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Recent Quant Links from Quantocracy as of 02/20/2026

This is a summary of links recently featured on Quantocracy as of Friday, 02/20/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Moneyball: Finding Undervalued Pairs Using Unconventional Metrics [Robot Wealth]

    Last time we established that stat arb is really about betting on divergence/convergence behaviour continuing. Two things that have historically moved together come apart, and you bet on them coming back together. Remember the forced flows example, some fund or whatever having to sell regardless of price? That sort of temporary dislocation creates opportunities. Conceptually simple. But the
  • Can LLMs Beat FinBERT for Stock Sentiment Trading? [Tommi Johnsen]

    Part 2 of a series. Part 1 covered building the hybrid classifier and validating it against Claude as ground truth on 991 headlines. This post reports whether the sentiment signals actually predict stock returns. The Question The academic evidence is clear: investor sentiment predicts short-term stock returns. Two decades of peer-reviewed research, anchored by Baker & Wurgler (2006) and
  • A Tale of Two Prices [Robot Wealth]

    It was the age of wisdom, it was the age of foolishness Ive seen heaps of stuff published online about stat arb lately. Some genuinely good takes. And some other material that, while academically interesting, isnt particularly useful for people like me and the people I write for: independent systematic traders looking for real edges we can realistically manage without a team. A lot of the

Filed Under: Daily Wraps

Recent Quant Links from Quantocracy as of 02/18/2026

This is a summary of links recently featured on Quantocracy as of Wednesday, 02/18/2026. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Combining Calendar Strategies into the Trading Portfolio [Quantpedia]

    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 appear inefficient at first glance. This impression, however, largely stems from evaluating these
  • Improving Performance with Fast Alphas; A Tactical Overlay for Intraday Trend Trading [Concretum Group]

    Predictive signals operating at very short horizons often exhibit strong gross performance in backtests but fail to survive realistic transaction costs due to prohibitive turnover. This research note argues that the inability to monetize such signals directly does not imply the absence of economic value. We distinguish between monetizable alpha, which survives trading frictions as a standalone
  • New Contributor: A Linear Regression’s Predictions are a Relevance-Wtd Avg of Past Outcomes [Yannick Kalber]

    The book Asset Allocation: From Theory to Practice and Beyond by Kinlaw et al. (2021) is one of my favorites as it gets a few myths about mean variance optimization right, which are constantly parroted, even in academic papers to motivate some fancy new method as solution. It also provides useful solutions to a portfolio managers or allocators practical questio like when to rebalance.
  • Volatility Clustering Across Asset Classes: GARCH and EGARCH Analysis with Python (2015 2026) [Jonathan Kinlay]

    If youve been trading anything other than cash over the past eighteen months, youve noticed something peculiar: periods of calm tend to persist, but so do periods of chaos. A quiet Tuesday in January rarely suddenly explodes into volatility on Wednesdaymarket turbulence comes in clusters. This isnt market inefficiency; its a fundamental stylized fact of financial markets, one that

Filed Under: Daily Wraps

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

Filed Under: Daily Wraps

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

Filed Under: Daily Wraps

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

Filed Under: Daily Wraps

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

Filed Under: Daily Wraps

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

Filed Under: Daily Wraps

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

Filed Under: Daily Wraps

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

Filed Under: Daily Wraps

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

Filed Under: Daily Wraps

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