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@quantocracy.bsky.social

Curated links from the quantitative trading blogosphere. https://Quantocracy.com/

246 Followers  |  14 Following  |  644 Posts  |  Joined: 13.11.2024  |  1.5674

Latest posts by quantocracy.bsky.social on Bluesky

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Why TAA is Performing Well Now: Outperformance Attribution from @allocatesmartly.bsky.social 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

Why TAA is Performing Well Now: Outperformance Attribution from @allocatesmartly.bsky.social

11.02.2026 13:31 — 👍 2    🔁 2    💬 1    📌 0
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 […] The post Recent Quant Links from Quantocracy as of 02/08/2026 appeared first on Quantocracy.

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

09.02.2026 06:56 — 👍 1    🔁 0    💬 0    📌 0
Pragmatic Asset Allocation Across Market Cycles from @quantpedia.bsky.social 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

Pragmatic Asset Allocation Across Market Cycles from @quantpedia.bsky.social

09.02.2026 06:38 — 👍 1    🔁 0    💬 0    📌 0
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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

EMNLP 2025 in Suzhou [Gautier Marti]

09.02.2026 06:25 — 👍 0    🔁 0    💬 0    📌 0
Herding in Commodities and Cryptocurrencies from @harbourfrontquant.substack.com 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

Herding in Commodities and Cryptocurrencies from @harbourfrontquant.substack.com

09.02.2026 06:11 — 👍 0    🔁 0    💬 0    📌 0
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

Build Better Strategies, Part 6: Evaluation [Financial Hacker]

06.02.2026 00:05 — 👍 1    🔁 0    💬 0    📌 0
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Stock Sentiment Indicators in U.S. Equities: and the research that supports them from @tommijohnsen.bsky.social 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

Stock Sentiment Indicators in U.S. Equities: and the research that supports them from @tommijohnsen.bsky.social

05.02.2026 23:52 — 👍 1    🔁 0    💬 0    📌 0
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 […] The post Recent Quant Links from Quantocracy as of 02/02/2026 appeared first on Quantocracy.

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

03.02.2026 07:22 — 👍 0    🔁 0    💬 0    📌 0
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More Bootstrap Simulations with Portfolio Optimizer: the Autoregressive Online Bootstrap [Portfolio Optimizer] #quant 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

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

03.02.2026 04:08 — 👍 0    🔁 0    💬 0    📌 0
Sampling Stock Prices Directly from Option Prices [Sitmo] #quant 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

Sampling Stock Prices Directly from Option Prices [Sitmo] #quant

03.02.2026 03:55 — 👍 1    🔁 0    💬 0    📌 0
Member Note: Our Approach to Selecting Strategies for the Platform from @allocatesmartly.bsky.social #quant 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,

Member Note: Our Approach to Selecting Strategies for the Platform from @allocatesmartly.bsky.social #quant

03.02.2026 03:41 — 👍 1    🔁 1    💬 0    📌 0
Do S&P500 0DTEs Options Increase Market Volatility? from @quantpedia.bsky.social #quant 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

Do S&P500 0DTEs Options Increase Market Volatility? from @quantpedia.bsky.social #quant

03.02.2026 03:28 — 👍 1    🔁 0    💬 0    📌 0
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 […] The post Recent Quant Links from Quantocracy as of 02/01/2026 appeared first on Quantocracy.

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

02.02.2026 06:50 — 👍 0    🔁 0    💬 0    📌 0
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Seasonality in Bitcoin Intraday Trend Trading [Concretum Group] #quant 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

Seasonality in Bitcoin Intraday Trend Trading [Concretum Group] #quant

01.02.2026 13:40 — 👍 0    🔁 0    💬 0    📌 0
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Target-aware Financial Sentiment: Why Structure Beats Confidence with LLMs from @tommijohnsen.bsky.social #quant 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

Target-aware Financial Sentiment: Why Structure Beats Confidence with LLMs from @tommijohnsen.bsky.social #quant

01.02.2026 13:26 — 👍 0    🔁 0    💬 0    📌 0
Revaluation Alpha: Why Past Factor Returns May Be Misleading [Alpha Architect] #quant 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

Revaluation Alpha: Why Past Factor Returns May Be Misleading [Alpha Architect] #quant

01.02.2026 13:13 — 👍 1    🔁 0    💬 0    📌 0
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 […] The post Recent Quant Links from Quantocracy as of 01/27/2026 appeared first on Quantocracy.

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

28.01.2026 07:09 — 👍 1    🔁 0    💬 0    📌 0
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Data: Data structures as lifecycle engineering [Trading the Breaking] #quant 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

Data: Data structures as lifecycle engineering [Trading the Breaking] #quant

28.01.2026 02:21 — 👍 1    🔁 0    💬 0    📌 0
Who Is the Counterparty to the Pro-Cyclical Investors from @quantpedia.bsky.social #quant 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

Who Is the Counterparty to the Pro-Cyclical Investors from @quantpedia.bsky.social #quant

28.01.2026 02:07 — 👍 1    🔁 0    💬 0    📌 0
Modern Pairs Trading: What Still Works and Why from @harbourfrontquant.substack.com #quant 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

Modern Pairs Trading: What Still Works and Why from @harbourfrontquant.substack.com #quant

28.01.2026 01:54 — 👍 0    🔁 0    💬 0    📌 0
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 […] The post Recent Quant Links from Quantocracy as of 01/25/2026 appeared first on Quantocracy.

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

26.01.2026 07:00 — 👍 0    🔁 0    💬 0    📌 0
Is The Optimal Long-term Portfolio Share of Bitcoin Negative? from @quantpedia.bsky.social #quant 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.

Is The Optimal Long-term Portfolio Share of Bitcoin Negative? from @quantpedia.bsky.social #quant

26.01.2026 05:36 — 👍 1    🔁 0    💬 0    📌 0
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The Age of AI Attractor Markets: One Possible Trajectory from @tommijohnsen.bsky.social #quant 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

The Age of AI Attractor Markets: One Possible Trajectory from @tommijohnsen.bsky.social #quant

26.01.2026 05:22 — 👍 1    🔁 0    💬 0    📌 0
Stock selection with macro factors: the case for simple neural networks [Macrosynergy] #quant 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

Stock selection with macro factors: the case for simple neural networks [Macrosynergy] #quant

26.01.2026 05:09 — 👍 1    🔁 0    💬 0    📌 0
Is Value Investing Dead? [Alpha Architect] #quant 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

Is Value Investing Dead? [Alpha Architect] #quant

26.01.2026 04:55 — 👍 0    🔁 0    💬 0    📌 0
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 […] The post Recent Quant Links from Quantocracy as of 01/20/2026 appeared first on Quantocracy.

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

21.01.2026 06:39 — 👍 0    🔁 0    💬 0    📌 0
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Gold Cross-Asset Momentum from @allocatesmartly.bsky.social #quant 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

Gold Cross-Asset Momentum from @allocatesmartly.bsky.social #quant

21.01.2026 04:39 — 👍 3    🔁 2    💬 0    📌 0
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Portfolio Optimization [Quantitativo] #quant 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

Portfolio Optimization [Quantitativo] #quant

21.01.2026 04:26 — 👍 0    🔁 0    💬 0    📌 0
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AI is no longer an experimental tool in finance from @tommijohnsen.bsky.social #quant 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)

AI is no longer an experimental tool in finance from @tommijohnsen.bsky.social #quant

21.01.2026 04:12 — 👍 0    🔁 0    💬 0    📌 0
The Many Facets of Stock Momentum [Alpha Architect] #quant 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

The Many Facets of Stock Momentum [Alpha Architect] #quant

21.01.2026 03:59 — 👍 0    🔁 0    💬 0    📌 0

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