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Quantocracy’s Daily Wrap for 10/22/2020

This is a summary of links featured on Quantocracy on Thursday, 10/22/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Correlation and correlation structure (4) – asymmetric correlations of equity portfolios [Eran Raviv]

    Here I share a refreshing idea from the paper Asymmetric correlations of equity portfolios which was published in the Journal of financial Economics, a top tier journal in this field. The question is how much the observed conditional correlation on the downside (say) differs from the conditional correlation you would expect from a symmetrical distribution. You can find here an explanation
  • Building Factor Portfolios Based with the Lowest Correlations [Alpha Architect]

    The two basic rules of asset allocation are: i) identify assets with positive expected payoffs, and ii) ensure that the assets are not too highly correlated, so that diversification benefits can be harvested. Although the rules are simple, implementation is often complex. Equities have a positive expected return over the long-term as stocks represent risk capital in for-profit companies. Bonds pay

Quantocracy’s Daily Wrap for 10/21/2020

This is a summary of links featured on Quantocracy on Wednesday, 10/21/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Comparison of Stock Market Performance Among Countries [Grzegorz Link]

    The performance of stock market indices varies between different countries. Market-to-market and stock-to-stock correlations tend to get high during downturns[5], but differ considerably during other, more peaceful market environments. MSCI, a financial market data company, offers great quality data on historical market indices of different countries. Some of the data is available through their

Quantocracy’s Daily Wrap for 10/20/2020

This is a summary of links featured on Quantocracy on Tuesday, 10/20/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • ArbitrageLab Release Update [Hudson and Thames]

    ArbitrageLab is a python library that helps traders who want to exploit mean-reverting portfolios by providing a complete set of algorithms from the best academic journals. How to Get Access Recently there has been a lot of interest in the development of our most recent library which focuses specifically on algorithms to enhance mean-reverting strategies related to statistical arbitrage. In the
  • Don’t Get Carried Away by Carry [Factor Research]

    Carry across asset classes has not performed strongly over the most recent decade Currency carry and Value & Size equity factors exhibited the same trends in performance since 1999 All three factors are likely driven by risk sentiment, essentially offering the same risk exposure INTRODUCTION There are folks in finance who know and folks who dont know. The latter group often drives the
  • A Temporal Clustering Function [Dekalog Blog]

    Recently a reader contacted me with a view to collaborating on some work regarding the Delta phenomenon but after a brief exchange of e-mails this seems to have petered out. However, for my part, the work I have done has opened a few new avenues of investigation and this post is about one of them. One of the problems I set out to solve was clustering in the time domain, or temporal clustering as I

Quantocracy’s Daily Wrap for 10/18/2020

This is a summary of links featured on Quantocracy on Sunday, 10/18/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Discrimination of Correlated Random Walk Time Series using GNPR [Hudson and Thames]

    Discriminating random variables on time-series on both their distribution and dependence information is motivated by the study of financial assets returns. For example, given two assets where their returns are perfectly correlated, are these returns always similar from a risk perspective? According to Kelly and Jiang (2014), the answer is no, because we did not take into account the distribution
  • Prospect theory value as investment factor [SR SV]

    Prospect theory value is a valid investment factor, particularly in episodes of apparent market inefficiency. Prospect theory is a popular model of irrational decision making. It emphasizes a realistic mental representation of expected gains and losses and an individuals evaluation of such representations. Prospect theory explains asymmetric loss aversion (view post here) and gambling

Quantocracy’s Daily Wrap for 10/17/2020

This is a summary of links featured on Quantocracy on Saturday, 10/17/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Knapsack problem implementation in R [Quantpedia]

    Our own research paper ESG Scores and Price Momentum Are More Than Compatible utilized the Knapsack problem to make the ESG strategies more profitable or Momentum strategies significantly less risky. The implementation of the Knapsack problem was created in R, using slightly modified Simulated annealing optimization algorithm. Recently, we have been asked about our implementation and the code. The
  • Equity Trend Following Performance Around the Globe [Alpha Architect]

    Time-series momentum (TSMOM) historically has demonstrated abnormal excess returns. Also called trend following, it is measured by a portfolio that is long assets that have had recent positive returns and short assets that have had recent negative returns. Trend following has attracted a lot of attention over the past decade due to its strong performance during the global financial crisis and the
  • Research Review | 16 October 2020 | Index Investing [Capital Spectator]

    Does Joining the S&P 500 Index Hurt Firms? Benjamin Bennett (Tulane University), et al. July 20, 2020 We investigate the impact on firms of joining the S&P 500 index from 1997 to 2017. We find that the positive announcement effect on the stock price of index inclusion has disappeared and the long-run impact of index inclusion has become negative. Inclusion worsens stock price

Quantocracy’s Daily Wrap for 10/14/2020

This is a summary of links featured on Quantocracy on Wednesday, 10/14/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Factor Exposure Analysis 101 [Factor Research]

    Linear regression is widely used for factor exposure analysis However, a high R2 and low p-value can be misleading Unsurprisingly the data quality matters INTRODUCTION Some fields of science like math or statistics seem to be too dry to be joking about, but a quick Google search for jokes on statisticians reveals that even this area is a fertile ground for humor. Sample these for a quick laugh: A
  • Clustering S&P500 using Fully Convolutional Autoencoders [Quant Dare]

    Clustering data into groups that share common characteristics can be very useful, but using experts to perform this grouping is costly and in many cases decisions are influenced by emotions. That is why clustering is one of the main topics of Unsupervised Machine Learning algorithms, that doesnt require labels to find patterns in data. We have shown how to use clustering techniques to find
  • News and its Impact on Risk and Returns Around the World [Alpha Architect]

    News is now data. But how is this data associated with changes in stock market returns and risks, and is there predictive power in the news via the words used? This innovative paper asks and answers nine important questions about the interrelationship of news and stock market outcomes. How should one best measure news using word flow? Which aspects of word flow should be the focus of measurement?

Quantocracy’s Daily Wrap for 10/09/2020

This is a summary of links featured on Quantocracy on Friday, 10/09/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Corr-correlation [OSM]

    We recently read two blog posts from Robot Wealth and FOSS Trading on calculating rolling pairwise correlations for the constituents of an S&P 500 sector index. Both posts were very interesting and offered informative ways to solve the problem using different packages in R: tidyverse or xts. Well use those posts as a launchpad to explore the rolling correlation concept with respect to
  • L pez de Prado on machine learning in finance [Mathematical Investor]

    Marcos Lpez de Prado, whom we have featured in previous Math Scholar articles (see Article A, Article B and Article C), has been invited to present a keynote presentation at the ACM Conference on Artificial Intelligence in Finance, to be conducted virtually October 14-16, 2020. Lpez de Prado is a faculty member of Cornell University and also CEO of True Positive Technologies, LP, a private
  • Value Investing Factor Research: How to Improve the Piotroski F-Score Measure [Alpha Architect]

    This project builds on research conducted by J. Piotroski, who published his paper Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers in 2000, offering a simple yet powerful framework to separate the winners from the losers in a value-investing context (summary here). You can read about how Piotroskis research is utilized as a quality screen

Quantocracy’s Daily Wrap for 10/08/2020

This is a summary of links featured on Quantocracy on Thursday, 10/08/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • “Please Send Me a Trading System!” [Financial Hacker]

    It should produce 150 pips per week. With the best indicators that you know. How much does it cost? Please also send live histories of your top systems. Although we often get such requests, we still dont know the best indicators and cant send live histories. We do not invent systems, but program them after clients specifications. And we do not trade them, except for testing. But
  • Profiling Diversification Attributes With Principal Components [Capital Spectator]

    The holy grail of portfolio design is combining assets so that returns are relatively stable if not higher, risk is generally lower and the overall mix delivers stronger risk-adjusted performance thats not otherwise available through owning the components separately. Diversification, as the saying goes, is the only free lunch in investing. The practical realities, of course, are messy and and

Quantocracy’s Daily Wrap for 10/07/2020

This is a summary of links featured on Quantocracy on Wednesday, 10/07/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Site: Cleaning Tick and Quote Data [Machine Factor Tech]

    Every business that cares about machine learning needs its Sandor Straus. Cleaning and enriching data to make it more useful is the secret ingredient to every successful AI strategy. Sandor Straus was Renaissanse Technologies data guru responsible for cleaning, storing and enriching the data used in machine learning models. Straus was obsessive about two things. First, he took painstaking efforts
  • FX Swap pricing and the mystery of Covered Interest Parity [Quant Dare]

    Sometimes described as a sort of physical law in international finance [1], Covered Interest Parity (CIP) has failed to hold after the Global Financial Crisis (GFC) of 2008. This has given rise to an interesting debate during the last decade that has resulted in relevant insights regarding international financial markets in general and FX Swaps in particular. This post will be the first of a

Quantocracy’s Daily Wrap for 10/06/2020

This is a summary of links featured on Quantocracy on Tuesday, 10/06/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Should I run my trading system at a fixed expected volatility target? [Investment Idiocy]

    This is a blog post which has been coming for a while. It relates to a lot of themes I've discussed before, and a recurring conversation I've had with a few people. As most regular readers will know, I run my trading strategy to hit a particular risk target. That risk target is expressed as an annual standard deviation of percentage returns, and happens to be 25%. But those details