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

The financial research literature has found that the performance of assets (and factors) can vary substantially across regimes (for example, see here and here)—factor premiums can be regime dependent. Unfortunately, the real-time identification of the current economic regime is one of the biggest

I was recently talking to someone about data mining as an approach to finding edges to trade. I get the appeal. Feed enough data into a computer, run enough tests, and surely something profitable will emerge, right? Maybe. But almost certainly not. But the worst thing about this approach is that it

Pragmatic Asset Allocation (PAA) represents a portfolio construction approach that seeks to balance the benefits of systematic trend-following with the realities faced by semi-active investors (mainly taxes and lack of time to manage positions). Building upon the insights presented in Quantpedia’s

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! Asset Allocation

This is a test of a dynamic seasonality strategy from Quantpedia that selects from 23 individual country ETFs. We’ve extended the author’s test by 30+ years using MSCI index data. Backtested results from 1971 follow versus an equal-weight benchmark of those 23 country ETFs (1). Learn more about

Hello all, Just little over 24 hours remain until the end of the deadline for QUANTPEDIA AWARDS 2025 – April 30th, 2025, at 23:59 UTC. Join the competition now, and don’t miss out on this chance to showcase your skills! Alternatively, if you can’t (or don’t want) to join, then please help us

Ever heard the term "IPO" thrown around in financial news? Let's break down what it means and why it might be interesting for systematic traders. What Exactly is an IPO? "IPO" stands for "Initial Public Offering." It's the very first time a private company

Compared to traditional futures or equities, crypto markets offer greater transparency—thanks primarily to the public blockchain and also to the unique culture that shaped the industry. This opens up new opportunities for investors and traders to monitor and measure liquidity dynamics that are

This article explains how researchers studied small investors’ trading habits by looking at tiny price differences, called subpennies, in stock trades. They found that the current method to identify these trades isn’t very accurate. By using a new approach, they improved the accuracy, helping to

Short-term reversal strategies in U.S. large-cap equity indexes, such as the S&P 500, are well-documented and widely followed. These reversals often occur in response to brief periods of market stress, where sharp declines are followed by quick recoveries (as we have experienced in the last few

Boosting is a machine learning ensemble method that combines the predictions of a chain of basic models, whereby each model seeks to address the shortcomings of the previous one. This post applies adaptive boosting (Adaboost) to trading signal optimisation. Signals are constructed with macro factors

Geopolitical tensions, such as wars, threats, or major international conflicts, are known to affect economies and markets. In recent years, several papers have examined the impact of geopolitical risk on stock returns. For example, Sheng et al. (2025) construct a risk index based on news articles

In Part II with Kevin, he delves into the intricate mechanics behind his systematic futures trading approach, offering advanced quantitative traders a window into the finer points of strategy design, walk forward analysis, robustness testing, and portfolio construction. Drawing on decades of

“The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin.” Richard Sutton. Richard Sutton is one of the greatest minds of our time. He is a founding figure in modern AI and a

As the world’s attention shifts from the US-centric equity markets to international equity markets (which strongly outperform on the YTD basis), we could review some interesting anomalies and patterns that exist outside of the United States. In the world of monetary policy, traders have long

Members: See the complete list of Meta Walk-Forwards In our previous post, we introduced this concept of “walking forward” optimal strategy combinations. In other words, we’re finding the optimal combination of strategies, in real-time, based only on data available at that moment in time. We

This is part 1 of our in-depth investigation of how quantitative risk management could help improve risk-adjusted returns: I'll explain what volatility targeting is, explore a seemingly paradoxical phenomenon, and highlight its blindspots. Volatility targeting’s goal is to keep an asset or

It’s time for another round of great investing research. Below is a curated selection of last week’s highlights, each linked to the original source for easy reading. If you’re enjoying these posts, a like or subscribe is always appreciated, thank you for your support! And may I kindly ask you

This article explores the difference between tradable and on-paper (theoretical) risk factors in investing. Risk factors are strategies that help explain stock market returns, but many work only in theory and not in real life. Researchers developed ways to make these factors tradable by using mutual

Machine learning (ML) has made a lot of progress in recent years. However, there are still skeptics, especially when it comes to its application in finance. In this post, I will feature articles that discuss the pros and cons of ML. In future editions, I’ll explore specific techniques. How

Given the recent market downturn, marked by the S&P 500 and the Nasdaq trading well below their 200-day moving averages, the familiar adage “Nothing good happens below the 200-day moving average” has once again gained traction in financial media. The 200-day simple moving average (200SMA) is

When you look at a chart of Bitcoin’s price from 2010 to today, it tells a story of volatility, resilience, and long-term gains. But what about the thousands of coins that launched, pumped, and then disappeared along the way? Most commonly used crypto datasets, especially those tied to current

In this week’s edition of Research Insights, I break down three recent papers. The first examines look-ahead bias in large language models. The second introduces a new approach to enhancing momentum strategies. The third explores how sensitive many cross-country return anomalies are to

There’s this Neumann quote: "With four parameters I can fit an elephant, and with five I can make him wiggle his trunk." Funny, but also true. It’s very fitting (haha) to our job at Unravel where we scan tens of thousands of time series in order to identify the ones that can be used as