Quantocracy

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Recent Quant Links from Quantocracy as of 09/02/2026

This is a summary of links recently featured on Quantocracy as of Wednesday, 09/02/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 2x Your Portfolio Every Year: How I Turbocharged the Overnight Effect [Paper to Profit]

    For those unaware, you generally make more money overnight than you do during the day when holding stocks. So much so that even if you just held the SPY overnight (buy at close, sell at market open the next day), you would net considerably more than if you held it during the day (buy at open, sell at close) Chart, line chart Description automatically generated This is known as the Overnight
  • A Sharpe of 2.1 From Nothing: The Second Number Your Agent Doesn’t Log [Jonathan Kinlay]

    I gave a research agent four years of prices with no predictable structure in them none, by construction and it came back with a long/short book, an in-sample Sharpe of 2.1, and a paragraph explaining the economics of an effect that does not exist. That is the measurement in this post. The more useful result is the second one: 88% of that number is accounted for by two integers how
  • All Weather built and tested against inverse vol, ERC and a 60/40 [Quanter Lab]

    People usually take All Weather for the fixed allocation Dalio gave Tony Robbins, which is the one every retail article prints, but that one is All Seasons and it carries no borrowing at all. All Weather proper is risk parity borrowed up until the book carries a 60/40's volatility, which means the borrowing is the strategy itself. We built that one and charged it the three-month Treasury bill
  • Stock-Bond Correlation: The Sign Flips and So Do Its Drivers [Aligrithm]

    A 50/50 stock-bond portfolio that earned its target return with moderate volatility before 2000 requires an 80/20 allocation after 2000 to maintain the same profile. That 30-point shift is not a style choice or a bet on equities. It is the portfolio adjustment needed to compensate for the stock-bond correlation flipping from positive to negative, which McMillan documents across the G7 using
  • Backtest Said “SURVIVES.” Per-Year Numbers Said No. I Learned to Trust the Second One [Jan Heger]

    Twice in a single afternoon, it told me an idea was good. Twice, it was wrong, and the thing that caught the error both times was a number the headline verdict had quietly averaged away. Heres what the aggregate hid, and why I now trust the breakdown over the bottom line every time. The idea that survived The first idea was a zone-cycle pattern, price making a particular round-trip
  • Volatility Clustering in Bitcoin: Regime Persistence as a Forecast [Aligrithm]

    Bitcoin's volatility does not mix randomly across time. High-vol periods follow high-vol periods, low-vol stretches extend themselves, and the transition probabilities are stable enough to build a forecast from. Borrego Roldn's 2024 study quantifies this with hourly and daily data from August 2018 to November 2024, constructing 3-state Markov chains around rolling log-volatility and

Recent Quant Links from Quantocracy as of 08/31/2026

This is a summary of links recently featured on Quantocracy as of Monday, 08/31/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Does Trading TAA Strategies More Often Improve Performance? [Allocate Smartly]

    Most Tactical Asset Allocation (TAA) strategies trade once per month. Thats by design. Short-term market movement is mostly noise, and trying to time every zig and zag is a fools errand. A unique feature of our platform is the ability to follow these monthly strategies on any day of the month. Were not just executing the same signal on a different date were recalculating the signal
  • Dual Momentum for a Collapsing Currency [Michael Emre Tulum]

    Turkish savers have argued about the same question for decades: keep the money in a lira time deposit and collect the interest, or convert it to dollars and hold. The dilemma is common enough to have a household name faiz mi, dolar m?, interest or dollars? Both answers have had long stretches of looking right, which is why the argument never ends. Figure 1 settles it the only way it
  • Can ChatGPT Forecast Stock Price Movements? [Alpha Architect]

    Financial markets process an enormous volume of corporate news every day. Earnings announcements, management changes, clinical trial results, insider transactions, partnerships, and regulatory developments can all affect a companys value. The challenge is not simply identifying whether a headline sounds positive or negative. Investors must understand its economic implications, anticipate how
  • Boundaries of Time Series Momentum [Quantpedia]

    Time-series momentum stands as one of the most reliable and heavily backtested anomalies in quantitative finance, serving as a foundational alpha source for modern managed futures and trend-following strategies. However, a recent academic paper by Matti Suominen and Erik Hjalmarsson, titled Boundaries of Time Series Momentum, uncovers a structural vulnerability that every practitioner must
  • Podcast: From market problems to quantitative trading systems [Trading the Breaking]

    In this episode of House of Quants, listeners will discover: Why quantitative trading is engineering: The episode challenges the myth of the lone trader searching for magical chart patterns and explains how institutional research operates as an industrial-scale system built around data, statistics, software, execution, and risk. How ideas become executable trading systems: Every strategy passes

Recent Quant Links from Quantocracy as of 08/27/2026

This is a summary of links recently featured on Quantocracy as of Thursday, 08/27/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Magic Formula tested against each half: quality alone beat the combination [Quanter Lab]

    Joel Greenblatt's Magic Formula ranks companies on two numbers and buys the ones that score well on both. One asks whether a business is cheap. The other asks whether it is any good. The claim in the book is that the pair works better than either number on its own. We tested that claim by running all three books over the same twenty one-year windows of the S&P 500, from 2006 to 2025.
  • What Happens After Congress Buys or Sells a Stock? Evidence… [Equibles]

    The obvious rule is to buy what members of Congress buy and avoid, or short, what they sell. Across 17,859 investable common-stock disclosure events, that rule fails. One year after a disclosure became public, the typical purchase event trailed the S&P 500 by 5.36 percentage points and the typical sale event trailed by 6.39 points. Purchases did only 1.13 points better than sales on matched
  • Crypto Isn’t Structurally Alien: Roll/VPIN/Amihud Predict Distribution Shifts [Aligrithm]

    Easley, O'Hara, Yang and Zhang take five textbook microstructure measures, compute them on Binance one-minute bars for BTC, ETH, XRP, SOL and ADA over January 2021 to July 2023, and ask a random forest to call the sign of tomorrow's change in the return distribution. Two numbers carry the paper. Crypto VPIN averages 0.47 against the 0.22 that the same authors found in E-mini and crude
  • Markowitz, Estimation Error and 1/N [Spatium Novum]

    Markowitz's 1952 paper deliberately begins after investors have formed beliefs about future returns. Portfolio choice is the second stage; estimating its inputs is the first. Rebuilding Robert Shiller's three-asset classroom example with two defensible US equity series moves the recommended equity weight from 38% to 26%, while estimated portfolio risk and return barely move. Repeating

Recent Quant Links from Quantocracy as of 08/25/2026

This is a summary of links recently featured on Quantocracy as of Tuesday, 08/25/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • FX Edge Lives in Other Markets (cross-asset series) [Aligrithm]

    A currency futures book built from other currency futures scores a Sharpe of 0.16. The same contracts, wired into a graph that also contains equities, bonds, and commodities, jump the FX sleeve to 0.66. That is Pu, Roberts, Dong, and Zohren, 64 futures, 2000 to 2022, volatility-targeted to 15%. Phylaktis and Yamani ran the pairwise version of the same instinct and got a harsher split: a
  • Form 4 Insider Trading in Python: A Filing-Date Event Study [Quant Insti]

    Form 4 looks unusually convenient for quantitative research. It is public, structured and tied to a regulatory deadline: in general, a reporting person must file by the end of the second business day after a reportable transaction. The SEC's Form 4 instructions state the rule and also show why a filing is more complicated than one insider, one trade. A filing can contain many
  • The Crash Switch Worked and the Strategy Starved Anyway: Keller’s Menu Tested [Quanter Lab]

    In July 2022 Wouter Keller published Bold Asset Allocation. This paper walks the aggressive variant, BAA-G4: hold the single strongest of QQQ, VWO, VEA and BND by relative momentum, or retreat into bonds and bills when a canary basket says so. We ran the published rules on the real tradable funds through seventeen registered one-year windows, 2009 through 2025. The crash switch is real: in 2022 it
  • The Sharpe Stability Ratio: Evaluating the Sharpe Ratio Temporal Consistency [Portfolio Optimizer]

    The Sharpe Ratio1, one of the most commonly used measure of risk-adjusted performance2, is usually reported as a point estimate (Morningstar, Quantalys, etc.). Thanks to the work of Lo3, Opdyke4 and more recently5 de Prado et al.6, it is nevertheless well understood that such a point estimate [] does not convey information about statistical significance6, so that a more meaningful way to

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,