This is the first of a multipart series examining the selection of Tactical Asset Allocation (TAA) strategies based on recent performance. We are proponents of combining multiple TAA strategies together into what we call Model Portfolios to limit the risk of any single strategy going of the rails. In this study we ask, what if, each month, we selected strategies for our portfolio that had

Why the path to making money in trading runs through work youd better find interesting Data mining and vibe quanting are essentially the same thing. Both fundamentally and philosophically. Fundamentally, data mining says: Ill try enough rules until something sticks. Vibe quanting says: Ill get AI to try enough rules until something sticks. Same thing, different packaging.

The growing dominance of passive investing has fundamentally altered the dynamics of equity markets. A substantial share of trading volume is now driven by index-tracking strategies, which mechanically allocate capital based on index membership rather than company-specific fundamentals. This raises an important question: can predictable flows associated with index rebalancing be systematically

A research team in Zurich has shown that the financial sentiment classifiers running inside many automated trading and risk pipelines can be flipped quietly, undetectably, and for pennies by anyone with access to GPT-4o. Thanks for reading! Subscribe for free to receive new posts and support my work. The paper has been out a few weeks. It deserves more attention than its getting. The

Cohen, Malloy and Nguyen's Lazy Prices paper found that small year-on-year changes in 10-K filings predict large negative returns. Here is what the paper actually says, and how Snowflake Cortex AI and Semantic Views collapse the original eight-year engineering pipeline into an afternoon's work. On 23 February 2010, Baxter International filed its annual report with the SEC. The stock did

In Beyond 60/40 I argued that the classic balanced portfolio rests on an assumption that stocks and bonds will hedge each other and that the assumption fails when the macroeconomic regime changes. The argument was built on the post-2005 ETF era, the only window where clean real-price data exists for the three assets needed to test it. Twenty years made the case. Fifty-eight years sharpens

New research challenges a long-standing rule in momentum investingand reveals surprising insights about when to use it For decades, investors using momentum strategies have followed a simple rule: ignore last months returns. This skip-month convention has been standard practice since the 1990s, designed to avoid short-term reversal effects where stocks that jump up one month tend to

In the previous blogs, we examined supervised learning algorithms like linear regression in detail. In this blog, we look at what unsupervised learning is and how it differs from supervised learning. Then, we move on to discuss some use cases of unsupervised learning in investment and trading. We explore two unsupervised techniques in particular- k-means clustering and PCA with examples in Python.

Who Wins and Who Loses In Prediction Markets? Evidence from Polymarket Pat Akey (ESSEC Business School), et al. April 2026 We study pricing efficiency in decentralized prediction markets by comparing marketimplied probabilities from Polymarket with benchmarks derived from option-implied riskneutral distributions extracted from the derivatives market. We study Bitcoin and Ethereum prediction bets

One of your first thoughts when looking at a strangers backtest is probably that its overfit, or that there is some look-ahead somewhere. When you go a step further, you are probably constantly worried about overfitting your own backtests too! In this article, we will introduce a framework that allows you to identify both! Its a two-stage approach introduced in D. Nikolopoulos (2026). We

Forward records for strategies people can actually inspect. TradeLock helps managers and signal providers turn live strategy intent into a forward-tracked public record that is harder to fake than a backtest, PDF, or spreadsheet.

The decomposition of risks and returns into overnight and intraday components is an emerging area of research. In this post, we examine how these components differ in terms of volatility clustering and the variance risk premium, and what this implies for forecasting, risk management, and strategy design. Breaking Down the Volatility Risk Premium: Overnight vs. Intraday Returns The decomposition of

The fact that mean-variance optimizers are highly sensitive to changes in expected returns [] is well known in investment practice1, with a couple of practical solutions already described in this blog, for example using near efficient portfolios or subset resampling-based efficient portfolios. In this blog post, I will introduce another approach originally described in Sharpe2 and known as

What if a meaningful part of a usual trading strategys performance has nothing to do with your signalbut simply when you rebalance? A recent paper written by Carlo Zarattini & Alberto Pagani highlights a largely underestimated risk in systematic investing: rebalance timing luck (RTL). For practitioners running rotation or factor strategies, this is not noiseits a structural source

A long-bond ETF sits in almost every balanced portfolio. Ours included TLT is one of the three core holdings in the risk-parity base of our portfolio architecture. And yet when TLT lost 48% between 2020 and 2024, most holders experienced it as a shock. It should not have been. The mechanics were entirely predictable from the yield level at which investors bought in, and the historical

This study examines the profitability of mean-reversion trading strategies applied to binary outcome contracts on Polymarket, the worlds largest decentralized prediction market platform. We analyze three distinct contracts representing varying risk profiles: a quasi-risk-free instrument (No to Will Jesus Christ return in 2025?) and two high-yield speculative contracts (No to Will China

Simple inflation-based trading factors have proven their predictive power in global financial markets over the past decades. Excess inflation ratios measure the average difference between CPI growth and a countrys effective inflation target (relative to that target). Inflation pressure ratios combine excess inflation ratios with recent CPI growth surprises. Both can be calculated for headline

By the Fourier theorem, any price curve is a mix of many long-term and short-term cycles. Once in a while a dominant market cycle emerges and can be exploited for trading. In his TASC 5/2026 article, John Ehlers described an algorithm for detecting such dominant cycles, using them to tune a bandpass filter, and creating a profitable trading system. Heres how to do it. Ehlers Easylanguage

People often criticise how ML models are just black boxes that take in some features and spit out a prediction. While some models (like linear regression) are naturally a lot more interpretable than others (like neural networks), its wrong that you cant figure out why a model made a certain prediction and how the different features affect the prediction. In this article, we will look at some

It starts with a basic problem every quantitative researcher faces: you have a universe of stocks, a universe of news, and a question. Which of todays headlines actually matter for tomorrows price? Step One: Finding the News Before you can classify sentiment, you have to collect articles. The naive approach search for a company name and take everything produces a lot of noise. An

RayforceDB is a recently open sourced time series database that offers blazingly fast performance. It is built with inspiration from kdb+, which is also known for its fast performance and minimal application size. RayforceDB offers similar benefits, being written in pure C and having a binary size of less than 1MB. Another benefit of RayforceDB is that it offers Python bindings with minimal

This is how I started last years update: "Mad out there isn't it? Tarrifs on/off/on/partially off/on… USD/SP500/Gold/US10/Bitcoin all yoyoing like crazy." Well the orange peril is still at it, and as I write this the global supply of oil has been severly curtailed for several weeks now; with a certain amount of reaction in oil futures (which some of it perhaps supressed since

The most common question we hear from advisors is whats the optimal stack? So we ran the optimizer bootstrapping 10,000 simulated 25-year histories across five asset classes to find the portfolio that would have maximized return at 60/40 volatility. The answer is mathematically elegant and practically unusable. In this piece, we walk through why the optimal portfolio would have been

April 15th is tax day. Tax day has historically been a good day for the market. A reason tax day may be bullish is that it is the last day that people can make IRA contributions to count for the previous tax year. This can create a last-minute rush and you will often have an inflow of funds heading into the market right around and on April 15th (or whenever tax day ends up falling, since it is

Stock momentum has long been a workhorse idea. Buy recent winners. Sell recent losers. Critics argue those profits mostly come from riding factor trends like value, size, or industry tilts. This paper pushes back. It shows there is a durable, stock-specific momentum component tied to how prices react to firm news around earnings dates. The result is a cleaner, lower-risk way to capture momentum

This is a test of Meb Fabers Tactical Yield from T-Bills and ChillMost of the Time. Backtested results from 1930 follow compared to a benchmark of 50% int-term US Treasuries (IEF) and 50% US corporate bonds (LQD). Results are net of transaction costs see backtest assumptions. Learn about what we do and follow 100+ asset allocation strategies like this one in near real-time.

Imagine that a team downloads a price series, defines a target, applies a transformation, and moves on to signal design, model fitting, validation, and execution. That sequence looks efficient. However, the transformation of the data is is the first act of model construction. That is why data-shape transformation sits at the true front line of feature engineering. The problem is whether the

A portfolio of seasonal strategies presents a problem that modern portfolio theory was not designed for. Most of these strategies are active fewer than sixty days per year. Many pairs share zero overlapping observations. The covariance matrix the standard tool for combining return streams produces nothing but noise. You need a different approach. The Foundation The IVOL three-asset core

Someone asked me recently whether strategies based on mean reversion, trend following, and momentum are good or just data mining. Its a reasonable question, but it reveals some confusion that arises from mixing up two things that sound similar but are very different. Mean reversion, trend, momentum: these arent edges. Theyre labels for how prices move. They describe patterns, not

Investment professionals have long relied on factor investingstrategies built around characteristics like value, momentum, and qualityto generate returns beyond the broad market. But predicting which factors will perform well in the future has remained challenging. Liyao Wang and Ming Zeng, authors of the December 2025 study Factor MAX and Predictable Factor Returns, introduced an

The global investment environment is going through a period of meaningful structural change. The dominance of the U.S. dollar is increasingly being questioned, geopolitical tensions are rising, and macroeconomic uncertainty remains elevated. Together, these forces challenge the post-Global Financial Crisis environment in which U.S. equities consistently outperformed most international markets. As

This is a test of a novel strategy from David Varadi: Growth and Inflation Sector Timing. Backtested results from 1991 follow. Results are net of transaction costs see backtest assumptions. Learn about what we do and follow 100+ asset allocation strategies like this one in near real-time. Logarithmically-scaled. Click for linearly-scaled results. Members know that we are especially interested

I just returned from a two-day conference in New York, FutureAlpha (formerly QuantStrats). This year, the theme focused largely on data, machine learning, and AI. While some speakers were very enthusiastic about the potential of AI to generate alpha, our panel was more conservative. The consensus among the panelists was to use ML and AI to enhance and improve risk management. Along this theme, in

You have to know that it didnt get that way overnight. And more often than not, it didnt get that way quietly. On every step of the way down, companies like this are forced by regulators to publicly share every detail on exactly how business is going and what theyve got planned. All you have to do is look for it. So, thats exactly what we did. A Primer on Advanced Event-Driven Trading

This article examines two distinct effects that share the same calendar window and the same tickers. The first is a pure bond seasonality: TLT tends to weaken in the first week of each month and rally in the last few days, regardless of what equities do. The second is a conditioned reversal trade: when stocks outperform bonds during the first half of the month, the underperformer tends to recover

Doubt is not a pleasant condition, but certainty is a ridiculous one. Voltaire Voltaire was arguably the most influential intellectual of 18th-century France. More than that, he was a provocateur. He spent his life as a one-man war against dogma, against anyone who claimed to know the truth with absolute certainty. The Age of Enlightenment didnt begin with answers: it began with

It is 3:47 AM Pacific Time. Elon Musk, reportedly on his fourth espresso and second viewing of a documentary about Roman emperors, picks up his phone. He types something. He posts it. Within eleven minutes, Teslas stock has moved. Within forty, three semiconductor companies have been dragged along for the ride. By morning, a fund manager in Oslo is explaining to her clients why their portfolio

Systematic trading strategies with macroeconomic information often rely on panel data that aggregate cross-country experiences over time. Panel regression is more information-efficient than single-time-series regression and allows for easier detection and assertion of the predictive power of macro factors. However, panels are often unbalanced, with factors missing for certain periods in specific

One year ago, in our article Can We Finally Use ChatGPT as a Quantitative Analyst?, we explored the feasibility of leveraging ChatGPT for quantitative analysis. Since then, a lot has changed: newer models are now available (from OpenAI and also other vendors), and the ecosystem around AI-assisted analysis has evolved significantly. Back then, we encountered numerous challenges, ranging from