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

In recent months, financial markets have experienced heightened volatility as Donald Trump, in his second term as President of the United States, increasingly uses executive orders to steer economic policy. While he also made use of this presidential power during his first term (2017–2021), the

I trust everyone is having a relaxing Passover week and is ready to devour some trading wisdom from Kevin Davey, an algorithmic trader with over 30 years of experience and a background in aerospace engineering and quality assurance, who exemplifies the importance of a disciplined process in trading.

Momentum is a financial anomaly in which buying stocks with positive past returns and selling the negative yielding ones has delivered positive returns. After Jegadeesh and Titman (1993)’s seminal paper “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency”

With financial markets increasingly whipsawed by geopolitical tensions and unpredictable policy shifts from the Trump administration—investors are once again questioning how to understand risk, fear, and the true drivers of returns. A recent and compelling paper dives into this debate with a

In this webinar, James explores a simple seasonality effect and finds that there’s more to the story than an upwardly sloping equity curve. Watch the video to see how you can use Excel to explore market phenomena efficiently and gather evidence that you can use to make practical trading decisions.

The SPX is going to experience a Death Cross today at the close. I’ve written many times in the past about “Death Crosses”. A Death Cross is when the 50ma crosses below the 200ma. It is confirmation of a downtrend. Some people view it as a bearish signal. As you’ll see, it is not a great

Time for another round of great 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. Bonds

nd Strategies Subscribe to newsletter Calendar anomalies in the stock market refer to recurring patterns or anomalies that occur at specific times of the year, month, or week, which cannot be explained by traditional financial theories. These anomalies often defy the efficient market hypothesis and

Mad out there isn't it? Tarrifs on/off/on/partially off/on... USD/SP500/Gold/US10/Bitcoin all yoyoing like crazy. Seems a good moment to be slightly reflective. I skipped my annual performance update last year, a little sad given it was my tenth anniversary. Mainly this is because it had become

Quantamental economic surprises are point-in-time measures of deviations of economic indicators from expected values. There are two types of surprises: first-print events and pure revisions. First-print events feature new observation periods, and the surprise element depends on market expectations

A catastrophe (CAT) bond is a debt instrument designed to transfer extreme event risks from insurers to capital market investors. They’re important for financial institutions, especially insurers and reinsurers, because they offer a way to manage large, low-probability. In this post, I feature

In this week’s “Research Insights,” I cover three interesting papers. The first examines the performance of crypto breakout strategies. The second questions the reliability of the 4% withdrawal rule amid today’s market turmoil and inflation concerns, while the third explores how commodity

One of the simplest form of trend trading opens positions when the price crosses its moving average, and closes or reverses them when the price crosses back. In the latest TASC issue, Perry Kaufman suggested an alternative. He is using a linear regression line with an upper and lower band for trend

This post gives a high-level introduction to Resampled Portfolio Stacking, which is a method for portfolio optimization with fully general parameter uncertainty introduced in Chapter 6 of the Portfolio Construction and Risk Management book1. The fundamental perspectives for the Resampled Portfolio

It’s time for another roundup of the latest investing research. Below is a carefully curated selection of last week’s highlights, with each title linking directly to its source for further reading. Thanks for your ongoing support! If you enjoy this content, please consider hitting the like

This article explores why stocks that have been performing well tend to continue doing so, a phenomenon known as “momentum.” Researchers analyzed data from various countries to see if explanations found in U.S. markets also apply internationally. They discovered that when information about a

The usual things people first look at when designing new trading systems is trend following / mean reversion — while in practice there are a wide range of other: liquidity, macroeconomic & sentiment factors that also heavily influence an asset’s returns (sometimes even cross-asset lead-lag

Last week, global stock markets faced a sharp and sudden correction. The S&P 500 dropped 10% in just two trading days, its worst weekly since the Covid crash 5 years ago. Big drops like this remind us that market volatility isn’t random, it tends to stick around once it starts. When markets

In this week’s “Research Insights”, I cover three interesting papers. The first is a timely study on how tariffs impact exchange rates. The second explores how volatility scaling can improve Sharpe ratios in crypto strategies. The third studies whether simple pairs trading in U.S. stocks

The key takeaway: The Portfolio Optimizer is effective at selecting optimal strategy combinations, even when “walked-forward” (i.e. when limited to data it would have had at that moment in time). First, a bit of background knowledge you’ll need to understand this analysis… Background

Understanding seasonality in financial markets requires recognizing how predictable return patterns can be influenced by investor behavior. One underexplored aspect of this is the impact of front-running—where traders anticipate seasonal trends and act early, shifting returns forward in time. We

It's time once again to explore some of the most compelling investing research from the past week. Below, you'll find a hand-picked selection of recent papers, each linked directly to the original source for further reading. Thanks for your ongoing support! If you enjoy this content,

Implied volatility is an important concept in finance and trading. In this post, I further discuss its breakdown into diffusive volatility and jump risk components. Decomposing Implied Volatility: Diffusive and Jump Risks Implied volatility is an estimation of the future volatility of a security’s

The idea “We don't have better algorithms; we just have more data.” Peter Norvig. Peter Norvig is one of the greatest computer scientists of all time and a leading figure in artificial intelligence. As the former Director of Research at Google, he played a key role in shaping the

Prerequisites To fully grasp the bias-variance tradeoff and its role in trading, it is essential first to build a strong foundation in mathematics, machine learning, and programming. Start with the fundamental mathematical concepts necessary for algorithmic trading by reading Stock Market Math:

Imagine you have to backtest a strategy on 50 stocks and for that you have to download price data of 50 stocks. But traditionally you have to download ticker by ticker. This sequential download process can be painfully slow, especially when each API call requires waiting for external servers to

The dominance of mega-tech stocks, particularly the “Magnificent 7,” in both U.S. and global equity indexes has a profound impact on factor portfolios. When constructing value-weighted smart beta strategies, these portfolios often end up heavily concentrated in a few individual stocks. This

I had a thought this week about what constitutes my "trading edge". You know, the question every trader is expected to be able to answer. It's supposed to constitute some kind of evidence that you can out-perform the market, your peers, or whatever. Something Bob Pardo mentioned made

Macro risk premium scores are differences between market-implied risk and point-in-time quantified macroeconomic risk. Two principal types of scores can be calculated for credit markets: spread-based risk premium scores and rating-based risk premium scores. This post proposes a small set of these

In Trade Like a Quant Bootcamp, we talk about win-win risk premia harvesting. It’s a game where no one’s really competing for the edge. Think about VTI (Vanguard’s Total Stock Market ETF). You expect to make more than implied by the stock market’s cash flows (a risk premium) because holding

Cross-Asset Momentum Capturing Time-Varying Return Predictability: The Multi-Asset Time Series Momentum Strategy (Harris, Taylor, and Wang) While standard time-series momentum strategies rely only on each asset's own return history, research shows that incorporating cross-asset predictability

Cryptocurrencies are becoming mainstream. In this post, I feature some strategies for trading and managing risks in cryptocurrencies. Arbitrage Trading in the Cryptocurrency Market Arbitrage trading takes advantage of price differences in different markets and/or instruments. Reference [1] examined

Imagine yourself, a great retail trader with an algorithm that flawlessly predicts stock movements for months—until a surprise Fed rate hike sends markets into chaos. Overnight, the model’s accuracy plummets. Why? Concept drift: your model no longer finds patterns in historical data and now

Here we present a yield curve interpolation method, one that’s based on conditioning a stochastic model on a set of market yields. The concept is closely related to a Brownian bridge where you generate scenario according to an SDE, but with the extra condition that the start and end of the

A good trading or investment strategy is only as good as the data behind it. High-quality data is essential if you are backtesting a quant model, analyzing market trends, or building an algorithmic trading system. Prerequisites: To make the most of this blog, it is essential to have a strong

ChatGPT and Deepseek: Can They Predict the Stock Market and Macroeconomy? Jian Chen (Xiamen University), et al. February 2025 We study whether ChatGPT and DeepSeek can extract information from the Wall Street Journal to predict the stock market and the macroeconomy. We find that ChatGPT has