Quantocracy

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Quantocracy’s Daily Wrap for 07/14/2017

This is a summary of links featured on Quantocracy on Friday, 07/14/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trend-Following with Valeriy Zakamulin: Moving Average Basics (Part 1) [Alpha Architect]

    One of the basic principles of technical analysis is that prices move in trends. Traders firmly believe that these trends can be identified in a timely manner and used to generate profits and limit losses. Consequently, trend following is arguably one of the most widespread market timing strategies; it tries to jump on a trend and ride it. Specifically, when stock prices are trending upward
  • Trend Following Research [Dual Momentum]

    There have been hundreds of research papers on relative strength momentum since the seminal work by Jegadeesh and Titman in 1993. [1] Relative momentum has been shown to work in and out-of-sample within and across most asset classes. Theoretical results have been consistent, persistent, and robust. Research on trend following absolute momentum got a much later start. The first paper on Time
  • Breadth Momentum and Vigilant Asset Allocation (VAA) [TrendXplorer]

    Breadth momentum extends traditional absolute momentum approaches for crash protection. Breadth momentum quantifies risk at the universe level by the number of assets with non-positive momentum relative to a breadth protection threshold. Vigilant Asset Allocation matches breadth momentum with a responsive momentum filter for targeting offensive annual returns with defensive crash protection.
  • Is Equity Premium Predictable? [Quantpedia]

    We study the performance of a comprehensive set of equity premium forecasting strategies that have been shown to outperform the historical mean out-of-sample when tested in isolation. Using a multiple testing framework, we find that previous evidence on out-of-sample predictability is primarily due to data snooping. We are not able to identify any forecasting strategy that produces robust and
  • Identifying Asset Pairs For Pairs Trading [Koppian Adventures]

    Last time, we talked about how to identify stationary time series. Today we continue this line of thought by defining cointegration and looking at its usage in trading. In particular, we will discuss how a pairs trading strategy works. Motivation If we remember that stationarity assumes that a mean of a time series exist, we can conclude that if the time series wanders too far off its mean, it
  • The profitability factor [Investing For A Living]

    What would you think of a quant strategy that only invests in the most profitable companies? Would it under perform the market or beat the market? If youre an efficient market person you may think that higher profitability must be priced into equities and therefore at best the strategy would match the market. Not so. Turns out that profitability is quite a durable factor and is only beaten by

Quantocracy’s Daily Wrap for 07/12/2017

This is a summary of links featured on Quantocracy on Wednesday, 07/12/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Out-of-sample testing and luck [Alvarez Quant Trading]

    Continuing from the last post, I will show how using different definitions of passing our out-of-sample test can change our results. How luck can play a role if you use only one strategy to test in out-of-sample. How you split your in-sample(IS) and out-of-sample(OOS) can change results. The Strategy I will be using a stock mean reversion strategy with an average hold of three days. Some of my
  • Avoiding Overpriced Winners: A Better Way to Capture the Momentum Premium? [Alpha Architect]

    Any frequent reader of our blog knows we are fans of momentum investing. At this point, investment professionals should know that momentum historically works, that momentum is painful, and we have our own opinions on how to implement momentum investing via our Quantitative Momentum Index. Sometimes we feel there is nothing new when it comes to momentum. However, a new momentum investing paper,
  • “Past performance is no guarantee of future results”, but helps a bit [Quant Dare]

    We are rather used to reading this disclaimer (or some variation thereof) in mutual fund prospectuses or investment vehicle webpages. Despite warnings, investors and advisors insist on considering past performance (and some other related metrics) as important factors in asset selection. But, are they really wrong? In this post, I will try to shed some light on this topic by means of some metrics

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