Quantocracy

Quant Blog Mashup

Quantocracy’s Daily Wrap for 01/25/2016

This is a summary of links featured on Quantocracy on Monday, 01/25/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Advanced Trading Infrastructure – Position Class [Quant Start]

    At the end of last year I announced that I would be working on a series of articles regarding the development of an Advanced Trading Infrastructure. Since the initial announcement I haven't mentioned the project to any great extent. However, in this article I want to discuss the progress I've made to date. At the outset I decided that I wanted to re-use and improve upon as much of the
  • Dissecting a trend following strategy in 2015 [Flirting with Models]

    Summary We run a U.S. sector-based, long-or-flat trend following strategy. With largely sideways market action, 2015 was a tough year for trend following, especially for long-only trend following, since the large negative trend in the Energy sector could not be monetized. We believe portfolio construction can be broken down into the signals generated and the rules used to transform these signals
  • Following Opportunistic insiders Earns over 1% Monthly Alpha [Alpha Architect]

    We show that opportunistic insider traders can be identified through the profitability of their trades prior to quarterly earnings announcements (QEAs), and that opportunistic trading is associated with various other kinds of managerial and firm misconduct. The subsequent buys and sells of opportunistic insiders (insiders with high past pre-QEA profits) are substantially more profitable than those

Quantocracy’s Daily Wrap for 01/23/2016

This is a summary of links featured on Quantocracy on Saturday, 01/23/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • [Academic Paper] Using Financial Reports to Predict Stock Market Trends With Machine Learning Techniques

    Stock markets as a fundamental component of financial markets play an important role in the countries economies. The factors that a ? ect the price of stocks include the political situations, company performance, economics activities, and some other unpredicted events. The traditional prediction approach is based on historical nu- merical data such as the previous trend, trading volume,
  • The Few Rule the Many Power Laws in Market Returns [Investor’s Field Guide]

    As index investing has grown in popularity, investors focus more and more on the markets overall return and less on the return of its component parts (individual stocks). But underneath the hood of each market index we find many inequalities. The top 20% of stocks represent 85% of the overall markets size in 2015. Similarly, the top 20% of the stocks account for 85% of the markets total

Quantocracy’s Daily Wrap for 01/20/2016

This is a summary of links featured on Quantocracy on Wednesday, 01/20/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Bootstrapping avoids seductive backtest results [Predictive Alpha]

    Nothing gets the adrenaline rushing as strong backtesting results of your latest equity trading idea. Often, however, it is a mirage created by a subset of equities, which have performed particularly well or poorly thereby inflating the results beyond what seems reasonable to expect going forward. The investment community has come a long way in terms of becoming more statistically sound, but it is
  • The Betting Against Beta Anomaly: Fact or Fiction? [Quantpedia]

    This paper suggests an alternative explanation for the recently documented betting against beta anomaly. Given that the equity of a levered firm is equivalent to a call option on firm assets and option returns are non-linearly related to underlying stock returns, linear CAPM-type regressions are generally misspecified. We derive theoretical expressions for the pricing error and analyze its
  • Quantitative Trading Strategy Using R: A Step by Step Guide [Quant Insti]

    In this post we will discuss about building a trading strategy using R. Before dwelling into the trading jargons using R let us spend some time understanding what R is. R is an open source. There are more than 4000 add on packages,18000 plus members of LinkedIns group and close to 80 R Meetup groups currently in existence. It is a perfect tool for statistical analysis especially for data
  • Numerical Analysis – JavaScript for Financial Analysts – Chapter 13 [John Orford]

    First draft of 'JavaScript for Financial Analysts' Chapter 13. Download all the code here and give it a test run! ~ The central theme of this book has been transforming data in a stateless manner using map and reduce. Data flows through our code until it's in the form we want it to be. Data comes in all shapes and sizes, but when when we are only working with numbers we can set
  • A Few Notes on DIY Financial Advisor (@AlphaArchitect) [CXO Advisory]

    Wesley Gray, Jack Vogel and David Foulke preface their 2015 book, DIY Financial Advisor: A Simple Solution to Build and Protect Your Wealth, by stating that: This book is a synopsis of our research findings developed while serving as a consultant and asset manager for large family offices. Our book is meant to be an educational journey that slowly builds confidence in ones own ability to
  • Stock Correction Sets Lowly Record [Dana Lyons]

    Persistent selling has resulted in an unprecedented stretch of elevated levels of New 52-Week Lows. One of the most noteworthy aspects to the ongoing stock market correction has been the persistence of selling pressure. Unlike declines in recent years, there has been little to no let up, even when indicators or chart levels suggest the high probability of a bounce. This has left the V-bottomers

Quantocracy’s Daily Wrap for 01/18/2016

This is a summary of links featured on Quantocracy on Monday, 01/18/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Growth and Trend: A Simple, Powerful Technique for Timing the Stock Market [Philosophical Economics]

    Suppose that you had the magical ability to foresee turns in the business cycle before they happened. As an investor, what would you do with that ability? Presumably, you would use it to time the stock market. You would sell equities in advance of recessions, and buy them back in advance of recoveries. The following chart shows the hypothetical historical performance of an investment strategy that
  • Automated Trading: Order Management System [Quant Insti]

    After graduation I moved into a small, empty, apartment in the city. My grandmother, Ill never forget, told me that moving into a new house is like meeting someone for the first time, you need to pick one room and make it yours, go slowly through the house, be polite and introduce yourself, so that it can introduce itself to you. It is with the same logic that I like to look on the different

Quantocracy’s Daily Wrap for 01/15/2016

This is a summary of links featured on Quantocracy on Friday, 01/15/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Book Added from @QuantAtRisk: Python for Quants [Amazon]

    Python for Quants is the first book-series in the market that takes you from the absolute beginner level in Python programming towards instant applications in Quantitative Analysis, Mathematics, Statistics, Data Analysis, Finance, and Algo Trading. Written with passion, this book of unprecedented quality and in-depth coverage teaches you the essentials of Python that allow you to start coding your

Quantocracy’s Daily Wrap for 01/14/2016

This is a summary of links featured on Quantocracy on Thursday, 01/14/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Data Sources for R [Revolutions]

    Over the past few months, a number of new CRAN packages have appeared that make it easier for R users to gain access to curated data. Most of these provide interfaces to a RESTful API written by the data publishers while a few just wrap the data set inside the package. Some of the new packages are only very simple, one function wrappers to the API. Others offer more features providing functions to
  • Value Investing: Accruals, Cash Flows, and Operating Profitability [Alpha Architect]

    Accruals are the non-cash component of earnings. They represent adjustments made to cash flows to generate a profit measure largely unaffected by the timing of receipts and payments of cash. Prior research finds that expected returns increase in firm profitability. However, firms with high accruals generate lower returns than firms with low accruals, and this "accrual anomaly"
  • Noise Kills Profits (Machine Learning with Genotick) [Throwing Good Money]

    A reader on my blog (Thanks Kris!) suggested that I explore how much noise is needed to send Genotick off the deep end. Youll recall from my earlier post on the subject that I was looking for hidden biases that Genotick might have, and explored how it responded to pure and noisy sine waves of data. For those just catching up, Genotick is a free, open-source machine-learning price-prediction
  • A Multiples-Based Decomposition of the Value Premium [Quantpedia]

    We use industry multiples-based market-to-book decomposition of Rhodes-Kropf, Robinson and Viswanathan (2005) to study the value premium. The market-to-value component drives all of the value strategy return, while the value-to-book component exhibits no return predictability in both portfolio sorts and firm-level return regressions controlling for other stock characteristics. Prior results in the
  • Panic Selling, a Pause, Then Another Smash… [Don Fishback]

    Early this week, Rob Hanna at Quantifiable Edges put out some research showing what happens when you get Repeated Hard Selling at Intermediate-Term Lows. The definition he used was: S&P 500 ($SPX) closes down more than 1% for three straight days. Each close is a new 20-day low. The final close on the third day is below the 200-day moving average. Last Friday satisfied that criteria. Rob
  • Complacent Correction Cause For Concern? [Dana Lyons]

    Despite recent stock market carnage, the reaction by the VIX has been a relative yawner. Well, the New Year hangover continues. Another day, another drubbing in the stock market. With indices pushing double digit losses just 8 days into the new year, it certainly seems reasonable to expect some panic on the part of investors. However, at least based on one metric, market participants have remained

Quantocracy’s Daily Wrap for 01/13/2016

This is a summary of links featured on Quantocracy on Wednesday, 01/13/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • On The Relationship Between the SMA and Momentum [QuantStrat TradeR]

    Happy new year. This post will be a quick one covering the relationship between the simple moving average and time series momentum. The implication is that one can potentially derive better time series momentum indicators than the classical one applied in so many papers. Okay, so the main idea for this post is quite simple: Im sure were all familiar with classical momentum. That is, the
  • Could the Stochastic Oscillator be a good way to earn money? [Quant Dare]

    If you knew the future price of assets when you are creating a portfolio, it would be the best way to be a millionaire. However, this is difficult if not impossible, so what if you knew what would be the movement of the price? It could be enough to earn money! To predict the trend of the price is not a silly job and there is no method that tells you what the movement will be with a 100%
  • Does Science Advance One Funeral at a Time? [Alpha Architect]

    A really interesting paper hit the NBER wires recently. The central argument of the paper is that rock star thought leaders dominate a field, but when they die, new thought leaders are able to emerge. In summary, there seems to be a cost and a benefit attached to a powerful intellectual: On one hand, powerful leaders drive ideas forward and other researchers can stand on the shoulders of
  • Tactical Alpha Part III – Asset Allocation – Security Selection [GestaltU]

    By far the greatest source of personal consternation as a professional in markets is investors obsession with finding the best stocks, or the best stock pickers. The fact that investors pursue this objective at all undermines all meaningful arguments about efficient markets. After all, why on earth would the well informed, rational actors that constitute efficient markets spend all their time
  • RUT Straddle – Backtest Results Summary [DTR Trading]

    Over the last seven weeks we reviewed the backtest results of 28,840 short options straddles on the Russell 2000 Index (RUT). In this post, I won't discuss how these trades were structured and managed. For background on the setup for the backtests, as well as the nomenclature used in the charts and tables below, please see the introductory article for this series: Option Straddle Series –

Quantocracy’s Daily Wrap for 01/10/2016

This is a summary of links featured on Quantocracy on Sunday, 01/10/2016. 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, 01/09 as voted by our readers. Exploring mean reversion and cointegration: part 2 [Robot Wealth] Dear Brokers [Financial Hacker] Quant Strategies: From Idea to Execution in Python [Quant Insti] New Book from Meb Faber: Invest With The House: Hacking The Top Hedge Funds [Amazon] We also welcome one blog making its first ever appearance on
  • Interview with Michael Cook [Better System Trader]

    Weve been very lucky to have a number of trading champions on the podcast before and this episode we get to talk to another champion trader, Michael Cook, who won the World Cup Trading championships in 2007 (Futures), 2011 (Stocks) and 2014 (Futures). Michael worked in the institutional world for a number of years before leaving behind the banks and hedge funds to trade for himself. In this
  • Give me good data, or give me death [Quantum Financier]

    A good discussion not to long ago led me to start a revolution against some data management aspects of my technology stack. Indeed it is one of the areas where the decisions made will impact every project undertaken down the road. Time is one of our most valuable resources and we need to minimize the amount of it we have to spend dealing with data issues. Messy and/or hard to use data is the

Quantocracy’s Daily Wrap for 01/09/2016

This is a summary of links featured on Quantocracy on Saturday, 01/09/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Components of a black box, humans versus computers, and high frequency trading w/ @RishiKNarang [Chat With Traders]

    To start the year with a bang, I have a very special guest on the podcast this week, who Id like you to meet his name is, Rishi Narang. Rishi has an impressive background, and has been involved with financial markets for over 20 years now. He originally started out as an analyst at Citibank, prior to co-founding TradeWorx with his brother Manoj Narang (a fintech company, turned
  • RSI(2)?25 X6! [Throwing Good Money]

    Today marks the sixth day in the row that the S&P 500s RSI(2) value was under 25.* Since Jan 1 2000, this has happened only 15 times prior to today. So I thought it would be fun to see what the forward return has been after these events. Heres a handy spreadsheet. I calculated the forward return from the following days open to the close of either the 5th or 20th day. I.e. if you were