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Quantocracy’s Daily Wrap for 01/12/2020

This is a summary of links featured on Quantocracy on Sunday, 01/12/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Market Structure Part 1: Order Volume Density [Reproducible Finance]

    Welcome to another installment of Reproducible Finance! Inspired by a great visualization in Hands on Time Series with R by Rami Krispin, today well investigate some market structure data and get to know the Midas data source provided by the SEC. Lets start by importing data from the SEC website for the 2nd quarter of 2019. If you navigate to the SEC website here
  • The Idiosyncratic Volatility Puzzle: Then and Now [Alpha Architect]

    One of the interesting puzzles in finance is that stocks with greater idiosyncratic volatility (IVOL) have produced lower returns (see an earlier post here). This is an anomaly because idiosyncratic volatility is viewed as a risk factorgreater volatility should be rewarded with higher, not lower, returns. Studies such as Arbitrage Asymmetry and the Idiosyncratic Volatility Puzzle, which
  • The predictive superiority of ensemble methods for CDS spreads [SR SV]

    Through R or Python we can nowadays apply a wide range of methods for predicting financial market variables. Key concepts include penalized regression, such as Ridge and LASSO, support vector regression, neural networks, standard regression trees, bagging, random forest, and gradient boosting. The latter three are ensemble methods, i.e. machine learning techniques that combine several base models

Quantocracy’s Daily Wrap for 01/09/2020

This is a summary of links featured on Quantocracy on Thursday, 01/09/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Inverse Volatility Position Sizing [Alvarez Quant Trading]

    Recently Ive had several of my consulting clients come with a strategy that uses Inverse Volatility Position Sizing. The basic idea is that the more volatile positions have smaller size while the less volatile ones get a larger size. I have always been a fan of equal position sizing for several reasons. One, it is simple to do. Two, it is one less variable to optimize on and thus overfit on.

Quantocracy’s Daily Wrap for 01/08/2020

This is a summary of links featured on Quantocracy on Wednesday, 01/08/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Testing a Yield-Based Asset Class Rotation Strategy [Allocate Smartly]

    By reader request, this is a test of a tactical strategy from Harrison Schwartz that considers various economic yields in order to rotate among asset classes. Strategy results versus the 60/40 benchmark follow. Weve extended Schwartzs original test by an additional 6+ years, and accounted for transaction costs (see backtest assumptions). Learn about what we do and follow 50+ asset allocation
  • Forecasting US Equity Market Returns with Machine Learning [Alpha Architect]

    Shillers CAPE ratio is a popular and useful metric for measuring whether stock prices are overvalued or undervalued relative to earnings. Recently, Vanguard analysts Haifeng Wang, Harshdeep Singh Ahluwalia, Roger A. Aliaga-Daz, and Joseph H. Davis have written a very interesting paper on forecasting equity returns using Shillers CAPE and machine learning: The Best of Both Worlds:

Quantocracy’s Daily Wrap for 01/07/2020

This is a summary of links featured on Quantocracy on Tuesday, 01/07/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Stop Loss: Explained & The Best Strategy [Analyzing Alpha]

    A stop-loss order protects profit or limits risk on an investors open position by exiting at a predetermined price. Placing an order to sell a long stock position if the price drops 5% below the purchase price is an example of a stop-loss order. In this post, were going to dig into what a stop loss is, the different types of stop-losses, understand what a trailing stop-loss is, and analyze

Quantocracy’s Daily Wrap for 01/06/2020

This is a summary of links featured on Quantocracy on Monday, 01/06/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Python Investigation of a New Proposed Short Vol ETF – SVIX [QuantStrat TradeR]

    This post will be about analyzing SVIXa proposed new short vol ETF that aims to offer the same short vol exposure as XIV used towithout the downside of, well, blowing up in 20 minutes due to positive feedback loops. As Im currently enrolled in a Python bootcamp, this was one of my capstone projects on A/B testing, so, all code will be in Python (again). So, first off, with those not
  • Quant Tools for Private Equity and Real Assets [Alpha Architect]

    Variance and covariance are widely accepted risk measures for liquid assets that trade in public markets. Illiquid assets are not part of this framework because of their lack of regular price quotes and thus time variance. Due to the difficulty in using standard risk measures to assess non-traded assets, illiquid assets are often analyzed using different tools and analyzing the risk of these
  • Factor Scoring Smart Beta ETFs [Factor Research]

    The difference between the cheapest and most expensive smart beta ETF in the US is 59 bps on average Some smart beta ETFs offer negative factor exposure, which requires explanation Factor scores can be used to identify which smart beta ETFs offer the best ratio of factor exposure per dollar in fees INTRODUCTION Buying a Big Mac at McDonalds in Basel, Switzerland, costs CHF6.50, compared with
  • Pursuing Factor Purity [Flirting with Models]

    Factors play an important role for quantitative portfolio construction. How a factor is defined and how a factor portfolio is constructed play important roles in the results achieved. Naively constructed portfolios such as most academic factors can lead to latent style exposures and potentially large unintended bets. Through numerical techniques, we can seek to develop pure factors
  • Most popular posts 2019 [Eran Raviv]

    As every year, I checked my analytics so that I can let you know what was popular. This year I have also experimented with a survey where I asked one question at the end of each relevant post. About 120 replies recieved, but the free Survey Monkey account (the survey provider I went with) only lets out the first 100 replies, and no exports*. Here are the results: Partial survey results Looks nice,

Quantocracy’s Daily Wrap for 01/03/2020

This is a summary of links featured on Quantocracy on Friday, 01/03/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Is Active Investing Doomed as a Negative Sum Game? A Critical Review [Alpha Architect]

    In an influential piece, Sharpe (1991) 1 put forward the proposition that active investing must be a losing pursuit in aggregate, as it amounts to a zero-sum game in gross terms and hence must be a negative-sum game after costs. I take a critical look at the underlying concepts and assumptions behind Sharpes proposition and link it to the issue of whether it is worthwhile for investors to
  • Factor Olympics 2019 [Factor Research]

    As in 2018, Low Volatility produced the best and Value the worst performance Value did not recover significantly further after a short rally in Q3 2019 However, Momentum broke its upward trajectory since then INTRODUCTION We present the performance of five well-known factors on an annual basis for the last 10 years. We only present factors where academic research highlights positive excess returns

Quantocracy’s Daily Wrap for 12/30/2019

This is a summary of links featured on Quantocracy on Monday, 12/30/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 2019 Research Compendium [Flirting with Models]

    In 2019, we published 45 research notes (not including video + audio commentary), totaling over 100,000 words. Our research spanned a number of topics, including: ensemble techniques, deep dives on trend following, factor and sector rotation, fixed income analysis, and of course rebalance timing luck. Our 2019 research compendium contains all this research, categorically organized for easy
  • Our Most Popular Posts of 2019 [Two Centuries Investments]

    We are closing 2019 with much gratitude to our clients, collaborators and online visitors. We have launched this blog less than a year ago and have had the pleasure of seeing many visitors from all over the world ranging from buy-side investors, financial advisors, asset owners, thought leaders, academics, and individual investors. We applaud all of you for joining us on the journey of becoming
  • Top Ten Blog Posts on Quantpedia in 2019 [Quantpedia]

    The end of the year is a good time for a short recapitulation. Apart from other things we do (which we will summarize in our next blog in a few days), we have published around 50 short blog posts / recherches of academic papers on this blog during the last year. We want to use this opportunity to summarize 10 of them, which were the most popular (based on Google Analytics tool). Maybe you will be
  • Asset Allocation vs. Factor Allocation – Can We Build a Unified Method? [Alpha Architect]

    Weve taken a lot of time reviewing multi-factor allocation techniques within the equity portion of a portfolio here and here. But thus far we have only written on the concept of utilizing a multi-factor investment technique in contrast with traditional asset allocation here. In this post, we are again going to engage the idea of using factors as a supplement to more traditional asset allocation

Quantocracy’s Daily Wrap for 12/28/2019

This is a summary of links featured on Quantocracy on Saturday, 12/28/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Hundreds of quant papers from #QuantLinkADay in 2019 [Cuemacro]

    I probably tweet too much. Some tweets are on burgers (well, they are pretty important I suppose). Other tweets will constitute puns, which I find mildly amusing, but most term as dad jokes. Among all of these tweets, there is also stuff about Python and quant more generally. In particular, I tweet out #QuantLinkADay, which largely consists of links to recent quant papers on financial
  • How market liquidity causes prices distortions [SR SV]

    Liquidity is a critical force behind market price distortions (and related trading opportunities). First, the cost of trading in and out of a contract gives rise to a liquidity premium. Second, the risk that transaction costs will rise when market conditions necessitate trading commands a separate liquidity risk premium. Third, actual changes in liquidity can precipitate large price changes

Quantocracy’s Daily Wrap for 12/27/2019

This is a summary of links featured on Quantocracy on Friday, 12/27/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • From Fragility to Robustness: The Value of Ensembles [Invest Resolve]

    Google dictionary defines the word robust thusly: sturdy in construction able to withstand or overcome adverse conditions and offers the following definitions for the word fragile: easily broken or damaged flimsy or insubstantial; easily destroyed not strong or sturdy; delicate and vulnerable How can an investment model be sturdy in construction and able to withstand or overcome
  • Quant Investing: Greenblatt Value Strategy [Investing For A Living]

    In this post I take a look a popular and quite simple quant strategy that combines value and profitability, the Greenblatt Value Strategy. Results are impressive and the strategy has held up better than most value strategies over the last 10 years. And even more impressive it has even outperformed the index over the last 3 years, which is saying something. Lets dive right in. The strategy is
  • International Evidence on Factor Premiums [Alpha Architect]

    Klaus Grobys contributes to the literature on asset pricing models with his October 2019 paper, Another Look on Choosing Factors: The International Evidence. Using bootstrap simulations, Grobys examined international markets, specifically the four regions of North America (NA), Europe, Japan and Asia Pacific (AP), as out-of-sample tests of the performance of the leading factors and asset

Quantocracy’s Daily Wrap for 12/25/2019

This is a summary of links featured on Quantocracy on Wednesday, 12/25/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The market impact of rebalancing factor investing strategies [Alpha Architect]

    Transaction costs are a major concern for practitioners attempting to implement factors investing strategies identified in academic literature. Naturally, this is a subject that has been covered before here, here, and here, but a new look at transaction costs never hurts. The authors of this paper look to analyze the trading costs of rebalancing factor portfolios by employing a model of implicit
  • The Best Investment Writing Volume 3: Wes Gray (@AlphaArchitect) [Meb Faber]

    Author: Wes Gray. Wes is the CEO/CIO of Alpha Architect. He has published multiple academic papers and four books, including Quantitative Value (Wiley, 2012), DIY Financial Advisor (Wiley, 2015), and Quantitative Momentum (Wiley, 2016). After serving as a Captain in the United States Marine Corps, Wes earned an MBA and a PhD in finance from the University of Chicago where he studied under Nobel