Quantocracy

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Recent Quant Links from Quantocracy as of 02/13/2026

This is a summary of links recently featured on Quantocracy as of Friday, 02/13/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Why Bonds Still Belong: Rethinking Fixed Income in Modern Portfolios [Return Stacked]

    n recent years, the bond market has disappointed many investors. Rising rates and inflation have driven high interest rate volatility, while long-duration bonds have underperformed, dramatically underperforming cash and generating outright negative returns. With a flat term structure, its tempting to see duration as uncompensated risk, especially when yields offer little cushion and price
  • FinBERT Is Wrong 83% of the Time on Positive Headlines: an LLM is Here to Help [Tommi Johnsen]

    If youve ever plugged financial news into a sentiment model and used it to trade, youve probably noticed something: the signals are garbage. The model says positive all the time, your positions lose money, and you start wondering whether sentiment analysis is just astrology for quants. Thanks for reading! Subscribe for free to receive new posts and support my work. The problem isnt
  • Point-in-time economics and financial market forecasting [Macrosynergy]

    Standard macroeconomic theory assumes that economic activity and financial market developments influence each other contemporaneously. This is incomplete and implausible. While some direct interaction occurs, financial investors typically require reliable statistics to adapt to economic conditions and trends. Such information takes time to compile and is often revised. A more appropriate
  • Why TAA is Performing Well Now: Outperformance Attribution [Allocate Smartly]

    We track 100+ published Tactical Asset Allocation (TAA) strategies, so these results are broadly representative of TAA as an investment style. TAA did reasonably well in 2025 and very well in these early days of 2026, relative to the ubiquitous 60/40 benchmark. How much of that is due to TAA correctly timing the market and how much is simply due to the types of assets TAA generally holds? In the

Recent Quant Links from Quantocracy as of 02/08/2026

This is a summary of links recently featured on Quantocracy as of Sunday, 02/08/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Pragmatic Asset Allocation Across Market Cycles [Quantpedia]

    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 classesincluding equities, bonds, commodities, gold, and cash-like instrumentsusing momentum-based signals and disciplined
  • EMNLP 2025 in Suzhou [Gautier Marti]

    This year at EMNLP 2025 in Suzhou, my colleague Khaled Al Nuaimi and I attended the conference so that Khaled could present his paper on Evasive Answers in Financial Q&A, and also to explore current R&D trends in empirical NLP. While walking through the poster sessions, we saw a dozen of papers closely related with our recent contributions and joint research program with Khalifa
  • Herding in Commodities and Cryptocurrencies [Relative Value Arbitrage]

    Herding behavior has been extensively studied and is well understood in equity markets, but far less so in other asset classes such as commodities and cryptocurrencies. In this post, we explore key aspects of herding behavior in crypto and commodity markets. Investor Behavior in Crypto During Geopolitical Shocks Herd behavior refers to the tendency of investors to follow the actions of a larger
  • Build Better Strategies, Part 6: Evaluation [Financial Hacker]

    Developing a successful strategy is a process with many steps, described in the Build Better Strategies article series. At some point you have coded a first, raw version of the strategy. At that stage youre usually experimenting with different functions for market detection or trade signals. The problem: How can you determine which indicator, filter, or machine learning method works best with
  • Stock Sentiment Indicators in U.S. Equities: and the research that supports them [Tommi Johnsen]

    Academic research treats investor sentiment as a systematic component of beliefs or demand that is not justified by available fundamentals, and whose price impact is amplified when limits to arbitrage make it difficult for rational traders to offset mispricing (e.g., Shleifer and Vishny, 1997; Baker and Wurgler, 2007). Sentiment indicators are therefore empirical proxies for an unobserved latent

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

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

  • More Bootstrap Simulations with Portfolio Optimizer: the Autoregressive Online Bootstrap [Portfolio Optimizer]

    In a previous article, I described several classical bootstrap techniques i.i.d. bootstrap, circular block bootstrap, and stationary block bootstrap and showed how the stationary block bootstrap could be used to simulate future price paths for financial assets by following the methodology of Anarkulova et al.1. In this blog post, I will detail another bootstrap technique called the
  • Sampling Stock Prices Directly from Option Prices [Sitmo]

    For a single maturity, European call prices encode the risk-neutral distribution of the underlying. You can turn them into Monte Carlo samples without fitting a model or estimating a density. For strikes K_0 < < K_n with call prices C_0, , C_n, define [F_i = 1 + e^{rT} frac{C_{i+1}-C_i}{K_{i+1}-K_i}, quad F_0 = 0, quad F_n = 1] This is a discrete approximation of the cumulative
  • Member Note: Our Approach to Selecting Strategies for the Platform [Allocate Smartly]

    A long-time member who has been a valuable source of feedback over the years sent us the following note about the most recent strategy added to the platform: Gold Cross-Asset Momentum. The strategy has performed poorly relative to other strategies on the platform. Youve turned down other stuff that was marginal like this, so Im surprised it made the cut. Hes right. Viewed in isolation,
  • Do S&P500 0DTEs Options Increase Market Volatility? [Quantpedia]

    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 instability, with particular attention paid to the growing prominence of S&P 500

Recent Quant Links from Quantocracy as of 02/01/2026

This is a summary of links recently featured on Quantocracy as of Sunday, 02/01/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Seasonality in Bitcoin Intraday Trend Trading [Concretum Group]

    As our readers are aware, futures trend trading, particularly at higher frequencies, represents a core area of Concretums expertise, with a meaningful share of our trading risk allocated to this family of models. Over recent years, we have also published several papers presenting simple and accessible variations of trend-following strategies, all of which have been well received. Building on
  • Target-aware Financial Sentiment: Why Structure Beats Confidence with LLMs [Tommi Johnsen]

    Sentiment analysis in finance typically treats sentiment as a property of text as a whole, but what matters to investors is sentiment about specific entities. This paper investigates target-level sentiment attribution in financial news headlines, demonstrating that widely used sentiment models, including domain-specific financial models, systematically fail to attribute sentiment correctly when
  • Revaluation Alpha: Why Past Factor Returns May Be Misleading [Alpha Architect]

    Robert Arnott, Sina Ehsani, Campbell Harvey, and Omid Shakernia, authors of the September 2025 study Revaluation Alpha, examined how much of a factors historical returns have been derived from changes in valuation levels (revaluation alpha). Their hypothesis was that this return component is typically nonrecurring, making it dangerous to extrapolate historical returns as indicators

Recent Quant Links from Quantocracy as of 01/27/2026

This is a summary of links recently featured on Quantocracy as of Tuesday, 01/27/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Data: Data structures as lifecycle engineering [Trading the Breaking]

    Most performance failures in trading systems dont look like failures. You know, impressive microbenchmarks, clean profiles, and average latency that inspire confidence. Then the market compresses time. A volatility microburst lands, the arrival process stops behaving like expected and the engine doesnt slow down uniformly. One thread stays current, another drifts into backlog, and a third
  • Who Is the Counterparty to the Pro-Cyclical Investors [Quantpedia]

    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 interest-rate cash and derivatives markets to classify counterparties by sector and to measure, at
  • Modern Pairs Trading: What Still Works and Why [Relative Value Arbitrage]

    Pairs trading, or statistical arbitrage (stat arb), is a classic, well-established quantitative trading strategy, and it is still in use today. I discussed its profitability in a previous post, and in this installment, we continue that discussion. Pairs Selection Methods Reference [1] provides a thorough review of the pairs trading literature between 2016 and 2023. Pair selection is a critical

Recent Quant Links from Quantocracy as of 01/25/2026

This is a summary of links recently featured on Quantocracy as of Sunday, 01/25/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Is The Optimal Long-term Portfolio Share of Bitcoin Negative? [Quantpedia]

    The crypto-enthusiasts mantrajust add Bitcoin and watch the efficient frontier flyruns 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 Milnes new SSRN paper applies a textbook Markowitz meanvariance framework to a two-asset universe (S&P 500 vs.
  • The Age of AI Attractor Markets: One Possible Trajectory [Tommi Johnsen]

    This essay explores one curious, if speculative, scenario for how markets might evolve. Its not a forecast, just a framework worth examining. The core question: could modern markets be drifting from an exploratory regime with messy human disagreement, diverse models, and largely uncorrelated mistakes, toward a convergent regime where machines (and humans using machine-like tools) increasingly
  • Stock selection with macro factors: the case for simple neural networks [Macrosynergy]

    Point-in-time macroeconomic information provides a valid basis for stock selection, as economic developments affect firms differently and with a time lag. The principal challenge lies in identifying which stocks benefit from which economic trends, a task for which theoretical priors are limited. Machine learning with neural networks, therefore, offers a compelling approach, as such models can
  • Is Value Investing Dead? [Alpha Architect]

    Value investing is dead. Value investing remains dead. And we have killed it. After years in what can be now called one of the worst (if not the worst) period for value investing, many investors have packed their bags and called it quits. Their claim? This time is different. HML factor returns since 2015 The results are hypothetical results and are NOT an indicator of future results and do NOT

Recent Quant Links from Quantocracy as of 01/20/2026

This is a summary of links recently featured on Quantocracy as of Tuesday, 01/20/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Gold Cross-Asset Momentum [Allocate Smartly]

    This is a test of a simple and effective gold trading strategy from Cyril Dujava of Quantpedia with his research: Cross-Asset Price-Based Regimes for Gold. Backtested results from 1970 follow. Results are net of transaction costs see backtest assumptions. Learn about what we do and follow 100+ asset allocation strategies like this one in near real-time. Logarithmically-scaled. Click for
  • Portfolio Optimization [Quantitativo]

    An investor who knew future returns with certainty would invest in only one security. Harry Markowitz We dont know the future. This is why we intuitively spread our bets. Harry Markowitz turned that intuition into algebra. In 1952, he published a paper that gave diversification a rigorous mathematical foundation, proving not just that it works, but exactly how much of each asset to
  • AI is no longer an experimental tool in finance [Tommi Johnsen]

    But it is increasingly the way markets get read, sized up, and traded. If you work with stocks, youve probably felt it already. The advantage isnt just speedy spreadsheets. The advantage is the ability to chew through messy, human language like headlines, filings, earnings-call transcripts, social chatter, and turn it into usable signals. The unfair advantage of large language models (LLMs)
  • The Many Facets of Stock Momentum [Alpha Architect]

    Stock momentum has long been a workhorse idea. Buy recent winners. Sell recent losers. Critics argue those profits mostly come from riding factor trends like value, size, or industry tilts. This paper pushes back. It shows there is a durable, stock-specific momentum component tied to how prices react to firm news around earnings dates. The result is a cleaner, lower-risk way to capture momentum

Recent Quant Links from Quantocracy as of 01/19/2026

This is a summary of links recently featured on Quantocracy as of Monday, 01/19/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • What Investors Should Know About Common Sentiment Models: Tone Isn t Attribution [Tommi Johnsen]

    Sentiment analysis has become a foundational tool in modern investing. From academic papers to hedge fund dashboards, models like FinBERT and RoBERTa variants are routinely used to classify news as positive or negative for stocks. However, there is a critical assumption hiding in plain sight. Thanks for reading! Subscribe for free to receive new posts and support my work. That assumption is that
  • The Fallacy of Concentration Risk [Quantpedia]

    Market concentration has become one of the most discussed structural risks in todays equity markets. A small group of mega-cap stocksoften the largest five to ten namesnow accounts for an unusually large share of major market indices. This has led to widespread concerns that such concentration makes markets more fragile and that elevated index weights at the top may foreshadow weaker
  • Can AI Read the News Better Than You? How ChatGPT Could Transform Momentum Investing [Alpha Architect]

    Momentum investing has been a cornerstone of quantitative finance for decades. Researchers Nikolas Anic, Andrea Barbon, Ralf Seiz, and Carlo Zarattini hypothesized that the ability of large language models (LLMs) to interpret and synthesize textual information in real time can be used to identify news that is likely to trigger price momentums. Their study, ChatGPT in Systematic Investing,
  • New Feature: The Underperformer Watchlist [Allocate Smartly]

    Weve added a new feature for members, the Underperformer Watchlist. All investment strategies go through rough patches. Its the nature of taking risks in inherently unpredictable financial markets. One of the difficulties of investing is knowing when a rough patch is just a normal period of poor performance, and when its significant enough to warrant further concern. The Underperformer
  • Implied vs. Realized Volatility in Delta Hedging Strategies [Relative Value Arbitrage]

    Delta hedging is a fundamental topic in portfolio and risk management. In this post, we discuss which volatility measure should be used in the delta hedging process, while a future edition will examine the appropriate hedging frequency and time horizon. Which Free Lunch Would You Like Today Sir? Reference [1] is a classic article on delta hedging that addresses the following question: if an

Recent Quant Links from Quantocracy as of 01/13/2026

This is a summary of links recently featured on Quantocracy as of Tuesday, 01/13/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Data: Building micro-machines [Trading the Breaking]

    Most performance debates in trading start at the wrong layer. Quants argue about languages, compilers, and fast code, as if latency were a property of instructions. In production, latency is a property of structure: how state is laid out, how it is shared, how often the machine is forced to translate addresses, and how predictable the control flow remains. The micro-pauses that ruin fills are
  • Much Ado About Variance [Robot Wealth]

    Lets be honest: 2025 was a pretty good year to be a systematic trader. If you had a diversified portfolio of risk premia, you probably did alright. Claiming we did anything overly special in such a favourable environment would be a tad arrogant. That said, theres a big difference between the market was kind and I was prepared to capture what the market offered. So heres a look
  • How dollar invoicing and dollar debt shape FX risk premia [Macrosynergy]

    Persistent differences in the USD returns of international currencies point to long-run differences in risk premia. Empirical work has identified two dominant factors: dollar risk and carry-trade risk. These factors, in turn, can be linked to countries macroeconomic positions. In particular, extensive dollar invoicing and higher net foreign debt relative to GDP make currencies more procyclical:

Recent Quant Links from Quantocracy as of 01/09/2026

This is a summary of links recently featured on Quantocracy as of Friday, 01/09/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Are markets that are good for trend good just bc they have also gone up a lot, or bc carry, or… [Investment Idiocy]

    I have a friend, ex-colleague and TTU co-host who runs a fixed income focused CTA. We have regular coffees (he pays, so his fund is doing ok) and one of our favourite topics for arguing debating making polite conversation about is why fixed income is so much better for trend than anything else. He's biased, but then arguably so am I; I started my career trading rates options, and we both
  • The fourth quarter effect in small caps [Klement on Investing]

    In the US, the full-year/Q4 earnings season begins next week, and investors will pay particular attention to these earnings and the guidance for 2026. But did you know that companies, in particular smaller companies, are much more likely to miss analyst expectations in Q4? Previously, I have written about research that shows that US company earnings are most truthful (least deceptive) in Q4,
  • Completing a Correlation Matrix: Another Problem from Finance [Portfolio Optimizer]

    The previous post of this series on mathematical problems related to correlation matrices introduced the nearest correlation matrix problem1, which consists in determining the closest2 valid correlation matrix to an approximate correlation matrix In this blog post, I will now describe the correlation matrix completion problem, which consist in filling in the missing coefficients of a partially
  • Do Breakouts of Strong Swings Work? I Tested 40 Futures Markets and the Data Is Clear [Cracking Markets]

    Many traders spend hours hunting for strong support and resistance levels with the goal of entering counter-trend trades. They bet on price hitting a wall and reversing. However, when I tested over 15 years of history across 40 futures markets, I found something different. Peter Do Breakouts of Strong Swings Work? I Tested 40 Futures Markets and the Data Is Clear Holding a position mechanically in