Trends in gold returns can be partly explained by the macroeconomic environment. Persistent monetary easing, dollar stability risks, and weak economic sentiment can each drive sustained demand for gold. This article shows how to construct simple point-in-time macro factors that capture these themes(...)

One of the most enduring puzzles in finance is the apparent disconnect between Wall Street and Main Street—markets sometimes soar while the underlying economy stumbles, and vice versa. Paul Samuelson famously quipped that “the stock market has predicted nine out of the last five recessions” —(...)

Beta began as a way to compress a covariance matrix. The CAPM turned it into the equilibrium measure of exposure to aggregate market risk, priced by the market premium. Markowitz maps beliefs to portfolios. With common beliefs, a risk-free asset and market clearing, the CAPM adds one decisive(...)

The literature on crypto quant trading keeps citing cross-sectional momentum as a durable edge with Sharpe around 1.1 to 1.5. I built exactly the paper spec, ran it against 303 weekly rebalances across 10 majors, no parameter tuning, no regime filters, no cheating. Annualized Sharpe came out at(...)

For the past months, almost every free hour I had went into one thing. Today it’s live. eigenscore.com In quant, everyone claims to be good, and there's no arena to settle it. Competitive programming solved that twenty years ago with rated contests. I built the same thing for us. What it is(...)

The most liquid fund shelf a person can buy holds 3.3 independent bets. Dalio's chart needs fifteen. The Holy Grail of investing, in his words: find fifteen good, uncorrelated return streams and risk falls by roughly eighty percent while return stands still. At correlation zero, fifteen equal(...)

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(...)

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(...)

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(...)

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(...)

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. Here’s what the aggregate hid, and why I now trust the breakdown over the bottom line every time. The idea that(...)

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 Roldán's 2024 study quantifies this with hourly and daily data from(...)

Most Tactical Asset Allocation (TAA) strategies trade once per month. That’s by design. Short-term market movement is mostly noise, and trying to time every zig and zag is a fool’s errand. A unique feature of our platform is the ability to follow these monthly strategies on any day of the month.(...)

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 mı?, “interest or dollars?” Both answers have had long stretches(...)

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 company’s value. The challenge is not simply identifying whether a headline(...)

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(...)

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,(...)

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(...)

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(...)

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(...)

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(...)

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%.(...)

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(...)

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(...)

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 […](...)

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(...)

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(...)

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(...)

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(...)

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(...)

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. Here’s what the control saw that my eyes couldn’t. Thanks for reading! Subscribe for free to receive new posts and support my work. What a control(...)

Hernández-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(...)

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(...)

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(...)

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(...)

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(...)

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(...)

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(...)

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(...)

I recently came across López 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(...)

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(...)

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(...)

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(...)

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(...)

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(...)

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(...)

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(...)

The dividend discount model (DDM) says that a stock’s 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(...)

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(...)

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(...)