Quantocracy

Quant Blog Mashup

Recent Quant Links from Quantocracy as of 04/05/2026

This is a summary of links recently featured on Quantocracy as of Sunday, 04/05/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Junior Quant’s Guide to Event-Driven Trading [Quant Galore]

    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
  • Two Calendar Effects at the Month Boundary [Beyond Passive]

    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
  • Uncertainty [Quantitativo]

    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
  • When Elon Musk Can t Sleep, Your Portfolio Feels It [Tommi Johnsen]

    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
  • How imputation helps statistical learning for macro trading signals [Macrosynergy]

    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 Later: Is ChatGPT Finally Worth Using for Quantitative Analysis? [Quantpedia]

    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

Recent Quant Links from Quantocracy as of 03/31/2026

This is a summary of links recently featured on Quantocracy as of Tuesday, 03/31/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Selling Volatility: The Most Seductive Backtest in Finance [Quantt]

    Here is a strategy with a thirty-year track record. Sell one-month at-the-money put options on the S&P 500, collateralised by Treasury bills. Roll monthly. That is the entire strategy. The CBOE PutWrite Index (PUT) tracks exactly this approach. From June 1986 through December 2018, it returned 9.54% annually – within touching distance of the S&P 500's 9.80%. But here is the part that
  • Coding the largest strategy ever [Financial Hacker]

    Recently I got a quite unusual job: Heres a trading system for the TradeStation platform. Its a bit large about 3000 lines of EasyLanguage code. Please replicate that monster in C++ so that it runs on the Zorro platform, but still produces the same trades as on TS. While youre at it, fix any bugs that you encounter in the EasyLanguage code. You have 2 weeks. Good luck. The strategy in
  • Breaking the Rules of Intraday Trading [Concretum Group]

    Quantitative research is, at its core, about following rules. As in any other STEM discipline (science, technology, engineering, and mathematics), precise frameworks give research rigor, discipline, and comparability. Yet, because such frameworks often remain unquestioned, challenging one of their constraints on purpose can sometimes be an informative experiment. In intraday trading, the first and

Recent Quant Links from Quantocracy as of 03/29/2026

This is a summary of links recently featured on Quantocracy as of Sunday, 03/29/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Brave New Backtest [Robot Wealth]

    My last two articles on AI and trading research got more engagement than almost anything Ive written. More of the Disease, Faster argued that LLMs cant answer the critical question: who pays you and why? AI Will Create Millions of Quants went deeper on the why: AI makes beautiful backtests trivially easy to produce, which means more false discoveries, more overfitting dressed up
  • Data transformations: Preprocessing time series [Trading the Breaking]

    Raw market data is the closest thing we have to the markets observable dynamics, so the instinct to leave it untouched feels sensible. But predictive models consume numerical representations. Between the tape and the optimizer there is always a translation step, whether explicit or hidden, and that translation determines which structures remain visible, which ones are attenuated, and which ones
  • Fridays for Gold, Tuesdays for Stocks: Two Sides of the Same Fear Cycle [Beyond Passive]

    The previous article showed that gold drifts higher on Fridays specifically when the VIX term structure compresses into the extended fear zone, driven by institutional risk managers adding weekend protection on Thursday afternoons. The same day-of-week chart that opened that article contains a second significant bar on the opposite side of the cycle. Equities on Tuesday. This article examines
  • When Crypto Stopped Diversifying: The ETF Regime Shift [Quantpedia]

    Can crypto still help diversify an equity portfolioor has that edge disappeared? Thats the practical question behind Crypto Contagion. The paper looks at how shocks move between crypto and U.S. equities, and more importantly, how that relationship changed after the launch of crypto ETFs. Instead of relying on simple correlations, the authors use a combination of jump detection (to isolate
  • Unlocking Hidden Patterns: How Daily Returns Predict Future Stock Performance [Alpha Architect]

    Nusret Cakici, Christian Fieberg, Gabor Neszveda, Robert Bianchi, and Adam Zaremba, authors of the January 2026 study A Unified Framework for Anomalies based on Daily Returns, challenged how we think about short-term return patterns in stock markets. Their research reveals that the wealth of information contained in daily stock returns has been hiding in plain sightand when properly
  • Evaluating Option-Based Strategies and Dollar-Cost Averaging [Relative Value Arbitrage]

    In past issues, we discussed popular investment strategies such as covered calls and collars. In this post, we continue by examining other strategies, focusing on their performance, limitations, and how they behave under different market conditions. Reexamining the Performance of Passive Options Strategies More than 40 years ago, Merton et al. published two papers [1,2] examining the performance
  • 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 outperformance out-of-sample is exceptionally hard to achieve. In this post, we build a complete,
  • 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 measurement problem. When CAPE is constructed using aligned index constituents and market-cap weights,

Recent Quant Links from Quantocracy as of 03/22/2026

This is a summary of links recently featured on Quantocracy as of Sunday, 03/22/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 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 how you trade it. This article builds a hypothesis about the mechanism behind the Friday effect,
  • 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 signals are composite scores of small sets of theoretically favorable macroeconomic factors for
  • 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 followinglong considered a source of crisis alphaexperiences one of its worst drawdowns in history.1 Whoomp, whoomp.

Recent Quant Links from Quantocracy as of 03/19/2026

This is a summary of links recently featured on Quantocracy as of Thursday, 03/19/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 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 this approach is going to cost people a lot of money. And worse, its going to prevent them from
  • 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 Fama and French 23 and 55 size and book-to-market portfolios, we construct the small value minus
  • Diversification Has Been a Huge Drag on TAA Performance for 15+ Years [Allocate Smartly]

    (but that wont 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 stock market dominance over almost all other asset classes over that period. TAA must use market
  • 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 and MAGA idiots waiting patiently to be educated; and I have a million more dad jokes to inflict on

Recent Quant Links from Quantocracy as of 03/16/2026

This is a summary of links recently featured on Quantocracy as of Monday, 03/16/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 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 inaccessible. In practice, most successful candidates do not "have everything." They are
  • 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 FamaFrench 48 industry portfolios. Using monthly data from July 1, 1926, to December 1, 2025, we analyze whether selected strategies are capable of generating superior risk-adjusted returns compared to both the standalone RlEst index and the
  • Brave New Backtest [Edge Alchemy]

    My last two articles on AI and trading research got more engagement than almost anything Ive written. More of the Disease, Faster argued that LLMs cant answer the critical question: who pays you and why? AI Will Create Millions of Quants went deeper on the why: AI makes beautiful backtests trivially easy to produce, which means more false discoveries, more overfitting dressed up
  • 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 lag of the usual adaptive averages. This is achieved by dynamically adapting its time period. This is

Recent Quant Links from Quantocracy as of 03/15/2026

This is a summary of links recently featured on Quantocracy as of Sunday, 03/15/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 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 than a low-Sharpe one. We also hinted at something more subtle: strategies with positively skewed
  • 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 next step in that evolution: the Transformerand asks whether it deserves a place in the
  • Infra: Financial APIs [Trading the Breaking]

    Before moving on to the next series, theres 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 rather than st. The transformation from st to xt includes vendor capture, transport, buffering,

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