Quantocracy

Quant Blog Mashup

ST

Quantocracy’s Daily Wrap for 07/10/2018

This is a summary of links featured on Quantocracy on Tuesday, 07/10/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Do Fundamentals Still Drive Market Prices? Or Have ETFs Taken Over? [Alpha Architect]

    What causes a stocks price to move? A great question, and one that puzzles most market observers every day. Since this is puzzling, it is an entertaining topic to discussenter CNBC and the financial media. In the short-run, stocks prices can move for a variety of reasons noise trading, announcements from the FED, and 6 A.M. tweets from the President :). Kidding aside, short-run moves in
  • How Does VelocityShares ZIV Work? [Six Figure Investing]

    Just about anyone whos looked at a multi-year chart for a long volatility fund like Barclays VXX has thought about taking the short side side of that trade. VelocityShares ZIV is an Exchange Traded Product (ETP) that allows you to hold a short volatility position while avoiding some of the issues associated with a direct short position in VXX. Because ZIV is tied to VIX futures with at
  • Arbitrage Opportunities in Cryptocurrency Markets [Quantpedia]

    This paper studies the efficiency and price formation of bitcoin and other cryptocurrency markets. First, there are large recurrent arbitrage opportunities in cryptocurrency prices relative to fiat currencies across exchanges that often persist for several days or weeks. These price dispersions exist even in the face of significant trading volumes on many of the exchanges. The total size of

Quantocracy’s Daily Wrap for 07/09/2018

This is a summary of links featured on Quantocracy on Monday, 07/09/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Simulating Variable FX Swaps in Zorro and Python [Robot Wealth]

    One of the ongoing research projects inside the Robot Wealth community involves an FX strategy with some multi-week hold periods. Such a strategy can be significantly impacted by the swap, or the cost of financing the position. These costs change over time, and we decided that for the sake of more accurate simulations, we would incorporate these changes into our backtests. This post shows you how
  • Vol Targeting and Trend Following [Investment Idiocy]

    I was moved to write this by a post on LinkedIn, which you can find here, and which is worth quoting (and thanks to Helder Palaro for pointing me at this): "Volatility tends to cluster. Recent high(low) volatility is followed by high(low) volatility in the near-term (ARCH). VT says lever the portfolio during low-vol regimes & scale-down leverage during high-vol regimes. Target
  • Artificial Intelligence and Value Investing [Alpha Architect]

    The paper investigates the following research question: Can machines allocate capital in the classic style of a value investor like Benjamin Graham or Warren Buffett? What are the Academic Insights? By connecting two broad and disparate areas (the innovative AI domain and the more old-fashioned value investing), the author speculates: YES- With the right kind of AI it will be possible. The author
  • Is Friday s Sharp Drop in VXO Meaningful? [Quantifiable Edges]

    The rally on Friday was accompanied by a sizable drop in the VIX (and even more so for the VXO, which is the old calculation for the VIX). This triggered some old studies for me in which I noted that big drops in the VXO have had much different connotations depending on whether SPX is in a long-term uptrend or downtrend (as defined by its proximity to the 200ma). I decided to review those studies,
  • State of Trend Following in June [Au Tra Sy]

    Slight positive uptick in the State of Trend Following Performance report last month, leaving the YTD performance in negative territory at half-time. Please check below for more details. Detailed Results The figures for the month are: June return: 0.25% YTD return: -2.67% Below is the chart displaying individual system results throughout June: StateTF June And in tabular format: System June Return
  • Factor Crowding Model [Factor Research]

    Crowded factors exhibit higher drawdowns than uncrowded factors A multi-metric approach can be successfully applied to measure factor crowding Effective in reducing factor drawdowns and volatility, but less meaningful for returns INTRODUCTION Architects devoted to creating large public facilities like sports stadiums are tasked with finding a good balance between aesthetics, commercialism and
  • On Performance Commentary [Flirting with Models]

    With the end of another quarter, the time of performance commentaries is upon us. We generally find most performance commentaries to be devoid of much substance, taking a heads Im skilled, tails Im unlucky mentality to describing short-term results. While quants may be reluctant to offer any substantial reasoning as to why performance occurred, quants can offer radical transparency

Quantocracy’s Daily Wrap for 07/08/2018

This is a summary of links featured on Quantocracy on Sunday, 07/08/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 07/07/2018

This is a summary of links featured on Quantocracy on Saturday, 07/07/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Seasonal effects in commodity futures curves [SR SV]

    Seasonal fluctuations are evident for many commodity prices. However, their exact size can be quite uncertain. Hence, seasons affect commodity futures curves in two ways. First, they bias the expected futures price of a specific expiry month relative that of other months. Second, their uncertainty is an independent source of risk that affects the overall risk premia priced into the curve.
  • Are economics and finance “lost in math”? [Mathematical Investor]

    In a provocative new book, Lost in Math: How Beauty Leads Physics Astray, quantum physicist Sabine Hossenfelder argues that the scientific world in general, and the field of physics in particular, has repeatedly clung to notions that have been rejected by experimental evidence, or has pursued theories far beyond what can be tested by experimentation, mainly because these theories and the
  • June 2018 Trend Following [Wisdom Trading]

    June 2018 Trend Following: UP +0.48% / YTD: -6.01% Please find this months report of the Wisdom State of Trend Following. Performance is hypothetical. Chart for June: And the 12-month chart: Below are the summary stats: Horizon Return Ann. Vol. Last month 0.48% 13.45% Year To Date -6.01% 15.36% Last 12 months -5.23% 13.21% Last calendar year (2017) -16.27% 10.94% Since Index Launch (08-13)

Quantocracy’s Daily Wrap for 07/06/2018

This is a summary of links featured on Quantocracy on Friday, 07/06/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • My Experiments with Data Science Techniques to beat the Stock Market [Auquan]

    am a recent Computer Science graduate from IIT Kanpur. I first came to know about QuantQuest through IITK placement cell during our final year placement season. During my mid-semester recess when I was chilling out at my home, I received an email from Ms. Chandini Jain (founder of Auquan) with the subject Optiver Amsterdam is recruiting traders from IIT Kanpur. Like other recruiter spams, I

Quantocracy’s Daily Wrap for 07/05/2018

This is a summary of links featured on Quantocracy on Thursday, 07/05/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • To be or not to be (correlated) [Quant Dare]

    There are many problems that a data scientist encounters when fighting financial data for the first time: nothing is normally distributed, most problems are tough (low signal to noise ratio) and non-stationary high-dimensional time series are ubiquitous. In Quantdare we have spoken many times about one of the main sources of non-stationarity in financial time series: volatility. It is very
  • The Impact of Volatility Targeting on Equities, Bonds, Commodities and Currencies [Quantpedia]

    Recent studies show that volatility-managed equity portfolios realize higher Sharpe ratios than portfolios with a constant notional exposure. We show that this result only holds for risk assets, such as equity and credit, and link this to the so-called leverage effect for those assets. In contrast, for bonds, currencies, and commodities the impact of volatility targeting on the Sharpe ratio

Quantocracy’s Daily Wrap for 07/02/2018

This is a summary of links featured on Quantocracy on Monday, 07/02/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation in June [Allocate Smartly]

    This is a summary of the recent performance of a wide range 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. Learn more about what we do or let AllocateSmartly help you follow these strategies in
  • The New Glide Path [Flirting with Models]

    In practice, investors and institutions alike have spending patterns that makes the sequence of market returns a relevant risk factor. All else held equal, investors would prefer to make contributions before large returns and withdrawals before large declines. For retirees making constant withdrawals, sustained declines in portfolio value represent a significant risk. Trend-following has
  • Bitcoin Volatility, Skew, and Options Pricing [Only VIX]

    As I wrote before, Bitcoin volatility is quite different from volatility of other assets. I will continue with the same topic here. Bitcoin prices shot up to all-time highs this winter, and have sharply declined since. When an equity index declines, volatility typically moves up, but in the case of BTC, volatility has actually declined. This behavior is similar to VIX index. Although I know that
  • Improving The Moving Average Crossover System [System Trader Success]

    Lets take a look at a simple moving average crossover system and see if we can improve it. Specifically, can we improve the moving average systems performance by reducing the number of whipsaws during those dreaded range bound markets? Whipsaws occur when a market moves from a trending mode to a consolidation mode. During this consolidation mode the system gets whipsawed from long to short
  • Factor Olympics 1H 2018 [Factor Research]

    Factor performance in 1H 2018 is comparable to 2017 The Size factor has taken the lead, likely reflecting the threat of global trade wars Value has generated the most negative returns across regions INTRODUCTION We present the performance of seven well-known factors on an annual basis for the last 10 years and the first half of 2018. It is worth mentioning that not all factors have strong academic
  • For Consistency Across Market Conditions, Try a Quant Manager [Alpha Architect]

    What are the research questions? Using eVestment, a source of data for asset managers, the authors evaluate U.S. fundamental and quantitative managers, specifically core, growth and value styles. Eighteen strategy types (six in each style) were identified and results calculated using averages for the median manager over the period including January 1996 to December 2016. Is the observed

Quantocracy’s Daily Wrap for 06/30/2018

This is a summary of links featured on Quantocracy on Saturday, 06/30/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Round Turn Trade Simulation in R [Open Source Quant]

    I was fortunate enough to talk about my latest open source work with Brian Peterson at the R/Finance conference in Chicago just less than 1 month ago. It was my first time to the conference, and I will be back again for sure. The topics and their presentations are available on the website. With this post, I hope to share the main ideas of my talk. Back in August 2017 I wrote a post about a new
  • Wonderful Generosity From Quantifiable Edges Readers [Quantifiable Edges]

    As many readers of this blog are aware, on this weekend I will be doing a 150 mile bike ride from Boston, MA around Cape Cod to its tip in Provincetown for the Multiple Sclerosis Society. I have offered anyone that donates any amount of money a copy of the QE Fed Day MS Ride package, which includes a pdf copy of the Quantifiable Edges Guide to Fed Days, along with Fed Day code to allow people to

Quantocracy’s Daily Wrap for 06/29/2018

This is a summary of links featured on Quantocracy on Friday, 06/29/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Loss aversion is not a behavioral bias [EP Chan]

    In his famous book "Thinking, Fast and Slow", the Nobel laureate Daniel Kahneman described one common example of a behavioral finance bias: "You are offered a gamble on the toss of a [fair] coin. If the coin shows tails, you lose $100. If the coin shows heads, you win $110. Is this gamble attractive? Would you accept it?" (I have modified the numbers to be more realistic in a
  • Podcast: Momentum in Theory, Momentum in Practice [Alpha Architect]

    Here is a link to our podcast on Flirting with Models. Today I am speaking with Jack Vogel, co-CIO of boutique ETF issuer Alpha Architect. Ive known Jack for some time now and was particularly excited to bring him on the show for two reasons. The first, which you will quickly learn in the episode, is his near encyclopedic knowledge of investing literature. Ive met few investors who have both
  • This 2-Day Pattern Suggests the Bulls May Have A Short-Term Edge [Quantifiable Edges]

    On Wednesday the bulls tried to make a move higher and failed, making for a higher high and a lower close. On Thursday the opposite happened. The bears failed in their attempt at a move lower. A study from the Quantifinder looked at 2-day moves like this. I found results to be substantially different based on whether the market is near the top or the bottom of its short-term range. When the
  • The Evolution of Investing [Dual Momentum]

    I began my investment career in 1974. In 1976 I left a large retail brokerage firm to join a premier investment bank. I had both retail and institutional clients. So I had a well-rounded knowledge of Wall Street. The 1970s was soon after the dark ages of investing. Modern portfolio theory existed in the academic but not the real world. Looking at how investing has progressed since then, I thought
  • Research Review | 29 June 2018 | Factor Investing [Capital Spectator]

    When Does Cap-Weighting Outperform? Factor-Based Explanations Roger G Clarke (Ensign Peak Advisors), et al. May 1, 2018 Equity mutual fund performance can be partially explained by commonly-followed equity market factors, and the proposition that fund managers in the aggregate have more equally-weighted positions that the capitalization weighted market. Currently, the aggregate mutual funds

Quantocracy’s Daily Wrap for 06/28/2018

This is a summary of links featured on Quantocracy on Thursday, 06/28/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Julia Build any time resolution using 1 minute data [Flare 9x]

    Reliable data makes for more accurate models. It is not the end of the world if there are minor discrepancies although data does need to be representative to build models and make good assumptions. Common data errors are known to be found at market closing times. We want the auction price not the last price. Last price might be some fluff trade with 1 lot. We want the real close or the auction
  • Excerpt, Part I: Quantitative Investment Portfolio Analytics In R [Capital Spectator]

    Heres an excerpt from my new book, Quantitative Investment Portfolio Analytics In R: An Introduction To R For Modeling Portfolio Risk and Return, which was published last week. In this two-part excerpt of Chapter 5, well look at a basic procedure for downloading factor premia from Professor Ken Frenchs web site to run a simple factor analysis using R code. Ill publish the second half
  • Explaining the Beta Anomaly [Alpha Architect]

    The superior performance of low-beta and low-volatility stocks was documented in the literature back in the 1970s by Fischer Black (in 1972) among others even before the size and value premiums were discovered. The low-beta/low-volatility anomaly has been demonstrated to exist in equity markets around the globe. Ive already written about the low-volatility (i.e., low-risk or
  • Bootstrapping time series data [Quant Dare]

    For those of us working with time series, the autocorrelation function (ACF) is a fundamental tool to understand how the values in a series correlate with others certain distance away. Indeed, we could even say that autocorrelation plots (a.k.a correlogram) are probably the most common visualizations in econometrics and time series analysis. This is why functions to compute and plot the ACF are