Quant Mashup Profitability Retrospective: Key Takeaways for Investors [Alpha Architect]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(...) Are you blind to the tail risks lurking in calm markets? [Trading the Breaking]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(...) Are Sector-Specific Machine Learning Models Better Than Generalists? [Quantpedia]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(...) The Virtue of Complexity in Return Prediction [Alpha Architect]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(...) How I Fused Momentum and Mean-Reversion to Achieve 20% CAGR on ETFs Since 2000 [Paper to Profit]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(...) Bias-Variance Decomposition for Trading: ML Pipeline with PCA, VIF & Evaluation [Quant Insti]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(...) Beta hedging [Quantitativo]"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(...) Equity trend-following with market and macro data [Macrosynergy]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(...) The Calendar Effects in Volatility Risk Premium [Relative Value Arbitrage]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(...) Andrea Unger - 672% Returns? Sure! Would You Like Some Risk with That? [Algorithmic Advantage]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(...) Can I build a scalping bot? A blogpost with numerous double digit SR [Investment Idiocy]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(...) The Aggregated Equity Risk Premium [Alpha Architect]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(...) Stock-Bond Correlation: What Drives It and How to Predict It [Relative Value Arbitrage]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?(...) Correlation-Based Clustering: Spectral Clustering Methods [Portfolio Optimizer]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(...) A New Approach to Regime Detection and Factor Timing [Alpha Architect]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(...) Why data mining risks your trading career [Robot Wealth]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(...) Revisiting Pragmatic Asset Allocation: Simple Rules for Complex Times [Quantpedia]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(...) Front-Running Seasonality in Country ETFs: An Extended Test [Allocate Smartly]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(...) Quantpedia Awards 2025 – Countdown [Quantpedia]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(...) Finding an Edge in IPOs: Research and a Backtested Mechanical Trading System [Cracking Markets]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(...) How Speculative Money Flows into Crypto [Unexpected Correlations]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(...) How Tiny Price Differences Help Track Small Investors’ Trades [Alpha Architect]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 Correlated Stress Reversal Trading [Quantpedia]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 macro trading signals [Macrosynergy]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(...) Kevin Davey II - Selecting Optimal Strategies for Peak Performance [Algorithmic Advantage]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 Bitter Lesson [Quantitativo]“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(...) Uncovering the Pre-ECB Drift and Its Trading Strategy Applications [Quantpedia]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(...) New Feature: Walked-Forward Optimal Strategy Combinations (aka "Meta Walk-Forwards") [Allocate Smartly]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(...) The unreasonable effectiveness of volatility targeting - and where it falls short [Unexpected Correlations]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(...) Making Factor Strategies Work for Everyone [Alpha Architect]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 in Financial Markets: When It Works and When It Doesn’t [Relative Value Arbitrage]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(...) Building a Survivorship Bias-Free Crypto Dataset with CoinMarketCap API [Concretum Group]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(...) The Least-Amount of Assumptions Backtest [Unexpected Correlations]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(...) Trump’s Executive Orders and Their Impact on Financial Markets [Quantpedia]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(...) 036 - Kevin Davey Part I - It's All About Process in Algo Trading [Algorithmic Advantage]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.(...) Enhancing Industry Momentum Strategies: Finding Hidden Neighbors [Alpha Architect]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”(...) Fear, Not Risk, Explains Asset Pricing [Quantpedia]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(...) Researching trading ideas in Excel [Robot Wealth]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.(...) 97 Years of Death Crosses [Quantifiable Edges]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(...) Do Calendar Anomalies Still Work? Evidence and Strategies [Relative Value Arbitrage]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(...) Annual performance update returneth - year 11 [Investment Idiocy]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 surprise indicators: a primer [Macrosynergy]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(...) Catastrophe Bonds: Modeling Rare Events and Pricing Risk [Relative Value Arbitrage]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(...) Trading the Channel [Financial Hacker]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(...) Resampled Portfolio Stacking [Anton Vorobets]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(...) Understanding What Drives Momentum in Global Stock Markets [Alpha Architect]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(...) Turning on-chain data into a profitable, systematic strategy (with code) [Unravel Markets]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(...) Forecasting Current Market Turbulence with the GJR-GARCH Model [Sitmo Machine Learning]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(...) Walking Forward Optimal Strategy Combinations [Allocate Smartly]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(...) Front Running in Country ETFs, or How to Spot and Leverage Seasonality [Quantpedia]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(...)