Quant Mashup The Limits of Out-of-Sample Testing [Relative Value Arbitrage]In trading system design, out-of-sample (OOS) testing is a critical step to assess robustness. It is a necessary step, but not sufficient. In this post, I’ll explore some issues with OOS testing. How Well Overfitted Trading Systems Perform Out-of-Sample? In-sample overfitting is a serious problem(...) We interrupt this service for an important message [Klement on Investing]Hi everyone Usually, I don’t comment too much on current affairs on this substack. Still, Trump firing the Head of the BLS, Erika McEntarfer, because he didn’t like the labour market data, is extremely dangerous for investors everywhere. If you have investments in the US, you should be highly(...) A Quant's Guide to Covariance Matrix Estimation [OS Quant]In this article, we explore three techniques to improve covariance matrix estimation: evaluating estimates independently of backtests, decoupling variance and correlation, and applying shrinkage for more robust outputs. Author Adrian Letchford Published 2 August 2025 Length 12 minutes Like what you(...) Overnight Crypto Returns [Falkenblog]On Monday, I examined the flaw in capturing the overnight equity return anomaly. The basic issue was that the anomaly shrank considerably after the 2008 bear market, and given that one has to turn over the entire portfolio twice a day, the minuscule transaction costs eliminate any alpha. The guys(...) A Different Way of Looking at Returns [Mark Best]It would be nice if it were possible to trade a moving average cross. The problem with this is always that the data lags. It’s not possible to trade the current value of a moving average since it requires trading prices in the past. The advantage to doing so is that, due to the smoothing,(...) 100 Papers an Hour: 10x'ing Your Strategy Research Speed With AI [Paper to Profit]As much as LLMs and AI seem to be writing our code, creating our art, and potentially replacing (or at least supplementing) our own artistic souls, they also still excel at pretty mundane tasks. When applied correctly, they can chew through hundreds of research papers at a time and give you deeper(...) First trading day of the month has generally been strong…except August [Quantifiable Edges]I’ve shown the chart below several times over the years. It breaks down by month the performance of the first trading day of the month. July has long had the strongest Day 1. But August is also notable for it’s lack of Day 1 performance. As you can see it is the only month with a negative Day 1(...) Options: Iron Condor Strategy [Trading the Breaking]The iron condor’s appeal is statistically seductive: a high-probability, defined-risk structure promising steady income from time decay and volatility erosion. Yet beneath its deceptively flat payoff profile lies a quantitatively intricate reality—one where theoretical win rates often mask a(...) The Equity Overnight Anomaly ETFs [Falkenblog]TL;DR The overnight return anomaly became much less anomalous around 2009 The failed ETFs designed to capture it suffered from horrible timing, but also transaction costs Transaction costs are much greater than fees, and also greater than fees + (ask-bid)/2 The overnight equity anomaly is that most(...) From Defense to Offense: A Tactical Model for All Seasons [Quantitativo]“Basketball is a game of adjustments.” Bob Knight. Bob Knight was the last coach to lead an NCAA team to a perfect season: 32 wins, zero losses. That record still stands nearly half a century later. His secret? He was a masterful tactician. Obsessed with preparation, relentless on fundamentals,(...) How to Identify Ponzi Funds? [Quantpedia]Can we spot a Ponzi scheme before it collapses? That question haunts regulators, investors, and journalists alike. But what if some modern investment funds operate on dynamics that, while not technically illegal, closely resemble Ponzi-like behavior? A new paper by Philippe van der Beck,(...) The Risks of Passive Investing Dominance [Alpha Architect]Fueled by the persistent failure of active management (as evidenced, for example, by the annual SPIVA scorecards), passive investing now commands the majority of assets under management. This structural shift is not without consequence. Chris Brightman and Campbell Harvey’s May 2025 paper(...) Sentiment as Signal: Forecasting with Alternative Data and Generative AI [Relative Value Arbitrage]Quantitative trading based on market sentiment is a less developed area compared to traditional approaches. With the explosion of social media, advances in computing resources, and AI technology, sentiment-based trading is making progress. In this post, I will explore some aspects of sentiment(...) Why the Last Few Minutes of Trading Might Matter More Than You Think [Alpha Architect]This paper reveals a striking pattern in U.S. stock markets: the prices of individual stocks often reverse direction at the very end of the trading day. Using high-frequency data, the authors find that the last few minutes—particularly the closing auction—are dominated by large institutional(...) Carlson's "Defense First" [Allocate Smartly]This is a test of Thomas Carlson’s “Defense First” strategy from his paper Defense First: A Multi-Asset Tactical Model for Adaptive Downside Protection. Strategy results from 1971 follow. Results are net of transaction costs – see backtest assumptions. Learn about what we do and follow 90+(...) When your strategy works, is it just dumb luck? How to stack the odds in your favour [Robot Wealth]Recently, we had an excellent question on the Trade Like a Quant Discord server: “How do you know if your strategy is working out of coincidence rather than actual edge? The strategy might work over a long period just because of blind luck.” Damn good question. It hits right in the insecurity(...) The Memorization Problem: Can We Trust LLMs’ Forecasts? [Quantpedia]Everyone is excited about the potential of large language models (LLMs) to assist with forecasting, research, and countless day-to-day tasks. However, as their use expands into sensitive areas like financial prediction, serious concerns are emerging—particularly around memory leaks. In the recent(...) Behavioral Biases and Retail Options Trading [Relative Value Arbitrage]Why Do Investors Lose Money? Behavioral finance is the study of how financial behavior affects economic decisions and market outcomes, and how those decisions and outcomes are affected by psychological, social, and cultural factors. Behavioral finance research has shown that people do not always(...) Do Smart Machines Make Smarter Trades? [Alpha Architect]Can machine learning models help us exploit stock market anomalies more effectively? This paper says yes—but with a few important caveats. By applying gradient boosting algorithms to a wide array of established anomalies (like value, momentum, and quality), the authors show that machine learning(...) The Unintended Consequences of Rebalancing [Quantitativo]“I picked up one or two pieces and examined them attentively... I then collected four or five pieces and went to Mr. Scott... I said, ‘I believe this is gold.’” James W. Marshall. He found gold… and died broke. James W. Marshall unintentionally sparked one of the greatest migrations in(...) How Fragile is Liquidity Across Asset Classes? [Quantpedia]The paper “Through Stormy Seas: How Fragile is Liquidity Across Asset Classes?” is a very interesting examination of how liquidity properties have evolved over the past decade. Although the average bid–ask spread has declined, the kurtosis and skewness of the spread distribution have(...) The Rise of 0DTE Options: Cause for Concern or Business as Usual? [Relative Value Arbitrage]Zero DTE (Days to Expiration) options are contracts that expire on the same day they are traded. They were introduced in 2022 and have been gaining popularity. In this post, I discuss their impact on the market and how options traders use them. Impact of Zero DTE Options on the Market Zero DTE(...) The 10 Most Popular TAA Strategies Ranked [Allocate Smartly]We’re in a unique position to analyze the behavior of Tactical Asset Allocation (TAA) investors. We track 90+ TAA strategies. Members combine these strategies into what we call “Model Portfolios”. By analyzing how members form these Model Portfolios, we can understand the choices that TAA(...) Testing Strategies [Trading the Breaking]Introduction. Risks and method limitations. Circular-shift (lag-invariant) permutation test. Random sign-flip (direction-neutral) test. Stationary bootstrap of returns. White's reality check and Hansen's superior predictive ability. Jittered-entry (temporal perturbation) test. Parameter(...) Research Review | 11 July 2025 | Risk Factors [Capital Spectator]Factoring in the Low-Volatility Factor Amar Soebhag (Erasmus University Rotterdam), et al. June 2025 Low-volatility stocks have historically delivered higher risk-adjusted returns than their high-volatility peers. Despite extensive evidence and widespread adoption in the investment industry, the(...) Volatility is a Reliable and Convenient Proxy for Downside Risk [Alpha Architect]Javier Estrada, author of the June 2025 study “Volatility: A Dead Ringer for Downside Risk” tackled a longstanding debate in finance: Is volatility (the standard deviation of returns) a good measure of the risk that investors actually care about? While volatility is the most widely used risk(...) The Lumber-Gold Strategy [Allocate Smartly]The Lumber-Gold Strategy was first published a decade ago, won the 2015 NAAIM Wagner Award, and continues to be cited today. The strategy trades based on the relative strength of lumber as a leading economic indicator, versus gold. How has the strategy performed since publication? Strategy results(...) Backtesting [Trading the Breaking]Introduction You know, after more than a decade in this business, I've come to think of backtesting as the ultimate paradox of our profession. It's like being handed the top one lie detector in the world, only to discover it's been calibrated exclusively on your own personal brand of(...) Should Investors Combine or Separate Their Factor Exposures? [Alpha Architect]If you’re a factor investor, there will come a time when you will have to choose between mom and dad: Should you combine or separate your factor exposures? And make no mistake: You will have to make a decision! While there’s no right answer, the way you structure your portfolio can have(...) How Machine Learning Enhances Market Volatility Forecasting Accuracy [Relative Value Arbitrage]Machine learning has many applications in finance, such as asset pricing, risk management, portfolio optimization, and fraud detection. In this post, I discuss the use of machine learning in forecasting volatility. Using Machine Learning to Predict Market Volatility The unpredictability of the(...) PCA analysis of Futures returns for fun and profit, part 1 [Investment Idiocy]I know I had said I wouldn't be doing any substantive blog posts because of book writing (which is going well, thanks for asking) but this particular topic has been bugging me for a while. And if you listened to the last episode of Top Traders Unplugged you will hear me mention this in response(...) Testing 87 Different Stop Loss Strategies [Paper to Profit]Stop losses are a way of life for a trader. They are often ‘do or die’ in situations of intraday and leverage trading. However, we think that the ‘gold standard’ is the average trailing stop. Maybe we add an ATR band to it if we are feeling fancy. But the reality is that there are much more(...) Learn from the Source [Anton Vorobets]This newsletter gives you the last opportunity to get access to the Applied Quantitative Investment Management course at the best price. I will share the first lecture on Thursday, July 3rd. After that, the subscription price will increase from the current €100 per year. The subscription will give(...) 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(...) 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 it’s brilliant. It’s satire dressed up as nonsense. A dream disguised as a joke. A(...) 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. That’s where(...) Can We Profit from Disagreements Between Machine Learning and Trend-Following Models? [Quantpedia]When using machine learning to forecast global equity returns, it’s tempting to focus on the raw prediction—whether some stock market is expected to go up or down. But our research shows that the real value lies elsewhere. What matters most isn’t the level or direction of the machine learning(...) A Cheat Code for Crypto? [Robot Wealth]The best trading edges are often found in places most people don’t think to look. It’s why being a bit of a pirate – zigging when others zag and finding opportunities in the market’s blind spots – is such an effective approach. Today, I want to share something that has me genuinely(...) 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(...) 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(...) Navigating Economic Downturns with Survey-Based Recession Indicators [Allocate Smartly]This test was inspired by Yulong Sun’s 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(...) I Tuned the Radio on My Stock Returns [Paper to Profit]It’s 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(...) Model: Advances in clustering [Trading the Breaking]Look, here’s the thing—we’ve all been drinking the correlation Kool-Aid for decades, right? It’s elegant, sure. Clean math. Easy to explain to the PMs. But let’s get real: relying on a correlation matrix in today’s markets is like trying to sail a speedboat with an anchor chained to your(...) 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(...) Comprehensive Comparison of Algorithmic Trading Platforms [Jonathan Kinlay]This comprehensive analysis examines three leading algorithmic trading platforms—Build Alpha, Composer, and StrategyQuant X—across five critical dimensions: comparative reviews and rankings, asset class applicability, ensemble strategy capabilities, walk-forward testing and robust optimization,(...) 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(...) 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(...) 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 Euan’s 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(...) The Surefire Ratio: My Custom Risk Ratio that Supercharged My Investing [Paper to Profit]We’ve all used it. It’s 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(...) Model: Clustering [Trading the Breaking]Alright, let’s 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(...)