Quantocracy

Quant Blog Mashup

Recent Quant Links from Quantocracy as of 05/11/2026

This is a summary of links recently featured on Quantocracy as of Monday, 05/11/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • “Surfing the Equity Curve”: Using Trend-Following to Switch Strategies On and Off [Allocate Smartly]

    This is the third installment in a series on selecting Tactical Asset Allocation (TAA) strategies based on recent performance. Read Part 1 and Part 2. We advocate combining multiple TAA strategies together into Model Portfolios to limit the risk of any single strategy underperforming. In our previous studies we selected strategies for our Model Portfolio based on recent return. In this
  • Martyn Tinsley – Beyond the BackTest [Algorithmic Advantage]

    Even if you have skill, you can look wrong for a very long time. Cliff Asness A backtest (or even many of them) can tell you whether a strategy survived a historical test. It cannot tell you whether you were testing the right idea, in the right way, for the right purpose. That gap matters. There are plenty of methodologies for minimising over-fitting and increasing confidence that an
  • A Day Is Now What a Decade Used to Be [Tommi Johnsen]

    Why does sentiment predict returns at all? The textbook answer is that markets are slow. A positive headline drops at 4:01 PM. By 4:30, sell-side analysts at maybe a dozen banks are scrambling to update their models. By 6 PM, three of them have published preliminary notes. By 9 AM the next day, the buy-side has read those notes, decided, and placed orders. By close on day one, the price reflects

Recent Quant Links from Quantocracy as of 05/10/2026

This is a summary of links recently featured on Quantocracy as of Sunday, 05/10/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The NAAIM Exposure Index: Incorporating Active Investment Mgr Sentiment into Asset Alloc [Portfolio Optimizer]

    The NAAIM Exposure Index represents the average exposure to U.S. equity markets as reported by members of the National Association of Active Investment Managers (NAAIM) in a weekly survey. That index, like any other sentiment indicator, is a useful gauge of the possible future direction of a market1 that can be incorporated into ones asset allocation process. In this blog post, I will analyze
  • The Currency You Didn t Choose [Beyond Passive]

    Run the same three-asset strategy out of New York and out of Frankfurt. The American gets Sharpe 0.97 and a 22% drawdown. The European, holding identical positions but spending in euros, gets Sharpe 0.65 and a 45% drawdown. The trades are the same. The difference is a currency position the European never chose to take, sized by the strategys gross exposure rather than by any view on EUR/USD.
  • AI Forex Backtesting with LLM Regime Labels: DeepSeek vs KMeans in Python [Quant Insti]

    TL;DR: This post builds a forex backtest where a DeepSeek LLM labels market regimes from compact numeric summaries. We compare it to a KMeans baseline, apply monthly walk-forward optimization, and report out-of-sample results from 2023 onward. Prerequisites To fully grasp the regime-labeling approach in this blog, it helps to have a basic familiarity with clustering methods and market regimes. For
  • Reinforcement Learning for Optimal Execution [Jonathan Kinlay]

    Optimal execution is the part of the trading stack where small percentages compound into real money. A long-only equity manager turning over 80% a year on a USD 5bn book pays roughly 4 bps 1.6m for every basis point of slippage. The textbook approach AlmgrenChriss (AC) or its risk-neutral cousin TWAP has been the operating standard for two decades, and for good reason: it is
  • Designing State-of-the-Art Logging in Python [Hanguk Quant]

    Hello friends~ This post, we will discuss the introduction of a state of the art performance Python logging subsystem in quantpylib, and discuss some of the key design principles that allow us to achieve this. To my knowledge, among all Python logging frameworks, it is the lowest latency implementation out there. As an aside, I am focused on making quantpylib into a more mature platform for
  • Why Momentum Investing Has Been Struggling And What Volatility Has to Do With It [Alpha Architect]

    A look at recent academic research connecting market volatility spikes to the underperformance of momentum strategies (especially for long/short versions of the strategy) The Big Picture If youve used momentum as part of your investment strategy over the past decade and found it disappointing, youre not imagining things. Haim Mozes, author of the study Volatility Spikes and Momentum,

Recent Quant Links from Quantocracy as of 05/06/2026

This is a summary of links recently featured on Quantocracy as of Wednesday, 05/06/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Selecting TAA Strategies Based on Recent Performance, Part 2: Recent Sharpe Ratio [Allocate Smartly]

    This is the second installment in a multipart series on selecting Tactical Asset Allocation (TAA) strategies based on recent performance. Read Part 1. We advocate combining multiple TAA strategies together into Model Portfolios to limit the risk of any single strategy underperforming. In our previous study we selected strategies for our portfolio with the highest recent return. In this
  • Dual Momentum Allocation Between Physical Gold and Bitcoin (Digital Gold) [Quantpedia]

    From the trading desk to the portfolio committee, investors face a familiar question: how should alternative stores of value fit into a diversified portfolio? This research explores that question through a systematic dual-momentum framework comparing Bitcoin and physical gold in a rules-based tactical allocation model. Rather than debating ideology, we focus on practical portfolio construction and
  • Bad Month for Your Strategy? Should You Change It? [Alvarez Quant Trading]

    A strategy you have been trading for years has just had a terrible month. Looking at the market environment, you think these trades should not have been taken. You make some small changes to your strategy and now the backtest shows that the terrible month is OK and the overall strategy statistics improve. Should you keep that change in your strategy? For the longest time, I would keep that
  • Modeling with the NAAIM Exposure Index [Quantifiable Edges]

    For much of last week I was at the National Association of Active Investment Managers (NAAIM) Uncommon Knowledge conference. NAAIM is a terrific organization that I have become more involved with over the years. NAAIM has published its NAAIM Exposure Index since 2006. I did some research a few years ago on the index to determine whether the numbers might be valuable as part of a model. I
  • Sentiment is not one signal [Tommi Johnsen]

    Most sentiment research treats the question as one thing. Take a corpus of news articles. Classify each as positive, negative, or neutral. Aggregate to the daily level. Correlate with next-day returns. Report a coefficient. Argue about which classifier is best. This is tidy. It is also wrong, in a specific way that took us a while to see clearly. The articles being classified are not the same kind
  • The Attention Factor: The Link That Connects Crypto and Public Equity Markets [Quantpedia]

    In an era of increasingly fragmented market microstructure, the emergence of cross-asset connectedness between Crypto and public equity markets presents a critical challenge for modern portfolio construction. This blog post examines the recent working paper by Harin de Silva, The Attention Factor: The Speculative Risk You May Already Own, which identifies a previously underappreciated

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