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Quantocracy’s Daily Wrap for 08/27/2021

This is a summary of links featured on Quantocracy on Friday, 08/27/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Countercyclical Trend Following [Allocate Smartly]

    This is a test of a tactical strategy based on contrarian timing of the business cycle, increasing risk during periods of stress and decreasing risk during periods of calm. The strategy adds trend-following to this countercyclical approach to manage short-term market shocks. Backtested results from 1980 follow. Results are net of transaction costs see backtest assumptions. Learn about what we
  • International Tests of Factor Anomalies: Most Don t Survive [Alpha Architect]

    Since the development of the capital asset pricing model (CAPM) about 50 years ago, academic researchers have documented hundreds of anomalies that generate significant positive alpha. There are now so many that economist John Cochrane, in his 2011 presidential address to the American Finance Association, referred to them as the factor zoo, which I hit on in my post Is there a

Quantocracy’s Daily Wrap for 08/26/2021

This is a summary of links featured on Quantocracy on Thursday, 08/26/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Use Exotic Assets to Improve Your Trading Strategy [Quantpedia]

    As we have mentioned several times, the best course of action for a quant analyst who wants to develop a new trading strategy is to understand a well-known investment anomaly/factor fundamentally and then improve it. Quantpedia is a big fan of transferring ideas derived from academic research from one asset class to another. But thats not the only possibility of improvement we can try to
  • 4 Ways to Trade the Trend Intensity Indicator [Raposa Trade]

    Determining the strength of a trend can provide a valuable edge to your trading strategy and help you determine when to go long and let it ride, or not. This is what the Trend Intensity Indicator (TII) was designed to do. This indicator is as simple to interpret as more familiar values like the RSI. Its scaled from 0-100 where higher numbers indicate a stronger upward trend, lower values a
  • Linear regression on market data using Python and R [Quant Insti]

    This is the second installment of my series on regression analysis used in finance. In the first installment, we touched upon the most important technique in financial econometrics: regression analysis, specifically linear regression and two of its most popular flavours: univariate linear regression, and multivariate linear regression. In this post, we apply our knowledge of regression to actual

Quantocracy’s Daily Wrap for 08/25/2021

This is a summary of links featured on Quantocracy on Wednesday, 08/25/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mean Reversion Entry: At Open vs. Intraday Pullback vs Confirmation [Alvarez Quant Trading]

    For the mean reversion strategies that I have created in the past and are trading now, they typically enter at the next days open or wait for a further pullback intraday before entering. My current mean reversion strategy, which enters on a limit down, was doing great until a few months ago when the performance started to slip. Looking at the missed trades and the trades taken, it seemed like
  • The Value Premium Might be Smaller Than We Originally Thought [Alpha Architect]

    Remember HML? It was the original formulation for estimating the value premium published by Fama & French in 1992. In that seminal article, FF argued based on the results they obtained, that the risk of owning equity is multidimensional. One of those dimensions of risk they used was financial distress proxied by the BE/ME ratio 1, which itself was originally based on the distress factor

Quantocracy’s Daily Wrap for 08/23/2021

This is a summary of links featured on Quantocracy on Monday, 08/23/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Correlation Matrix Stress Testing: Shrinkage Toward an Equicorrelation Matrix [Portfolio Optimizer]

    Financial research has consistently shown that correlations between assets tend to increase during crises and tend to decrease during recoveries1. The recent COVID-19 market crash was no exception, as illustrated on Alvarez Quant Trading blog post Correlations go to One for both the individual constituents of the S&P500 and several broad ETFs commonly used in tactical asset allocation
  • The Best Systematic Trading Strategies in 2021: Part 2 [Quantpedia]

    The year 2021 has been an incredible year for passive equity investors so far. However, in the first part of our article, we talked about quantitative strategies which achieved even better results in 2021 than passive US equity investors. Indeed, there do exist such strategies, at least definitely in Quantpedias database of 650+ trading strategies. We focused the first part of the article more
  • Building a Long Volatility Strategy without Using Options [Factor Research]

    Long volatility strategies can be built without using options Securities can be selected on different risk metrics like the VIX or high yield spread Although portfolios differ, the strategies exhibited similar trends INTRODUCTION We started our exploration of long volatility strategies by analyzing the Eurekahedge Long Volatility Hedge Fund Index and highlighted that this would have provided
  • Macro trends for trading models [SR SV]

    Unlike market price trends, macroeconomic trends are hard to track in real-time. Conventional econometric models are immutable and not backtestable for algorithmic trading. That is because they are built with hindsight and do not aim to replicate perceived economic trends of the past (even if their parameters are sequentially updated). Fortunately, the rise of machine learning breathes new life

Quantocracy’s Daily Wrap for 08/20/2021

This is a summary of links featured on Quantocracy on Friday, 08/20/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Crypto Trading Depth [Tr8dr]

    I have a collection of crypto stat/arb strategies I plan to trade as a portfolio of strategies. Each strategy trades a small mean-reversion portfolio of loosely cointegrated coins, based on a bayesian state-based model. The returns in cryptos for this sort of strategy are phenomenal, however, finding enough size can be difficult for some coin portfolios. In my universe of roughly 220 coins, there
  • Optimising the rsims package for fast backtesting in R [Robot Wealth]

    rsims is a new package for fast, quasi event-driven backtesting in R. You can find the source on GitHub, docs here, and an introductory blog post here. Our use case for rsims was accurate but fast simulation of trading strategies. Ive had a few questions about how I made the backtester as fast as it is after all, it uses a giant for loop, and R is notoriously slow for such operations so
  • The Impact of Goodwill on Stock Returns [Alpha Architect]

    A firms stock price should reflect the value of both its tangible and intangible capital. While tangible capital has been widely studied, intangible capital has been receiving more attention due to its increasing importance in economic values. According to a December 29, 2020, Forbes article, In 1975, less than 20% of the S&P 500s market value was derived from intangible assets such

Quantocracy’s Daily Wrap for 08/18/2021

This is a summary of links featured on Quantocracy on Wednesday, 08/18/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Testing Turtle Trading: The System that Made Newbie Traders Millions [Raposa Trade]

    In 1982, a group of inexperienced traders were recruited to be a part of an experiment that would make many of them multi-millionaires. Richard Dennis bet his partner William Eckhardt that anyone could be a successful trader given they had training and a system to follow. It was a re-hash of the nature vs nurture debate, but now with millions of dollars on the line. Dennis and Eckhardt trained

Quantocracy’s Daily Wrap for 08/17/2021

This is a summary of links featured on Quantocracy on Tuesday, 08/17/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Designing Neural Networks [Enjine]

    Unfamiliar terms have a way of impressing us. I remember the first time I heard about the Monte Carlo method. The name conjured up an image of a sophisticated technique, born out of deep discussions by brilliant mathematicians in a Spanish cafe. Turns out, its just a by-word for running lots of randomized simulations. Numerous other fancy terms likewise dress up simple concepts. Linear
  • Financial Media, Price Discovery, and Merger Arbitrage [Alpha Architect]

    This paper contributes to the literature on understanding the limits of arbitrage and the resulting dynamics of price discovery. Specifically, it studies the context of "merger arbitrage," which is a well-known investment strategy and unless there are limits to arbitrage, this market segment should be highly efficient. The authors ask the following question: Do texts in financial media

Quantocracy’s Daily Wrap for 08/16/2021

This is a summary of links featured on Quantocracy on Monday, 08/16/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Free Resources to Learn Machine Learning for Trading [Quant Insti]

    Machine learning is a need in almost every sector today. Sectors like medicine, transportation, healthcare, advertising and financial technology are tremendously reliant on machine learning. Speaking about the financial technology domain, algorithmic trading practice is extremely efficient with the machine learning algorithms. There are various resources available to learn machine learning for
  • Better Indicators with Windowing [Financial Hacker]

    If indicators didnt help your trading so far, just pimp them by preprocessing their input data. John Ehlers proposed in his TASC September article the windowing technique: multiply the input data with an array of factors. Lets see how triangle, Hamming, and Hann factor arrays can improve the SMA indicator. We are going to define some windowing functions that operate on a data series and thus
  • Chinese Stocks from a Factor Lens [Factor Research]

    Foreign stock ownership is low in China and the market is dominated by retail investors This provides an opportunity for investors to deploy quant strategies Factor investing has been far more attractive in Chinese than U.S. equities in recent years INTRODUCTION The latest chapter in the complicated relationship between international investors and Chinese equities has interesting elements that

Quantocracy’s Daily Wrap for 08/13/2021

This is a summary of links featured on Quantocracy on Friday, 08/13/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Embeddings of Sectors and Industries using Graph Neural Networks [Gautier Marti]

    You can find the reproducible experiment in this Colab Notebook. In econometrics and financial research, categorical variables, and especially sectors and industries, are usually encoded as dummy variables (also called one-hot encoding in the machine learning community). You can find plenty of such examples in the SSRN literature, where authors are regressing the performance of their signal on
  • Exploring the rsims package for fast backtesting in R [Robot Wealth]

    rsims is a new package for fast, realistic (quasi event-driven) backtesting of trading strategies in R. Really?? Does the world really need another backtesting platform?? Its hard to argue with that sentiment. Zipline, QuantConnect, Quantstrat, Backtrader, Zorro there are certainly plenty of good options out there. But allow me to offer a justification for why we felt the need to build
  • Community Alpha of QuantConnect – Part 2: Social Trading Factor Strategies [Quantpedia]

    This blog post is the continuation of series about Quantconnects Alpha market strategies. This part is related to the factor strategies notoriously known from the majority of asset classes. Although the results are insightful, they are not straightforward, and further analysis could be made. Therefore, stay tuned for the next parts! Introduction We have already established that we can construct
  • Research Review | 13 August 2021 | Market and Asset Analytics [Capital Spectator]

    Decomposing Momentum: Eliminating its Crash Component Pascal Bsing (University of Muenster), et al. July 15, 2021 We propose a purely cross-sectional momentum strategy that avoids crash risk and does not depend on the state of the market. To do so, we simply split up the standard momentum return over months t-12 to t-2 at the highest stock price within this formation period. Both resulting

Quantocracy’s Daily Wrap for 08/12/2021

This is a summary of links featured on Quantocracy on Thursday, 08/12/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Relative Sentiment and Market Returns [Alpha Architect]

    This paper studies the relationship between aggregate investor attention and subsequent market returns over the following week. The authors create two different investor attention indicatorsone for aggregate retail attention (ARA) and one for aggregate institutional attention (AIA). ARA is found by taking the market-weighted average of stock-level retail attention, which itself is found by