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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.

Quantocracy’s Daily Wrap for 08/09/2021

This is a summary of links featured on Quantocracy on Monday, 08/09/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Extended Optimal Arbitrage Strategies [Hudson and Thames]

    In our previous article, weve discussed a couple of trading strategies exploiting arbitrage between similar stocks using stochastic optimal control methods. A major shortcoming of those approaches is that we restricted ourselves to constructing delta-neutral portfolios. Along with this, the ratio between the stocks in the portfolio is fixed at the start of the investment timeline. These
  • Building an Inflation Portfolio Using Stocks [Factor Research]

    An inflation portfolio can be created by systematically selecting stocks correlated to inflation This would have resulted in a portfolio with strong sector and factor biases However, the correlation to inflation would not have been significantly higher than for stocks overall INTRODUCTION Measuring inflation is as challenging as calculating our body weight changes using only a mirror. We might
  • Should you Trade with the Kelly Criterion? [Raposa Trade]

    The Kelly Criterion gives an optimal result for betting based on the probability of winning a bet and how much you receive for winning. If you check out Wikipedia or Investopedia, youll see formulas like this: f=p1pb1f^{*} = p – frac{1-p}{b-1} f=pb11p which gives you the optimal amount to bet (ff^*f) given the probability of winning (p) and the payout youre
  • Machine learning for portfolio diversification [SR SV]

    Dimension reduction methods of machine learning are suited for detecting latent factors of a broad set of asset prices. These factors can then be used to improve estimates of the covariance structure of price changes and by extension to improve the construction of a well-diversified minimum variance portfolio. Methods for dimension reduction include sparse principal components analysis,

Quantocracy’s Daily Wrap for 08/05/2021

This is a summary of links featured on Quantocracy on Thursday, 08/05/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Paper Review: Algorithmic Financial Trading with Deep Convolutional Neural Networks [Enjine]

    Of the major machine learning algorithms, the convolutional neural network (CNN) is my favourite. CNNs form some of our companys most cherished elements that give strength to our investment algorithms. My curiosity was therefore piqued when I came across Sezer and Ozbayoglus paper titled Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion
  • The Active vs Passive: Smart Factors, Market Portfolio, or Both? [Alpha Architect]

    While there may be debates about passive and active investing, and even blogs about the numbers of active funds that were outperformed by the market, history taught us that the outperformance of active or passive investing is cyclical. As a proxy for active investing, the paper examines factor strategies and their smart allocation using fast or slow time-series momentum signals, the relative
  • 10 Free Swing Trading Strategies That Work (Backtested Buy And Sell Signals) [Quantified Strategies]

    The internet is flooded with anecdotal evidence about how to swing trade and how to make money. Unfortunately, almost all articles consist of unproven and untested swing trades. To make money swing trading is difficult, but we believe you face much better odds the more you backtest and generate trading ideas. Below we provide you with 10 free swing trading strategies that work. They are all