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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

Quantocracy’s Daily Wrap for 03/03/2023

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

  • Hybrid Asset Allocation [Allocate Smartly]

    This is a test of the latest tactical asset allocation strategy from Dr. Wouter Keller and JW Keuning and their paper: Dual and Canary Momentum with Rising Yields/Inflation: Hybrid Asset Allocation (HAA). Backtested results from 1971 follow. Results are net of transaction costs see backtest assumptions. Learn about what we do and follow 70+ asset allocation strategies like this one in near

Quantocracy’s Daily Wrap for 03/01/2023

This is a summary of links featured on Quantocracy on Wednesday, 03/01/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • I got more than 99 instruments in my portfolio but butter ain’t one of them [Investment Idiocy]

    As those of you who follow me on the Elon Musk Daily News App will know, I received physical copies of my new book last week (exciting!). Global supply chains being what they are, you lot will have to wait until April to get your copies. Sorry. Anyway one of the themes I touch on in the book is the truely amazing diversification that trend following strategies offer when run across multiple
  • International diversification – does it work (when you need it)? [Alpha Architect]

    In this article, the authors examine the research on the benefits of international diversification. Some argue that because equity markets generally crash simultaneously, there are no benefits to having equity diversification. The evidence from this paper rejects this hypothesis. Diversification During Hard Times Najah Attig and Oumar Sy Financial Analyst Journal, 2023 A version of this paper can

Quantocracy’s Daily Wrap for 02/27/2023

This is a summary of links featured on Quantocracy on Monday, 02/27/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Performance attribution of a crypto market-neutral book on a statistical risk model [Gautier Marti]

    In this short blog post, we investigate whether a simple systematic market-neutral stat arb crypto book loads on the main components of a statistical risk model. from datetime import timedelta import pandas as pd from tqdm import tqdm import statsmodels.formula.api as smf def compute_pnl_attribution( symbol, date, weights, returns, factor_returns, info, fexp_cols, ): if symbol not in
  • ETF Crusades [Finominal]

    This research note is a guest post from Rodolfo Martell, PhD, Head of Portfolio Strategy, of Pluribus Labs LLC, a San Francisco-based systematic active equity manager that is part of Exos Financial. SUMMARY Religious-themed ETFs have increased their AUM to roughly $1 billion 3 / 4 products outperformed their benchmarks since 2020 The outperformance can be attributed to their factor exposures

Quantocracy’s Daily Wrap for 02/25/2023

This is a summary of links featured on Quantocracy on Saturday, 02/25/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Inside the Minds of Expected Stock Returns [Alpha Architect]

    Financial literature has produced a long list of firm characteristics (referred to as factors) that provide information as to future stock returns, with the explanation for the casual relationship between the characteristics and returns being either risk- or behavioral-based. The traditional finance (risk-based) explanation is that stocks with certain characteristics tend to perform worse when the
  • How to Deal With Missing Financial Data [Quantpedia]

    The problem of missing financial data is widespread yet often overlooked. An interesting insight into the structure of missing financial data provides a novel research paper by authors Bryzgalova et al. (2022). Firstly, examining the dataset of the 45 most popular characteristics in asset pricing, the authors found that missing data is frequent among almost any characteristic and affects all kinds
  • Predicting base metal futures returns with economic data [SR SV]

    Unlike other derivatives markets, for commodity futures, there is a direct relation between economic activity and demand for the underlying assets. Data on industrial production and inventory build-ups indicate whether recent past demand for industrial commodities has been excessive or repressed. This helps to spot temporary price exaggerations. Moreover, changes in manufacturing sentiment should