Quantocracy

Quant Blog Mashup

ST

Quantocracy’s Daily Wrap for 07/12/2018

This is a summary of links featured on Quantocracy on Thursday, 07/12/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Stock Prediction with ML: Feature Selection [Alpha Scientist]

    This is the third post in my series on transforming data into alpha. If you haven't yet see the data management and guide to feature engineering, please take a minute to read those first… This post is going to delve into the mechanics of feature selection to help choose between the many variations of features created in the feature engineering stage. By design, many of the features
  • Announcing Defensive Asset Allocation (DAA) [TrendXplorer]

    Defensive Asset Allocation (DAA) builds on the framework designed for Vigilant Asset Allocation (VAA) For DAA the need for crash protection is quantified using a separate canary universe instead of the full investment universe as with VAA DAA leads to lower out-of-market allocations and hence improves the tracking error due to higher in-the-market-rates In our brand new SSRN-paper Breadth
  • Deconstructing the Low Volatility/Low Beta Anomaly [Alpha Architect]

    One of the big problems for the first formal asset pricing model developed by financial economists, the Capital Asset Pricing Model (CAPM), was that it predicts a positive relationship between risk and return. However, the historical evidence demonstrates that, while the slope of the security market line is generally positive (higher-beta stocks provide higher returns than low-beta stocks), it is

Quantocracy’s Daily Wrap for 07/11/2018

This is a summary of links featured on Quantocracy on Wednesday, 07/11/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Excerpt, Part II: Quantitative Investment Portfolio Analytics In R [Capital Spectator]

    A couple of weeks back I published the first part of a full-chapter excerpt from my new book, Quantitative Investment Portfolio Analytics In R: An Introduction To R For Modeling Portfolio Risk and Return. Heres the second half of this two-part excerpt of Chapter 5, which reviews the basics for factor analysis via R code. The chapter sample below focuses on additional analytics, including a
  • Our Conversation with Adam Butler [Flirting with Models]

    This post is the first of a series where we will be providing some of our own thoughts and commentary the conversations we had in the first season of our new podcast. This post covers our conversation with Adam Butler, which you can listen to here. 1:57 – Corey introduces Adam via a blog post Adam wrote about his experience with the emerging market and commodity super cycle theory of the early
  • Multiple Managers vs A Single Manager: Return Predictability [Rayner Gobran]

    This is the seventh in my Hedge Fund Hacks series. It is a natural follow-up to my sixth hack on Hedge Fund Return Predictability in which I identified the following conundrum: You need a track record of 8+ years of monthly data to have reasonable confidence in a managers expected returns. The longer the track record you demand, the fewer managers you will have to choose from. A long track
  • Hierarchical Risk Parity [Quant Dare]

    Building profitable portfolios has been giving investment managers headaches for decades. Many approaches have been used up until now, some of the most well-known being Markowitzs Efficient Frontier and Risk Parity. Today, we are presenting a brand new approach to this recurrent problem developed by Dr. Marcos Lpez de Prado applying Modern Graph Theory and Machine Learning techniques. Lpez
  • Impact of Single Stocks On Factor Returns [Factor Research]

    Factor portfolios are typically created by equal weighting stocks The impact of single stocks is therefore reduced compared to market-cap weighted indices The FAANG stocks impacted factors differently INTRODUCTION The famous FAANG quintet of Facebook, Amazon, Apple, Netflix, and Google has driven much of the performance of the Nasdaq 100 in 2018 and currently accounts for approximately 35% of the
  • Double Gaps and Hens Teeth [Throwing Good Money]

    I looked at the chart for SPY just now, and thought, Huhtwo days in a row that have gapped up. Wonder if thats significant in any way? By gap, I mean that todays low was higher than yesterdays high. When this happens two days in a row, does it mean we should use quintuple leverage to buy everything we can? Sell at the opening bell and hide under a rock? Something else?

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