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Recent Quant Links from Quantocracy as of 06/30/2025

This is a summary of links recently featured on Quantocracy as of Monday, 06/30/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • An Empirical Analysis of Conference-Driven Return Drift in Tech Stocks [Quantpedia]

    Corporate conferences have long been recognized as pivotal events in financial markets, serving as catalysts that signal upcoming innovations and strategic shifts. Scheduled corporate events induce market reactions that can be systematically analyzed to reveal predictable return patterns. In this work, we focus on examining the return drift exhibited by technology stocks in the days surrounding
  • The Derivative Payoff Bias [Quantitativo]

    You're nothing but a pack of cards! Alice, standing up to the Queen of Hearts. I used to think Alice in Wonderland was just a goofy kids story. After re-reading it as an adult, I actually think its brilliant. Its satire dressed up as nonsense. A dream disguised as a joke. A mirror hiding in plain sight. The White Rabbit? Thats obsession and urgency. The Queen of Hearts?
  • Mastering the Tri-Timeframe Trend-Following System [Alina Khay]

    Most losing trades happen for one reason: traders fight the trend on the wrong timeframe. You see a beautiful setup on the 15-minute chart, but the daily is turning, and the 4-hour is in no man's land. The result? You get chopped, faked out, or stopped at the exact top. Thats where Tri-Timeframe Trend-Following comes in. This strategy aligns three layers of market structure macro,
  • Can We Profit from Disagreements Between Machine Learning and Trend-Following Models? [Quantpedia]

    When using machine learning to forecast global equity returns, its tempting to focus on the raw predictionwhether some stock market is expected to go up or down. But our research shows that the real value lies elsewhere. What matters most isnt the level or direction of the machine learning models forecast but how much it differs from a simple, price-based benchmarksuch as a naive
  • A Cheat Code for Crypto? [Robot Wealth]

    The best trading edges are often found in places most people dont think to look. Its why being a bit of a pirate zigging when others zag and finding opportunities in the markets blind spots is such an effective approach. Today, I want to share something that has me genuinely excited. Its a little like finding a cheat code in a video game that gives you special powers the other
  • Insider Trading Increases Market Efficiency [Alpha Architect]

    The empirical research (for example, here, here, here and here) on insider trading demonstrates that insider transactions have significant predictive power for future stock returns as they reveal helpful information that may affect the price of stocks. George Jiang and Yun Ma contribute to the literature with their November 2023 study, Does Insider Trading Correct Mispricing? which examined
  • Predicting Corrections and Economic Slowdowns [Relative Value Arbitrage]

    Being able to anticipate a market correction or an economic recession is important for managing risk and positioning your portfolio ahead of major shifts. In this post, we feature two articles: one that analyzes indicators signaling a potential market correction, and another that examines recession forecasting models based on macroeconomic data. Predicting Recessions Using The Volatility Index And

Recent Quant Links from Quantocracy as of 06/25/2025

This is a summary of links recently featured on Quantocracy as of Wednesday, 06/25/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Navigating Economic Downturns with Survey-Based Recession Indicators [Allocate Smartly]

    This test was inspired by Yulong Suns paper Navigating Economic Downturns: Insights from Survey-Based Recession Indicators. Strategy results from 1970 follow. Results are net of transaction costs see backtest assumptions. Learn about what we do and follow 90+ asset allocation strategies like this one in near real-time. Logarithmically-scaled. Click for linearly-scaled results. These results
  • I Tuned the Radio on My Stock Returns [Paper to Profit]

    Its dated technology. The radio. But underpinning its humble origins is an electrical engineering field known as signal processing that has applications in literally everything around us: WiFi routers, making music sound better, and even detecting earthquakes. While many readers may assume that I built my system with bespoke approaches tuned specifically for financial applications, the reality
  • Weekly Research Recap [Quant Seeker]

    Genetic Mimicking Portfolios for ETF Arbitrage (Crego, Kvaerner, Sommervoll, Sommervoll, and Stevens) Many ETFs trade at a premium or discount to their net asset value (NAV). This paper targets corporate bond ETFs trading at a premium by shorting them and hedging the position with a portfolio of other liquid ETFs that replicate the NAV. Even after accounting for trading costs, the strategy

Recent Quant Links from Quantocracy as of 06/24/2025

This is a summary of links recently featured on Quantocracy as of Tuesday, 06/24/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Model: Advances in clustering [Trading the Breaking]

    Look, heres the thingweve all been drinking the correlation Kool-Aid for decades, right? Its elegant, sure. Clean math. Easy to explain to the PMs. But lets get real: relying on a correlation matrix in todays markets is like trying to sail a speedboat with an anchor chained to your hull. Its not just simplisticits dangerously misaligned with how the game actually works.
  • Experimental Control for Machine Learning of Temporal Effects in Quantitative Trading [Hanguk Quant]

    Experimental control is one of the foundational principles of sound scientific experimentation. Its importance lies in ensuring that the conclusions drawn from an experiment are valid and attributable to the factor(s) being investigated, rather than to confounding variables. Experimental control allows researchers to manipulate only the independent variable(s), establishing causal relationships by
  • Comprehensive Comparison of Algorithmic Trading Platforms [Jonathan Kinlay]

    This comprehensive analysis examines three leading algorithmic trading platformsBuild Alpha, Composer, and StrategyQuant Xacross five critical dimensions: comparative reviews and rankings, asset class applicability, ensemble strategy capabilities, walk-forward testing and robust optimization, and strategy implementation with broker connectivity. Through extensive research of platform
  • Rethinking Leveraged ETFs and Their Options [Relative Value Arbitrage]

    A leveraged Exchanged Traded Fund (LETF) is a financial instrument designed to deliver a multiple of the daily return of an underlying index. Despite criticism, LETFs are frequently used by institutional investors. In this post, I discuss the practicality of LETFs and show that they are not as risky as they may seem. Information Content of Leveraged ETFs Options Leveraged ETFs, or exchange-traded

Recent Quant Links from Quantocracy as of 06/22/2025

This is a summary of links recently featured on Quantocracy as of Sunday, 06/22/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Science and Practice of Trend-following Systems: paper and presentation [Artur Sepp]

    I would like to introduce the updated draft of my paper co-authored with Vladimir Lucic and entitled The Science and Practice of Trend-following Systems. Trend-following systems have been employed by many quantitative and discretionary funds, also known as commodity trading advisors (CTAs), or managed futures, since the early 1980s. Richard Dennis, a commodity trader on the CME, organised
  • Ask Me Anything with Euan Sinclair [Robot Wealth]

    In this Ask Me Anything, Euan addressed the following questions: Key lessons from wacky genius Victor Niederhoffer Euans journey from market maker to sports bettor to options trader What are the most important predictors for options trades? Where can we find good long vol trades? What is the minimum viable tech stack for an options beginner? Is it reasonable to harvest a VRP with ETPs vs
  • The Surefire Ratio: My Custom Risk Ratio that Supercharged My Investing [Paper to Profit]

    Weve all used it. Its seen as the gold standard of investment metrics. But the Sharpe ratio is a dated formula that takes a naive assumption on the market and runs us into walls. It has no concept of prolonged drawdowns or causality, and those that continue to use it as the gospel are quantitative luddites handicapping their own success. But get this: With a simple financial ratio, we

Recent Quant Links from Quantocracy as of 06/19/2025

This is a summary of links recently featured on Quantocracy as of Thursday, 06/19/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Model: Clustering [Trading the Breaking]

    Alright, lets establish first principles. Before deploying capital into algorithmic strategies, one must confront the paradigm shift that distinguishes durable firms from those erased by structural blind spots: financial markets are not monolithic stochastic processes but non-stationary systems governed by latent regime dynamics. This is not heuristic philosophyits an empirical reality
  • I Used a Thermostat s Logic to Control My Portfolio And Achieved 24% CAGR [Paper to Profit]

    As traders, we scour the internet, books, and articles for industry specific information to create our new fancy algorithms. The Black-Scholes model, Markowitz Mean-Variance portfolio optimization, the Capital Asset Pricing Model (CAPM) These are all systems designed for investment purposes solely. derivatives – Proof Black Scholes Theta – Quantitative Finance Stack Exchange Enough of this math
  • Deep Reinforcement Learning for Portfolio Optimization [Gatambook]

    We wrote a lot about transformers in the last three blog posts. Their sole purpose was for feature transformation / importance weighting. These transformed and attention-weighted features will be used as input to downstream applications. In this blog post, we will discuss one such application: portfolio optimization via deep reinforcement learning. We will based our example on a paper by Sood et.
  • Weekly Research Recap [Quant Seeker]

    Decoding the Bond Market (Haghani and White) Investors often look to bonds for clues about future interest rates and inflation. This paper explains how to extract such signals from current market yields. After adjusting for convexity and risk premia, the authors find that markets expect long-term real rates to be near 1.75% and inflation to be around 2.1%. Given the low compensation for risk,
  • Why Most Markets and Styles Have Been Lagging US Equities? [Quantpedia]

    Over the past decade and a half, the US equities have set the hard-to-beat performance benchmark. Nearly all of the other countries, no matter if small or big, emerging or developed, have lagged behind. However, what are the forces behind this outperformance? Why did most of the other markets and even investing styles bow to the US large-cap growth dominance? A new paper written by David Blitz
  • Using Skewness and Kurtosis to Enhance Trading and Risk Management [Relative Value Arbitrage]

    Skewness is a measure of the asymmetry of a return distribution. In this post, Ill discuss the skewness risk premium and how skewness can be used to forecast realized volatility. Skewness Risk Premium in the Options Market Skewness of returns is a statistical measure that captures the asymmetry of the distribution of an assets returns over a specified period. It is particularly important in

Recent Quant Links from Quantocracy as of 06/14/2025

This is a summary of links recently featured on Quantocracy as of Saturday, 06/14/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Absolute Valuation Models for the Stock Market: Are Indexes Fairly Priced? [Quantpedia]

    Valuation models for equity indexes are essential tools for investors seeking to assess long-term market conditions. Traditional models like the CAPE ratio, introduced by Robert J. Shiller, or the Buffett Indicator often rely on macroeconomic variables such as corporate earnings or GDP. While informative, these models can be complex and dependent on data that may be revised or vary across regions.
  • Cesar Alvarez – A Novel Way to Combine Trend, Reversion, ETFs, Volatility & More [Algorithmic Advantage]

    When I sat down recently with Cesar Alvarez of Alvarez Quant Trading, I knew I'd be tapping into a deep reservoir of quantitative trading wisdom. Cesars journey into systematic trading began similarly to many of usstarting with discretionary trades, dabbling in mutual funds, and eventually stumbling into the quant world. From his early days at Connors Research to managing sophisticated
  • Generating Synthetic Equity Data with Realistic Correlation Structure [Quant Start]

    Recently on QuantStart we have begun looking at generating synthetic asset price paths using Stochastic Differential Equation models such as the Brownian Motion, Geometric Brownian Motion (GBM), Ornstein-Uhlenbeck and Vasicek Models. Historically, we have also considered more sophisticated models such as the Heston Stochastic Volatility Model. What we have not considered to any great extent in
  • Don’t Over-Engineer your Trading Business Make Money Instead [Robot Wealth]

    Someone sent me their trading technology blueprint. It was a thing of beauty: timeseries databases, Grafana dashboards, message queues, and all sorts of fancy architecture. My first question: What are you currently trading? Their answer: Nothing yet. But Im planning a medium frequency stat arb basket trade. I almost spat out my coffee. Look, I get it. If you come from a tech
  • Enhancing Momentum Strategies [Alpha Architect]

    Paul Calluzzo, Fabio Moneta, and Selim Topaloglu, authors of the April 2025 study Momentum at Long Holding Periods investigated a key aspect of how academic momentum strategies are typically constructed when forming a portfolio. Specifically, at the end of each month t1, the standard 12-2 momentum strategy sorts stocks based on their cumulative returns from month t12 to month t2 and
  • Research Review | 13 June 2025 | Analyzing And Monitoring Risk [Capital Spectator]

  • Short-Term Basis Reversal [Quantitativo]

    A single hair from the head of a woman is worth more than all the books of Galen and Avicenna. Paracelsus. Paracelsus (14931541) was one of the most radical and influential physicians and philosophers of the Renaissance. A restless traveler, alchemist, and fierce critic of medical orthodoxy, he believed that true knowledge came not from ancient books but from direct observation of nature
  • Weekly Research Recap [Quant Seeker]

    The Reaction of Corn Futures Markets to US and Brazilian Crop Reports (Silveira, Silva, Mattos, Junior, and Capitani) Crop reports from the US and Brazil inform markets about expected corn production, but not all reports have the same impact. The authors find that US reports prompt sharp price movements and trading spikes in both US and Brazilian futures markets while Brazilian reports, on the

Recent Quant Links from Quantocracy as of 06/09/2025

This is a summary of links recently featured on Quantocracy as of Monday, 06/09/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Supervised Portfolios: A Supervised Machine Learning Approach to Portfolio Optimization [Portfolio Optimizer]

    Standard portfolio allocation algorithms like Markowitz mean-variance optimization or Choueffati diversification ratio optimization usually take in input asset information (expected returns, estimated covariance matrix) as well investor constraints and preferences (maximum asset weights, risk aversion) to produce in output portfolio weights satisfying a selected mathematical objective like
  • Reduce Trading Costs and Boost Profits with the “No-Trade Region” Strategy [Robot Wealth]

    An easy, practical way to harness an edge in the face of trading costs is the no trade region technique. It nearly always improves after-cost performance. Heres a real example: And it does so with only one-tenth of the trading of the baseline strategy! Uncertain edge vs certain costs A good thing to remember is that your edge is uncertain not only do you usually not know how big it
  • Cross-Attention for Cross-Asset Applications [Gatambook]

    In the previous blog post, we saw how we can apply self-attention transformers to a matrix of time series features of a single stock. The output of that transformer is a transformed feature vector r of dimension 768 1. 768 is the result of 12 64: all the lagged features are concatenated / flattened into one vector. 12 is the number of lagged months, and 64 is the dimension of the embedding
  • Gold Ratios as Stock Market Predictors [Relative Value Arbitrage]

    The ratio of gold prices to other asset classes has been shown to be a useful predictor of stock market returns. In this post, we discussed several gold-based ratios and how they can be used to forecast equity market performance. Gold Oil Price Ratio As a Predictor of Stock Market Returns Analyzing intermarket relationships between assets can help identify trends and predict returns.
  • Pre-Announcement Drift for BoE, BoJ, SNB: Do Markets Move Before the Word Is Out? [Quantpedia]

    Weve previously examined how central bank policy decisionsparticularly those by the Federal Reserve and the European Central Bank (ECB)impact stock market behavior. The price drift in U.S. equities around the Federal Open Market Committee (FOMC) meetings is a well-documented phenomenon. Likewise, our research study of the ECB revealed a pre-announcement drift, underscoring the

Recent Quant Links from Quantocracy as of 06/03/2025

This is a summary of links recently featured on Quantocracy as of Tuesday, 06/03/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Off to the Races: A Universal Metastrategy [Paper to Profit]

    We often have baskets of assets that we turn into trading strategies. But also, we have baskets of trading strategies that we need to allocate our capital into. In my last post (here), I demonstrated how to use generative AI to create a theoretically limitless supply of trading strategies. But, this is no good unless those strategies actually make money. Backtests tell us what happened, but
  • Weekly Research Recap [Quant Seeker]

    Anomaly Persistence and Nonstandard Errors (Coqueret and Perignon) Many investing anomalies seem compelling, but their performance often depends on how they're tested. This paper demonstrates that overlapping design choices, such as holding periods and weighting, create strong correlations between results, making strategies appear more robust than they actually are. The authors propose a
  • Quickies #1: Overfitting and EWMAC forecast scalars [Investment Idiocy]

    I'm now in full book writing mode, so I don't have the time to do full blog posts. Instead I plan to do a series of quick posts where I share some research I did for the book. Cynically, there is also a chance it will encourage you to buy the book, as long as I don't overshare like one of those movie trailers that gives away the plot and includes all the best action scenes.
  • Data: Range, Renko, Filter and Volatility bars [Trading the Breaking]

    You are observing the markets in real-timethousands of price ticks cascading across your screen, each reflecting a momentary shift in supply, demand, and sentiment. At first glance, the data appears evenly spaced, structured, and regular. Yet beneath this surface lies a deeper asymmetry: the rhythm of market activity is not governed by the uniform cadence of the wall clock, but by the irregular
  • Explaining overnight returns in the US [Joachim Klement]

    Older people among my readers will remember the time when there was for a while a discussion about how the US stock market had significantly higher returns between yesterdays close and todays open (when there were no trades at all) than during the day. Those were the innocent days of an era long gone, aka 2018, when we were all nave and enthralled by a bull market that couldnt
  • Volatility of Volatility: Insights from VVIX [Relative Value Arbitrage]

    The volatility of volatility index, VVIX, is a measure of the expected volatility of the VIX index itself. In this post, we will discuss its dynamics, compare it with the VIX index, and explore how it can be used to characterize market regimes. Dynamics of the Volatility of Volatility Index, VVIX The VVIX, also known as the Volatility of Volatility Index, is a measure that tracks the expected

Recent Quant Links from Quantocracy as of 05/31/2025

This is a summary of links recently featured on Quantocracy as of Saturday, 05/31/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quantpedia Awards 2025 Winners Announcement [Quantpedia]

    Hello all, Welcome to the Quantpedia Awards 2025 winners announcement. This is the moment we all have been waiting for, and today, we would again like to acknowledge the accomplishments of the researchers behind innovative studies in quantitative trading. So, what do the top five look like, and what will the authors of the papers receive? 1st Place Harvey, Mazzoleni, Melone: The Unintended
  • 164 Profitable Trading Strategies [Paper to Profit]

    As mentioned in my last post (here), I designed and developed a way to quickly produce trading systems with the help of generative AI. And while this sounds like a recipe for disaster, because I constrained the problem to a very specific subset and I focused on only a few factors, the results were actually quite amazing. From 875 candidate strategies, I manually filtered out 259 contenders. Using
  • Can We Finally Use ChatGPT as a Quantitative Analyst? [Quantpedia]

    In two of our previous articles, we explored the idea of using artificial intelligence to backtest trading strategies. Since then, AI has continued to develop, with tools like ChatGPT evolving from simple Q&A assistants into more complex tools that may aid in developing and testing investment strategiesat least, according to some of the more optimistic voices in the field. Over a year has
  • Weekly Research Recap [Quant Seeker]

    Time for another batch of top-tier investing research. Below is a carefully curated list of great papers from last week, each linked to the original source for easy access. If youre enjoying these posts, a like or subscribe is always appreciated, thank you for your support! Bonds Book-to-Market, Mispricing, and the Cross Section of Corporate Bond Returns (Bartram, Grinblatt, and Nozawa) Many
  • Probabilistic Inferencing for Trading Strategies [Hanguk Quant]

    Previously, we have discussed classical non-parametric approaches to making probabilistic inferences on attributes of trading strategies based on typical artefacts available. In this post, we discuss and implement in Python a finite-sample probabilistic bounding method, a unique approach coined Rademacher Anti-Serum by Paleologo, in this new book; The Elements of Quantitative Investing. We show
  • I Asked 6 LLMs for Better Exit Strategies [Rogue Quant]

    You start writing a trading strategy. The entry? Solid. Sharp. Thought-out. The exit? Let me guess Fixed dollar profit target? A stop based on some ATR multiple? Maybe a hard-coded dollar loss? Or the classic: "Just close it after 7 bars I guess?" Same old, same old. What if thats your weakest link? Stop exiting like everyone else does. We obsess over entries. We optimize
  • Unlocking Cross-Asset Potential: A New Approach to Portfolio Construction [Alpha Architect]

    Christian Goulding and Campbell Harvey, authors of the study Investment Base Pairs, proposed a groundbreaking framework for portfolio construction that challenges traditional approaches in modern finance. Their research focused on leveraging cross-asset information to optimize investment strategies and improve returns across diverse asset classes. Heres an overview of their investigation,

Recent Quant Links from Quantocracy as of 05/26/2025

This is a summary of links recently featured on Quantocracy as of Monday, 05/26/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • What are your bars hiding from you? [Trading the Breaking]

    The electronic marketplace generates vast amount of databillions of timestamped trades, quotes, and cancellationsthat demand processing to extract actionable insights. For quantitative traders, the central challenge lies not in designing strategies but in constructing a robust framework to interpret this data. Raw tick data, while granular, is computationally intensive and often contains
  • Market Timing with Macro Surveys [Quant Seeker]

    Hi there. In recent months, there has been increased chatter about the possibility of a recession triggered by President Trumps tariff war. The recent pause in tariffs appears to have eased some of those concerns. For example, JP Morgan now sees the likelihood of a U.S. recession to be below 50%, and the recession odds on Polymarket currently hover around 40%. For investors, recessions are
  • Simplicity or Complexity? Rethinking Trading Models in the Age of AI and ML [Relative Value Arbitrage]

    When it comes to trading system design, there are two schools of thought: one advocates for simpler rules, while the other favors more complex ones. Which approach is better? This newsletter explores both perspectives through the lens of machine learning. Use of Machine Learning in Pairs Trading Machine learning has become an essential tool in modern finance, transforming the way financial