Quant Mashup Sovereign debt sustainability and CDS returns [SR SV]Selling protection through credit default swaps is akin to writing put options on sovereign default. Together with tenuous market liquidity, this explains the negative skew and heavy fat tails of generic CDS (short protection or long credit) returns. Since default risk depends critically on(...) Organization Capital and the Cross-Section of Expected Returns [Alpha Architect]This paper focuses on “organization capital,” representing intangible assets in a firm’s key employees that is not captured by classic value measures such as book-to-market. The authors propose a structural model to analyze the impact of organizational capital on asset prices and argue that(...) CLOs - Diversifier, or another Equity Clone? [Finominal]Multiple collateralized loan obligation (CLO) ETFs have been launched since 2020 CLOs are promoted as low-risk fixed-income products However, these simply represent diluted equity exposure and offer limited diversification benefits INTRODUCTION The U.S. leveraged loan market has increased from $100(...) Technology Spillover Impacts Stock Returns [Alpha Architect]The increasing role of intangible assets compared to physical assets in our economy has been accompanied by increased research into their impact on asset prices and returns. Studies such as the 2020 papers “Explaining the Recent Failure of Value Investing,” “Intangible Capital and the Value(...) Inflation and Stock/Treasury Correlation [Allocate Smartly]There has been a surge in correlation between US stocks and Treasuries over the last couple of years. To illustrate, below we’ve shown the rolling 3-year correlation between US stocks and 10-year Treasuries since 1900 (based on monthly returns). Note the spike at the far right of the chart. What(...) Big boost for quant_rv going long/short SPY/SH (Part 11) [Babbage9010]sum: quant_rv was already beating SPY in our last post, with a long-only approach. Testing the remaining (flat) days finds that they’re dominated by losers, so if we go short (using SH ETF) instead of flat, boom! performance jumps again recap quant_rv is a simple quant strategy written in R that(...) Index Tracking: Reproducing the Performance of a Financial Market Index (and more) [Portfolio Optimizer]An index tracking portfolio1 is a portfolio designed to track as closely2 as possible a financial market index when its exact replication3 is either impractical or impossible due to various reasons4 (transaction costs, liquidity issues, licensing requirements…). In this blog post, after reviewing(...) Higher Volatility, Higher Alpha? [Finominal]Intuitively fund managers should create more alpha when volatility is higher However, neither mutual nor hedge fund managers have been able to do so Likely explained by fund managers being less rational than assumed INTRODUCTION “However, a period of higher volatility is ‘a good environment for(...) Dissecting the Idiosyncratic Volatility Puzzle [Alpha Architect]Idiosyncratic volatility (IVOL) is the volatility of a security that cannot be explained by overall market volatility—it is the risk unique to a particular security. IVOL contrasts with systematic risk, which is the risk that affects all securities in a market (such as changes in interest rates or(...) Part 10 quant_rv: getting somewhere now, by adding normalized ATR [Babbage9010]TL;DR ~ This post explores adding a normalized Average True Range (nATR) measure that behaves in a similar fashion to other volatility measures, including a “low volatile anomaly” and nATR gives quant_rv a nice boost in backtesting performance. Not all goals for quant_rv are met yet, but we’re(...) Demystifying Equity Market Neutral Investing [Simplify]The 60/40 portfolio, the bread-and-butter portfolio of today’s wealth management industry, is limited to just two core drivers of returns: equities and bonds. Is there someplace else we can turn to for a compelling yet distinct source of returns? Yes, there are several places to look in fact, one(...) 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(...) 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(...) 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(...) 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(...) How an old Nintendo baddie boosts portfolio analysis [PyQuant News]Today’s newsletter is based on a reader’s suggestion. We look at k-medoids which is a villain in the popular Nintendo game Metroid. No it’s not. But if you know Metroid, you have to agree: It sounds like one! It’s actually a powerful method used in data science to cluster similar data(...) Volatility Forecasting: Simple and Exponentially Weighted Moving Average Models [Portfolio Optimizer]One of the simplest and most pragmatic approach to volatility forecasting is to model the volatility of an asset as a weighted moving average of its past squared returns1. Two weighting schemes widely used by practitioners23 are the constant weighting scheme and the exponentially decreasing(...) 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(...) 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.(...) 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(...) Use OpenAI prompts for stock news sentiment [PyQuant News]In today’s newsletter, you’ll use the OpenBB SDK to download news for a topic. Then, you’ll use OpenAI and build a prompt to predict the sentiment of a news headline. You’ll bring it all together with LangChain. The result is a pandas DataFrame with a column of news headlines and a column(...) 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(...) 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(...) AutoRegressive Fractionally Integrated Moving Average (ARFIMA) model [Quant Insti]Usually, in algorithmic trading, we talk about AutoRegressive Integrated Moving Average (ARIMA) models. They’re 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? We’ll learn about such(...) 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 stock’s exposure to non-fundamental demand risk – stock price fragility. However, this approach may be biased by confounding(...) Beyond Stocks: The Surprising Volatility Returns of Oil and Gold [Robot Wealth]I’ve 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,(...) 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(...) 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(...) 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(...) The ESG-efficient frontier (Part II) [Quantifying ESG]In the previous week, we looked at the first part of a paper by AQR’s 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(...) 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(...) 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. Here’s an excellent recent look from Portfolio Optimizer. How predictable?(...) 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(...) Diving Deep: My Personal Approach to Equity and Volatility Risk Premia [Robot Wealth]Lately, I’ve 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(...) 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(...) 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(...) 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(...) Key insights: Imbalance in the order book [OS Quant]I summarise key insights from a few papers studying the limit order book. You’l 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(...) 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 today’s guest post, you’ll use VectorBT PRO to algorithmically detect chart patterns from 230 million unique pattern and window combinations. All in about 2 minutes.(...) AI case study: Long/Short strategy [Quant Dare]In today’s 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(...) 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(...) 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(...) 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(...) 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(...) 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, we’ll explore theoretically these models using Python.(...) An Introduction to Machine Learning Research Related to Quantitative Trading [Quantpedia]Following the recent release of the popular large language model ChatGPT, the topic of machine learning and AI seems to have skyrocketed in popularity. The concept of machine learning is, however, a much older one and has been the topic of various research and technology projects over the last(...) Reducing Whipsaws When Using 200-day Moving Average for Market Timing [Alvarez Quant Trading]I was working on testing a market timing indicator that I read about it. It was showing some promise and the next step was to compare it to my benchmark. My benchmark is using the 200-day moving average. But an additional rule removes a lot of the whipsaws that can happen. After doing the(...) Super-Secret Proprietary Black Box Strategies [Allocate Smartly]Note: This is a rare non-geeky, non-quantitative, stream of thought blog post. Because we’re so deep into this world of Tactical Asset Allocation (TAA), we’re sometimes asked for our thoughts on such-and-such black box TAA strategy. By “black box” we mean a strategy for which the trading(...) Geographic investing: business activity vs. domicile [Alpha Architect]The article explores the limitations of traditional country-level stock market indexes constructed based on issuing firms’ domicile. Additionally, it introduces a new type of national stock market index called the EMindex, which is based on companies’ business activities rather than their(...) Code Walkthrough for Alpha Simulator: Simple Trend Rule with Vol Targeting [Hanguk Quant]In the last post we did a line-by-line walkthrough of the alpha simulator, using a uniform random variable to represent the signal generating component of the alpha backtest. We also raised a few questions: How can we manage the varying levels of risk profiles of different stocks in our asset(...)