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Quantocracy’s Daily Wrap for 03/20/2023

This is a summary of links featured on Quantocracy on Monday, 03/20/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 03/19/2023

This is a summary of links featured on Quantocracy on Sunday, 03/19/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Avoid Equity Bear Markets with a Market Timing Strategy Part 3 [Quantpedia]

    In the last third installment, we will finish exploring the world of market timing strategies (see parts 1 & 2). We will focus on yield curve predictors and incorporate all three ideas (price-based, macro-economic, and yield curve predictors) into one final trading strategy that yields an annual return above that of the stock market while doubling its Sharpe ratio and reducing maximal drawdown
  • R & D, Expected Profitability, and Expected Returns [Alpha Architect]

    Since the development of the CAPM, academic research has attempted to find models that increase the explanatory power of the cross-section of stock returns. We moved from the single-factor CAPM (market beta) to the three-factor Fama-French model (adding size and value), to the Carhart four-factor model (adding momentum), to Lu Zhangs q-factor model (beta, size, investment, profitability), to

Quantocracy’s Daily Wrap for 03/16/2023

This is a summary of links featured on Quantocracy on Thursday, 03/16/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Is it Possible to Know the Daily High or Low Intraday with 80% Accuracy? [Black Arbs]

    This is an old concept concerning the opening range. The idea is that the opening range often sets the days high or low within the first hour of cash equities trading (9:30 am – 10:30 am EST). Recently a trader on [Youtube] made the claim that you can know with 88% probability the high or low of the day after the first hour of trading. He managed to successfully repopularize the idea of using

Quantocracy’s Daily Wrap for 03/15/2023

This is a summary of links featured on Quantocracy on Wednesday, 03/15/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Avoid Equity Bear Markets with a Market Timing Strategy Part 2 [Quantpedia]

    In this second installment in a series of three articles, we will continue with our goal to construct a market timing strategy that would sidestep the equity market during bear markets. A few days ago, we started with price-based market timing strategies. Today, we will focus on macroeconomic indicators and predictors derived from the movements in the commodity markets. Market Timing Using Trend
  • More Intuitive Joins in dplyr 1.1.0 [Robot Wealth]

    dplyr 1.1.0 was a significant release that makes several common data operations more syntactically intuitive. The most significant changes relate to joins and grouping/aggregating operations. In this post well look at the changes to joins. First, install and load the latest version of dplyr: install.packages("dplyr") library(dplyr) Copy A new approach to joins The best way to explore

Quantocracy’s Daily Wrap for 03/14/2023

This is a summary of links featured on Quantocracy on Tuesday, 03/14/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Avoid Equity Bear Markets with a Market Timing Strategy – Part 1 [Quantpedia]

    In this series of three articles, our goal is to construct a market timing strategy that would reliably sidestep the equity market during bear markets, thereby reducing market volatility and boosting risk-adjusted returns. We will build trading signals based on price-based indicators, macroeconomic indicators, and a leading indicator, a yield curve, that would try to predict recessions and bear
  • Twitter Sentiment Analysis Using Zero-Shot Classification [Analyzing Alpha]

    Are you looking for a way to quickly assess the sentiment of public companies through their tweets without previously training any ML models? The OpenAI API provides powerful, zero-shot classification capabilities so that text data can be classified into multiple categories regardless of whether or not the model has encountered those categories. This guide explains each step using excellent
  • Multi-Strategy Hedge Funds: Jack of All Trades? [Finominal]

    A few select multi-strategy hedge funds generated outsized returns in 2022 However, the average fund lost money The average fund can be simply replicated via the S&P 500 & cash INTRODUCTION Citadel made $16 billion in profits in 2022, Millenium $8.0 billion, and Point 72 $2.4 billion. These returns are spectacular as all three are multi-strategy funds that allocate capital to hundreds of

Quantocracy’s Daily Wrap for 03/11/2023

This is a summary of links featured on Quantocracy on Saturday, 03/11/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • SetFit: Fine-tuning a LLM in 10 lines of code and little labeled data [Gautier Marti]

    This blog is a follow-up to the series of posts Snorkel Credit Sentiment – Part 1 (May 2019) May the Fourth: VADER for Credit Sentiment? (May 2019) Experimenting with LIME – A tool for model-agnostic explanations of Machine Learning models (May 2019) Using LIME to explain Snorkel Labeler (August 2019) which share a common dataset of portfolio managers comments focused on the CDS market.
  • Algorithmic Trading in Python with Machine Learning: Walkforward Analysis [Ed West]

    Implementing a successful trading strategy with code can be a challenging task. While some traders prefer to use basic trading rules and indicators, a more advanced approach involving predictive modeling may be necessary. In this tutorial, I will guide you through the process of training and backtesting machine learning models in PyBroker, an open-source Python framework that I developed for
  • Research Review | 10 March 2023 | ETFs [Capital Spectator]

    ETF Dividend Cycles Pekka Honkanen (University of Georgia), et al. February 2023 Exchange-traded funds (ETFs) collect approximately 7% of all U.S. corporate dividends, which they are required to redistribute to investors. How do the funds manage these dividend flows, and does such management have spillover effects on other financial markets? In this paper, we document a new stylized fact of the

Quantocracy’s Daily Wrap for 03/11/2023

This is a summary of links featured on Quantocracy on Saturday, 03/11/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • SetFit: Fine-tuning a LLM in 10 lines of code and little labeled data [Gautier Marti]

    This blog is a follow-up to the series of posts Snorkel Credit Sentiment – Part 1 (May 2019) May the Fourth: VADER for Credit Sentiment? (May 2019) Experimenting with LIME – A tool for model-agnostic explanations of Machine Learning models (May 2019) Using LIME to explain Snorkel Labeler (August 2019) which share a common dataset of portfolio managers comments focused on the CDS market.
  • Algorithmic Trading in Python with Machine Learning: Walkforward Analysis [Ed West]

    Implementing a successful trading strategy with code can be a challenging task. While some traders prefer to use basic trading rules and indicators, a more advanced approach involving predictive modeling may be necessary. In this tutorial, I will guide you through the process of training and backtesting machine learning models in PyBroker, an open-source Python framework that I developed for
  • Research Review | 10 March 2023 | ETFs [Capital Spectator]

    ETF Dividend Cycles Pekka Honkanen (University of Georgia), et al. February 2023 Exchange-traded funds (ETFs) collect approximately 7% of all U.S. corporate dividends, which they are required to redistribute to investors. How do the funds manage these dividend flows, and does such management have spillover effects on other financial markets? In this paper, we document a new stylized fact of the

Quantocracy’s Daily Wrap for 03/08/2023

This is a summary of links featured on Quantocracy on Wednesday, 03/08/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Candlestick Subplots with Plotly and the AlphaVantage API [Quant Start]

    AlphaVantage were founded in 2017 following the demise of the Yahoo Finance API. They offer OHLC data on 100,000+ securities, ETFs and mutual funds. Along with Forex, Crypto and Fundamental data, all accessible via their REST API. They offer free or premium membership which depend on the number API calls you require. Their premium packages range from 49.99 to $249.99 a month. While most of their
  • Risk contribution in portfolio management [Quant Dare]

    We usually compute return attribution to know how much each asset contributes to portfolio return. This calculation is quite easy because return formula is linear and sub-additive. In that context, one can split the whole portfolio return in smaller parts corresponding to each asset. However, although risk measures have to be coherent (monotonous, sub-additive, homogeneous and translational

Quantocracy’s Daily Wrap for 03/07/2023

This is a summary of links featured on Quantocracy on Tuesday, 03/07/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Turbulence Index: Regime-based Partitioning of Asset Returns [Portfolio Optimizer]

    The turbulence index, introduced in the previous blog post, is a measure of statistical unusualness of asset returns popularized by Kritzman and Li1. It provides a way to measure how much the behavior of a group of assets differs from its historical pattern. In this post, based on the paper Optimal Portfolios in Good Times and Bad by Chow et al.2, I will describe how the turbulence index can be
  • Active versus index funds: Latest results [Mathematical Investor]

    Fifty years ago, Princeton economics professor Burton Malkiel published A Random Walk Down Wall Street. He boldly asserted that a blindfolded chimpanzee throwing darts could pick a stock portfolio that would do as well as one created by many expert practitioners in the field. At the time, Malkiel envisioned a strategy of owning a broad-based set of stocks, saying mimicking a major stock index such
  • Shorting Lousy Stocks = Lousy Returns? [Finominal]

    Shorting stocks with poor features was unattractive throughout most of the last decade Combining features would not have improved performance It only started working again in 2022 INTRODUCTION Playing the stock market should be easy. When the economy is booming, buy equities. When its deteriorating, short them. Stock selection shouldnt take much effort either we just need to apply the
  • Salience Theory: How does it impact Momentum Profit? [Alpha Architect]

    This research examines the potential of enhancing a standard momentum strategy using signals derived from Salience Theory (ST). The strategy presented here is to exclude stocks with extreme salience scores and then analyze the risk and return properties of the ST strategy. Salience theory and enhancing momentum profits Myounghwa Sim, Hee-Eun Kim Finance Research Letters A version of this paper can

Quantocracy’s Daily Wrap for 03/04/2023

This is a summary of links featured on Quantocracy on Saturday, 03/04/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Slava Ukraini! Latest from Quantocracy contributor in Ukraine: DVOL Futures [Only VIX]

    The biggest news this week is that Deribit is moving ahead with launching futures on their DVOL Bitcoin volatility index. Like with every new product launch, I am cautiously optimistic, but given that Deribit has ~ 90% market share in cryptocurrency options volume, I think that the product has a great chance of success! If you trade volatility on Deribit, in addition to straddles and strangles you
  • Applying Corrective AI to Daily Seasonal Forex Trading [EP Chan]

    We applied Corrective AI (Chan, 2022) to a trading model that takes advantage of the intraday seasonality of forex returns. Breedon and Ranaldo (2012) observed that foreign currencies depreciate vs. the US dollar during their local working hours and appreciate during the local working hours of the US dollar. We first backtested the results of Breedon and Ranaldo on recent EURUSD data from
  • Intangibles and the Value Factor [Alpha Architect]

    Traditional value strategies use common valuation metrics, such as book-to-market (B/M), price-to-earnings (P/E), price-to-sales (P/S) or price-to-cash flow (P/CF), to establish a ratio between a market value and a fundamental anchor to assess the cheapness of a stock. The largest historical drawdown for traditional value strategies over the period November 2016-October 2020 raised the question of