Quantocracy

Quant Blog Mashup

ST

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

Quantocracy’s Daily Wrap for 08/11/2021

This is a summary of links featured on Quantocracy on Wednesday, 08/11/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • New Feature: Cluster Analysis [Allocate Smartly]

    We track a lot of tactical strategies, and it can be difficult to understand how they all fit together in the big picture. The usual correlation matrix (example) is helpful when drilling down on a single strategy, but its near impossible to see the forest for the trees among the 1000s of data points. In response, weve added a new feature that we hope will provide clarity: a Cluster
  • Modeling US Stock Market Expected Returns, Part III [Capital Spectator]

    I recently outlined two models for estimating the US stock markets return for the decade ahead. Lets add a third model to the mix with the plan to take the average as a relatively robust forecast. The previous two models (see here and here) used valuation to estimate ex ante performance for the S&P 500 Index. One used Professor Robert Shillers Cyclically Adjusted Price Earnings Ratio
  • Value Investing and the Role of Intangibles [Alpha Architect]

    Recent research, including the 2020 studies Explaining the Recent Failure of Value Investing and Intangible Capital and the Value Factor: Has Your Value Definition Just Expired?, have investigated the impact on U.S. value strategies of the increase in the relative importance of intangible assets compared to physical assets. 1 Because global accounting standards require companies to

Quantocracy’s Daily Wrap for 08/10/2021

This is a summary of links featured on Quantocracy on Tuesday, 08/10/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Valuing Bitcoin using USD Index [Recession Alert]

    Of the dozen indicators and metrics we have researched, the fortunes of the US Trade-Weighted U.S Dollar Index (TWDI) has the biggest impact on Bitcoin USD prices. When the TWDI depreciates, this boosts Bitcoin prices strongly. When the TWDI becomes stronger, Bitcoin prices face significant headwinds. The TWDI is a weekly index created by the U.S Federal Reserve to measure value of the U.S.