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Quantocracy’s Daily Wrap for 01/18/2020

This is a summary of links featured on Quantocracy on Saturday, 01/18/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Breaking Down 50 Years of Industry Data [Fortune Financial]

    It has long been a belief of mine that the industry in which a company operates has a huge impact on its performance, and that most industries simply are not worthwhile for long-term investment consideration. To further this discussion, I took the detailed industry data found in Professor Ken Frenchs data library, and analyzed the performance of each over the fifty-year period ending in
  • Research Review | 17 January 2020 | Volatility [Capital Spectator]

    Macro News and Long-Run Volatility Expectations Anders Vilhelmsson (Lund University) December 10, 2019 I propose a new model-free method for estimating long-run changes in expected volatility using VIX futures contracts. The method is applied to measure the effect on stock market volatility of scheduled macroeconomic news announcements. I find that looking at long-run changes gives qualitatively

Quantocracy’s Daily Wrap for 01/16/2020

This is a summary of links featured on Quantocracy on Thursday, 01/16/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Timing Low Volatility with Factor Valuations [Alpha Architect]

    Funds flows are frequently analyzed by investors to gauge the demand for investment strategies, but it represents a challenging exercise. Key issues are data availability as few market participants disclose their holdings as well as reporting frequency as limited data is published in real-time. The resulting headlines in media, therefore, are often confusing, although they naturally also reflect
  • Predicting Bank Nifty Open Price Using Deep Learning [Quant Insti]

    With the advent of several machine / deep learning models, there have been several theories emerging in applying these techniques for stock market prediction because of the difficulty and complexity it involves. In this project, were trying to solve the problem using a classifier to predict whether the Bank Nifty index listed in NSE will go up or down, on the next day open using two Deep

Quantocracy’s Daily Wrap for 01/15/2020

This is a summary of links featured on Quantocracy on Wednesday, 01/15/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Petra on Programming: A New Zero-Lag Indicator [Financial Hacker]

    I have been recently hired to code a series of indicators based on monthly articles in the Stocks & Commodities magazine, and to write here about the details of indicator programming. Looking through the magazine, I found many articles useful, some a bit weird, some a bit on the esoteric side. So I hope I wont have to code Elliott waves or harmonic figures one day. But this first one is a
  • Autoencoder based outlier detection in Forex [Quant Dare]

    In FOREX, both the EURCHF and USDCHF series have outliers that can be a problem when applying Machine Learning techniques to them. So, in this post, the performance of an autoencoder detecting these anomalies is going to be studied. Analyzing the EURCHF and USDCHF returns, it can be seen that there are days in which there was a very abrupt change in the price. The reasons for this are as follows:
  • Top 5 Most Interesting Papers from the Annual Finance Geek Fest [Alpha Architect]

    The American Finance Association Annual Meetings have now come and gone (here is information on the broader conference). The conference was in sunny San Diego this year and Im told it did not disappoint! 1 This 3-day conference collects the brightest minds in academia to discuss hundreds of new research papers a gold mine for new and exciting ideas! We always look at the mounds of papers

Quantocracy’s Daily Wrap for 01/14/2020

This is a summary of links featured on Quantocracy on Tuesday, 01/14/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Skew and Kurtosis as trading rules [Investment Idiocy]

    This is part X of my series of blog posts on skew and kurtosis, where 2 A post on skew: measuring, and it's impact on future returns A post on kurtosis: measuring, it's impact on future returns, and it's interaction with skew. A post on trend following and skew (which I actually wrote first, hence the confusion!) This post: on using skew and kurtosis as trading rules This series
  • The Hierarchical Risk Parity Algorithm: An Introduction [Hudson and Thames]

    Portfolio Optimisation has always been a hot topic of research in financial modelling and rightly so a lot of people and companies want to create and manage an optimal portfolio which gives them good returns. There is an abundance of mathematical literature dealing with this topic such as the classical Markowitz mean variance optimisation, Black-Litterman models and many more. Specifically,
  • Bitcoin plus Harry Brown s Permanent Portfolio A mix in heaven? [Sanz Prophet]

    What would happen if you took $5,000 out of your $100,000 permanent portfolio and allocated it to Bitcoin? From 3.6% annual to 15% annual returns? Got to love the Permanent Portfolio I have been somewhat obsessed with the simplicity and fundamental thinking behind the permanent portfolio. I have written and analyzed it various times (here and here ) a well as created variations from it that are
  • How ESG Affects Valuation, Risk, and Performance [Alpha Architect]

    We have done a fair amount on the investment merits of ESG investing, but the question of how ESG affects the fundamental performance of a firm (in a causal fashion) is addressed in this study. For example, this paper askes questions such as, Are high ESG scoring firms more adept at managing their risks, thus leading to higher valuations? Or is it the reverse: are firms with higher valuations

Quantocracy’s Daily Wrap for 01/13/2020

This is a summary of links featured on Quantocracy on Monday, 01/13/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Beware Strategies That Fall Down on Good Data [Allocate Smartly]

    Sources of long-term historical data are few and far between. Because its been generously provided for free, one of the most often used is data from Professor French (of Fama-French fame). Others include Shiller and Ibbotson. These data sets are fine for a first pass at testing out ideas, but they often dont remotely match up to something that can actually be traded in todays market.
  • How Expensive Are ESG Stocks? [Factor Research]

    Highly ranked ESG stocks trade at higher valuation multiples than the stock market However, the difference in multiples is minor and far less than extreme than for Growth stocks ESG ETFs generated lower returns than the stock market, but were also less volatile INTRODUCTION Europeans seem far more focused on the environment than Americans, which might be considered unusual given that both share an
  • Principal Component Analysis in Trading [Quant Insti]

    As trading becomes automated, we have seen that traders seek to use as much data as they can for their analyses. But we all know that adding more variables leads to more complications and that in turn might make it harder to come to solid conclusions. Think about it, we have more than 3000 companies in the New York Stock Exchange. A simple exercise to find pairs between them will be really

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,