Quantocracy

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Quantocracy’s Daily Wrap for 01/29/2016

This is a summary of links featured on Quantocracy on Friday, 01/29/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Correlations, Weights, Multipliers…. (pysystemtrade) [Investment Idiocy]

    This post serves three main purposes: Firstly, I'm going to explain the main features I've just added to my python back-testing package pysystemtrade; namely the ability to estimate parameters that were fixed before: forecast and instrument weights; plus forecast and instrument diversification multipliers. (See here for a full list of what's in version 0.2.1) Secondly I'll be
  • Computers vs Humans – considering the median [Investment Idiocy]

    Or why you aren't, and will never be, John Paulson The systematic versus discretionary trading argument is alive and well; or if you prefer, computers versus humans*. In this post I pose the question – who is better the average systematic trader, or the average discretionary human? * Though even fully discretionary traders will be relying on a computer at some point; fully manual trading and
  • The most concise explanation of behavioral finance I’ve ever seen [Alpha Architect]

    One of the most overused and misunderstood terms Ive seen used by finance practitioners is behavioral finance. Many professionals consider themselves to be behavioral finance experts because they identify irrational investors.1 Newsflash: Identifying irrational investors is not behavioral finance. But here is a great summary from a Baker, Bradley, and Wurgler paper weve
  • Peak Crashes: Are They a Shortable Opportunity? [Throwing Good Money]

    In hindsight, its fun to look at stocks that have had a huge surge, only to collapse violently after they peak. And by fun I mean sitting on the sidelines watching, as opposed to pulling ones hair out when youre long that particular trade. Above you can see Apple (AAPL) in 2008, where it hit its 52-week high, only to collapse a few days later, for a very significant loss. Are these
  • Latest Twist In The Stock Market s Wild 2016 Ride [Dana Lyons]

    The stock markets wild ride to begin the year continues, with the latest twist reminiscent of a roller coaster. Over the past 4 days, the Dow Jones Industrial Average (DJIA) has moved at least 1%, with each day alternating up and down. Since 1900, this is the 68th such streak and just the 17th in the past 70 years (actually, today narrowly missed making it 5 days in a row which would have been

Quantocracy’s Daily Wrap for 01/27/2016

This is a summary of links featured on Quantocracy on Wednesday, 01/27/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Case For High Volatility Strategies [EconomPic]

    Which investment would you prefer to invest in to diversify your existing stock allocation? Asset A with an expected: 3% annualized return 3.5% annualized standard deviation 0.00 correlation with your existing investment Asset B with an expected: -5% annualized return > 50% annualized standard deviation 0.00 correlation with your existing investment Easy question right? Perhaps not. Asset B may
  • Technologies Screening II [Algorythmn Trader]

    In my last post I introduced how I identified possible technologies and learned the basics about different languages, operating systems and IDEs. I also mentioned that my choice was to build on top of .Nets C# and Visual Studio, as my base for developing. In this post I want to dig a little deeper into the overall tool chain for my trading system development. When I was starting to work with
  • How Does Analyst Optimism Affect Momentum Strategies? [Alpha Architect]

    We examine the effect of security analyst recommendations on stock price momentum. Results show that momentum profits are directly linked to analyst optimism. Specifically, we find that a 1-unit change in recommendation quintile translates to about a 50 basis point change in subsequent 3-month momentum profits. We also examine uncovered stocks by using parallel projection methods to project
  • The Internal Bar Strength Indicator [Backtest Wizard]

    The internal bar strength or (IBS) is an oscillating indicator which measures the relative position of the close price with respect to the low to high range for the same period. The calculation for Internal Bar Strength is as follows IBS = (Close Low) / (High Low) * 100; For example, on 13/01/2016 the QQQ etf had a high price of $106.23, a low price of $101.74 and a close price of
  • 7 Best Backtesting Platforms for Quantitative Trading [Quant Insti]

    We have a large number of vendor-developed backtesting platforms available in market which can be very efficient in backtesting automated strategies; but to decide which once will suit your requirements, needs some research. Ideally custom development of a backtesting environment within a first-class programming language provides the most flexibility and third party platforms might make a number
  • ‘Tis the Season for Bold Prediction [GestaltU]

    It is with a giddy sense of schadenfreude that every year around this time, we get to read bold prediction rubbish like this: A best-selling personal finance guru, a behavioral economics columnist at MarketWatch, a Harvard-educated economist and other notable financial experts all warn the stock market is going to hell in 2016. Wall Streets major powerhouses, on the other hand, beg to differ.

Quantocracy’s Daily Wrap for 01/26/2016

This is a summary of links featured on Quantocracy on Tuesday, 01/26/2016. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Random portfolios: correlation clustering [Predictive Alpha]

    We investigate whether two clustering techniques, k-means clustering and hierarchical clustering, can improve the risk-adjusted return of a random equity portfolio. We find that both techniques yield significantly higher Sharpe ratios compared to random portfolio with hierarchical clustering coming out on top. Our debut blog post Towards a better benchmark: random portfolios resulted in a lot of
  • Linear regression assumes nothing about your data [Eran Raviv]

    We often see statements like linear regression makes the assumption that the data is normally distributed, Data has no or little multicollinearity, or other such blunders (you know who you are..). Lets set the whole thing straight. Linear regression assumes nothing about your data It has to be said. Linear regression does not even assume linearity for that matter, I argue. It is
  • Breadth Diffusion Predicts a Bounce? [Throwing Good Money]

    Recently I posted a number of articles on various breadth diffusion indicators and their relative effectiveness in predicting the health of the S&P 500. The big winner was the system that compared the number of stocks in the historical constituents of the Russell 3000 that were up 30% or more over the last quarter (60 trading days) vs those were 30% or down over the same period. You can read
  • RUT Straddle – Normalized Return Charts [DTR Trading]

    In the last two articles (here and here), we reviewed the backtest results of 28,840 short options straddles on the Russell 2000 Index (RUT). If you haven't read the last two articles, you may want to first read the introductory article for this series Option Straddle Series – P&L Exits. In this post, I am going to show the P&L results in line-chart form rather than the heat map
  • Are Stocks Cheap? Checking in on Current Valuations [EconomPic]

    I'll leave it to others to chime in whether forward P/E's are useful or not given the fact they typically overstate earnings and I'll ignore that earnings may be at a cyclical peak (more on the latter here). As an aside, technicals in the market are filthy, as most short-term signals I look at are providing caution (example here). BUT, based purely on current forward P/E's

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