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Quantocracy’s Daily Wrap for 04/09/2017

This is a summary of links featured on Quantocracy on Sunday, 04/09/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Pop or Drop part 1: Stock Behavior After Big Moves [Throwing Good Money]

    When stocks are moving gently from one day to the next, there is often no discernible pattern. However when they start rockin' and rollin' one direction or the other, they show certain similarities. I'm always curious how stocks behave when they show a significant drop, or when they pop upward unexpectedly. I ran some simple statistics and noticed a couple of things. The Drop First,
  • Visualizing Data with Python [Build Alpha]

    In this post I will go over a few different ways to manipulate price data to create visuals to aid in the investing and trading research process. I have attached a ten minute YouTube video that has explanations, etc. However, this post also attempts to briefly walk you through the Python code. First, we will use some Python code to download some free data from the Yahoo Finance API. The code below

Quantocracy’s Daily Wrap for 04/08/2017

This is a summary of links featured on Quantocracy on Saturday, 04/08/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Can We Use Mixture Models to Predict Market Bottoms? (Part 2) [Black Arbs]

    In the previous post I gave a basic "proof" of concept, where we designed a trading strategy using Sklearn's implementation of Gaussian mixture models. The strategy attempts to predict an asset's return distribution such that returns that fall outside the predicted distribution are considered outliers and likely to mean revert. It showed some promise but had many areas in need

Quantocracy’s Daily Wrap for 04/07/2017

This is a summary of links featured on Quantocracy on Friday, 04/07/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Market State Impact on Cross-Sectional and Time-Series Momentum Strategy [Quantpedia]

    Recent evidence on momentum returns shows that the time-series (TS) strategy outperforms the cross-sectional (CS) strategy. We present new evidence that this happens only when the market continues in the same state, UP or DOWN. In fact, we find that the TS strategy underperforms the CS strategy when the market transitions to a different state. Our results show that the difference in momentum
  • Settle For Oil [Throwing Good Money]

    Above: long-exposure nighttime shot of oil rigs off the coast of California. Strange things happen to options and futures on fairly predictable dates. Options expiration dates, contract settlement datesthese are trading days where and this is just my theory some traders just want to get out of a trade by any means necessary. So it can, in theory, lead to behaviors that cant easily be

Quantocracy’s Daily Wrap for 04/05/2017

This is a summary of links featured on Quantocracy on Wednesday, 04/05/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Philosophical Economics’ Growth-Trend Timing [Allocate Smartly]

    This is a test of the Growth-Trend Timing (GTT) model from the always thought-provoking Philosophical Economics. GTT combines trends in price and key economic indicators to switch between US equities and cash. Results from 1970, net of transaction costs, follow. Read more about our backtests or let AllocateSmartly help you follow this strategy in near real-time. Logarithmically-scaled. Click for
  • March 2017 Trend Following down [Wisdom Trading]

    March 2017 Trend Following: DOWN -6.56% / YTD: -10.95% More of the same for the State of Trend Following in March. The index continues its downward progress and established a new Max Drawdown figure last month. Below is the full State of Trend Following report as of last month. Performance is hypothetical. Chart for March: WSTF 201703 Index And the 12-month chart: WSTF 201703 Index 12months Below

Quantocracy’s Daily Wrap for 04/04/2017

This is a summary of links featured on Quantocracy on Tuesday, 04/04/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • What are the Best & Worst Times of Day to Buy the Stock Market? [MKTSTK]

    I awoke this morning to find the S&P 500 futures down about 11 points. My immediate thought was oh goodie, theres some candy for the old early morning BTFDers to enjoy [translation, Ill be the market goes up from here]. This instinct was based on many such mornings of observing and interacting with the stock market. But how good is this intuition, and more importantly are
  • Can We Use Mixture Models to Predict Market Bottoms? [Black Arbs]

    In Part 1 we learned about Hidden Markov Models and their application using a toy example involving a lazy pet dog. In Part 2 we learned about the expectation-maximization algorithm, K-Means, and how Mixture Models improve on K-Means weaknesses. If you still have some questions or fuzzy understanding about these topics, I would recommend reviewing the prior posts. In those posts I also provide
  • Understanding False Discovery Rate [Eran Raviv]

    False Discovery Rate is an unintuitive name for a very intuitive statistical concept. The math involved is as elegant as possible. Still, it is not an easy concept to actually understand. Hence i thought it would be a good idea to write this short tutorial. We reviewed this important topic in the past, here as one of three Present-day great statistical discoveries, here in the context of
  • Looking to Improve your Factor Investing? Examine the Trend of Profits [Alpha Architect]

    A few years ago, the profitability quality factor was originally proposed by Robert Novy-Marx. Here is a snippet from the abstract of the paper: Profitability, measured by gross profits-to-assets, has roughly the same power as book-to-market predicting the cross-section of average returns. Profitable firms generate significantly higher returns than unprofitable firms, despite having
  • Prospect Theory, Bias, and Chalk: Our 2017 March Madness Wrap [Invest Resolve]

    Congrats to the First Place Loser Lets start off in the obvious place: Mike Philbrick, the poor-mans Gronkowski, went wire-to-wire in last place. That makes us happy, and so first and foremost, we come to bury him. Its entirely his fault. We know because the scoring rules were such that, assuming public betting markets are reasonably good proxies for the true odds of a team winning a
  • Do Price Multiples Predict Market Returns? [Larry Swedroe]

    A large body of work demonstrates that price multiples, such as the dividend-to-price ratio, predict stock returns. As a result, modern asset pricing theory increasingly incorporates time-varying expected returns. The majority of the empirical work underpinning these findings uses U.S. stock market data going back to 1926. Benjamin Golez and Peter Koudijs contribute to the literature on return

Quantocracy’s Daily Wrap for 04/03/2017

This is a summary of links featured on Quantocracy on Monday, 04/03/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Diversification, Adaptation, and Stock Market Valuation [Philosophical Economics]

    Looking back at asset class performance over the course of market history, we notice a hierarchy of excess returns. Small caps generated excess returns over broad equities, which generated excess returns over corporate bonds, which generated excess returns over treasury bonds, which generated excess returns over treasury bills (cash), and so on. This hierarchy is illustrated in the chart and table
  • Early April s Bullish Inclination [Quantifiable Edges]

    The study below is one I have shown here on the blog a few times over the years. It examines the bullish inclination the market has had in early April. 2017-04-03 Numbers here appear impressive. Of further note, sixteen of the 1st eighteen years were higher on day 4, but the 2012-2014 instances saw mild declines. Meanwhile, the 2-day time period has been positive 10 of the last 11 years, with 2015
  • The Curious Case of the Missing Credit Premium [Flirting with Models]

    Before we dive into this weeks commentary, we want to extend a very heartfelt thank you to everyone who nominated us for ETF.coms 2016 ETF Strategist of the Year Award. The award ceremony was held on Thursday night and we were fortunate enough not to leave empty handed! Were incredibly honored and humbled and would like to thank ETF.com, our partners, our advisor community, and all the
  • A Tensorflow Exercise [Quintuitive]

    A previous post in this series, implemented the Walk Forward Loop on top of Microsofts CNTK. There was interest in a Googles Tensorflow implementation, which seems to be the more popular framework in this domain, I decided to put what have already done with Tensorflow. The full source code is here. It will not work without modifications it needs data, and some of my modules. These are

Quantocracy’s Daily Wrap for 04/01/2017

This is a summary of links featured on Quantocracy on Saturday, 04/01/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation in March [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
  • N-CryptoAsset Portfolios: Identifying Highly Correlated Cryptocurrencies using PCA [Quant at Risk]

    IMHO, there is nothing more exciting these days than researching, analysing, and a good understanding of cryptocurrencies. Powered by blockchain technology, we live in a new world that moves fast forward as we sleep. In my first post devoted to that new class of tradable assets we have learnt how to download daily sampled OHLC time-series for various coins. Having any of them, we can think
  • Do ETFs Harvest Factors & Shrink Premiums? [Larry Swedroe]

    Financial research has uncovered many relationships between investment factors and stock returns. For investors, an important question is whether the publication of this research can impact the future size of factor premiums. Asking this question is crucial on two fronts. First, if anomalies are the result of behavioral errors, or even investor preferences, and the publication of research into

Quantocracy’s Daily Wrap for 03/31/2017

This is a summary of links featured on Quantocracy on Friday, 03/31/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Dead-Simple Hedge Ratio API [MKTSTK]

    As the title suggests, I created a dead simple hedge ratio API called Risk Hedger. Also its free and the Python client is open source. So if youre in to that kind of thing feel free to read on: What is a Hedge Ratio? Traders and investors buy/sell hedges when they want to reduce the risk of their portfolio. Additionally, certain strategies such as Pairs Trading rely on accurate estimation of
  • Factor Investing: The Fama French 5-Factor Model on Chinese A-Shares [Alpha Architect]

    Each year I teach my seminar in investments course at Drexel, which consists of the Masters in Finance students and a handful of geeky MBA students. The first few weeks of the course involve an introduction to various investment frameworks and how to navigate the source academic literature. The rest of the course is dedicated to research. Yeah, baby! I divide the class into research groups
  • Research Review | 31 March 2017 | Managing Portfolio Risk [Capital Spectator]

    Bubbles for Fama Robin M. Greenwood (Harvard Business School), et al. February 2017 We evaluate Eugene Famas claim that stock prices do not exhibit price bubbles. Based on U.S. industry returns 19262014 and international sector returns 19852014, we present four findings: (1) Fama is correct in that a sharp price increase of an industry portfolio does not, on average, predict unusually low

Quantocracy’s Daily Wrap for 03/30/2017

This is a summary of links featured on Quantocracy on Thursday, 03/30/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Free Friday #14 and #14a [Build Alpha]

    Happy Friday. The trader in me could not risk doing Free Friday #13 so I decided to release 2 strategies this week (14 and 14a). The first strategy shorts $GDX, the Gold Miners ETF, and the second strategy goes long $GLD, the Gold ETF. ff14a gdx_ff14 The strategy above is the GDX short strategy. The left chart is from Build Alpha (which now highlights out of sample trades – new feature) and the
  • Why have asset price properties changed so little in 200 years? [Quantpedia]

    We first review empirical evidence that asset prices have had episodes of large fluctuations and been inefficient for at least 200 years. We briefly review recent theoretical results as well as the neurological basis of trend following and finally argue that these asset price properties can be attributed to two fundamental mechanisms that have not changed for many centuries: an innate preference

Quantocracy’s Daily Wrap for 03/29/2017

This is a summary of links featured on Quantocracy on Wednesday, 03/29/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Podcast: Trading technology, alternative data, and originality w/ Manoj Narang [Chat With Traders]

    High-speed trading veteran, Manoj Narang, originally worked on Wall St for the likes of Credit Suisse and Goldman Sachs prior to founding Tradeworx, which became one of the larger trading firms in the U.S. (in terms of volume). Hes since parted ways with Tradeworx to start MANA Partnersan innovative quant fund which raised almost one billion dollars for its launch in January this year
  • How to use bootstrapping in Portfolio Management [Quant Dare]

    Faced with growing uncertainty in financial markets, investors are worried about the future of their investments. Travelling in time to check the future reality is not yet a possibility. For that reason, we use techniques and create measures to gain confidence in our investments future behaviour. Bootstrapping is a financial technique that allows us to get a return confidence interval for a
  • Podcast w/ @GaryAntonacci: You Get a Synergy That Happens When You Use Dual Momentum [Meb Faber]

    Gary has over 40 years experience as an investment professional focusing on underexploited investment opportunities. Since receiving his MBA degree from the Harvard Business School, Gary has concentrated on researching, developing, and applying innovative investment strategies that have their basis in academic research. His innovative research on momentum investing was the first-place winner in
  • A More Complex View On Value [Larry Swedroe]

    Eugene Fama and Kenneth Frenchs 1992 paper, The Cross-Section of Expected Stock Returns, resulted in the development of the FamaFrench three-factor model. This model added the size and value factors to the market beta factor. As my co-author, Andrew Berkin, and I demonstrate in Your Complete Guide to Factor-Based Investing: The Way Smart Money Invests Today, the value premium has
  • The Case For Using Random Benchmarks In Portfolio Analysis [Capital Spectator]

    Benchmarks are indispensable for investment analytics. The challenge is picking a relevant one. The stakes are high because the wrong benchmark can be worse than none at all. The good news is that the potential for error can be dramatically reduced by choosing a set of random benchmarks that are generated from a portfolios holdings. As an example, consider a money manager with a mandate to beat