The question was simple: what do you find if you forget everything you think you know and search the data from scratch? No own setups, no favourite ideas, no weighting by gut feeling. Instead, every pattern family that books, forums and papers call profitable, on the same data and under the same rules. The rules were strict, because a pattern search always finds something otherwise. Test 900,000

US crude oil (WTI) rose 50% or more within a year 13 times from 1970 to August 2026, and US CPI inflation was higher at every one of the 13 oil peaks than a year earlier, by a median 2.0 percentage points. In the 8 oil spikes since 1970 that took WTI crude to a 5-year high, the S&P 500 fell in 5 over the 12 months to the oil peak (median 11.3%); in the 5 spikes that stayed under the

In the 7 episodes from 1963 to 2026 when the 10-year Treasury yield rose 1 point or more in a year and the S&P 500 fell, 10-year Treasuries lost money all 7 times (median 5.5%) and 1-month Treasury bills gained all 7 times (median +6.1%). In those 7 episodes a 60/40 portfolio of US stocks and 10-year Treasuries lost a median 6.6%, barely less than the 7.4% lost by US stocks alone; the same

Huang and Shum Nolan buy three names. Each month they take the tickers WallStreetBets mentioned most, keep a name only when the posts were majority bullish, and hold the equal-weight book for the next month. Regress that book's daily percent return on the market, size, value, and momentum portfolios (the Fama-French-Carhart regression) and the intercept is 0.433 percent a day. Multiply by

This is a summary of links recently featured on Quantocracy as of Wednesday, 09/30/2026. To see our most recent links, visit the Quant Mashup. Read on readers! A New Stage, a New Deadline: Quantpedia Awards 2027 Are Here Again! [Quantpedia] Hello everyone, The Quantpedia Awards are backand this time, were taking the winners announcement to […] The post Recent Quant Links from Quantocracy as of 09/30/2026 appeared first on Quantocracy.

Hello everyone, The Quantpedia Awards are backand this time, were taking the winners announcement to the stage! For the 2027 edition, were bringing together an attractive prize pool, a panel of investment professionals and academics, and a new opportunity to put outstanding quantitative research in front of the industry. If you have been developing a systematic trading strategy,

The previous note inaugurated the replication arc with an anomaly that died by decay. The present replication examines a more recent published case of finer mechanics: the market intraday momentum documented by Gao, Han, Li and Zhou in 2018, according to which the sign of the first half hour of the session predicts the return of the last half hour. The outcome, stated upfront in one line, is less

On the non-microcap book, 0.50% times the Gu-Kelly-Xiu neural net's turnover of 0.869 costs 0.4345% a month. Its Fama-French six-factor alpha on that book is 0.312%. The ticket is larger than the alpha. Instrumented principal components, the linear model that lets betas move with firm characteristics, posts a six-factor alpha of 0.613% against a cost of 0.565% and clears by 0.048 percentage

What do systematic managers actually do with their money? This post distils 335 podcast episodes, about 3.7 million words of interviews with CTAs, quants and allocators, into practical lessons. Every claim links to the episode it came from. It is organised around the questions people ask most: which strategies they run, which signals and models they use, how they build portfolios and execute,

Correlation is an important component of portfolio and risk management. However, unlike volatility, which has received significant attention and for which numerous models have been developed, correlations have received considerably less attention from a modeling perspective. In this post, we give correlations the attention they deserve and examine their role in volatility dynamics, portfolio

This is a summary of links recently featured on Quantocracy as of Monday, 09/28/2026. To see our most recent links, visit the Quant Mashup. Read on readers! Do Stocks Fall When Bond Yields Rise? 63 Years of Data [Portfolio Terminal] From January 1963 to August 2026, the 10-year Treasury yield rose 1 percentage point or […] The post Recent Quant Links from Quantocracy as of 09/28/2026 appeared first on Quantocracy.

From January 1963 to August 2026, the 10-year Treasury yield rose 1 percentage point or more within 12 months in 20 separate episodes; the S&P 500 fell over the same 12 months in 7 of them. All 7 of those S&P 500 losses came with US consumer-price inflation at 3% or more. In the 6 episodes where inflation was under 3%, including 1994, 2013 and 2021, the S&P 500 rose every time. Across

A feature that matches the sign of the next E-mini return on 4 days out of 5 leaves 0.722 bits of uncertainty about that sign. The part the feature removes is 0.278 bits of mutual information, out of the 1 bit in a fair coin. The binary channel that flips 1 transmitted bit in 5 has that 0.278 as its capacity, and no choice of how often the feature calls up versus down raises it. What this actually

Many trend rules can be drawn on the chart, and the 200-day line is the best known of them: hold the fund while the price is above its average of the last 200 days, step aside when it falls below. A Kalman filter comes at the same prices from statistics. It treats every close as a noisy reading of a trend nobody can see and updates its estimate of that trend each day, the way a guidance system

The Euro Stoxx 50 future's trade price led the DAX future's midpoint on 99% of trading days from January to June 2021, and the lag that maximized the correlation was 100 microseconds. Bender, Cestonaro, and Schmidt measure that lead on 19 Xetra and Eurex instruments, across nine microstructure series, with a lag-shifted Hayashi-Yoshida correlation: the sum of price changes whose time

This is a summary of links recently featured on Quantocracy as of Saturday, 09/26/2026. To see our most recent links, visit the Quant Mashup. Read on readers! Can Weakening Morning Order Flow Predict SPY Reversals? [Quantpedia] In a previous article Building and Testing Trend-Following Strategies on One-Minute SPY Data, we investigated whether retail activity indicators […] The post Recent Quant Links from Quantocracy as of 09/26/2026 appeared first on Quantocracy.

In a previous article Building and Testing Trend-Following Strategies on One-Minute SPY Data, we investigated whether retail activity indicators derived from one-minute SPY data could be used to construct profitable trend-following strategies. The results suggested that we are able to construct strategies that are often able to achieve superior risk-adjusted performance. In this article, we

In a previous blog post, I described the NAAIM Exposure Index, which 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. At the end of August 2026, the NAAIM Exposure Index hits a value of 102.66, meaning that U.S. active investment managers were in aggregate leveraged long in terms of

Mean reversion is the idea that a price pushed too far in a few days tends to come part of the way back. The research explains it as a trade: holders who must sell at once push the price below what the news justifies, and whoever buys from them is paid when it drifts back. We test its plainest trading form, the two-day RSI dip rule: buy a stock after a sharp two-day fall while its long trend is

On S&P 500 names from 2000 to 2022, the best book in Raymond Leung's grid is multi-view co-regularized spectral clustering with 25 clusters, a plain long-short spread, and an Ornstein-Uhlenbeck entry and exit. After a 5 basis point charge on each leg at entry and again at exit, that book has an annualized Sharpe ratio of 0.830, a mean excess return of 2.7% a year, and a maximum drawdown

A pairwise screen of the 64-futures book, at a 5% level and five lags, expects 1,008 false links when every cross-link is null. That book is the one in the old article "Network Momentum as a Cross-Asset Factor," and the screen is the bivariate test the old article "Stop Using Pairwise Granger: PCMCI for Financial Causality" took apart. The sequel is the object you trade. Both

This is a summary of links recently featured on Quantocracy as of Thursday, 09/24/2026. To see our most recent links, visit the Quant Mashup. Read on readers! Tech companies that spend more on capex than their operating cash flow [Quanter Lab] Before the dot-com crash, the telecom companies building the internet's networks spent more on […] The post Recent Quant Links from Quantocracy as of 09/24/2026 appeared first on Quantocracy.

Before the dot-com crash, the telecom companies building the internet's networks spent more on buildings and equipment than their businesses brought in, and borrowed or sold shares to pay the difference. Our thirty-year study of the tech industry found that their spending passed their own cash in 2000, and within two years they had cut it in half. Today's AI giants spend a similar share

Conformal prediction is not one technique - it is a family of methods, each with its own strengths and weaknesses. Split Conformal, Mondrian, ACI, CQR - they all produce valid prediction intervals, but they behave very differently depending on the signal you feed them. It is not an implementation detail. On the same alpha signal with the same data, switching from Split Conformal to ACI turns a

Divide a stock's 21-day moving average by its 200-day moving average. That is the whole signal, and it is about as retail as a signal gets. Avramov, Kaplanski and Subrahmanyam ran it across 13,828 US firms and 1,353,679 monthly returns from July 1977 through December 2018, and the value-weighted hedge portfolio produced an annual alpha of 9.05% against the five Fama-French factors plus

Part 1 was survival first: start slow, start boring, and treat the early years as skill acquisition rather than a race to 100% a year. This part is the bridge from that idea into the actual research. The subject is relative momentum: rank a universe of stocks by how strongly theyve risen, hold the strongest, and rotate as the ranking changes. It suits a part-time trader almost perfectly. It

This is a summary of links recently featured on Quantocracy as of Tuesday, 09/22/2026. To see our most recent links, visit the Quant Mashup. Read on readers! Building and Testing Trend-Following Strategies on One-Minute SPY Data [Quantpedia] Intraday trading strategies have gained increasing attention as advances in computing power and market data availability have made […] The post Recent Quant Links from Quantocracy as of 09/22/2026 appeared first on Quantocracy.

Intraday trading strategies have gained increasing attention as advances in computing power and market data availability have made intraday strategy analysis more accessible. While many trading strategies are traditionally developed and evaluated using daily price data, shorter timeframes can provide additional opportunities to identify and exploit market trends within a single trading session. In

Since the dawn of the LLM age (only few years ago), there has been a flurry of alternative approaches and fundamental remixed on the classic ChatGPT style attention transformer. In 2023, Bloomberg released BloombertGPT which was the first financial LLM and claimed that it could outperform the current state-of-the-art in financial sentiment analysis. Just last year, a PhD student in

Barak, Razmi and Mousavi report an out-of-sample Sharpe ratio of 1.34 against 0.59 for the best static version of the identical trading logic, on 50 crypto futures from January 2022 to January 2024, with a Ledoit-Wolf bootstrap p-value of 0.008 on the difference. The machine doing the work is a LightGBM classifier that picks tomorrow's directional-change threshold out of {0.01, 0.02, 0.04}.

In 1968 Edward Altman took sixty-six manufacturers, half of which had gone bankrupt, and found five numbers from their accounts that together told the two halves apart. His Z-score is still taught, and it is now used for a different job: as a quality screen, a way of choosing which shares to own. The reasoning is easy to follow. A low score means trouble, so a high score should mean a sound

The panel in my September post was supposed to have zero alpha. It didnt quite. The market factor carried a drift of 0.0002 per day and the betas were drawn N(1, 0.3), so a book that tilted towards high-beta names had a true Sharpe of about +0.22 on a panel I described as containing nothing. The generator also clipped daily returns asymmetrically, at [0.5, +1.0], which leaves a name whose

Take a library of rules that loses money, put a model on top and let it decide which trades to take, then measure what that recovers across 420 rules, two markets and 43 months. A common pitch for machine learning in systematic trading leaves the strategies alone and puts a model on top of them. The rules keep generating signals, while a model trained on their past trades decides which signals to

Pham, Han, Nguyen, Pham and Do build one index and then trade it backwards. In Section 5.4 they write that an RCI near zero marks "widespread panic selling and a potentially buying opportunity," and that exuberance at the top "can indicate a market peak and a potential selling opportunity." Nine pages later, in Section 6, they lever the portfolio up to 125% when the same index

Crypto exchanges list new perpetual futures every week, and Binance alone has listed 832 USDT-margined perpetuals, so a research universe built on that exchange mixes contracts listed last week with contracts that have traded for years. Both kinds usually share one cost assumption and one strategy library, even though a new contract has no settled basis, no settled funding schedule and far more

Vance Harwood passed away in September 2026 after a brief and very unexpected illness. Any clients who have posted orders can get their payments refunded via the platform chosen for payment. May his best live on in others. Should you have any important questions or concerns, contact Heidi Nordberg: hlnordberg at gmail

This is a summary of links recently featured on Quantocracy as of Sunday, 09/20/2026. To see our most recent links, visit the Quant Mashup. Read on readers! A sad day for the quant vol trading community, Vance is gone [Six Figure Investing] Vance Harwood passed away in September 2026 after a brief and very unexpected […] The post Recent Quant Links from Quantocracy as of 09/20/2026 appeared first on Quantocracy.

Holidays put people in motion. In the days surrounding major U.S. holidays, airports become busier as travelers visit their families or take advantage of extended weekends. Financial markets themselves are known to display a holiday-related seasonality. In our previous research on the Pre-Holiday Effect in Commodities, we identified a short-term price drift in crude oil and gasoline before major

Every quant hits the same wall. You have hundreds of features that look predictive in isolation. Now you need to combine them into a single portfolio. This is a worked example of that problem: 576 features, 25 instruments, 5 asset classes, daily bars. What to select, how to combine, and where it quietly breaks. 1. The Problem The investment universe is 25 instruments across 5 asset classes: equity

A regime tilt is bought as insurance: lean into momentum while the market is calm, into quality when it is stressed, and keep your head in a crash at little cost in between. The premium is rarely priced with the rule held fixed, because most tests choose the thresholds after seeing the crashes. This one fixed them before the walk and printed the bill year by year. Two portfolios hold thirty

There is 150 years of data on stocks, on bonds, on gold, and on property. On the asset most people actually hold, there is almost nothing. Cash has no volatility, so there seems to be nothing to measure. That turns out to be the wrong conclusion. What is actually guaranteed A Treasury bill promises a number. You put in a hundred, and in three months you get back a hundred plus a little, and the

Trend following is one of the oldest and most persistent anomalies in finance. The evidence that recent winners continue to outperform recent losers has been documented across virtually every liquid asset class, stretching back at least two centuries. It stands in direct opposition to the Efficient Market Hypothesis, yet it has survived out-of-sample testing, multiple market regimes, and decades

The overnight gain is real. Since 1993 nearly everything the S&P 500 paid came between the close and the next morning's open, and a dollar held in SPY only overnight ended more than ten times above a dollar held only through the trading day. Every year someone puts this back into circulation as a discovery, with a table of small stocks whose night returns run to thousands of times and a

A 70.8% continuation rate looks like a trade. Greene sorts 4,061 Kalshi contracts by the quality of their price trend over the window from 30 to 12 minutes before close, and the top decile keeps moving in the trend's direction 70.8% of the time against 51.2% in the bottom decile. Ex-post forecast error falls from 14.75 cents to 6.07 cents across the same sort. Both gaps carry contract-level

When we published that piece we said a re-test was scheduled and that we would report it whichever way it came out. It has now run, on data through 18 September. Here is what it found, and what it changes. The short version. On all the data together the result is stronger and cleaner than what we published. On the five weeks of genuinely new data considered alone, the size of the effect held up

We wanted to see whether a long-only, rules based algorithm applied to US industries could remain profitable over a full century. Our paper written with Gary Antonacci, A Century of Profitable Industry Trends, answers exactly that. Using Kenneth Frenchs industry data from 1926 to 2024, the strategy delivers 18.2% compounded per year, with 12.6% volatility and a Sharpe ratio of 1.39. For

Macroeconomic theory suggests that aggregate demand for goods and services is a key determinant of interest rates. Interest rates regulate demand strength or weakness through market-based financing conditions and central-bank reaction functions. If financial markets do not immediately incorporate all information contained in macroeconomic trends, excess-demand pressures should help predict

This is a summary of links recently featured on Quantocracy as of Wednesday, 09/16/2026. To see our most recent links, visit the Quant Mashup. Read on readers! Fama-French factors inside the S&P 500: walk-forward 2006 to 2025 [Quanter Lab] Fama and French sort every US stock on the signal at the end of June, hold […] The post Recent Quant Links from Quantocracy as of 09/16/2026 appeared first on Quantocracy.

Fama and French sort every US stock on the signal at the end of June, hold the top thirty percent weighted by market value, and quote the spread against the bottom thirty percent. A fund that sells the factor holds something near that, restricted to large caps. A private investor who reads about the same factor buys thirty names and weights them equally. The three are assumed to be one trade at

As many of you know, I trade the US, ASX (Australian), and TSX (Canadian) markets systematically. And hopefully in the near future Ill be adding even more markets. It puzzles people why I trade markets other than the US. The US has to be the best, right? Its the biggest and has the most volume, so surely every trading opportunity I could ever need lives in the US market... right? I get this