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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

Quantocracy’s Daily Wrap for 10/06/2023

This is a summary of links featured on Quantocracy on Friday, 10/06/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Long-term Returns for US Treasury Funds Are Predictable: What Do We Do with That Information? [Allocate Smartly]

    Long-term returns for US Treasury bond funds with a constant maturity (like IEF, TLT and SHY) have been quite predictable simply using initial Treasury yields as an estimate. This observation was popularized by John Bogle. Heres an excellent recent look from Portfolio Optimizer. How predictable? Below weve shown the intial yield on 10-Year US Treasury notes since 1871 in blue, versus the
  • International Value Stocks Offering “More Bang for the Buck” [Alpha Architect]

    Over the very long term, while value stocks have been less profitable and have had slower growth in earnings than growth stocks, they have provided higher returns. Among the reasons are that value stocks have traded at substantial valuation discounts compared to growth stocks, and reversion to the mean of abnormal (both abnormally high and abnormally low) growth in earnings has been greater than

Quantocracy’s Daily Wrap for 10/04/2023

This is a summary of links featured on Quantocracy on Wednesday, 10/04/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Diving Deep: My Personal Approach to Equity and Volatility Risk Premia [Robot Wealth]

    Lately, Ive been thinking a lot about the Volatility Risk Premium (VRP). The VRP makes much more sense (to me, at least) when I have the Equity Risk Premium (ERP) for context and comparison. So, in this article, I want to discuss the ERP and the VRP, their similarities and differences, and how I seek exposure to both. Ill do a follow-up article where we analyse the VRP using the most
  • Trading Technical Indicators the Right Way: Digital to Analog Signals [Hanguk Quant]

    In the previous lecture, we did a line by line walk through and intuitive explanation of the need for volatility targeting: Code Walkthrough for the Alpha Simulator: Simple Trend Rule with Volatility Targeting HangukQuant Sep 23 Code Walkthrough for the Alpha Simulator: Simple Trend Rule with Volatility Targeting Read full story In particular, the expectation for risk-adjusted returns shall be
  • The State Of Vol [Investment Idiocy]

    I'm sometimes asked where I get my ideas for new trading strategies from. The boring truth is I rarely test new trading strategies, and I mostly steal ideas when I feel in the mood. Today for example I saw this tweet post on twitter X: The original paper is here (requires subscription or academic institution membership) Now I've written in the past about how volatility levels affect the
  • AutoRegressive Moving Average (ARMA) models: Using R [Quant Insti]

    In the AutoRegressive Moving Average (ARMA) models: A Comprehensive Guide of my ARMA article series, I covered the theoretical aspects of Autoregressive Moving Average models (ARMA). In the AutoRegressive Moving Average (ARMA) models: Using Python, I simulated different ARMA models, their autocorrelations and their partial autocorrelations. We also provided a strategy based on these models. In

Quantocracy’s Daily Wrap for 10/02/2023

This is a summary of links featured on Quantocracy on Monday, 10/02/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Key insights: Imbalance in the order book [OS Quant]

    I summarise key insights from a few papers studying the limit order book. Youl learn how to measure volume imblanace in the limit order book and how well it predicts price moves. Author Adrian Letchford Published 1 October 2023 Length 5 minutes Like what you see? Follow Adrian on Twitter to be notified of new content. Follow This write up is my notes on a few papers looking at using order book
  • How to use VectorBT PRO to algorithmically find chart patterns [PyQuant News]

    VectorBT PRO (VBT) is a proprietary Python package designed for backtesting and analyzing quantitative trading strategies. In todays guest post, youll use VectorBT PRO to algorithmically detect chart patterns from 230 million unique pattern and window combinations. All in about 2 minutes. Ready? Using VectorBT PRO to algorithmically find chart patterns VectorBT PRO provides a comprehensive
  • AI case study: Long/Short strategy [Quant Dare]

    In todays post we will be using AI to improve a module of the Alternative Data-Driven Investment (ADDI) strategy developed by ETS Asset Management Factory, which is an automatic Long Short investment strategy that aims to obtain stable performance de-correlated from the market and with a limited drawdown risk. This comes after our recent posts, where we have been looking into key aspects of
  • Is $SPX Selloff Near An End? [Quantifiable Edges]

    This past week was the 4th week in a row that the SPX declined. It is quite unusual to see SPX close down for 4 weeks in a row, but still remain above its 40-week moving average. Below is a look at other times since 1975 that this action has occurred. SPX down 4 weeks in a row but above 40-week average These results are suggestive of an upside edge over the next several weeks. Below I have listed
  • ETF Evolution: what does it mean for investors? [Alpha Architect]

    The first ETFs emerged in 1993 and closely tracked broad-based indexes for a low fee. Since then, the competitive situation in the ETF industry today has differentiated itself by adding a new breed of ETFs that reflected specialization into popular investment themes. When the evolution of the ETF industry is compared to the evolution of mutual funds, the picture that emerges is different in two
  • ESG Preferences Negatively Affecting Market Efficiency [Alpha Architect]

    Environmental, social, and governance (ESG) investing continues to increase in popularity, with many institutional and individual investors incorporating ESG criteria into their investment decision-making process. Three main themes have driven this massive shift of assets: 1) Many investors are motivated by nonfinancial reasons to tilt their portfolios toward firms with robust ESG criteria; 2)

Quantocracy’s Daily Wrap for 09/28/2023

This is a summary of links featured on Quantocracy on Thursday, 09/28/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Researching the Quality Factor with Alphalens and Zipline [Quant Rocket]

    Buying high-quality stocks and avoiding low-quality ones can improve investment returns. In this post, I use Alphalens and Zipline to analyze the Piotroski F-Score, a composite measure of a firm's financial health and quality. This post is part of the fundamental factors series, which explores techniques for researching fundamental factors using Pipeline, Alphalens, and Sharadar US
  • AutoRegressive Moving Average (ARMA) models: Using Python [Quant Insti]

    In the first part of my ARMA article series, I covered the background theory of lag operators, the stationarity and invertibility of Autoregressive Moving Average models (ARMA) and the different types of versions you can create from it. Here, well explore theoretically these models using Python. Here you'll learn about ARMA model Python examples. From the simulation of these models to a