Quantocracy

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Quantocracy’s Daily Wrap for 07/10/2017

This is a summary of links featured on Quantocracy on Monday, 07/10/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Intratrade [Golden Compass]

    This article is on the analysis of intra-arrival times of future contract trades, analysing market behaviour, and identification of other particpants trading strategy. Intra-arrival time has close relationship with the quantity of each trade. One reason behind this is that many participants use high speed algorithmic trading to reduce price impact. One method is dividing one large order into a
  • Academic Research Insight: Facts about Factors [Alpha Architect]

    What are the research questions? Do factors offer superior diversification benefits relative to assets because factors are less correlated with each other? Does consolidating a larger set of assets into a smaller set of factors reduce noise? Are investors more skilled at relating current information to future factor behavior than to future asset behavior? Are factors and assets prone to the same
  • Four Important Details in Tactical Asset Allocation [Flirting with Models]

    Newfound specializes in systematic, factor-based approaches to constructing tactical portfolios. While we believe factors like value, momentum, carry, and trend are applicable at the asset class level, care must be taken in designing tactical allocation portfolios. We outline four considerations that we believe can have an outsized impact on tactical portfolio performance if ignored. For readers

Quantocracy’s Daily Wrap for 07/09/2017

This is a summary of links featured on Quantocracy on Sunday, 07/09/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Hacking a HFT system [Financial Hacker]

    Compared with machine learning or signal processing algorithms of conventional trading strategies, High Frequency Trading systems can be surprisingly simple. They need not attempt to predict future prices. They know the future prices already. Or rather, they know the prices that lie in the future for other, slower market participants. Recently we got some contracts for simulating HFT systems in
  • How to Build a Sequential Option Scraper with Python and Requests [Black Arbs]

    In the previous post I revealed a web scraping trick that allows us to defeat AJAX/JavaScript based web pages and extract the tables we need. We also covered how to use that trick to scrape a large volume of options prices quickly and asynchronously using the combination of aiohttp and asyncio. The Problem It worked beautifully until… I told people about it. Shortly after publishing, my code

Quantocracy’s Daily Wrap for 07/07/2017

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

  • Do Security Analyst Recommendations Bet on or Against Academic Findings? [Alpha Architect]

    As my co-author Andrew Berkin, the director of research for Bridgeway Capital Management, and I explain in our new book, Your Complete Guide to Factor-Based Investing, there is considerable evidence of cross-sectional return predictability. Citing more than 100 academic papers, we present the evidence of predictability for both equity and bond factors. And since the research is well known,

Quantocracy’s Daily Wrap for 07/05/2017

This is a summary of links featured on Quantocracy on Wednesday, 07/05/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Growth Optimal Portfolios [Flirting with Models]

    Traditional portfolio management focuses explicitly on the trade-off between risk and return. Anecdotally, investors often care more about the growth of their wealth. Due to compounding effects, wealth is a convex function of realized returns. Within, we explore geometric mean maximization, an alternative to the traditional Sharpe ratio maximization that seeks to maximize the long-term growth rate
  • Hacking Compound Annual Growth Rate [Rayner Gobran]

    This the third in my Hedge Fund Hacks series in which I dig just below the surface of some of the common hedge fund performance statistics. In the previous post I highlighted some of the ways in which Compound Annual Growth Rate can be distorted by chance. In this post I provide a simple hack to help bring into sharper focus the estimated returns derived from a value added monthly index. Compound
  • Capital Asset Pricing Model (CAPM) [No Noise Only Alpha]

    While I am a believer of APT more than of CAPM, I will share some of my findings on CAPM. CAPM has many flaws: there are capital taxes, high transaction cost on illiquid securities with few floating shares, licensed leveraged funds do influence prices with outsized positions, different analyst has a different expectations of a fair price in the same time horizon, we see animal spirits overwhelming
  • Lasso, Lasso, Lasso (and friends) [Eran Raviv]

    LASSO stands for Least Absolute Shrinkage and Selection Operator. It was first introduced 21 years ago by Robert Tibshirani (Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B). In 2004 the four statistical masters: Efron, Hastie, Johnstone and Tibshirani joined together to write the paper Least angle regression published in the Annals of
  • First half of 2017 down for Trend Following [Wisdom Trading]

    June 2017 Trend Following: DOWN -3.44% / YTD: -16.88% The whole first half of 2017 was negative, with the June result following the same trend. The YTD figure is now well in the red and it would take a good reversal of that equity curve to erase the losses through the second half of the year. Below is the full State of Trend Following report as of last month. Performance is hypothetical. Chart for

Quantocracy’s Daily Wrap for 07/03/2017

This is a summary of links featured on Quantocracy on Monday, 07/03/2017. 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. Read more about our backtests or let AllocateSmartly help you follow these strategies

Quantocracy’s Daily Wrap for 07/01/2017

This is a summary of links featured on Quantocracy on Saturday, 07/01/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mistakes when backtesting trading strategies [Cuemacro]

    You know how it feels. Time has come and gone, and still no progress. Then hours later, youve found the mistake, and youre absolutely kicking yourself. Coding is very much a case of trial and error. You just need enough stubbornness not to give up to get your code to run properly. Most of the coding Ive done over the past few years, has been geared towards developing systematic trading

Quantocracy’s Daily Wrap for 06/30/2017

This is a summary of links featured on Quantocracy on Friday, 06/30/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Does the Day of the Month Matter? [Allocate Smartly]

    Be sure to check out our guest post over at research supersite Alpha Architect: Tactical Asset Allocation: Does the Day of the Month Matter? Backtests of long-term strategies like tactical asset allocation are usually shown trading at the end of the month, both because it makes the analysis simpler and because monthly asset class data is easier to come by. In our guest post we talk about our
  • Research Review | 30 June 2017 | Searching For Alpha [Capital Spectator]

    US Sector Rotation with Five-Factor Fama-French Alphas G. Sarwar (University of Greenwich), et al. June 16, 2017 In this paper we investigate the risk-adjusted performance of US sector portfolios and sector rotation strategy using the alphas from the Fama-French five factor model. We find that five-factor model fits better the returns of US sector portfolios than the three factor model, but that
  • Using a Market Timing Rule to Size an Option Position, A Static Case [Relative Value Arbitrage]

    In the previous installment, we discussed the use of a popular asset allocation/market timing rule (10M SMA rule hereafter) to size a short option position. The strategy did not work well as it was the case in traditional asset allocation. We thought that the poor performance was due to the fact that the 10M SMA rule is more of a market direction indicator that is not directly related to the PnL
  • Are REITs a Distinct Asset Class? [Quantpedia]

    Real estate investment trusts (REITs) are often considered to be a distinct asset class. But, do REITs deserve this designation? While exact definitions for asset class may vary, a number of statistical methods can provide strong evidence either for or against the suitability of the designation. The authors step back from the established real estate and REITs literature and answer this broader
  • Free Friday #19 Long/Short Small Caps and June Update [Build Alpha]

    This Free Friday, Free Friday #19, is a user submission! It is a long/short strategy for $IWM – the Russell 2000 ETF. Both the long and the short strategy only have two rules each and only hold for 1 day. Below Ive posted the long strategy on the left and the short strategy on the right. Short edges have certainly been difficult to find over the past few years in the US equity indexes on a

Quantocracy’s Daily Wrap for 06/29/2017

This is a summary of links featured on Quantocracy on Thursday, 06/29/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation: Does the Day of the Month Matter? [Alpha Architect]

    Most long-term approaches to investing, like tactical asset allocation or factor investing, are designed to trade infrequently, generally once a month or once a quarter. This is a feature, not a limitation. Trading infrequently forces a strategy to ignore day-to-day noise and focus on long-term trends. This reduces the negative impacts of turnover, including transaction costs, taxes and whipsaw.
  • Dynamic Asset Allocation for Practitioners, Part 3: Risk-Adjusted Momentum [Invest Resolve]

    So far, weve discussed the importance of investment universe selection and price momentum in designing a robust asset allocation methodology. If you havent read those articles, we would strongly encourage you to do so before proceeding with this one. We lay most of the explanatory and theoretical groundwork for this article in the previous instalments, and we wont repeat them here. In our
  • Podcast: Strategy development – powered by machine learning w/ Morgan Slade [Chat With Traders]

    Youll recall, I had Andy Kershner on the podcast a few episodes back. Towards the end of that episode, Andy briefly mentioned a cloud-based algo development platform and fund, CloudQuant, which is a subsidiary of Kershner Trading Group I mention this, because with me on this episode is Morgan Sladethe CEO of CloudQuant. Morgans career as a trader and portfolio manager began 20-years
  • Dispersion Trading Using Options [Quant Insti]

    This article is the final project submitted by the author as a part of his coursework in Executive Programme in Algorithmic Trading (EPAT) at QuantInsti. Do check our Projects page and have a look at what our students are building. Introduction The Dispersion Trading is a strategy used to exploit the difference between implied volatility and its subsequent realized volatility. The dispersion

Quantocracy’s Daily Wrap for 06/27/2017

This is a summary of links featured on Quantocracy on Tuesday, 06/27/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Tell-Tale Sign of Short-Run Trading [Alex Chinco]

    Motivation Trading has gotten a lot faster over the last two decades. The term short-run trader used to refer to people who traded multiple times a day. Now, it refers to algorithms that trade multiple times a second. Some people are worried about this new breed of short-run trader making stock prices less informative about firm fundamentals by trading so often on information thats

Quantocracy’s Daily Wrap for 06/26/2017

This is a summary of links featured on Quantocracy on Monday, 06/26/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Struggling Quant Episode 1: How I lost USD 500,000 [Quant Journey]

    STRUGGLING QUANT episode 1: How I lost USD 500.000 while figuring out the link between questions, math, stats, coding and trading Say that you are 30 years old and you have a good 25 years to work hard. Instead of going down the easy way of working for someone else during the day and killing time in the evenings and weekends, you have chosen the hard path of quantitative trading and started the
  • Trading Decisions of Your Stone Age Grandpa can Make You Money in Forex [Quant Journey]

    Why Ferrari or Rolex does not price their products at 149.999 or 12.999 but most of the items you see in your supermarket is priced like 4.99? Because Ferrari never likes to position itself as a bargain. Did you know that we tend to chose the price with less syllables even if the two prices have the same written lenght? These are some of the pricing strategies used by marketers. This is a very
  • Density Estimation Using Regression [Eran Raviv]

    Density estimation using regression? Yes we can! I like regression. It is one of those simple yet powerful statistical methods. You always know exactly what you are doing. This post is about density estimation, and how to get an estimate of the density using (Poisson) regression. The go-to estimator for density is currently a nonparametric (or semiparametric) kernel. This is the estimator
  • Visualizing Time Series Data in R [R Trader]

    Im very pleased to announce my DataCamp course on Visualizing Time Series Data in R. This course is also part of the Time Series with R skills track. Feel free to have a look, the first chapter is free! Course Description As the saying goes, A chart is worth a thousand words. This is why visualization is the most used and powerful way to get a better understanding of your data. After this
  • Should You Buy or Rent a GPU-Based Deep Learning Machine for Quant Trading Research? [Quant Start]

    We've recently been considering the field of deep learning as a modelling methodology for forming new quantitative trading models. Such models have been shown to be 'unreasonably effective' in the fields of computer vision, natural language processing and games of strategy. This motivates us to see if these models can be applied to quant trading strategies. We've so far looked
  • The birth of a strategy a common effort [Quant Bear]

    Lets start an experiment! This post will be the first in a series on going through the process of creating a trading strategy. It will not only detail the steps that I myself curently follow when I am building a strategy, what Im hoping for is that others contribute to the process by adding their ideas, criticism, point out logical flaws etc. Maybe someone also wants to share their process.
  • Duration Timing with Style Premia [Flirting with Models]

    In a rising rate environment, conventional wisdom says to shorten duration in bond portfolios. Even as rates rise in general, the influence of central banks and expectations for inflation can create short term movements in the yield curve that can be exploited using systematic style premia. Value, momentum, carry, and an explicit measure of the bond risk premium all produce strong absolute and
  • Academic Research Insight: The Value of Crowsourced Earnings Forecasts [Alpha Architect]

    What are the research questions? Are crowdsourced earnings forecasts from a source such as Estimize, useful in the capital markets by capturing new information about future earnings? Does a site such as Estimize add incremental accuracy when combined with the conventional, sell-side earnings forecasts such as the IBES consensus as well as a statistical model of forecasts? Is the crowdsourced