussing in a series of articles (which you can find here, here and here), we now have a substantial body of evidence demonstrating that individual investors possess a preference for low-priced equities. This is anomalous behavior, because the level of a companys stock price is arbitraryfirms can manipulate it by adjusting the number of shares they have outstanding. The

Quantocracy’s Daily Wrap for 11/18/2016

This is a summary of links featured on Quantocracy on Friday, 11/18/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Pre-earnings Annoucement Strategies [EP Chan]

    Much has been written about the Post-Earnings Announcement Drift (PEAD) strategy (see, for example, my book), but less was written about pre-earnings announcement strategies. That changed recently with the publication of two papers. Just as with PEAD, these pre-announcement strategies do not make use of any actual earnings numbers or even estimates. They are based entirely on announcement dates
  • Tradelib Developments [Quintuitive]

    It has been a while since I posted about my back-testing framework tradelib, nevertheless, I have been constantly improving it. Among the new features, the most prominent is the support for SQLite. SQLite is a standalone, single file database, thus, its much easier to get software up and running with it compared to MySQL. I liked the SQLite support so much, that I actually started using it
  • In Calm Markets Should We Buy “Cheap” Put Protection? [Alpha Architect]

    Time for a little myth busting. Recently, the Motley Fool posted an article that argued the following: when market volatility is low, protective put options are cheap. From the article: Smart investors know that the time to buy most investments is when most investors arent paying attention to them. The same is true of options. Typically, put options are cheapest during big bull markets,

Quantocracy’s Daily Wrap for 11/17/2016

This is a summary of links featured on Quantocracy on Thursday, 11/17/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book Added: A Linear Algebra Primer for Financial Engineering [Amazon]

    This book covers linear algebra methods for financial engineering applications from a numerical point of view. The book contains many such applications, as well as pseudocodes, numerical examples, and questions often asked in interviews for quantitative positions. Financial Applications The ArrowDebreu one period market model One period index options arbitrage Covariance and correlation matrix
  • R-view: Backtesting Harvey & Liu (2015) [Open Source Quant]

    In this post i take an R-view of Backtesting Harvey & Liu (2015). The authors propose an alternative to the commonly practiced 50% discount that is applied to reported Sharpe ratios when evaluating backtests of trading strategies. The reason for the discount is due to the inevitable data mining inherent in research, both past and present. Harvey & Liu (HL) propose a multiple

Quantocracy’s Daily Wrap for 11/16/2016

This is a summary of links featured on Quantocracy on Wednesday, 11/16/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mean Reversion Trading System [Milton FMR]

    Many traders who managed to design and implement a mean reversion system correctly made a fortune. Fact is that financial markets move in patterns and especially in cycles. In simple words everything that goes up must come down and everything that goes down must come up. Nothing moves in one direction forever. When it comes to the markets we basically have two possible outcomes its either
  • Levy flights. Foraging in a finance blog [Quant Dare]

    Does this graph look like a kids drawing? Maybe a piece of art from the monkey Jeff? No, of course Jeff draws better than this. Actually, it is a representation of what is known as a Lvy flight, a mathematical concept that shows up in nature, marketing, cryptography, astronomy, biology, physics and nowadays even in social media. Here are a couple of examples of Lvy flights for
  • Quant investing: making momentum tolerable [Investing For A Living]

    For today s post and the next few Ill be going back to my favorite topic, quant investing. In this post I want to explore pure momentum quant portfolios and in particular ways to make pure momentum investing tolerable and implementable to more investors. Note: for a refresher on momentum and its power (arguably the most powerful factor in investing) see this great paper from AQR. You may
  • Is synthetic XIV/VXX data safe to use? [Alvarez Quant Trading]

    I have done several posts about trading XIV & VXX. In these posts (here, here and here) I refer to using synthetic data before these ETFs started trading. I supported the use of the data due to the very high correlation of daily returns during the overlap period. With a correlation of .97, I thought great the data should be good to use for backtesting. Then the head slapping moment. Run the
  • An Evidence-Based Low Volatility Investing Discussion [Alpha Architect]

    Jack and I had the honor of attending the Evidence-Based Investing conference, hosted by the team at Ritholz Wealth Management. Wow. What a great event and a great group of inspiring investors and thinkers. Abe, Meb, John, Mike, and I had the opportunity to chat about systematic investing. Mr. Lincoln was a little lost during the conversation, but thats okay hes old.
  • What is the Capacity of Smart Beta Strategies? [Quantpedia]

    Using a transaction cost model, and an assumption for the smart beta premium observed in data, we estimate the capacity of momentum, quality, value, size, minimum volatility, and a multi-factor combination of the first four strategies. Flows into these factor strategies incur transaction costs. For a given trading horizon, we can find the fund size where the associated transaction costs negate the

Quantocracy’s Daily Wrap for 11/15/2016

This is a summary of links featured on Quantocracy on Tuesday, 11/15/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Long-Short Investing Might Shorten Your Investment Lifespan [Alpha Architect]

    Over the past several decades, academics have identified numerous variables that seem to predict future expected returns. This has led to a proliferation of so-called factors identified in the literature, and created what John Cochrane has labeled the factor zoo. Now we we have a zoo of new factors. The Journal of Finance 2010 Presidential Address Enter the zoo at your own risk
  • Momentum: Letting the Cheap Get Cheaper? [Flirting with Models]

    As an investment strategy, momentum focuses solely on prior returns. Being valuation agnostic, however, does not mean that a momentum strategy does not have first-order valuation effects on portfolio construction. Using historical US sector data, we find that both cross-sectional and time-series momentum strategies may serve as good diversifiers to the potential risks of large structural repricing
  • Does Risk Parity Maximize Risk-adjusted Returns? [Markov Processes]

    While it is well known that risk parity strategies typically allocate more weight or apply leverage to asset classes with lower risk, it is not well understood how higher volatility affects the Sharpe ratios exhibited by the assets that get over- or under- weighted. We find that in practice the strategy increases an assets weight in periods of lower risk, which ultimately produces higher

Quantocracy’s Daily Wrap for 11/14/2016

This is a summary of links featured on Quantocracy on Monday, 11/14/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Central Moments [Eran Raviv]

    Sometimes I read academic literature, and often times those papers contain some proofs. I usually gloss over some innocent-looking assumptions on moments existence, invariably popping before derivations of theorems or lemmas. Here is one among countless examples, actually taken from Making and Evaluating Point Forecasts: Example from Making and Evaluating Point Forecasts If the second moment
  • Testing A Euro Currency Futures Scalping Strategy, Part 6 [System Trader Success]

    In an attempt to make this system more tradable, Ive looked at many different stop methods and filters. My hunt for a stop value resulted in concluding that a stop value really hurts its performance. This is not so unusual for a mean reverting strategy, such as the Euro scalping strategy. In an effort to improve the trading metrics Im going to look at an area that I looked at very early on