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Recent Quant Links from Quantocracy as of 08/23/2026

This is a summary of links recently featured on Quantocracy as of Sunday, 08/23/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Safe Assets: 150 Years of Bonds and Gold Across Sixteen Countries [Beyond Passive]

    A reader asked whether the birthplace lottery that decided equity outcomes also decided bond and gold outcomes. It did, and the shape of the answer is different from what the equity article found. The same measurement applied to bonds The construction matches the equity article. One unit of currency, invested at home in long government bonds, deflated by the home consumer price index, held from
  • Does Complexity Actually Help? The Virtue-of-Complexity Autopsy [Aligrithm]

    Kelly, Malamud and Zhou opened their Journal of Finance paper by reporting out-of-sample market timing Sharpe ratio improvements "relative to market buy-and-hold" of roughly 0.47 per year, earned by a model with 12,000 predictors trained on 12 monthly observations. Daniel Buncic re-ran it on the same data with the same code and found the first problem in the first sentence. Buy-and-hold
  • Regime-Based Sector Rotation Beats the Index at Full Deployment [Quanter Lab]

    Regime calls are a steering wheel, not a brake. We walked two strategies through the same registered one-year windows on the nine original Select Sector SPDRs. One holds all nine, equal weight, quarterly rebalance, always fully invested. The other holds three at a time, routed between a risk-on book of technology, discretionary and financials, a cyclical book of industrials, materials and energy,
  • Wealth management with macro factors [Macrosynergy]

    A critical decision in wealth management is the allocation of capital across asset classes and cash. Theory and empirical evidence suggest that out- and underperformance of broad asset classes depends on the macroeconomic environment. Consequently, the systematic, low-frequency adjustment of portfolio weights in response to macro factors can produce material excess returns. It is a practical and

Recent Quant Links from Quantocracy as of 08/20/2026

This is a summary of links recently featured on Quantocracy as of Thursday, 08/20/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Momentum Mini-Portfolio Development – Part 2: USA Pullback Momentum [TradeQuantiX]

    Recently we kicked off the momentum mini-portfolio series. The first article was a TSX market dual-factor momentum system with a hybrid trend following type exit. The system turned out to be pretty robust after being run through a vast set of robustness tests. We also took a deep dive into minimum system allocation, as I felt that topic is not as well explored online. If you missed that one you
  • Two Strategies Past 80 Meaningless Versions of Themselves. Control Group Told Them Apart [Jan Heger]

    The other I threw away, and the only thing that told them apart was a control group that most people building trading systems never bother to build. Heres what the control saw that my eyes couldnt. Thanks for reading! Subscribe for free to receive new posts and support my work. What a control group is, and why trading skips it In a drug trial you dont just give people the drug and check
  • Metaheuristics for Rule Optimization With Diversity as the Guardrail [Aligrithm]

    Hernndez-Romo and co-authors ran four population-based optimizers 31 times each, 1000 generations per run, population of 50, tuning an eight-parameter moving-average strategy on five-minute BTC/USDC bars from January 2020 to March 2025. Differential Evolution won on average annualized return with 107.36%, against 92.05% for the Whale Optimization Algorithm, 73.47% for Particle Swarm and 61.80%
  • Model-Based or Data-Mined: Lotter’s Framing of the Whole Problem [Aligrithm]

    Johann Christian Lotter opens his workshop deck with four lines that do more work than most methodology chapters. A model is not the reality. The reality is unknown. The same reality can be described with many different models. The best model must be selected by experiment. Every argument in Pillar 1 reduces to those four lines, and the deck states them in under thirty words before showing a
  • Sectoral Intramonth Momentum Cycle: Exploiting Turn-of-the-Month Patterns in Sector ETF Strategies [Quantpedia]

    We document a persistent intramonth momentum cycle in U.S. sector ETFs that yields meaningful risk-adjusted returns when properly sequenced. Using the nine original Select Sector SPDR ETFs and SPY as the market benchmark from December 1998 through June 2026, we show that trailing 252-day sector momentum generates a positive spread on the first trading day of the monthand then sharply reverses

Recent Quant Links from Quantocracy as of 08/16/2026

This is a summary of links recently featured on Quantocracy as of Sunday, 08/16/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Your Research Agent Is an Undisclosed Factor Exposure – And So Is Everyone Else’s [Jonathan Kinlay]

    In May I published a case study on running alpha research through a team of LLM agents: four roles, instrumented handoffs, and roughly a 2 lift in hypotheses tested per week. The post was careful about what it measured. It was silent about a risk I had not thought to price. Here is the risk. If your research agent and your competitors research agent are the same base model, then whatever that
  • Price-Path Convexity: A New Cross-Sectional Anomaly [Aligrithm]

    Take two stocks that both end the month flat. The first bled lower for two weeks, then clawed all the way back. The second ran up for two weeks, then gave it all back. Same start, same finish, same zero return. Sort every stock in the market by past return and these two land in the identical bucket, because return only sees the endpoints. Gulen and Woeppel show that the shape between the endpoints
  • Dual Momentum Between Gold and Bitcoin (Two Stores of Value) [Aligrithm]

    An 8-week dual-momentum switch between GLD and IBIT returns 79.91% a year at a Sharpe of 1.64. Vojtko and Dujava report that number on a Quantpedia note covering 31 December 2018 through April 2026, weekly rebalance at Wednesday's close, cash when both lookbacks print negative. Hold the other column: maximum drawdown is still -43.94%. They tested ten lookbacks and published the peak. The
  • VIX and Trend Following Revisited: Nearly a Decade of Out-of-Sample Evidence [Alpha Architect]

    In September 2017, Alpha Architect published VIX and Trend-Following, the Killer Combo?, an empirical examination of whether volatility information could improve a traditional trend-following allocation model. The central idea was intuitive: market volatility may contain useful information about how quickly an investor should measure momentum. A shorter momentum window may react more effectively
  • Factor Timing Mostly Fails the Honest Version [Aligrithm]

    Factor timing is the seductive idea that you can do better than holding a basket of factors: lean into value when value looks cheap, load momentum when momentum is running, cut a factor when its volatility spikes. Every one of those moves has a tidy formula and a plausible story. And when you run them out of sample on a clean universe, almost all of them lose to the dumbest thing you could have
  • Sharpe Ratio Distribution: When Normal’s A No-Go, Why Not SHASHo? [Krzysztof Ozimek]

    I recently came across Lpez de Prado et al.'s (2026) treatment of the Sharpe ratio estimator: Normal, with the true as its mean, and a variance (see the Normal section of the image below) conditioned on sample size, the return series' autocorrelation, its skewness, its kurtosis, and the level of itself. It's an elegant correction but stubbornly a bell curve underneath.
  • Market Regimes and Changing Market Dynamics [Relative Value Arbitrage]

    Markets have been behaving unusually lately. In May, equity indices rose while volatility and skew also increased, a relatively rare occurrence historically. Since last week, the same phenomenon has emerged again, with the spot/volatility correlation turning positive. Is this still a rare occurrence? We dont know. But one thing is clear: regime detection is becoming increasingly important in

Recent Quant Links from Quantocracy as of 08/12/2026

This is a summary of links recently featured on Quantocracy as of Wednesday, 08/12/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Spot a Fake ML Trading Paper [Aligrithm]

    Three papers crossed my desk this month. One detects market manipulation with graph neural networks and reports 98.7% accuracy at 8.3 milliseconds per decision. A second, same author cluster, detects anomalies in high-frequency trading and reports a 15% accuracy improvement with an F1 of 0.915. A third uses XGBoost on Bitcoin technical indicators and reports 92.40% accuracy with a ROC AUC of
  • I Scaled Out to Raise My Win Rate. It Didn’t Move and It Cost Me $115,000 [Jan Heger]

    You lock in gains, you stop giving winners back, and, the part everyone repeats, your win rate goes up. I tested it on 3,966 of my own trades. Two of those three claims are false, and the third one cost me a fortune. The setup I run an automated futures system with a fixed exit: each strategy takes its full target or its full stop. No partials, no breakeven moves; the trade rides to one of two
  • Autopsy No.005: The congressional-trading ETF that beats the market [Morgue Labs]

    Where this came from. A reader replied to Autopsy 001 with a Morningstar page showing NANC ahead of the index and a fair question: what am I missing? They were not missing anything. The fund really has outperformed. This is the answer to what that outperformance is made of. Unlike our other case files this is not a pre-registered test of our own hypothesis. It is a descriptive analysis of
  • The Rise of CTA ETFs [Concretum Group]

    Over the past few years, the liquid alternatives space has moved decisively into a new ETF era. What was once a narrow category now stretches from CTA to hedge-fund replication strategies, sometimes pairing those return streams directly with core stocks and bonds exposures. And thanks to their low-cost, daily liquidity and ease of access, these vehicles are attracting progressively more inflows.

Recent Quant Links from Quantocracy as of 08/11/2026

This is a summary of links recently featured on Quantocracy as of Tuesday, 08/11/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Autopsy No.003: Buying options for the big win [Morgue Labs]

    The pitch One screenshot: $6k into weekly calls, $1.2M out. The logic sounds like physics options have capped downside and uncapped upside, so keep buying cheap lottery tickets and one asymmetric win pays for everything. The post-2021 refinement adds a mechanism: find the gamma squeeze, ride the dealers' forced hedging, be the flywheel instead of the fuel. The test Twenty years of S&P
  • Does AI Still Read the News Better Than the Market? [Tommi Johnsen]

    A well-known finance paper showed that an AI model could read a news headline about a company and say, better than chance, which way the stock would move. The same paper predicted the effect would fade as more traders started using the same tools. Thanks for reading! Subscribe for free to receive new posts and support my work. We tested that prediction with 2026 data, a different AI model, and
  • Stop Using Pairwise Granger: PCMCI for Financial Causality [Aligrithm]

    Run pairwise Granger tests over a basket of thirty instruments and you get a causal network that looks like a plate of spaghetti. AUD/USD drives copper, copper drives the Aussie back, oil leads the loonie, the loonie leads oil, and somewhere in the mess a small-cap ETF appears to Granger-cause the ten-year yield. Most of those arrows are fake. They come from three things markets are drowning in:
  • Excessively Volatile? Or Inexplicably Precise? [Alex Chinco]

    The dividend discount model (DDM) says that a stocks current price ought to reflect the discounted value of its expected future dividend stream (1) begin{equation*}mathrm{Price} = sum_{t=1}^{infty} frac{mathbb{E}[mathrm{Div}_{t}]}{(1{+}r)^t}end{equation*} mathbb{E}[mathrm{Div}_t] is the companys expected dividend in t years, and r > 0% is the firms discount rate. The Gordon

Recent Quant Links from Quantocracy as of 08/09/2026

This is a summary of links recently featured on Quantocracy as of Sunday, 08/09/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 150 Years of Global Stock Returns – The Birthplace Lottery [Beyond Passive]

    Every investor who holds only their home market has made the same decision. Over the last century and a half that decision paid an Australian seven percent a year in real terms and a Portuguese investor less than one. Nobody chose which of those they were born into. The number everyone quotes Equities return about seven percent a year after inflation. That figure comes from one country, measured
  • Bid-Ask Spread From OHLC: The GMM Estimator That Beats Roll/CS [Aligrithm]

    You backtest a strategy, subtract "a spread" for costs, and move on. Where did that number come from? If you typed in a guess, or pulled a single quoted spread from a vendor, you are almost certainly wrong, and wrong in the one direction that flatters your worst assets. The effective spread you actually pay is unobservable without trade-and-quote data, and quote data for anything outside
  • Bond indices and systematic duration management [Macrosynergy]

    This article presents methods for adjusting the duration of major countries in global bond indices using point-in-time measures of local economic conditions. The macro factors include inflation, credit conditions, real estate price growth, yield-curve valuations, and economic surprises. Each has clear theoretical implications for interest-rate markets. An equally weighted composite score of these
  • Trading Strategy Comparison: Is B Really Better Than A? [Krzysztof Ozimek]

    How a single-value performance metric can distort the ranking of trading strategies and how to avoid falling for it and gain deeper insight into strategy comparison. Diagram comparing probability distributions of trading strategies A and B, showing why a single higher performance value (V_B) doesn't mean strategy B is truly better than A Picture two trading strategies, A and B. You run
  • Skewness as a Hidden Driver of Anomaly Returns [Alpha Architect]

    Behavioral finance research has established that investors dislike negative skewness because it exposes them to rare but severe losses, while they embrace positive skewness because it offers the chance of occasional outsized gains the lottery-like appeal that persists even when expected payoffs are modest. In behavioral models, this preference for positively skewed assets bids up their prices,

Recent Quant Links from Quantocracy as of 08/06/2026

This is a summary of links recently featured on Quantocracy as of Thursday, 08/06/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Taming the Wildcard: David Varadi’s “Inflation Compass” [Allocate Smartly]

    This is an independent test of a novel strategy from David Varadi: Inflation Compass. It builds on his earlier Growth and Inflation strategy by adding a direct market-based measure of expected inflation. Were testing two versions of his new strategy: Original and Enhanced (more on this later). Backtested results from 1990 follow. Results are net of transaction costs see backtest
  • Testing for Mean Reversion: ADF, Hurst Exponent and Half-Life [Quantt]

    A time series is mean-reverting if it tends to return to a stable long-run level after being displaced from it. In pure form, that means the process has a well-defined unconditional mean and a variance that does not grow without bound; shocks decay rather than accumulate. This is the opposite of a random walk, where each innovation is permanently absorbed into the level and variance grows linearly
  • The Mathematics of Machine Learning, for Traders [Aligrithm]

    You already ran the models. The old article "From One Tree to Forests to Boosting" walked you from a single decision tree to XGBoost, "How a Decision Tree Engineers a New Alpha" showed a tree carving conditional edges out of order-book features, and "Ridge Above 1h, XGBoost Below 5min" handed you a timeframe rule for which model to point at which horizon. None of
  • The Ornstein-Uhlenbeck Process in Finance: Theory, Simulation and Calibration [Quantt]

    What Is the Ornstein-Uhlenbeck Process? The OrnsteinUhlenbeck (OU) process is the simplest continuous-time model of a mean-reverting random process. It was introduced in 1930 by Leonard Ornstein and George Uhlenbeck as a physical model of the velocity of a Brownian particle experiencing friction (Uhlenbeck & Ornstein, 1930), and it has since become one of the most widely used stochastic

Recent Quant Links from Quantocracy as of 08/05/2026

This is a summary of links recently featured on Quantocracy as of Wednesday, 08/05/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Statistic That Passed Every Test I Had And Still Isn’t an Edge [Jan Heger]

    It cleared the control test that kills almost everything I try and that I still wont trade. Thats because theres a gap between those two things and it is the most useful thing Ive learned building this system, so let me show you what actually happened. The pattern For this particular example, I am trading the market on the daily gaps. I noticed what looked like a pull toward round
  • The Signal Ceiling: Why No Single-Bar OHLCV Edge Beats Costs in MNQ [Aligrithm]

    Open any retail futures forum and you will find the same fourteen setups traded as gospel: the opening range breakout, the gap fade, the gap continuation, the volume spike, the liquidity grab reversal, the Asia session expansion. Each comes with a chart, a win rate, and a story about why it works. Mathias Mesfin took the whole list, wired it to a bar-close signal and next-bar-open fill on Micro
  • Quick 5 ETF Rotational Strategy Returns the Upside of Stocks with Half the Risk [Paper to Profit]

    The last few posts have been a lot to chew through. So here is a simple ETF rotational strategy that you can do to capture the upside of equities while eliminating half the risk. The inspiration for this strategy comes from the paper Looking for Synergy with Momentum in Main Asset Classes which you can read for yourself here. Not a subscriber? Become one today for more research delivered to your

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

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

  • Quantitativo weekly #4 [Quantitativo]

    The only sustainable competitive advantage is to learn faster than your competition. Arie de Geus Implementing research papers can sometimes work, though a perfect replication often fails. Its never wasted effort, though: the ideas in the paper end up feeding new ideas and good conversations with other researchers. Heres the 4th edition of the Quantitativo weekly, featuring papers that
  • Supertrend, flipped to death: the most-taught indicator on the internet [The Refutation]

    If you learned to trade from YouTube, you probably learned the Supertrend. It topped our folklore hunt being the single most-taught beginner tool on the internet, complete with the green-line/red-line screenshots and the promise that the trend will tell you when to start riding the new trend. So we did the honest thing: we tested it exactly the way it's taught. Figure 1 is the tool itself, on
  • Down June & July: 11 of 13 Septembers Closed Lower [Quantifiable Edges]

    Both June and July saw SPX close lower. Seasonality for those two months it is generally favorable. But since 1950, two of the weakest months are August (3rd worst) and September (worst). So in this weekends letter I decided to see how August and September fared when we already had June and July slipping. The table below gives us an answer. After down closes in both June and July. August has
  • Conformal Prediction in Quantitative Finance [Vertox Quant]

    In one of our previous articles, we built a neural-network-based volatility forecaster that beats baseline models in all volatility regimes: Volatility Forecasting using Neural Networks Vertox Jun 22 Volatility Forecasting using Neural Networks Today, we are gonna look at something weve never done in any article before: Neural Networks. Read full story Now no need to be sceptical about the
  • Making Option Pricing Models More Practical [Relative Value Arbitrage]

    The Black-Scholes-Merton model is one of the cornerstones of modern quantitative finance. Despite its elegance and widespread use, its simplifying assumptions limit its ability to capture many features of real financial markets. As a result, researchers continue to extend the model to make it more realistic and applicable in practice. In this post, we discuss two such extensions. The first

Recent Quant Links from Quantocracy as of 07/31/2026

This is a summary of links recently featured on Quantocracy as of Friday, 07/31/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The 58% Win Rate That Was My Own Code Lying To Me [Jan Heger]

    Below I have shared my story of an idea that passed four checks Id set in advance and died on the fifth, and why the fifth one is now the first thing I run. The setup I like to day-trade MNQ, and Id built a framework around specific price levels that I have created. The core claim was simple: price reacts at certain levels within each hundred-point block. Falls into one, bounces. Thanks for
  • Fair Value as an Adaptive Low-Pass Filter: LAFO for Mean Reversion [Aligrithm]

    Every mean-reversion trade starts with a lie you tell yourself about where price "should" be. You call it fair value, you subtract it from the spot price, and you bet the gap closes. Most traders reach for a moving average and stop thinking. Xu, Firoozye, Koukorinis, Treleaven, and Zhu at UCL take the question seriously and reframe fair value as the output of a tunable low-pass filter,
  • We tested the 50/100 MA ribbon 55 different ways over five years [The Refutation]

    Move like the wind, be still as the mountain, the old strategists taught. The moving-average ribbon promises to tell you which moment you're in. The catch: it's built from the past, so the wind it reads has already blown. Stack a fan of moving averages on a chart and assign colours to them. Wait for the bands to fan out and align, and price is "in a trend." When the lines
  • Bitcoin’s Overnight Returns Forecast the VIX [Aligrithm]

    Split one Bitcoin day into two pieces and only one of them predicts anything. The piece that runs while US stock exchanges are closed, from yesterday's 4pm close to today's 9:30am open, carries a signal for tomorrow's VIX. The piece that runs while those exchanges are open carries nothing. Gu, Lin, and Liu ran five-minute Bitcoin data from 2018 to 2023 through that split and found