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Quantocracy’s Daily Wrap for 11/09/2017

This is a summary of links featured on Quantocracy on Thursday, 11/09/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Overnight Risk [Qusma]

    Why are overnight periods riskier? For one, you cant use stops to limit your risk. But more importantly, the distribution of overnight returns has far more extreme negative returns than the intraday or close-to-close periods. Lets take a look at some stats on close-to-open, open-to-close, and close-to-close returns for SPY: Some definitions: Skew: negative skew means longer tails on the

Quantocracy’s Daily Wrap for 11/08/2017

This is a summary of links featured on Quantocracy on Wednesday, 11/08/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • PDF: Two Centuries of Value and Momentum 1800-2014

  • 800 Years of Risk-Free Rate [Quantpedia]

    This paper presents a new dataset for the annual risk-free rate in both nominal and real terms going back to the 13th century. On this basis, we establish for the first time a long-term comparative investigation of bond bull markets. It is shown that the global risk-free rate in July 2016 reached its lowest nominal level ever recorded. The current bond bull market in US Treasuries which
  • Risk Parity in Python [Quant Dare]

    Once we are familiar with the theory surrounding Risk Parity, its time to put the strategy into practice and try out the algorithm for ourselves. We discover how it works, analyse the strategy and create our own portfolios. Thanks to the posts written by T.Fuertes and mplanaslasa we already know what the Risk Parity strategy is, its formulation and even how it differs from the Inverse
  • State of Trend Following in October [Au Tra Sy]

    Last month saw a strong upwards performance from the State of Trend Following index, single-handedly reversing half the negative performance for the year, which still stands close to double-digit territory. Please check below for more details. Detailed Results The figures for the month are: October return: 8.72% YTD return: -8.02% Below is the chart displaying individual system results throughout

Quantocracy’s Daily Wrap for 11/07/2017

This is a summary of links featured on Quantocracy on Tuesday, 11/07/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Timing TAA Strategies Based on Relative Strength: A Suboptimal Approach [Allocate Smartly]

    We track a wide range of tactical asset allocation strategies in near real-time (41 and counting), which members can combine into their own custom portfolios. We provide members with a wealth of data to understand how each strategy fits into a coherent trading plan, but we dont tell members the absolute best ones to trade or how to combine them. Thats because there isnt a single
  • Earning Money in Cryptocurrency Markets by Spotting Statistical Arbitrage Opportunities [Quant at Risk]

    When you come in contact with cryptocurrencies, e.g. Bitcoin (BTC), you quickly realise that there is no single price of BTC at any given moment. The reason is that Bitcoin is traded on different markets. It can be worth more on Coinbase exchange and less on Kraken exchange. In particular, the Coindesk Bitcoin Price Index (XBP) aims to unify the BTC price into a single number based on four markets
  • Replicating Indexes In R (Part III): Socially Responsible Investing [Capital Spectator]

    In previous installments of replicating indexes I profiled the style-analysis methodology and presented an example using a hedge fund index. Now lets turn to a strategy of replicating the S&P 500 Index with a handful of stocks that are considered socially responsible investments (SRI). Whats the rationale? A growing number of investors require that their equity portfolios match certain

Quantocracy’s Daily Wrap for 11/06/2017

This is a summary of links featured on Quantocracy on Monday, 11/06/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Cointegration, Correlation and Log Returns [Quantoisseur]

    The differences between correlation and cointegration can often be confusing. While there are some helpful explanations online, I wasnt satisfied with the visual examples. When looking at a plot of an actual pair of symbols where the correlation and cointegration test results differ, it can be difficult to pinpoint which portions of the time series are responsible for these separate properties.
  • Are we misidentifying seasonal patterns as genuine earnings news? [Alpha Architect]

    Changes in earnings are comprised of the expected earnings number plus any seasonal component of earnings. If the seasonal component is expected then it should not affect prices in an efficient market. However, unusual returns have been documented surrounding earnings announcements at the seasonal juncture in time. It is possible investors discount the complexity of seasonality even though it is a
  • It s Long/Short Portfolios All The Way Down [Flirting with Models]

    Long/short portfolios are helpful tools for quantifying the value-add of portfolio changes, especially for active strategies. In the context of fees, we can isolate the implicit fee of the managers active decisions (active share) relative to a benchmark and ask ourselves whether we think that hurdle is attainable. Bar-belling low fee beta with high active share, higher fee managers may actually
  • Integrated Value, Growth and Quality Portfolios [Factor Research]

    Integrated Value, Growth & Quality portfolios generated attractive returns year-to-date 2017 Sorting stocks on several characteristics results in relatively smooth performance Mitigates the issue of factor timing, but not of factor selection INTRODUCTION Year-to-date 2017 is shaping up as a terrible year for the consensus trade of the beginning of the year Value, which was based on animal

Quantocracy’s Daily Wrap for 11/02/2017

This is a summary of links featured on Quantocracy on Thursday, 11/02/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Application of Machine Learning Techniques to Trading [Auquan]

    Auquan recently concluded another version of QuantQuest, and this time, we had a lot of people attempt Machine Learning with our problems. It was good learning for both us and them (hopefully!). This post is inspired by our observations of some common caveats and pitfalls during the competition when trying to apply ML techniques to trading problems. IF you havent read our previous posts, we
  • The Herd Effect in Financial Markets [Quant Dare]

    Often in financial markets, as in our daily life, we imitate the decisions of predecessors, instead of analysing available information and making our own decisions. This decision imitation could lead to collective hysteria, and investment calls may be influenced by these panicked situations. Imagine that youre looking for a place to have dinner, and find a street with two restaurants. Both look
  • Trend Following Strong in October [Wisdom Trading]

    October 2017 Trend Following: UP +7.12% / YTD: -15.39% Below is the full State of Trend Following report as of last month, which saw our trend following index post a strong positive performance. Performance is hypothetical. Chart for October: Wisdom State of Trend Following – October 2017 And the 12-month chart: Wisdom State of Trend Following 12 months – October 2017 Below are the summary stats:

Quantocracy’s Daily Wrap for 11/01/2017

This is a summary of links featured on Quantocracy on Wednesday, 11/01/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Tactical Asset Allocation in October [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 ABCs of creating a mean reversion strategy Part 2 [Alvarez Quant Trading]

    This post is the continuation of the steps for creating a mean reversion strategy from the first part of The ABCs of creating a mean reversion strategy Part 1. You can also listen to part 2 of my interview on Better System Trader here. A quick recap of the topics covered in part 1. I covered trading universe, indicators to measure daily mean reversion, combining multiple mean reversion
  • What Will We Talk About at the Evidence-Based Investing Conference This Year? [Alpha Architect]

    ack and I will be attending the Evidence-Based Investing Conference tomorrow in NYC. Were excited to participate and be part of the crowd. Be sure to give us a holler love to discuss whatever is on your mind! Author rendering of the scene at EBI Historically, the conversations at EBI can end up covering fun topics. For example, Over the summer, Jack spoke at the West Coast version and

Quantocracy’s Daily Wrap for 10/31/2017

This is a summary of links featured on Quantocracy on Tuesday, 10/31/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Do Portfolio Factors or Characteristics Drive Expected Returns? [Alpha Architect]

    This article examines a somewhat over-looked, but important, discussion that raged among academic researchers in the late 1990s and early 2000s. The topic: factors versus characteristics. What do you mean, Factors versus characteristics? We often highlight that the value premium can be explained by either risk and/or mispricing. A core aspect of the risk argument is that a
  • Trading Using Decision Tree Classifier Part 1 [Quant Insti]

    The strategy in this blog will cover no normal technical indicators, but some of my own creation. Also, we will see the difference between strategy performance on test and train data along with respect to the changes in the size of the train data and the prediction length. Unlike in my previous blogs, in here I will use a dynamic time frame to fetch data for the past few days. But before we begin,

Quantocracy’s Daily Wrap for 10/30/2017

This is a summary of links featured on Quantocracy on Monday, 10/30/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Alternative Data: The Next Frontier of Quant? [Flirting with Models]

    The world is awash with new data. Satellite imagery, shipping manifests, agricultural sensors, and more can provide untapped insights. To understand how investors might benefit, we decompose investment strategies into three pieces: systematic rules, idiosyncratic decisions, and randomness. We explore how new and unique data might help investors enhance decision making in each of these categories.
  • Resist the Siren Call of High Dividend Yields [Factor Research]

    Buying high yielding and selling low yielding stocks has been an attractive strategy since 2000 However, it has been a highly unattractive strategy over the last century Investors should resist the Siren call of high yielding stocks and focus on other factors INTRODUCTION The search for yield has led investors and savers to consider quite adventurous investments in recent years. One contender for
  • Launching My Subscription Service [QuantStrat TradeR]

    After gauging interest from my readers, Ive decided to open up a subscription service. Ill copy and paste the FAQs, or my best attempt at trying to answer as many questions as possible ahead of time, and may answer more in the future. Im choosing to use Patreon just to outsource all of the technicalities of handling subscriptions and creating a centralized source to post
  • Information In Volatility Structure [Tr8dr]

    In the prior post Information In Volatility Structure [1] applied the SABR model to fit noisy raw option price data of approximatelty 700 million prices across a 10 year history of 2700 stocks. The point was to examine a hypothesis: does supply / demand imbalance in the options market express in terms of abnormal vol skew? can abnormal vol skew point to forward market behavior? First Application I
  • Broken Wing Butterfly Price and Volatility – CDN [DTR Trading]

    In the last two posts (here, and here), we looked at how implied volatility (IV) and price of the option strikes in two broken wing butterfly (BWB) strategies changed with time. In this post, we'll look at another BWB strategy, the centered delta neutral (CDN) BWB. In this strategy, the short put options are at-the-money (ATM), the lower long is at least 100 points below the market, and the
  • Autumn Readings about Factor Investing [Quantpedia]

    Factor returns, net of changes in valuation levels, are much lower than recent performance suggests. Value-add can be structural, and thus reliably repeatable, or situationala product of rising valuationslikely neither sustainable nor repeatable. Many investors are performance chasers who in pushing prices higher create valuation levels that inflate past performance, reduce potential future
  • Academic Research Insight: Sin Stocks May Earn a “Boycott” Risk Premium [Alpha Architect]

    In this study, a two-factor risk model is developed assuming differing preferences for sin or no-sin stocks for two groups of investors. Social screens are built into the model by assuming a small percentage of investors are self-restricting, declining to invest in sin stocks. Since each group of investors is facing a different set of investment opportunities, the derivation of a
  • Podcast: Building Mean Reversion trading strategies with @AlvarezQuant – Part 2 [Better System Trader]

    And were back for the 2nd episode in this 3-part series on building Mean Reversion strategies with Cesar Alvarez from Alvarez Quant Trading. In the first episode we discussed the goal of Mean Reversion trading, how to select a trading universe, a number of effective techniques to measuring Mean Reversion and how to combine indicators to identify better quality trades. If you havent listened

Quantocracy’s Daily Wrap for 10/27/2017

This is a summary of links featured on Quantocracy on Friday, 10/27/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • HillClimber.ai: A New Machine Learning Mashup from Long-Time Quantocracy Contributor @JacquesQuant

    HillClimber.ai is a curated machine learning mashup inspired by the quantitative finance blog aggregator Quantocracy. A special shout-out to Mike for all the help he provided in setting up this website. This mashup is very new and I would welcome all feedback from the community. There are many good blogs that I am sure to have missed. To request a blog / feed please send an email to
  • Replicating Indexes In R With Style Analysis (Part II): Global Macro [Capital Spectator]

    Imitation, Oscar Wilde famously observed, is the sincerest form of flattery that mediocrity can pay to greatness. The observation echoes the objective for using Professor Bill Sharpes style analysis to replicate investment indexes that, for one reason or another, cant be purchased directly. If we can obtain an indexs returns, theres a pretty good chance that we can reverse
  • Updating Historical Data Using Oanda’s API and R [Dekalog Blog]

    Following on from my previous post about downloading historical data, this post shows how previously downloaded data may be updated and appended with new, more recent data without having to re-download all the old data all over again. The main function to do this, HisPricesDates, downloads data between given dates as function inputs and is shown below. HisPricesDates = function( Granularity,

Quantocracy’s Daily Wrap for 10/26/2017

This is a summary of links featured on Quantocracy on Thursday, 10/26/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Want to Learn More About Factor Investing? Read This. [Alpha Architect]

    Replicating Anomalies is arguably a must read for anyone who thinks about factor investing and is looking to improve their understanding of the space. Lu Zhang, and his colleagues, Kewei Hou and Chen Xue, spent nearly 3 years carefully compiling and replicating 447 anomalies identified in the academic literature Thats a lot of programming and late nights burning the midnight oil
  • How universities are failing finance students [Mathematical Investor]

    One of us (Marcos Lopez de Prado) has been interviewed on the topic of educational training in the finance field by Institutional Investor. A brief synopsis of this interview is below. The full article is HERE. How Universities Are Failing Finance Students With investment shops fighting over mathematicians and engineers, a Guggenheim executive argues that finance degrees and departments face
  • Takeaways from a Non-PHD who Powered Through a 144-page Factor Investing Paper [Alpha Architect]

    Wes recently challenged me with a unique proposition: Hey Ryan, read through this Replicating Anomalies paper and tell me what you think. Its a bit long, but Im curious to hear your thoughts. Well, by a bit long, Wes really meant 144 pages of equations and reams of quantitative data on various factor analysis. Regardless, I jumped on the challenge grenade and decided to share a few