When inflation shifts from low to high, a traditional 60/40 equity-and-bond allocation breaks down because both fall together. You need real assets to act as the ballast. Ray Dalio Inflation is one of the most powerful forces in asset allocationand one of the hardest to measure in real time.Everyone understands why it matters. The harder question is how to capture it before markets

Alpha rarely arrives as one heroic discovery. It is more like an ant colony carrying a leaf many times its own size: dozens of small contributions, each unimpressive on its own, somehow producing an impressive result. Unfortunately, research also resembles an ant colony in another respect. Much of the work involves running in circles. The first version

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. In this second post of this series on sentiment indicators, I will show how that survey of professional money managers can be turned into a relative sentiment

I wrote my newest paper mainly to challenge the conventional way of judging an investment or trading strategy through a single observational point of its performance metric an approach that discards precious information about a strategy's effectiveness and can lead to false conclusions. Rather than routinely collapsing a strategy's performance metric to one observational scalar, I

Networks are everywhere. All you need is an eye for them. Albert-Lszl Barabsi. Albert-Lszl Barabsi is a Romanian-born Hungarian-American physicist, renowned for his pioneering discoveries in network science. In his seminal 1999 paper with Rka Albert, Emergence of Scaling in Random Networks, he reshaped how we understand connected systems. His 2002 book Linked carried the

Cross-sectional momentum has one job: at each rebalance, order a universe of assets from worst to best, buy the top, sell the bottom. Everyone agrees on that. Where strategies quietly disagree is on how they produce the order. Classic momentum sorts on the past twelve-month return. A neural net predicts each asset's next return and sorts on the prediction. Both treat the ranking as a

The first part described the idea and put a gross Sharpe of about one and a half on it. The second part set it in front of a brokers fees and an integer number of shares, and watched most of the edge go to the cost of trading. This part adds a second signal, drawn from the same spread. It does not raise the return by much. What it does is trade far less, and on a small account that is worth

This article shows a practical method for optimizing equity portfolios with point-in-time macroeconomic information and sequential statistical learning. The learning process relies on neural networks, as they learn portfolio weights directly from a full historical panel of macroeconomic divergence factors and return data. They do not require stock-by-stock theoretical priors for model

The CAPE that Cried Wolf Dino Palazzo (Board of Governors of the Federal Reserve System) May 2026 The Capital Spectators Takeaway The paper reports that traditional CAPE ratios false warnings of market overvaluation since the 1990s are an accounting illusion caused by mandatory R&D expensing and volatile special-item write-downs. By stripping out these regulatory distortions, CAPE-H

Key takeaways Neither entries nor exits controlled every result. Win rate was more sensitive to exits in all ten market-direction jobs, while maximum drawdown was more sensitive to entries in all ten. Changing an entry indicator was a much larger move than nudging that indicator's period. Treating both as "entry tuning" hides the useful distinction. For the study's

Recent interesting research from Cakici and Zaremba, highlights an often-overlooked aspect of machine learning for equity return prediction: the choice of prediction target. Rather than focusing on increasingly sophisticated model architectures or feature engineering, the authors show that how returns are represented during training has a much larger impact on predictive performance. In

Advances in computing power, declining hardware costs, and the rapid rise of machine learning and algorithmic trading have fundamentally transformed modern financial markets. While these technologies have improved market efficiency and execution, they have also introduced new challenges and risks. In this post, we examine research on the impact of algorithmic trading, from its influence on

Chaos theory is a fancy topic that alludes to a world almost as mystic and mysterious as quantum physics. Everyone has heard of it, but not many people really know what it means or how to use it. A Mandelbrot Set, or infinitely complex shape described by a simple formula, a classic example of chaos theory where simple rules create complex results. Fortunately for us, you cant judge a book by

Feed 920 firm characteristics into a Lasso to predict next month's stock returns and it will do something that should stop you cold: it loses to a model that predicts zero for every stock. Not "underperforms a good benchmark." Loses to the number 0. Shen and Xiu prove this is not bad luck or a coding bug. In the regime where economics and finance actually live, where signals are

I ran the same trend-following strategy across futures, stocks, FX, and crypto. On futures it produced a net Sharpe of 0.73. Crypto came in at 0.56. US stocks barely broke even at 0.42. FX was marginal at 0.24. Same signal, same methodology, four very different outcomes. All four have near-zero correlation to the S&P 500. The difference isn't the signal. It's the market. This is a

Are 0DTE straddles systematically overpriced? It is probably the most argued question in options trading since daily expirations took over SPY volume – and it has a testable answer. We replayed 193 Wednesday sessions from July 2022 through April 2026 on the FlashAlpha Historical 0DTE endpoint, snapshotting the same-day straddle at 10:00 ET and comparing its implied move to what SPY actually did

The short version The RSI 70/30 rule (above 70 = overbought, sell; below 30 = oversold, buy) is the most repeated pattern in trading. We measured what price actually does after the signal, across six timeframes and four assets, and ran the traded version through six market regimes. It has no edge: forward travel is ~0.00% where the sample is real, it points the wrong way on the 4-hour chart, and

An idea is nothing more nor less than a new combination of old elements. James Webb Young 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 3rd edition of the Quantitativo weekly, featuring papers that caught

The short version We wrote down four predictions before running a single test. Then we ran 360 measurements, tuned 320 variants, and put three complete systems through regime cross-validation. All four predictions held. What is actually inside the most famous momentum indicator in trading is not what its users think. The claim on trial MACD is on every platform, in every course, behind every

The current extreme in margin debt offers one way to gauge the speculative exuberance of investors. John Hussman Thinking about this chart from John Hussman, showing margin debt relative to GDP spiking to all-time highs, with previous such instances seeming to foreshadow major market downturns: Its very easy to look at a chart like this with hindsight and identify the top of each

Man AHL has recently published a research piece titled A Trend Following Deep Dive: Cash (Equities) Is King (Panjabi, Bordigoni, and Buchanan, 2026) which has resonated not only with researchers in the trend-following space but also with those specializing in equity markets. The authors show that cross-sectional momentum techniques can be successfully applied across equity-style factors,

Momentum is the oldest anomaly in the book, and a new momentum paper has to justify why it exists. Landolfi's percentile-rank framework does not sell you the momentum. It sells the plumbing around it: rank each move against its own sign-consistent history instead of a raw threshold, gate entries and exits with a hysteresis band so the signal stops flip-flopping, and validate with a grid of

The first part described the idea and put a gross Sharpe of about one and a half on it. Gross is the easy figure to produce and the least interesting one to quote, because it assumes you can trade for free. Here I put the strategy in front of a brokers fee schedule and an integer number of shares, and the single number becomes a curve one that depends almost entirely on the size of the

Two of the longest-running puzzles in accounting and asset pricing research are the accrual anomaly and the post-earnings-announcement drift, or PEAD. Both describe return patterns that standard one-period asset pricing models struggle to explain, and both have generated a huge literature. The recurring question has been the same: is the market mispricing the information, or is it rationally

Every few years a new architecture gets pointed at Bitcoin and a paper announces it won. This round it is Mamba, the selective state-space model that is genuinely reshaping language and vision. Sepehri, Mehradfar, Soltanolkotabi, and Avestimehr at USC built CryptoMamba, a compact Mamba network that reads 14 days of Bitcoin OHLCV and predicts the next day's close. The numbers are real and they

In this episode of House of Quants, listeners will discover: How quants separate genuine edge from market noise: The episode explains why financial markets are difficult to diagnose and how researchers distinguish persistent information from randomness, temporary anomalies, and misleading patterns. How data problems create false strategies: It examines survivorship bias, timestamp errors,

Large language models are already capable of summarizing financial research, but are they ready to conduct it? In their latest paper, researchers from Google, Boston College, and Columbia introduce a framework where a large language model doesnt just fetch datait acts as an autonomous AI research agent capable of navigating the hypothesis discovery loop. By placing an LLM within a

Here is the thesis, stated before the evidence: when we re-measured seven months of work on a pipeline that reads financial headlines and asks whether each one should move a stock, the mechanisms we had found held up. The magnitudes almost never did, including, twice, the magnitudes we ourselves had computed and believed. The pipelines job is narrow. For roughly 850 tickers a night, it reads

This is the fourth installment in our series on selecting Tactical Asset Allocation (TAA) strategies based on recent performance. Read parts 1, 2 and 3. In our previous studies we selected strategies based on recent return. In this study, we select distressed strategies, or strategies nearing or exceeding their previous max drawdown. We looked at this subject way back in 2020 and concluded

Moving averages are the first fools errand we make as traders. If only it was smoother, but still responsive. Predictive, not just reactive. And so many hours are lost clicking through TradingView PineScripts or MetaTrader indicators trying to find the Holy Grail amongst muck. 9 Types of Forex Trading Strategies Just one more indicator, bro. I swear. Were almost there. But maybe its not

Key Takeaways RMP (Random-Max Percentile) is a new per-row column in our scan results and paid-plan exports: the probability that the best result pure chance could produce, across the N trials your scan actually ran, lands below this row. An RMP of 0.97 reads: even the luck record of a search this size sits below this row with 97% probability. It exists because deflated Sharpe has a blind zone.

Momentum investing has been one of the most persistent and puzzling phenomena in finance for more than three decades. Traditional explanations typically focus on investor psychology, delayed information diffusion, or risk compensation. But this paper proposes something radically different. The authors argue that momentum profits are largely driven by institutional cash-management mechanics.

Momentum is the one factor nobody argues about. Winners keep winning, losers keep losing, and the effect shows up in stocks, bonds, commodities, and currencies across a century of data. The old article "From Intermarket Analysis to Network Momentum" pushed a harder claim: an asset's momentum can leak into the assets it is linked to, so the return of one contract carries information

The value of an idea lies in the using of it. Thomas Edison. In my experience, 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. Quant Trading Rules is a reader-supported publication. To receive new posts and support my

Ask a CTA salesperson why their fund makes money and you get a story: markets trend, we ride the trend, we cut losers and let winners run. That story is untestable. Sepp and Lucic did something the industry rarely does. They wrote down the exact profit-and-loss of a standard European trend-follower and factored it into two things you can measure directly: the autocorrelation of the traded returns

Feature selection is often presented as a simple cleanup step remove the weak variables, keep the useful ones, and move on. In practice, it is much closer to a research decision about what information the model is allowed to trust. Every feature added to a trading system creates a cost. It may require another data source, increase latency, make the model harder to interpret, or create another

You can watch a refiner's gross margin move in real time: it trades in the futures market, tick by tick, as the crack spread and the stocks that earn it are reliably slow to follow. That lag is small, but it is honest, market-neutral, and it rests on a mechanism rather than a curve fit. This is the first of three parts: the idea and the evidence here, the execution and its costs in the

Key Takeaways A single strategy, its trades bit-for-bit identical, grades as luck in one scan (deflated Sharpe 0.035, inside a 21.9M-trial search) and close to real in another (0.919, inside 14.8M). The strategy never changed; the crowd it was measured against did. We ran one 5.7-year BTC and ETH perpetuals search twice, changing one thing: run B removed the swept configurations of two

Key Takeaways The Deflated Sharpe Ratio (DSR) adjusts a strategy's Sharpe for how many strategies you tested. It sets the bar not at zero, but at the best Sharpe you would expect from luck alone after N tries, a bar that grows only with the logarithm of N. Of 99,878,688 BTC/USDT backtests, 336,818 cleared a plain significance test (PSR > 0.95), but only 3,130 (0.003%) cleared a full