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Quantocracy’s Daily Wrap for 10/29/2020

This is a summary of links featured on Quantocracy on Thursday, 10/29/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trading the US Election Profiting from Known Unknowns [Robot Wealth]

    Youve probably noticed that theres a US election on the horizon. This is an event of known uncertainty: a known unknown in the now immortal language of Donald Rumsfeld. In trading, we sometimes observe marginal pricing inefficiencies around these known unknowns. For example, ahead of stock earnings announcements or significant economic or policy announcements, we tend to find:
  • Slippage and low liquidity stocks [Alvarez Quant Trading]

    Recently, I have been working on a strategy that trades stocks with low dollar turnover. The initial performance was attractive and I was liking the strategy. But there were two issues that I needed to deal with in the backtesting. How much slippage to add to these stocks. The strategy enters and exits on the open and while looking over the trade list, I noticed some trades entered at the low of
  • Dream team: Combining classifiers [Quant Dare]

    When you are in front of a complex classification problem, often the case with financial markets, different approaches may appear while searching for a solution. These systems can estimate the classification and sometimes none of them is better than the rest. In this case, a reasonable choice is to keep them all and then create a final system integrating the pieces. At least we would have a more
  • Best Ways to Use Momentum [Dual Momentum]

    There are many ways to use momentum. Some are better than others. Let us look at some of the best approaches. Stock Momentum In 2018, Dimensional Fund Advisors (DFA) issued a report on the performance of all public momentum funds from June 2003 through 2017. Only one fund had outperformed the Russell 3000 broad market benchmark. That fund was the iShares Edge MSCI USA Momentum Factor ETF (MTUM).

Quantocracy’s Daily Wrap for 10/26/2020

This is a summary of links featured on Quantocracy on Monday, 10/26/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Kernel of error [OSM]

    In our last post, we looked at a rolling average of pairwise correlations for the constituents of XLI, an ETF that tracks the industrials sector of the S&P 500. We found that spikes in the three-month average coincided with declines in the underlying index. There was some graphical evidence of a correlation between the three-month average and forward three-month returns. However, a linear
  • Does Portfolio Timing Based on Volatility Signals Outperform Buy and Hold? [Alpha Architect]

    The popularity of using volatility to inform portfolio strategies has grown as the research tying volatility-managed techniques and improved risk/return portfolio performance has proliferated in the literature. The portfolios examined in the empirical literature generally utilize conservative positions in underlying factors (market, momentum, betting-against-beta, financial distress, size, and so

Quantocracy’s Daily Wrap for 10/25/2020

This is a summary of links featured on Quantocracy on Sunday, 10/25/2020. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Build a Financial Data Database with Python [Python For Finance]

    Hi all, and welcome back to the site I appreciate it has been an unexpectedly long time since I last postedin fact my last post was around this time last year. Hopefully I can get back on the treadmill and churn out some articles at a somewhat faster rate than 1 a year over the next couple of months! Well thats my aim anyway. Ok so this post will be based on how to build and

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