Quantocracy

Quant Blog Mashup

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

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

  • Deep Learning for Volatility Surface Repair [Jonathan Kinlay]

    A self-contained synthetic benchmark of a small mask-conditional CNN against calendar-projected linear interpolation and a per-slice SVI fit. A volatility surface marker is rarely a clean rectangle of quotes. Strikes go unobserved during illiquid hours, wings get crossed and then erased, broker stripes drop out across an entire maturity, and weeklies arrive at the desk with random missingness on
  • New Contributor: Modeling Asymmetric Volatility With EGARCH [Krzysztof Ozimek]

    This post presents an accessible introduction to the Exponential GARCH (EGARCH) modela widely used tool in financial econometrics for modeling time-varying volatility in asset returns. Unlike standard GARCH models, EGARCH captures both volatility clustering and the leverage effect, whereby negative shocks tend to increase future volatility more than positive shocks of equal magnitude. The post
  • A Strong Start to May Has Often Been Followed by a Short-Term Dip [Quantifiable Edges]

    May got off to a positive start. But a strong start to May has typically been followed by a dip in the next few days. This can be seen in the study below, which was featured in this weekends subscriber letter. Of the 25 instances that rose on the first day in May since 1987, 17 of them closed lower 4 days later. Below is an equity curve that shows how it has played out over time. Ill note
  • Overfitting and Parameter Selection in Trading Strategies [Relative Value Arbitrage]

    The risk of overfitting is serious and can lead to significant losses. It has been discussed in previous posts. In this edition, we revisit the topic, given its continued relevance to quantitative strategy development. Formal Study of Overfitting in Trading System Design A serious problem when designing a trading system is the overfitting phenomenon, wherein the system is excessively tuned to

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

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

  • I paper-traded 22 popular crypto strategies on real fees for 10 days. Here’s the data. [Strat Proof]

    Why I'm publishing this I wanted to build a trading bot like a lot of people did once Claude integrated with TradingView. Took the leap, my strategies kept failing, and the backtests kept being way too optimistic compared to what happened when I actually ran them. Started digging into why. This post is what 10 days of running 22 popular strategies on real Binance fees with real L2 spread
  • Where Risk Parity Hurts: A 58-Year Audit of Tails and Drawdowns [Beyond Passive]

    The previous article extended the inverse-volatility allocation across SPY, TLT, and GLD back to 1968 using a synthetic price construction. Over fifty-eight years the strategy delivered a CAGR of 7.1%, volatility of 7.5%, a Sharpe of 0.97, and a maximum drawdown of 22%. The volatility-targeting overlay, justified by the persistence of volatility across the same window, kept realised vol close to
  • Almost Explicit Implied Volatility [Chase the Devil]

    Several years ago, I had explored accuracy and performance of different ways to imply the Black-Scholes volatility. Jherek Healy proposed some improvements over my naive algorithm on his blog. Recently, a Linkedin post mentioned a new paper from Wolfgang Schadner which presents an almost explicit formula for the implied volatility. Almost because it actually relies on some implementation of the
  • Rethinking Trend Following: Optimal Regime-Dependent Allocation [Alpha Architect]

    Most trend-following research focuses on signal construction: how to detect trends better, faster, or earlier. The paper asks a different question, and arguably a more important one for investors: once a market regime has been identified, what is the optimal portfolio exposure in that regime? That is the central novelty of the paper which is available here. Traditional time-series momentum
  • Curve trades with macroeconomic signals [Macrosynergy]

    The shape of yield curves in developed swap markets reflects the state of growth, inflation, and credit supply. This is primarily because central banks adjust short-term policy rates in response to evolving economic conditions, while their credibility helps anchor longer-term forward rates. In monetary policy regimes committed to price stability, and when short rates are above the zero lower

Recent Quant Links from Quantocracy as of 04/29/2026

This is a summary of links recently featured on Quantocracy as of Wednesday, 04/29/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Selecting TAA Strategies Based on Recent Performance (Part 1) [Allocate Smartly]

    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
  • For The Love of The Game [Robot Wealth]

    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.
  • When Big Gets Small: Trading the Lower Tier of Large Caps and Upper Mid Caps [Quantpedia]

    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
  • How to Break a Financial Sentiment Model Without Changing What It Means [Tommi Johnsen]

    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
  • Lazy Prices, Lazy Investors – and the 22% Alpha Hidden in 10-Ks That Nobody Reads [Quantt]

    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
  • Revisiting Beyond 60/40: Five Decades of Risk-Weighted Allocation [Beyond Passive]

    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

Recent Quant Links from Quantocracy as of 04/25/2026

This is a summary of links recently featured on Quantocracy as of Saturday, 04/25/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Skip-Month Mystery: What Last Month s Returns Are Really Telling You [Alpha Architect]

    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
  • Unsupervised Learning for Trading: K-Means, PCA & Python Examples [Quant Insti]

    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.
  • Research Review | 24 April 2026 | Prediction Markets [Capital Spectator]

    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

Recent Quant Links from Quantocracy as of 04/22/2026

This is a summary of links recently featured on Quantocracy as of Wednesday, 04/22/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

Recent Quant Links from Quantocracy as of 04/20/2026

This is a summary of links recently featured on Quantocracy as of Monday, 04/20/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Mean-Variance Optimization in Practice: Reverse Optimization and Implied Expected Returns [Portfolio Optimizer]

    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
  • The Tranching Dilemma [Quantpedia]

    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
  • Sixty-four years of TLT: reconstructing the bond ETF everyone owns [Beyond Passive]

    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

Recent Quant Links from Quantocracy as of 04/18/2026

This is a summary of links recently featured on Quantocracy as of Saturday, 04/18/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Exploiting Mean-Reversion in Decentralized Prediction Markets: Evidence from Polymarket [Quantpedia]

    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
  • Inflation as a trading signal [Macrosynergy]

    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
  • The AutoTune filter [Financial Hacker]

    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
  • Looking Inside The Black Box [Vertox Quant]

    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

Recent Quant Links from Quantocracy as of 04/16/2026

This is a summary of links recently featured on Quantocracy as of Thursday, 04/16/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • We Trusted FinBERT to Filter the Noise. It Was Also Filtering the Signal [Tommi Johnsen]

    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
  • Time Series Database Review: RayforceDB [Anton Vorobets]

    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
  • Annual performance update- year 12 [Investment Idiocy]

    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

Recent Quant Links from Quantocracy as of 04/15/2026

This is a summary of links recently featured on Quantocracy as of Wednesday, 04/15/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • What’s the Optimal Stack? [Return Stacked]

    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
  • A Historical Look At $SPX on Tax Day [Quantifiable Edges]

    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
  • The Many Facets of Stock Momentum: Distinguishing Factor and Stock Components [Alpha Architect]

    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

Recent Quant Links from Quantocracy as of 04/13/2026

This is a summary of links recently featured on Quantocracy as of Monday, 04/13/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Meb Faber’s “Tactical Yield”, Simple and Intuitive [Allocate Smartly]

    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.
  • Data transformations: Data shape and predictive features [Trading the Breaking]

    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
  • When Correlations Fail: A Bayesian Approach to Sizing Sparse Overlays [Beyond Passive]

    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
  • To Trend or Not To Trend? (Wrong question) [Robot Wealth]

    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
  • Factor MAX: A New Signal for Predicting Factor Returns [Alpha Architect]

    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