Quantocracy

Quant Blog Mashup

Recent Quant Links from Quantocracy as of 03/12/2026

This is a summary of links recently featured on Quantocracy as of Thursday, 03/12/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 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 dont spend the majority of your life in code terminals and academic papers, the word cross-sectional might not mean much.
  • 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 pricing complex derivatives and identifying signals that may precede major market downturns.
  • 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 mechanically, through volatility drag: large losses require disproportionately large gains to

Recent Quant Links from Quantocracy as of 03/08/2026

This is a summary of links recently featured on Quantocracy as of Sunday, 03/08/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 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, and since then weve seen Black-Litterman, risk parity, hierarchical risk parity, and countless variations. Yet the fundamental challenge remains: markets are dynamic, regimes shift, and static optimization methods struggle to adapt. What if we
  • 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 youve 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 prescribe more of the disease, faster, skipping the learning that makes trading research actually
  • 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 model flexibility comes at a high cost of instability. To improve the trade-off, regression-based

Recent Quant Links from Quantocracy as of 03/05/2026

This is a summary of links recently featured on Quantocracy as of Thursday, 03/05/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 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 on your laptop perhaps a portfolio stress-testing model that grinds through a few hundred
  • 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 Feds policy rate as a flexible tool that reacts quickly to economic data, the actual behavior of the Federal Open Market Committee (FOMC) looks very
  • 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 market rank indicator (MRI), this time related to the notion of condition number2 of a matrix,
  • 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 generation of synthetic asset returns via various time series models. In this article we are going
  • 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 day? Thanks for reading! Subscribe for free to receive new posts and support my work. This article

Recent Quant Links from Quantocracy as of 03/02/2026

This is a summary of links recently featured on Quantocracy as of Monday, 03/02/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 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 but turn out to be unstable in practice. The natural next step is to start trading them the
  • 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 growth positively affects local-currency FX returns, owing to its support for higher real interest
  • 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 preferred risk level. Once borrowing is realistically restricted, the Sharpe ratio can stop lining up with
  • 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 and remote:
  • 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 learning for sequence modeling over the past seven years, but a new class of models State-Space Models

Recent Quant Links from Quantocracy as of 02/26/2026

This is a summary of links recently featured on Quantocracy as of Thursday, 02/26/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Systematic Allocation in International Equity Regimes [Quantpedia]

    This research examines the critical quantitative investment problem of systematic tactical allocation to international equity mandatesspecifically Emerging Markets (EM) and Europe, Australasia, and the Far East (EAFE)amidst conjectured macroeconomic regime transitions. The investigation is precipitated by observable deteriorations in USD hegemony, elevated geopolitical risk premiums, and
  • 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 series models, which can form the basis of realistic synthetic financial datasets. In this article we
  • 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 built the first wearable computer to beat roulette. He wrote Beat the Dealer and proved blackjack

Recent Quant Links from Quantocracy as of 02/23/2026

This is a summary of links recently featured on Quantocracy as of Monday, 02/23/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 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 mainstream equity or bond ETFs. This combination makes them intriguing: they offer exposure 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 operation. The explicit objective of this architecture is to expose what underlying stocks these
  • 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 derivative markets exhibit their own systematic return patterns. In this post, we review the latest

Recent Quant Links from Quantocracy as of 02/22/2026

This is a summary of links recently featured on Quantocracy as of Sunday, 02/22/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 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. Weve become remarkably good at squeezing insights from limited data, optimizing hyperparameters on in-sample windows, and
  • Multivariate Break-Even Correlation Tresholds [Yannick Kalber]

    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 set of signals is predictive for returns, you can do this more clearly and directly by regressing returns on the signals or measuring their correlations. But how strong do those correlations need to be for the signals to be good enough? A popular heuristic
  • 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 strong does that correlation need to be for the signal to be good enough? A popular heuristic
  • 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 level, based on current index components, to the index earnings of previous years, calculated using

Recent Quant Links from Quantocracy as of 02/20/2026

This is a summary of links recently featured on Quantocracy as of Friday, 02/20/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 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 price? That sort of temporary dislocation creates opportunities. Conceptually simple. But the
  • 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 stock returns. Two decades of peer-reviewed research, anchored by Baker & Wurgler (2006) and
  • A Tale of Two Prices [Robot Wealth]

    It was the age of wisdom, it was the age of foolishness Ive seen heaps of stuff published online about stat arb lately. Some genuinely good takes. And some other material that, while academically interesting, isnt particularly useful for people like me and the people I write for: independent systematic traders looking for real edges we can realistically manage without a team. A lot of the

Recent Quant Links from Quantocracy as of 02/18/2026

This is a summary of links recently featured on Quantocracy as of Wednesday, 02/18/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

Recent Quant Links from Quantocracy as of 02/13/2026

This is a summary of links recently featured on Quantocracy as of Friday, 02/13/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 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, its tempting to see duration as uncompensated risk, especially when yields offer little cushion and price
  • FinBERT Is Wrong 83% of the Time on Positive Headlines: an LLM is Here to Help [Tommi Johnsen]

    If youve ever plugged financial news into a sentiment model and used it to trade, youve 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. Thanks for reading! Subscribe for free to receive new posts and support my work. The problem isnt
  • Point-in-time economics and financial market forecasting [Macrosynergy]

    Standard macroeconomic theory assumes that economic activity and financial market developments influence each other contemporaneously. This is incomplete and implausible. While some direct interaction occurs, financial investors typically require reliable statistics to adapt to economic conditions and trends. Such information takes time to compile and is often revised. A more appropriate
  • Why TAA is Performing Well Now: Outperformance Attribution [Allocate Smartly]

    We track 100+ published Tactical Asset Allocation (TAA) strategies, so these results are broadly representative of TAA as an investment style. TAA did reasonably well in 2025 and very well in these early days of 2026, relative to the ubiquitous 60/40 benchmark. How much of that is due to TAA correctly timing the market and how much is simply due to the types of assets TAA generally holds? In the