Quant Mashup From Hype to Reality: Building a Hybrid Transformer-MVO Pipeline [Jonathan Kinlay]A Five-Way Decomposition of What Actually Drives Risk-Adjusted Returns in an AI Portfolio The quantitative finance space is currently flooded with claims of deep learning models generating massive, effortless alpha. As practitioners, we know that raw returns are easy to simulate but risk-adjusted(...) Increases CAPE Ratio Predictability with a Simple Adjustment [Alpha Architect]CAPE has long been a cornerstone of long-horizon return forecasting. High valuations imply lower future returns. Low valuations imply higher future returns. Critics argue that its predictive power has faded in recent decades. This paper pushes back. It shows that the apparent decline is largely a(...) The Friday Gold Trade: A Conditional Edge [Beyond Passive]Gold drifts higher on Fridays. The effect is statistically significant, it has persisted for decades, and most traders who know about it trade it unconditionally. The useful question is not whether this is true but under which market conditions it is true — and the answer changes everything about(...) Unlocking relative value across asset classes [Macrosynergy]A macro-systematic approach allows efficient allocation of risk capital across 12 conventional financial market asset classes. Positions in each asset class are taken via leveraged derivatives to equalize expected volatility. Targets are relative positions in one class versus all the others. Trading(...) The Return of the King: Trend Following Is Back – But Will It Last? [Alpha Architect]On April 2, 2025, one of the largest market shocks since 2020 hits financial markets. Out of left field, President Trump announces punitive tariffs left and right, and most financial assets begin bleeding. In the desperation, investors look for a safe haven. Ironically, trend following—long(...) More of the Disease, Faster (What happens when you ask an LLM to find you an edge) [Robot Wealth]This week I discovered the “vibe quant” movement (or rather, it discovered me). People using LLMs to find trading strategies, validate them, and put them into production. The pitch is seductive: the LLM reads the literature, implements the ideas, backtests them, and you just supervise. I think(...) Timing Value vs. Growth: Evidence from 100 Years of Small Value–Large Growth Spread [Quantpedia]The goal of our article is to examine the long-term relationship between small value and large growth stocks using more than 100 years of data and test whether the spread between small value and large growth portfolios shows trends that could help investors switch between the two styles. Using the(...) Diversification Has Been a Huge Drag on TAA Performance for 15+ Years [Allocate Smartly](…but that won’t always be the case) Over the last 15+ years, diversification (as opposed to market timing) has been a huge drag on Tactical Asset Allocation (TAA) performance, to the tune of 2.1% per year compared to the 60/40 benchmark. That “diversification drag” has been mostly due to US(...) How to write a tweet that gets over 300k views; and why diversification is probably good [Investment Idiocy]At eight words this is almost certainly* my most viewed and liked tweet ever (although I have nearly 25k followers, so thats 18,000 or so that didn't like it) . Short, pithy, funny; I should retire from my Xmaxxing game right now (just kidding; there are still plenty of gamblers, crypto nuts(...) Landing Your First Role - Breaking Into Quant Finance [Quantt]Why Breaking In Feels So Hard (and Why It Is Still Achievable) If you are trying to get your first role in quantitative finance, you have probably noticed two things: Job descriptions often look intimidating Everyone online seems to have a PhD, a perfect CV, or both That can make the whole path feel(...) Anomaly-Based Trading Strategies in the Real Estate Sector. Can the Market Be Beaten? [Quantpedia]This study examines the effectiveness of several anomaly-based trading strategies applied to the real estate sector represented by the RlEst index from the Fama–French 48 industry portfolios. Using monthly data from July 1, 1926, to December 1, 2025, we analyze whether selected strategies are(...) Brave New Backtest [Edge Alchemy]My last two articles on AI and trading research got more engagement than almost anything I’ve written. “More of the Disease, Faster” argued that LLMs can’t answer the critical question: who pays you and why? “AI Will Create Millions of Quants” went deeper on the why: AI makes beautiful(...) The One Euro Filter [Financial Hacker]Whenever John Ehlers writes about a new indicator, I crack it open and wire it straight into C for the Zorro platform. Or rather, I let ChatGPT do most of the work. The One Euro Filter is a minimalistic, yet surprisingly effective low-latency smoother that reacts instantly to volatility with less(...) The Calendar Ensemble: Building an Event-Driven Alpha Overlay [Beyond Passive]In the previous article, we established that the Sharpe ratio is the single most important number in portfolio construction. Variance drag scales with the square of volatility, which means a high-Sharpe portfolio can tolerate leverage, survive decumulation, and compound wealth far more efficiently(...) Transformer Models for Alpha Generation: A Practical Guide [Jonathan Kinlay]Quantitative researchers have always sought new methods to extract meaningful signals from noisy financial data. Over the past decade, the field has progressed from linear factor models through gradient-boosting ensembles to recurrent architectures such as LSTMs and GRUs. This article explores the(...) Infra: Financial APIs [Trading the Breaking]Before moving on to the next series, there’s an important point that any algorithmic trader needs to know: the point about APIs. A quant can state the issue in simple terms. Let st denote latent market state and let xt denote observed state at decision time. The strategy trades on xt(...) A Quant's Guide to Cross-Section Maxxing, Code Included [Quant Galore]Quantitative trading is about many things; buying low and selling high, chasing momentum, capturing yield, arbitraging price imbalances — the whole lot. But time and time again, it comes back to one idea: The cross-section, the cross-section, the cross-section. Now, if you don’t spend the(...) Machine Learning for Derivative Pricing and Crash Prediction [Relative Value Arbitrage]Applications of machine learning in finance continue to evolve rapidly. In previous posts, we discussed both the uses and the challenges of applying machine learning in financial markets. In this installment, we continue that discussion by highlighting new research on machine learning approaches for(...) The Best Defensive Strategies: Two Centuries of Evidence [Alpha Architect]Traditionally, balanced portfolios rely on the equity and bond risk premia to generate returns. The canonical 60% equity/40% bond allocation has produced strong long-run results, but its short- and medium-horizon experience includes large drawdowns. Those drawdowns matter not only behaviorally but(...) Reinforcement Learning for Portfolio Optimization: From Theory to Implementation [Jonathan Kinlay]The quest for optimal portfolio allocation has occupied quantitative researchers for decades. Markowitz gave us mean-variance optimization in 1952,¹ and since then we’ve seen Black-Litterman, risk parity, hierarchical risk parity, and countless variations. Yet the fundamental challenge remains:(...) AI Will Create Millions of Quants [Kris Longmore]AI makes it easier than ever to build trading strategies. Prompt a model, run a backtest, optimise some parameters, and suddenly you’ve got a beautiful equity curve staring back at you. It feels like progress. It feels like research. I wrote recently about how AI coding assistants tend to(...) Macro trading signals with regression-based machine learning [Macrosynergy]Regression-based machine learning is suitable for optimizing macro trading signals, particularly for combining multiple trading factors within a strategy. However, due to the low frequency of macroeconomic events and trends, the bias-variance trade-off in machine learning is very steep, meaning(...) New Contributor: Scaling Python Financial Models on AWS [Quantt]How to take a Python financial model from running 150 scenarios in a Lambda function to processing over a million using AWS Step Functions, Batch, and Fargate — without managing a single server. From Laptop to a Million Scenarios You've built a financial model in Python. It runs beautifully(...) 2-Year Notes Momentum: Extracting Term Structure Anomalies from FOMC Cycles [Quantpedia]For many investors, short-term interest rates are often treated as something the market “discovers.” In reality, the Federal Reserve has enormous control over how the front end of the yield curve evolves. While textbooks often portray the Fed’s policy rate as a flexible tool that reacts(...) The Market Rank Indicator: Measuring Financial Risk, Part 3 [Portfolio Optimizer]In the previous post of this series on measuring financial risk, I described the absorption ratio, a measure of financial market fragility based on principal components analysis, introduced in Kritzman et al.1. In this new blog post, I will describe another measure of financial distress called the(...) Correlated Time Series Generation using Object Oriented Python [Quant Start]This article is a continuation of a series of articles on generating synthetic equities datasets for the purposes of machine learning (ML) model training or synthetic backtesting of systematic trading strategies. We have previously considered the generation of synthetic correlation matrices and the(...) Sentiment Analysis Series Part 3: Three Ways the Sentiment Model Can Fail [Tommi Johnsen]Every day, financial news outlets publish thousands of articles about publicly traded companies. For investors, the obvious question is: does any of it actually matter? If a headline says a company just signed a major contract or passed a clinical trial, should you expect the stock to move the next(...) The Winter of our Pairs Trading Discontent: Problems, limitations, frustrations [Robot Wealth]In the last article, we built up a conceptual understanding of universe selection: how to find pairs that diverge and converge in a tradeable way. We talked about measuring the thing you actually care about directly, rather than reaching for statistical tests like cointegration that sound perfect(...) Systematic FX trading with point-in-time GDP growth estimates [Macrosynergy]Even a single basic macroeconomic factor applied to one derivatives market can generate material and consistent long-term risk-adjusted returns. This is illustrated using point-in-time GDP nowcasts in global FX forward markets. The deployment is based on a simple premise: relatively strong economic(...) Fund Selection When Borrowing Is Restricted [Alpha Architect]Selecting mutual funds is one of the most important jobs investors face. Yet the tool everyone reaches for, the Sharpe ratio, quietly assumes something most real people do not have: the ability, and willingness, to borrow at the risk free rate to lever the “best” fund up or down to their(...) Backtesting course from Rob Carver, March 7 and 8, in person and remote [Investment Idiocy]No, it's not one of those 'make $$$ easy by trading' courses, it's a dull and tedious one about robust fitting and backtesting. This is the first* time I've taught outside of a university. * and possibly last, we'll see. This could be a one-off opportunity. In person(...) State-Space Models for Market Microstructure [Jonathan Kinlay]n my recent piece on Kronos, I explored how foundation models trained on K-line data are reshaping time series forecasting in finance. That discussion naturally raises a follow-up question that several readers have asked: what about the architecture itself? The Transformer has dominated deep(...) Systematic Allocation in International Equity Regimes [Quantpedia]This research examines the critical quantitative investment problem of systematic tactical allocation to international equity mandates—specifically Emerging Markets (EM) and Europe, Australasia, and the Far East (EAFE)—amidst conjectured macroeconomic regime transitions. The investigation is(...) Time Series Models using Object Oriented Python [Quant Start]In the recent previous article on Correlation Matrix Generation using Object Oriented Python we created a Python object-oriented class hierarchy to develop an extensible, modular tool for generating synthetic correlation matrices. Such matrices can be used to generated synthetic correlated time(...) More Bets, Better Bets [Quantitativo]“Casino gambling with a system where you have the edge is a wonderful teacher for elementary money management.” Ed Thorp Ed Thorp is the money manager I admire most. Many people have never heard of him. They should have. In 1961, with Claude Shannon — the father of information theory — he(...) Evaluating Reversal Potential in Niche Alternative ETFs [Quantpedia]Alternative ETFs sit at an unusual intersection of public-market accessibility and hedge-fund-style investment techniques. They package managed futures, merger arbitrage, and option-based income strategies into exchange-traded products, yet they remain thinly traded and relatively niche compared to(...) Infra: Scraping financial data [Trading the Breaking]In fund research, the input that matters most is simple: what stocks are inside the funds right now, and in what weight. Without that look-through layer, fund momentum, category rotation, or risk exposure becomes a label-driven proxy. We are going to construct and execute a systematic fund scraping(...) Do Options Exhibit Momentum? [Relative Value Arbitrage]Momentum has been studied extensively across equities, commodities, and other asset classes, with well-documented evidence of cross-sectional and time-series continuation effects. More recently, an emerging line of research has shifted attention to momentum in option returns, examining whether(...) Kronos and the Rise of Pre-Trained Market Models [Jonathan Kinlay]The quant finance industry has spent decades building specialized models for every conceivable forecasting task: GARCH variants for volatility, ARIMA for mean reversion, Kalman filters for state estimation, and countless proprietary approaches for statistical arbitrage. We’ve become remarkably(...) Break-Even Correlation Thresholds for Linear Predictive Signals [Yannick Kalber]When Is a Signal Good Enough? As we all know, backtesting is not a research tool, but the very end of your research pipeline. If you want to evaluate if a given signal is predictive for returns , you can do this more clearly and directly by regressing on or measuring their correlation. But “how(...) Research Review | 20 February 2026 | Forecasting Returns [Capital Spectator]CAPE Ratios and Long-Term Returns Rui Ma (La Trobe University), et al. January 2026 We demonstrate that 10-year equity market returns are considerably more predictable in relation to price-earnings ratios than previously thought. The traditional approach involves relating the current index price(...) Moneyball: Finding Undervalued Pairs Using Unconventional Metrics [Robot Wealth]Last time we established that stat arb is really about betting on divergence/convergence behaviour continuing. Two things that have historically moved together come apart, and you bet on them coming back together. Remember the forced flows example, some fund or whatever having to sell regardless of(...) Can LLMs Beat FinBERT for Stock Sentiment Trading? [Tommi Johnsen]Part 2 of a series. Part 1 covered building the hybrid classifier and validating it against Claude as ground truth on 991 headlines. This post reports whether the sentiment signals actually predict stock returns. The Question The academic evidence is clear: investor sentiment predicts short-term(...) A Tale of Two Prices [Robot Wealth]It was the age of wisdom, it was the age of foolishness… I’ve seen heaps of stuff published online about stat arb lately. Some genuinely good takes. And some other material that, while academically interesting, isn’t particularly useful for people like me and the people I write for:(...) Combining Calendar Strategies into the Trading Portfolio [Quantpedia]Calendar strategies are often viewed as weak when assessed individually. Their annualized returns tend to be low, market exposure is limited, and trading activity is sparse. Compared to trend following or swing strategies, which can remain invested for extended periods, calendar strategies may(...) Improving Performance with Fast Alphas; A Tactical Overlay for Intraday Trend Trading [Concretum Group]Predictive signals operating at very short horizons often exhibit strong gross performance in backtests but fail to survive realistic transaction costs due to prohibitive turnover. This research note argues that the inability to monetize such signals directly does not imply the absence of economic(...) New Contributor: A Linear Regression's Predictions are a Relevance-Wtd Avg of Past Outcomes [Yannick Kalber]The book Asset Allocation: From Theory to Practice and Beyond by Kinlaw et al. (2021) is one of my favorites as it gets a few myths about mean variance optimization right, which are constantly parroted, even in academic papers to motivate some fancy new method as solution. It also provides useful(...) Volatility Clustering Across Asset Classes: GARCH and EGARCH Analysis with Python (2015–2026) [Jonathan Kinlay]If you’ve been trading anything other than cash over the past eighteen months, you’ve noticed something peculiar: periods of calm tend to persist, but so do periods of chaos. A quiet Tuesday in January rarely suddenly explodes into volatility on Wednesday—market turbulence comes in clusters.(...) Why Bonds Still Belong: Rethinking Fixed Income in Modern Portfolios [Return Stacked]n recent years, the bond market has disappointed many investors. Rising rates and inflation have driven high interest rate volatility, while long-duration bonds have underperformed, dramatically underperforming cash and generating outright negative returns. With a flat term structure, it’s(...) FinBERT Is Wrong 83% of the Time on Positive Headlines: an LLM is Here to Help [Tommi Johnsen]If you’ve ever plugged financial news into a sentiment model and used it to trade, you’ve probably noticed something: the signals are garbage. The model says “positive” all the time, your positions lose money, and you start wondering whether sentiment analysis is just astrology for quants.(...)