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Quantocracy’s Daily Wrap for 10/23/2023

This is a summary of links featured on Quantocracy on Monday, 10/23/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Unmasking Insights through Human-AI Differences in Earnings Conference Q&A [Alpha Architect]

    This paper acknowledges the pivotal role of earnings calls in disseminating value-relevant information, with particular emphasis on the Q&A segment. However, it confronts the inherent challenge posed by the unstructured nature of language in these calls, complicating quantitative analysis. In response, the authors innovate by introducing a novel measure designed to grasp the subtleties of
  • Determining the Optimal Benchmark for Funds [Finominal]

    SUMMARY Identifying the right benchmark for a fund or portfolio can be difficult Many common metrics like correlation or betas do poorly for benchmark selection Combining metrics is more effective INTRODUCTION Is gold the right benchmark for gold miners? Although these companies focus on excavating the precious metal, there are plenty of operational issues like staff strikes, collapsing mines, or

Quantocracy’s Daily Wrap for 10/22/2023

This is a summary of links featured on Quantocracy on Sunday, 10/22/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Market Cap vs Dollar Volume: Which to Use for Universe Selection? [Quant Rocket]

    Market cap and dollar volume are two commonly used metrics for filtering a trading universe by size of security. Does it matter which one you use? In this post, I quantify the difference between market cap and dollar volume and explain the kinds of stocks that may unexpectedly appear in your universe with each metric. Overview of market cap and dollar volume Market cap and dollar volume are both
  • Macro demand-based rates strategies [SR SV]

    The pace of aggregate demand in the macroeconomy exerts pressure on interest rates. In credible inflation targeting regimes, excess demand should be negatively related to duration returns and positively to curve-flattening returns. Indeed, point-in-time market information states of various macro demand-related indicators all have helped predict returns of directional and curve positions in
  • How an old Nintendo baddie boosts portfolio analysis [PyQuant News]

    Todays newsletter is based on a readers suggestion. We look at k-medoids which is a villain in the popular Nintendo game Metroid. No its not. But if you know Metroid, you have to agree: It sounds like one! Its actually a powerful method used in data science to cluster similar data together. Its robust to outliers so super useful when clustering features of a portfolio of financial

Quantocracy’s Daily Wrap for 10/20/2023

This is a summary of links featured on Quantocracy on Friday, 10/20/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 10/18/2023

This is a summary of links featured on Quantocracy on Wednesday, 10/18/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Hello ChatGPT, Can You Backtest Strategy for Me? [Quantpedia]

    You may remember our blog post from the end of March, where we tested the current state-of-the-art LLM chatbot: Time flies fast. More than six months have passed since our last article, and half a year in a fast-developing field like Artificial intelligence feels like ten times more. So, we are here to revisit our article and try some new hacks! Has the OpenAI chatbot made any significant

Quantocracy’s Daily Wrap for 10/16/2023

This is a summary of links featured on Quantocracy on Monday, 10/16/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Vector AutoRegression models: Implementation in Python and R [Quant Insti]

    Whenever you want to estimate a model for multiple time series, the Vector Autoregression (VAR) model will serve you well. This model is suitable for handling multiple time series in a single model. You will learn here the theory, the intricacies, the issues and the implementation in Python and R. What is a VAR model? Creating a VAR model A stationary VAR VAR Lag Selection Criteria Estimation of a
  • Momentum Research: a summary: high quality articles of note [Alpha Architect]

    The Jegadeesh and Titman (1993) paper on momentum established that an equity trading strategy consisting of buying past winners and selling past losers, reliably produced risk-adjusted excess returns. The Jegadeesh results have been replicated in international markets and across asset classes. As this evidence challenged and contradicted widely accepted notions of weak-form market efficiency, the

Quantocracy’s Daily Wrap for 10/15/2023

This is a summary of links featured on Quantocracy on Sunday, 10/15/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Use OpenAI prompts for stock news sentiment [PyQuant News]

    In todays newsletter, youll use the OpenBB SDK to download news for a topic. Then, youll use OpenAI and build a prompt to predict the sentiment of a news headline. Youll bring it all together with LangChain. The result is a pandas DataFrame with a column of news headlines and a column with the predicted sentiment. Lets go! Use OpenAI prompts for stock news sentiment Generative
  • Aliens made this rock: The post-hoc probability fallacy in biology, finance and cosmology [Mathematical Investor]

    While out hiking, I found this rock. Evidently it was created by aliens, as can be shown by a probability argument. The following table gives measurements made on the rock. The first two rows give the overall length and width of the rock. Each of the next six rows, after the first two, gives thickness measurements, made on a 3cm x 6cm grid of points from the top surface. All measurements are in

Quantocracy’s Daily Wrap for 10/13/2023

This is a summary of links featured on Quantocracy on Friday, 10/13/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trend-Following Filters Part 7 [Alpha Architect]

    Financial time series that are structured as data sampled at a uniform time interval, e.g., hourly, daily, weekly, or monthly, are called discrete-time time series and referred to, from a digital signal processing (DSP) perspective, as being in the time domain. Technical market analysts generally study financial time series in the time domain. For example, a price chart displays time series
  • AutoRegressive Fractionally Integrated Moving Average (ARFIMA) model [Quant Insti]

    Usually, in algorithmic trading, we talk about AutoRegressive Integrated Moving Average (ARIMA) models. Theyre very popular in the industry. You might remember that the d component in the model can be 0, 1, 2, etc. What if 'd' could take fractional values? Well learn about such models i.e. AutoRegressive Fractionally Integrated Moving Average (ARFIMA) here. Lets dive in
  • Research Review | 13 October 2023 | Market Volatility [Capital Spectator]

    An ETF-Based Measure of Stock Price Fragility Renato Lazo-Paz (University of Ottawa) July 2023 A growing literature employs equity mutual fund flows to measure a stocks exposure to non-fundamental demand risk stock price fragility. However, this approach may be biased by confounding fundamental information, potentially leading to underestimating risk exposure. We propose an alternative

Quantocracy’s Daily Wrap for 10/12/2023

This is a summary of links featured on Quantocracy on Thursday, 10/12/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Beyond Stocks: The Surprising Volatility Returns of Oil and Gold [Robot Wealth]

    Ive previously discussed the Volatility Risk Premium (VRP) and how it differs from the Equity Risk Premium (ERP). Probably the most interesting difference, from the perspective of the trader, is that the VRP may be somewhat amenable to timing more than the ERP at any rate. In this article, Ill use some of the excellent data from ORATS to explore the VRP. Well start by using the SPY

Quantocracy’s Daily Wrap for 10/11/2023

This is a summary of links featured on Quantocracy on Wednesday, 10/11/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Time Invariant Portfolio Protection [Quantpedia]

    In this article we are going to continue the discussion on portfolio insurance strategies. An exhaustive description of this methodology was already presented in the article Introduction to CPPI (https://quantpedia.com/introduction-to-cppi-constant-proportion-portfolio-insurance). This article will focus on an extension of the original model introduced by Estep and. Kritzman (1988), namely Time
  • Dynamically combining mean reversion and momentum strategies [Hudson and Thames]

    Exploring Mean Reversion and Momentum Strategies in Arbitrage Trading Our recent reading group examined mean reversion and momentum strategies, drawing insights from the article, Dynamically combining mean reversion and momentum investment strategies by James Velissaris. The aim of the paper was to create a diversified arbitrage approach that combines mean reversion and momentum strategies

Quantocracy’s Daily Wrap for 10/09/2023

This is a summary of links featured on Quantocracy on Monday, 10/09/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to measure the quality of a trading signal [SR SV]

    The quality of a trading signal depends on its ability to predict future target returns and to generate material economic value when applied to positioning. Statistical metrics of these two properties are related but not identical. Empirical evidence must support both. Moreover, there are alternative criteria for predictive power and economic trading value, which are summarized in this post. The
  • The ESG-efficient frontier (Part II) [Quantifying ESG]

    In the previous week, we looked at the first part of a paper by AQRs Lasse H Pedersen, Shaun Fitzgibbons and Lukasz Pomorski. This remains an influential paper in that it contains some really useful ideas on how to incorporate ESG in a portfolio optimization problem. You can read the first part here. Just to summarize, in Part I we covered the theory behind the ESG-efficient frontier, which is
  • Building Better High Yield Portfolios [Finominal]

    There is an inverse relationship between yield and total return The ideal yield strategy has a high yield, high Sharpe, and low correlation to stocks The yield-to-downside beta ratio enhances the strategy selection process INTRODUCTION @ChatGPT: What is a rhyme that includes high dividends are for suckers? @Nicolas: Im happy to help with rhymes, but its important to note that