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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

Quantocracy’s Daily Wrap for 10/05/2020

This is a summary of links featured on Quantocracy on Monday, 10/05/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Review of Modern Asset Allocation For Wealth Management, by David M. Berns, PhD [QuantStrat TradeR]

    This post will be a review of the book Modern Asset Allocation for Wealth Management, by Dr. David Berns, PhD. The long story short is that I think the book is a must-read for a new and different perspective on asset management, albeit one not without some fairly minor flaws that could be very easily covered with a second edition. So first off, Id like to give a large thank you to Dr. Berns,
  • Factor Olympics Q3 2020 [Factor Research]

    Momentum & Quality are leading the performance scoreboard in Q1-3 2020 Value & Size generated negative returns, like in recent years, and Low Volatility ended a 10-year fantastic run 2020 is shaping up as a year of highly dispersed factor returns INTRODUCTION We present the performance of five well-known factors on an annual basis for the last 10 years. We only present factors where
  • Institutional Investment Strategies: Keep it Simple [Alpha Architect]

    Historically Institutional investors have been considered the smart money in investment circles. What academic research has tended to show is that the smart money status of institutional investing has some chinks in its armor, as can be seen in a previous paper we summarized here. In this article, which was recently published in the Journal of Portfolio Management, Richard Ennis begins his

Quantocracy’s Daily Wrap for 10/04/2020

This is a summary of links featured on Quantocracy on Sunday, 10/04/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Exploring the PMFG Portfolios for Covid-19 Robustness [Hudson and Thames]

    Pozzi, Di Matteo, and Aste (2013) conclude that it is better to invest in the peripheries of the Planar Maximally Filtered Graph (PMFG), as investing in the peripheries lead to better returns, and reduced risk. This blog post explores the impacts of Covid-19 by simulating two investment portfolios a portfolio consisting of peripheral stocks, versus a portfolio consisting of central
  • The Next 5 Weeks All Are Among The Weakest And Strongest Of The Year [Quantifiable Edges]

    October is a month that is known for volatility. And that is a well-earned reputation. Crashes in 1929, 1987, and 2008 all occurred in October. But volatility cuts both ways. If you break the year down into 1-week periods, October also contains some of the strongest seasonal edges of the year, both bearish and bullish. Breaking the year down by week is something I have done numerous times over the
  • Lottery Preferences and Their Relationship with Factor Investing [Alpha Architect]

    Among the assumptions in the first formal asset pricing model, the CAPM, is that investors are risk-averse, they maximize the expected utility of absolute wealth, and they care only about the mean and variance of return. However, research has found that these assumptions dont hold. In the real world, there are investors who have a taste, or preference for lottery-like investments

Quantocracy’s Daily Wrap for 09/30/2020

This is a summary of links featured on Quantocracy on Wednesday, 09/30/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Using strength to exit a mean reversion trade [Alvarez Quant Trading]

    I had a long-time reader, Cristian Franchi, send me a mean-reversion strategy that he wanted me to test and write about. What caught my attention was the rules differing from what I typically see and use. Different ways of measuring strength of a sell-off and volatility expansion. Along with a different type of exit being used on a mean reversion strategy. Not simply waiting for the bounce. Rules