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

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

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

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 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

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,

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

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

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

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

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

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

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:

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

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

Skewness is a measure of the asymmetry of a return distribution. In this post, I’ll 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

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

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. Cesar’s journey into systematic trading began similarly to many of us—starting with discretionary trades, dabbling in mutual funds, and

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

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 I’m planning a medium frequency

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 t−1, the standard 12-2

“A single hair from the head of a woman is worth more than all the books of Galen and Avicenna.” Paracelsus. Paracelsus (1493–1541) was one of the most radical and influential physicians and philosophers of the Renaissance. A restless traveler, alchemist, and fierce critic of medical

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

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

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. Here’s 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

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 /

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

We’ve previously examined how central bank policy decisions—particularly 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.

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

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

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

You are observing the markets in real-time—thousands 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

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 yesterday’s close and today’s open (when there were no trades at all) than during the day. Those were the innocent days of

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

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

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

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

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 you’re enjoying these posts, a like or subscribe is always appreciated, thank you for your support! Bonds Book-to-Market,

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

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 that’s

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

The electronic marketplace generates vast amount of data—billions of timestamped trades, quotes, and cancellations—that demand processing to extract actionable insights. For quantitative traders, the central challenge lies not in designing strategies but in constructing a robust framework to

Hi there. In recent months, there has been increased chatter about the possibility of a recession triggered by President Trump’s 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%,

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

Often as traders, we equate complexity with profitability. A model’s edge comes from it doing something that no other person on Earth has tried yet. But the data shows that simple rules based on real market factors still outperform most models. Those that continue seeking complexity are headed

My kids love bedtime stories. Like most kids. But they’re not into fairy tales or superheroes. They’re obsessed with one thing: “Dad, can you tell a witch story? A mean witch, okay?” Every night. Same request. So I lie next to their bed and say, “Alright, buddies. A mean witch story it

Someone recently asked me if I have a checklist for adopting new trading strategies. You know, a neat little formula like “if backtested Sharpe > 1.8, trade it” or “if drawdown < 15%, green light.” I get the appeal. We all want clear, objective criteria to make these decisions easier.

In the previous blog post, we gave a very simple example of how traders can use self-attention transformers as a feature selection method: in this case, to select which previous returns of a stock to use for predictions or optimizations. To be precise, the transformer assigns weights on the

Everyone either naively thinks that an LLM will find alpha for them, or equally naively thinks LLMs cannot develop their own systems with any sort of edge. The reality is quite the opposite. When used properly, LLMs can supercharge your strategy research process by at least 10x. Those who aren’t