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Quantocracy’s Daily Wrap for 10/30/2024

This is a summary of links featured on Quantocracy on Wednesday, 10/30/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Understanding the Invisible Tail of a Power Law [Quant at Risk]

    Understanding the invisible tail of a power law distribution is crucial for accurate extreme value analysis, especially in fields where rare, extreme events have a large impact. In finance, natural disaster modeling, and engineering, rare events, or outliers, are disproportionately impactful. The theory behind the invisible tail demonstrates that traditional methods for estimating risk or
  • Day 8: Baseline effects [OSM]

    Yesterday, we discussed the size effects, their statistical significance (e.g., p-values), and some other summary statistics for the various momentum combinations namely, 3, 6, 9, and 12 week lookback and look forward returns. We found that size effects were small, but a few were significant, and that in the case of the 12-by-12 combination about 75% of the results clustered in the -10% to 10%
  • Day 7: Size effects [OSM]

    Welcome to the last day of the first week of 30 days of backtesting! We hope youre enjoying the ride. If you have any questions or concerns, you can reach us at the contact details listed at the bottom of this post. On Day 6 we defined momentum rather roughly and ran a bunch of tests to identify the linear relationship between different lookback and look forward periods. However, we didnt go

Quantocracy’s Daily Wrap for 10/28/2024

This is a summary of links featured on Quantocracy on Monday, 10/28/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Covariance Matrix Forecasting: Iterated Exponentially Weighted Moving Average Model [Portfolio Optimizer]

    In the previous post of this series on covariance matrix forecasting, I reviewed both the simple and the exponentially weighted moving average covariance matrix forecasting models, which are straightforward extensions of their respective univariate volatility forecasting models to a multivariate setting. With these reference models established, we can now delve into more sophisticated approaches
  • Day 6: Momentum [OSM]

    Yesterday we examined the eponymous Fama-French factors to see if we could find something that will help us develop an investment strategy to backtest. It turned out the best performing factor was the market risk premium, which is essentially the return to the market in excess of the risk-free rate. In other words, the best factor is buy-and-hold! I guess that means weve finished 24 days early.
  • Can Artificial Intelligence outsmart seasoned equity analysts? [Alpha Architect]

    If the task is to identify a firms true profitability, can AI outsmart seasoned analysts? Given the increasingly bloated nature of financial reports, decoding the twists and turns associated with events like obscure one-time gains and out-of-nowhere expenses to extract core earnings has become challenging even for accountants. This research will unravel how AI can successfully attack the

Quantocracy’s Daily Wrap for 10/26/2024

This is a summary of links featured on Quantocracy on Saturday, 10/26/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Build Mean Reversion Strategies in Currencies [Quantpedia]

    Our article explores a simple mean reversion trading strategy applied to FX futures, focusing on identifying undervalued and overvalued currencies to generate returns. Using FX futures rather than spot rates allows for the inclusion of interest rate differentials, simplifying the analysis. The strategy employs two position-sizing methodslinear and exponentialboth rebalanced monthly based on
  • Lognormal Stochastic Volatility Youtube Seminar and Slides [Artur Sepp]

    I would like to share the youtube video of my online seminar at Minnesota Center for Financial and Actuarial Mathematics and presentation slides. I discuss the motivation behind introducing the log-norml stochastic volatility (SV) model in our IJATF paper with Parviz Rakhmonov. I briefly highlight the advantages of this model over exisiting SV models. Then I focus on new features of the model. For
  • New YouTube Series Launched: Building Your AWS Trading Data Pipeline! [Black Arbs]

    I just published Part 1 of my new YouTube series, and I'm excited to share it with you all! After my recent post about automating trading strategies with AWS Cloud, many of you asked for a deeper dive into the technical implementation. Well, here it is! What's in Part 1? In this first video, we're tackling the foundation: configuring your AWS data pipeline and writing the Python
  • Day 5: Trifactor [OSM]

    The day has finally arrived! Time to start backtesting! Weve always wanted to test how Fibonacci retracements with Bollinger Band breakouts filtered by Chaikin Volatility would perform while implementing rolling stop-loss updates based on the ATR scaled by the 7-day minus 5-day implied volatility rank.1 Maybe were getting ahead of ourselves. Expeditions are fun and its always thrilling to
  • Day 4: First analysis [OSM]

    Were four days in and youre probably wondering when are we actually going to start backtesting?! The answer is that while it is natural to want to rush to the fun part the hope and elation of generating outsized returns and Sharpe Ratios greater than 2 the reality is getting the foundation right should serve us well in the future. Or so thats what we always hear from those longer
  • Day 3: Metrics [OSM]

    Yesterday we investigated the effect of using the 200-day simple moving average (200SMA) as a proxy for a rules-based investing method. The idea was to approximate what a reasonably rational actor/agent might do in addition to the buy-and-hold approach. When folks talk about research, backtesting, and forecast comparisons, they usually use a naive model against which one compares performance. In

Quantocracy’s Daily Wrap for 10/23/2024

This is a summary of links featured on Quantocracy on Wednesday, 10/23/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Day 2: Hello World [OSM]

    On Day 1, we decided on a few benchmarks to use for our backtest. That is, a 60-40 and 50-50 weighting of the SPY and IEF ETFs. What we want to add in now is the Hello World version of trading strategies the 200-DAY MOVING AVERAGE! Why are we adding this to our analysis? As we pointed out yesterday, the typical benchmark against which to compare a trading strategy is buy-and-hold. But, just as

Quantocracy’s Daily Wrap for 10/22/2024

This is a summary of links featured on Quantocracy on Tuesday, 10/22/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Day 1: Benchmarks [OSM]

    Yesterday we set out our plan to backtest a strategy using the SPY ETF, which tracks the S&P 500. Before we commence, we obviously need to establish a baseline. What metrics will we use to assess the strategy? How will we define success? What benchmarks will we use? Typically, for a single asset strategy the comparison is buy-and-hold performance. That is, if youre using Fibonacci
  • Reinforcement Learning in Finance: Resources and Expert Advice from Paul Bilokon [Quant Insti]

    Reinforcement learning (RL) is one of the most exciting areas of Machine Learning, especially when applied to trading. RL is so appealing because it allows you to optimise strategies and enhance decision-making in ways that traditional methods cant. One of its biggest advantages? You dont have to spend a lot of time manually training the model. Instead, RL learns and makes trading decisions

Quantocracy’s Daily Wrap for 10/20/2024

This is a summary of links featured on Quantocracy on Sunday, 10/20/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Accurately Forecasting Multi-period Stock Market Returns [Six Figure Investing]

    I recently posted a paper, Transforming Stock Market Forecasts with Variable Expected Returns, on the SSRN online repository. This paper resolves an issue that has been bugging me for years. The link is: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=495384 This paper is not about making money, but rather about a fundamental theoretical error regarding stock market forecasts that has
  • Mind the gap [Quantitativo]

    "What we know is a drop; what we don't know is an ocean. Isaac Newton. Many of Isaac Newton's early theories and ideas were met with skepticism or outright failure. Newton spent years working on problems related to motion, optics, and gravity, often facing dead ends and revisions. In fact, throughout most of his career, Newton was very loathe to publish due to his high
  • Artificial Intelligence, Textual Analysis and Hedge Fund Performance [Alpha Architect]

    Artificial Intelligence (AI) offers the intriguing potential to revolutionize investment decision-making by providing important advantages such as: Enhanced Data Analysis: AI can process and analyze vast amounts of data from various sources, including financial news, market trends, and company fundamentals, at a speed and scale far surpassing human capabilities. This enables investors to identify

Quantocracy’s Daily Wrap for 10/15/2024

This is a summary of links featured on Quantocracy on Tuesday, 10/15/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Pre-Holiday Effect in Commodities [Quantpedia]

    Our research will explore the intriguing phenomenon of the Pre-Holiday effect in commodities, particularly crude oil and gasoline. Historical data reveals a short-term price drift prior to major U.S. holidays, suggesting a trend in these markets. We hypothesize that this anomaly may be driven by increased demand for oil and its derivatives, such as gasoline, as people prepare for travel, often by
  • The Return of Simple and Exponentially Weighted Moving Average Models [Portfolio Optimizer]

    In the initial post of the series on volatility forecasting, I described the simple and the exponentially weighted moving average forecasting models, that are both easy to understand and relatively performant in practice. Beyond (univariate) volatility forecasting, these two models are also widely used in (multivariate) covariance matrix forecasting123, for the very same reasons. In this blog
  • The Sahm Rule as a Recession Indicator [Alpha Architect]

    A weaker-than-expected July jobs report, with the unemployment rate increasing to 4.3%, officially triggered the Sahm Rule, causing investors to worry that the Federal Reserve may be behind the curve in cutting interest rates to prevent a recession. (The August report showed an increase in payroll employment of 142,000, with the unemployment rate at 4.2%). Named after Claudia Sahm, a

Quantocracy’s Daily Wrap for 10/10/2024

This is a summary of links featured on Quantocracy on Thursday, 10/10/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Improve ETF Sector Momentum [Quantpedia]

    In this article, we explore the historical performance of sector momentum strategies and examine how their alpha has diminished over time. By analyzing the underlying causes behind this decline, we identify key factors contributing to the underperformance. Most importantly, we introduce an enhanced approach to sector momentum, demonstrating how this solution significantly improves the performance
  • How I Automated My Trading Strategy Using AWS Cloud for Free (Part 1) [Black Arbs]

    This year I launched a strategy subscription service for a long-only ETF strategy developed in house. I learned a lot through this process but I made several mistakes that pushed me to learn new skills and improve the product offering. In this series I will discuss my initial mistakes, and how correcting them led me to automate the system using AWS cloud and how you can too. Mistake #1 First

Quantocracy’s Daily Wrap for 10/08/2024

This is a summary of links featured on Quantocracy on Tuesday, 10/08/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Reading the WSJ May Make You a Better Economist [Alpha Architect]

    What are the Research Questions? The research questions are as follows: How can textual analysis of business news, specifically The Wall Street Journal (WSJ), be used to measure the state of the economy? What is the structure of news coverage related to economic events, and how do these topics evolve over time? Does news attention contain information that is distinct from standard numerical
  • The Hidden Cost of Index Replication [Alpha Architect]

    As the annual SPIVA studies demonstrate, index funds persistently outperform the vast majority of actively managed funds, even before considering taxes. With that said, most investors are unaware that there are weaknesses of index funds that result from their strategy to replicate the return of an index. Those weaknesses, which result from the desire to minimize what is called tracking error

Quantocracy’s Daily Wrap for 09/29/2024

This is a summary of links featured on Quantocracy on Sunday, 09/29/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A different indicator [Quantitativo]

    "Mathematical reasoning may be regarded rather schematically as the exercise of a combination of two facilities, which we may call intuition and ingenuity. Alan Turing. It's hard to find anyone in Computer Science who doesn't hold Alan Turing in deep admiration. Widely regarded as the father of modern computing, Turing's groundbreaking work during World War II on breaking
  • Vasicek Model Simulation with Python [Quant Start]

    Recently on QuantStart we wrote a tutorial article that discussed the mean-reverting Ornstein-Uhlenbeck process, outlining some of its applications as well as providing some Python snippets to generate sample paths. In this article we are going to introduce the Vasicek Model, which is example of a one-factor short rate model used to model interest rate behaviour for interest rate derivatives
  • Can Skewness Identify Future Outperforming Mutual Funds [Alpha Architect]

    The annual SPIVA has documented that retail mutual funds underperform with great persistence, with any persistence of outperformance not significantly greater than would be randomly expected. The large body of research on the failure of active management led Charles Ellis to famously call it a losers gameone that is possible to win, though the odds of doing so are so poor the surest