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

Risk parity is an investment strategy that allocates risk exposure equally across asset types through volatility-based calibration and leverage. A most profitable risk parity strategy in the past decades has been the equity-duration “long-long”, which harvests combined equity and long

Questions about this long-ago strategy from Cliff Smith land in our inbox periodically (here’s another recent take). Smith’s simple strategy trades senior loan (aka leveraged loan) ETFs like BKLN, and has continued to be effective at timing these ETFs in the 10+ years since it was published.

When building an intraday systematic strategy, the quality and consistency of historical data can make or break your trading results. Cost, however, is also a critical factor for many traders. We conducted a comprehensive analysis comparing three popular data providers offering REST APIs for

In the day-to-day grind of systematic trading, volatility isn’t just a market feature—it’s the atmosphere we operate in. It drives the edge, defines the risk, and sets the tempo. But while volatility creates the conditions for profit, it also contains the seeds of our destruction. That

Building machine learning models for trading is full of nuances, and one important but often overlooked question is: what exactly should we try to predict—the direction of the next market move or the actual value of the asset’s return? A recent paper by Cheng, Shang, and Zhao, titled

During the previous optimization cycle, I was tasked with enhancing inventory management protocols for a legacy trading system operating under low-latency constraints—order cycle times ≥ 500ms. While the academic corpus fixates on high-frequency trading paradigms—microsecond latency

Asset Allocation How Much Should You Pay for Alpha? Measuring the Value of Active Management with Utility Calculations (Ang and Basu) Many investors chase high-performing funds expecting them to beat the market, but rarely ask how much that outperformance is actually worth to them. Even when a fund

Oscillator-type indicators swing around the zero line. They are often used for opening positions when oscillator exceeds a positive or negative threshold. In his article series about no-lag indicators, John Ehlers presents in the TASC June issue the Cybernetic Oscillator. It is built by applying a

The recent market turbulence highlights the need for improved risk management and strategies to reduce portfolio volatility. In this post, I’ll explore how to enhance portfolio diversification using low-volatility stocks. Gold and Low-Volatility Stocks as Diversifiers Gold has long been regarded

For those of us who grew up before GenAI became a thing (e.g. Ernie), we often use tree-based algorithms for supervised learning. Trees work very well with heterogeneous and tabular feature sets, and by limiting the number of nodes or the depth of a branch, there is feature selection by default.

A friend of mine owns a Neapolitan-style pizzeria… this is the real pizzeria… When he first opened, he had one recurring headache: He could never guess how many pizzas he’d sell each night. Some days he ran out of dough by 9pm. Other days he overprepared and ended up tossing dozens of unused

Rethinking the Stock-Bond Correlation Thierry Roncalli (Amundi Asset Management & University of Evry) February 2025 The stock-bond correlation is a basics of finance and is related to some of the fundamentals of asset management. However, understanding the stock-bond correlation is not easy. In

What can we realistically expect from investing across different asset classes over the long run? That’s the kind of big-picture question the “Long-Run Asset Returns“ paper tackles—offering a sweeping look at how stocks, bonds, real estate, and commodities have performed over the past 200

In his 2013 paper “The Other Side of Value: The Gross Profitability Premium,” Robert Novy-Marx documented that profitability, broadly measured, has as much power as relative price in predicting cross-sectional differences in expected returns. With the publication of that paper, profitability

Algorithmic trading systems can give you this sleek, high-tech confidence—like the robots have everything under control. They’re fast, precise, and backtested to death, right? But that’s where the trap snaps shut. When your risk metrics are built on things like standard deviation or recent

Can machine learning models better predict stock returns if they are tailored to specific industries, or is a one-size-fits-all (generalist) approach sufficient? This question lies at the heart of a recent research paper by Matthias Hanauer, Amar Soebhag, Marc Stam, and Tobias Hoogteijling. Their

In the realm of investment strategies, simplicity has long been favored. Traditional models with a limited number of parameters are prized for their interpretability and ease of use. However, recent research challenges this convention, suggesting that embracing complexity can lead to more accurate

We think of momentum and mean reversion as opposing forces—pick one or the other. Yet, data from 2000 shows that blending both via a local adaptive learning filter produces 20% CAGR on liquid equities versus 8% buy-and-hold. Traders ignoring this hybrid edge are leaving significant extra returns

Welcome to the second part of this two-part blog series on the bias-variance tradeoff and its application to trading in financial markets. In the first part, we attempted to develop an intuition for bias-variance decomposition. In this part, we’ll extend what we learned and develop a trading

Time for another round of the latest investing research. Below is a curated list of last week’s highlights, each linked to the original source for easy access. Appreciate your continued support! If you’re finding value in these posts, feel free to like and subscribe if you haven’t already.

"If you're not thinking about risk, then you're not thinking." William Sharpe. William Sharpe is a Nobel Prize-winning economist renowned for his work on the Capital Asset Pricing Model (CAPM) and the Sharpe Ratio, both of which highlight the central role of risk in pricing and

The popularity of trend-following bears the risk of market excesses. Medium-term market price trends often fuel economic trends that eventually oppose them (”macro headwinds”). Fortunately, relevant point-in-time economic indicators can provide critical information on the sustainability of

I recently covered calendar anomalies in the stock markets. Interestingly, patterns over time also appear in the volatility space. In this post, I’ll discuss the seasonality of volatility risk premium (VRP) in more detail. Breaking Down the Volatility Risk Premium: Overnight vs. Intraday Returns

Bitcoin Arbitrage: The Role of a Single Exchange (Flowerday, Gandal, Halaburda, Olson, and Ardel) Cross-exchange arbitrage has historically been common in crypto markets. This paper analyzes Bitcoin price differences across major exchanges from 2017 to 2020 and finds that Bitfinex was responsible

Finishing our little mini-series on shorter-term futures trading we talk to Andrea Unger and happily inject some click-bait in the form of gloating about his 672% return in a single year when he won the World Trading Competition. Naturally, we know that this kind of return is generated by

Two minute to 30 minute horizon: Mean reversion works, and is most effective at the 4-8 minute horizon from a predictive perspective; although from a Sharpe Ratio angle it's likely the benefits of speeding up to a two minute trade window would overcome the slight loss in predictability. There

This article explores how researchers forecast market returns by aggregating expected returns from individual stocks. Using machine learning, they improve accuracy over traditional methods. The approach helps identify when to increase or reduce market exposure. This can lead to better-informed

The correlation between stocks and bonds plays a crucial role in portfolio allocation and diversification strategies. In this issue, I discuss stock-bond relationships, the factors that influence their correlation, and techniques for forecasting it. What Influences Stock-Bond Correlation?

Clustering consists in trying to identify groups of “similar behavior”1 - called clusters - from a dataset, according to some chosen characteristics. An example of such a characteristic in finance is the correlation coefficient between two time series of asset returns, whose usage to partition a