During periods of market stress, assets that normally behave differently often begin to move together. Correlations rise, diversification weakens, and different parts of the equity market increasingly respond to the same underlying risk factors. This raises a practical portfolio-management(...)

Leveraged ETFs provide amplified exposure to an underlying index, but their daily reset mechanism creates volatility drag, which can substantially reduce their long-term value. This raises the possibility of exploiting the effect by simultaneously shorting leveraged ETFs with opposite exposures. We(...)

For anyone trading or investing in Bitcoin, a familiar indicator can become a dangerous comfort blanket. Josh Molnar’s Bitcoin Runs on a Clock argues that classic cycle signals—such as MVRV, the Mayer Multiple and Pi Cycle Top—have weakened as Bitcoin’s price swings become less extreme. Thresholds(...)

This paper investigates whether longer historical training periods improve the out-of-sample performance of a Hidden Markov Model (HMM) used for identifying financial market regimes. Four four-state Gaussian HMMs are compared, differing only in the starting point of their training data (1970, 1980,(...)

We analyze a multi-asset calendar anomaly around U.S. federal elections that is consistent across assets and simple to trade. While our previous study documents a robust pre-election drift, the cross-asset pricing of its D+1 resolution remains unmapped beyond SPY. Using a diversified ETF basket(...)

Meme stocks are commonly studied through social-media activity, but this approach limits both the historical scope of research and the signals available to practitioners. In A Century of Meme Stocks and the Price of Coordination, Chad Schmerling develops an alternative: a machine-learning model(...)

Hello everyone, The Quantpedia Awards are back—and this time, we’re taking the winners’ announcement to the stage! For the 2027 edition, we’re bringing together an attractive prize pool, a panel of investment professionals and academics, and a new opportunity to put outstanding quantitative(...)

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(...)

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(...)

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(...)

The explosive growth of hyper-liquid 0-DTE markets has pushed traditional options pricing infrastructure to its breaking point, as continuous Black-Scholes calculus can collapse into an unusable point mass at expiration. Rather than patching a broken formula with hand-fitted tweaks, a new paper(...)

The integration of artificial intelligence into algorithmic trading has ignited a race to transform generative text into systematic alpha. A new paper written by Steven Edwards empirically investigates whether constructing a synthetic consensus using large language models can simulate information(...)

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(...)

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(...)

Trend following was one of the most persistent anomalies in finance for nearly two centuries, yet its performance deteriorated sharply after the 2008 financial crisis. An analysis of approximately 100 liquid futures contracts from 1995 to 2025 shows that this decline is highly selective. The(...)

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(...)

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 doesn’t just fetch data—it acts as an autonomous AI research(...)

We investigate a Donchian breakout rotation strategy between QQQ (NASDAQ-100) and Bitcoin (BTC), with a cash fallback during consolidation, and test it across eight lookback horizons (5–50 trading days) and two priority variants over a seven-year sample spanning 2019–2026. The strategy consistently(...)

An autonomous research agent replicated nine published US-equity anomalies on clean, survivorship-free data. The question is not only what it found (out-of-sample decay is the rule, and on a faithful build none survive — the lone apparent survivor turned out to be a construction error the(...)

Mean-Variance Optimization remains the intellectual cornerstone of modern portfolio theory, yet its real-world deployment via plug-in MVO often delivers unstable, over-leveraged portfolios that collapse out-of-sample. The core insight from VertoxQuant’s analysis is profound: raw plug-in MVO does(...)

Diversification is a key principle in portfolio construction, yet equal-weight portfolios often fail to deliver true risk diversification. This study shows that capital-based allocation can mask strong concentration in a small number of underlying risk factors. We analyze a simple multi-asset(...)

This study investigates short-term price reversals—temporary retracements following adverse daily returns—and develops a systematic trading framework to capture this effect across multiple asset classes. Using daily data from six liquid ETFs spanning equities, fixed income, currencies, gold, and(...)

Commodities represent a vital but highly volatile asset class, characterized by pronounced cyclicality, lack of yield, and susceptibility to severe macroeconomic drawdowns. While cross-sectional (relative) momentum is a well-documented anomaly, its application in commodities often forces portfolios(...)

How much do we really know about corporate bond returns before the modern data era? Until recently, the answer was: not enough. Most empirical work in corporate bond pricing has relied on relatively short samples, especially the post-2002 TRACE period, leaving open the question of whether observed(...)

If you’ve ever scrolled through Polymarket or Kalshi wondering whether the “wisdom of crowds” is actually wisdom—or just organized noise—you’re not alone. A new paper, “How Wise is the Crowd? Bias and Edge in Prediction Markets,” tears into the microstructure of modern prediction markets to ask a(...)

Welcome to the Quantpedia Awards 2026 winners announcement. For the third time, we are proud to celebrate excellence in quantitative research and recognize the researchers behind innovative studies in quantitative trading. We are also pleased to see that the Quantpedia Awards have become an(...)

Our study explores a weekly-rebalanced dual-momentum-based Global Tactical Asset Allocation (GTAA) strategy applied to a diversified set of ETFs. The strategy selects assets based on relative momentum and applies an absolute momentum filter to avoid declining investments. Ultimately, a single(...)

For U.S. equities, fixed income, and commodities, reconstructing long-term historical datasets is relatively straightforward, and we have already explored these challenges in several previous studies, including 100 Years of Multi-Asset Trend Following, Extending Historical Daily Bond Data to 100(...)

In the high-stakes arena of prediction markets, a counterintuitive pattern emerges: retail traders who correctly pick winners more than half the time still lose money, while automated traders with coin-flip accuracy pocket nine-figure profits. Using 222 million prediction market trades with(...)

Commodity markets are back in investors’ focus. After years in which equities and growth assets dominated portfolios, the recent rise in geopolitical tensions, inflation uncertainty, supply-chain fragmentation, and renewed resource nationalism has reminded allocators that commodities remain a(...)

From the trading desk to the portfolio committee, investors face a familiar question: how should alternative stores of value fit into a diversified portfolio? This research explores that question through a systematic dual-momentum framework comparing Bitcoin and physical gold in a rules-based(...)

In an era of increasingly fragmented market microstructure, the emergence of cross-asset connectedness between Crypto and public equity markets presents a critical challenge for modern portfolio construction. This blog post examines the recent working paper by Harin de Silva, “The Attention Factor:(...)

The growing dominance of passive investing has fundamentally altered the dynamics of equity markets. A substantial share of trading volume is now driven by index-tracking strategies, which mechanically allocate capital based on index membership rather than company-specific fundamentals. This raises(...)

What if a meaningful part of a usual trading strategy’s performance has nothing to do with your signal—but simply when you rebalance? A recent paper written by Carlo Zarattini & Alberto Pagani highlights a largely underestimated risk in systematic investing: rebalance timing luck (RTL). For(...)

This study examines the profitability of mean-reversion trading strategies applied to binary outcome contracts on Polymarket, the world’s largest decentralized prediction market platform. We analyze three distinct contracts representing varying risk profiles: a quasi-risk-free instrument (No to(...)

The global investment environment is going through a period of meaningful structural change. The dominance of the U.S. dollar is increasingly being questioned, geopolitical tensions are rising, and macroeconomic uncertainty remains elevated. Together, these forces challenge the post-Global(...)

One year ago, in our article “Can We Finally Use ChatGPT as a Quantitative Analyst?”, we explored the feasibility of leveraging ChatGPT for quantitative analysis. Since then, a lot has changed: newer models are now available (from OpenAI and also other vendors), and the ecosystem around AI-assisted(...)

Can crypto still help diversify an equity portfolio—or has that edge disappeared? That’s the practical question behind Crypto Contagion. The paper looks at how shocks move between crypto and U.S. equities, and more importantly, how that relationship changed after the launch of crypto ETFs. Instead(...)

The goal of our article is to examine the long-term relationship between small value and large growth stocks using more than 100 years of data and test whether the spread between small value and large growth portfolios shows trends that could help investors switch between the two styles. Using the(...)

This study examines the effectiveness of several anomaly-based trading strategies applied to the real estate sector represented by the RlEst index from the Fama–French 48 industry portfolios. Using monthly data from July 1, 1926, to December 1, 2025, we analyze whether selected strategies are(...)

For many investors, short-term interest rates are often treated as something the market “discovers.” In reality, the Federal Reserve has enormous control over how the front end of the yield curve evolves. While textbooks often portray the Fed’s policy rate as a flexible tool that reacts quickly to(...)

This research examines the critical quantitative investment problem of systematic tactical allocation to international equity mandates—specifically Emerging Markets (EM) and Europe, Australasia, and the Far East (EAFE)—amidst conjectured macroeconomic regime transitions. The investigation is(...)

Alternative ETFs sit at an unusual intersection of public-market accessibility and hedge-fund-style investment techniques. They package managed futures, merger arbitrage, and option-based income strategies into exchange-traded products, yet they remain thinly traded and relatively niche compared to(...)

Calendar strategies are often viewed as weak when assessed individually. Their annualized returns tend to be low, market exposure is limited, and trading activity is sparse. Compared to trend following or swing strategies, which can remain invested for extended periods, calendar strategies may(...)

Pragmatic Asset Allocation (PAA) is a systematic, multi-asset investment strategy designed to adapt dynamically to evolving market conditions. Rather than maintaining a static equity exposure, the model actively allocates capital across a diversified set of asset classes—including equities, bonds,(...)

Recent market action has once again underscored how rapidly volatility can surface across asset classes, as evidenced by pronounced price swings in gold, silver, and cryptocurrency markets. Such episodes routinely revive debate within the quantitative community about structural drivers of intraday(...)

An interesting transaction-level study we take a closer look at today asks who takes the other side of trades when the most pro-cyclical players in markets — primarily asset managers — buy in booms and sell in busts. The paper uses comprehensive transaction data across major European equity and(...)

The crypto-enthusiast’s mantra—“just add Bitcoin and watch the efficient frontier fly”—runs into a hard empirical wall when you extend the sample, tighten the econometrics, and force the asset to compete on identical risk-adjusted footing with equities. Alistair Milne’s new SSRN paper applies a(...)

Market concentration has become one of the most discussed structural risks in today’s equity markets. A small group of mega-cap stocks—often the largest five to ten names—now accounts for an unusually large share of major market indices. This has led to widespread concerns that such concentration(...)

This article develops a price-based macro–financial model of gold that formally links its medium-horizon return dynamics to cross-asset risk-premium configurations. Although gold has traditionally been conceptualized as a non-yielding inflation hedge or safe-haven asset, contemporary empirical(...)