Quantocracy

Quant Blog Mashup

Recent Quant Links from Quantocracy as of 11/16/2025

This is a summary of links recently featured on Quantocracy as of Sunday, 11/16/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Leveraged ETFs in Asset Allocation: Opportunity or Trap? [Quantpedia]

    In this article, we explore whether it makes sense to incorporate leveraged ETFs into static and dynamic long-only asset allocation strategies. Leveraged ETFs promise amplified exposure to the underlying asset, offering the potential for significantly higher returns during favorable market conditions. However, this comes at the cost of much higher volatility, path-dependency, and the well-known
  • Wordle (TM) and the one simple hack you need to pass funded trader challenges [Investment Idiocy]

    An unusual (but quick) mid month post, as this is a live issue I thought I'd publish this whilst it's relevant. There has been some controversy on X/Twitter about 'pay to play' prop shops (see this thread and this one) and in particular Raen Trading. It's fair to say the industry has a bad name, and perhaps this is unfairly tarnishing what may pass for good actors in this
  • Research Review | 14 November 2025 | Bubble Risk [Capital Spectator]

    Bubble Beliefs Christian Stolborg (Copenhagen Bus. School) and Robin Greenwood (Harvard) October 2025 We study expert beliefs during boom-bust episodes in which highly valued individual US stocks experience a price run-up followed by a crash. As prices surge, analysts forecast exceptional earnings growth and high near-term returns. Short interest stays low. Media coverage rarely mentions the word
  • How to Design a Simple Multi-Timeframe Trend Strategy on Bitcoin [Quantpedia]

    Bitcoin is one of the most widely discussed financial assets of the modern era. Since its inception, it has evolved from a niche digital experiment into a globally recognized investment instrument with institutional adoption and billions in daily trading volume. Despite its inherent volatility, Bitcoin has demonstrated a strong long-term growth trajectory, making it an attractive candidate for

Recent Quant Links from Quantocracy as of 11/11/2025

This is a summary of links recently featured on Quantocracy as of Tuesday, 11/11/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Is predicting vol better worth the effort and does the VIX help? [Investment Idiocy]

    I'm a vol scaler. There I've said it. Yes I adjust my position size inversely to vol. And so should you. But to this well we need to be able to predict future vol; where the 'future' here is roughly how long we expect to hold our positions for. Some people spend a lot of effort on this. They use implied vol from options, high(er) frequency data, GARCH or stochastic vol models.
  • Weekly Research Recap [Quant Seeker]

    What 200 Years of Data Tell Us About the Predictive Variance of Long-Term Bonds (Della Corte, Gao, Preve, and Valente) Over two centuries of data show that the risk of holding long-term foreign bonds without currency hedging increases with the investment horizon rather than mean-reverting. The dominant sources of uncertainty are exchange rate fluctuations and shifts in monetary and interest-rate
  • Denoising Correlation Matrices for More Stable Portfolio Optimization [Sitmo]

    Portfolio optimization lies at the heart of asset management, guiding investment strategies from risk minimization to return maximization. Many of the most widely used allocation methods such as minimum variance, maximum Sharpe ratio, and risk parity rely on the inverse of the correlation matrix to compute optimal portfolio weights. However, if the correlation matrix is poorly conditioned (i.e.,

Recent Quant Links from Quantocracy as of 11/04/2025

This is a summary of links recently featured on Quantocracy as of Tuesday, 11/04/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Value Overvalued MicroStrategy? [Quantpedia]

    MicroStrategy has become one of the most polarizing companies in public markets. Once a conventional business intelligence firm, it has transformed into the worlds largest publicly traded Bitcoin proxy, holding over a million BTC on its balance sheet and continuously raising capital to buy more. Supporters praise it as a visionary Bitcoin ETF with leverage, while critics argue it is an
  • Expressing an Indicator in Neural Net Form, Part 3 [Dekalog Blog]

    The results of the first set of tests of optimising an indicator via the framework of training a neural net are in, and this post is a presentation of these results and a reflection on this in more general terms. I would encourage readers to look at my previous 2 posts to put this one in context. The following chart plot shows 8 weeks of 10 minute price action in the EURUSD forex pair, with the
  • The Factor Mirage: How Quant Models Go Wrong [CFA Institute]

    Factor investing promised to bring scientific precision to markets by explaining why some stocks outperform. Yet after years of underwhelming results, researchers are finding that the problem may not be the data at all; its the way models are built. A new study suggests that many factor models mistake correlation for causation, creating a factor mirage. Factor investing was born from an
  • Weekly Research Recap [Quant Seeker]

    Hello! This weeks research recap brings you the most important investing insights from the past seven days, spanning academic papers, industry reports, social media, and blogs, with direct links to every source. Asset Allocation Dynamic Dragon: Integrating Regime Detection into Strategic Asset Allocation (Ni) This paper refines the Dragon Portfolio proposed by Artemis Capital Management by
  • Volatility vs. Volatility of Volatility: Conceptual and Practical Differences [Relative Value Arbitrage]

    Volatility and volatility of volatility are highly correlated and share many similar characteristics. However, there are subtle but important differences between them. In this post, we will examine some of these differences and explore an application of volatility of volatility in portfolio management. Improving Portfolio Management with Volatility of Volatility Managing portfolios using

Recent Quant Links from Quantocracy as of 11/02/2025

This is a summary of links recently featured on Quantocracy as of Sunday, 11/02/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • R squared and Sharpe Ratio [Investment Idiocy]

    Here's some research I did whilst writing my new book (coming next year, and aimed at relatively inexperienced traders). Imagine the scene. You're a trader who products forecasts (a scaled number which predicts future risk adjusted returns, or at least you hope it does) who wants to evaluate how good you are. After all you've read Carver, and you know you should use your expected
  • Thanksgiving and Christmas Trading Strategies [Quantpedia]

    This article examines the impact of major consumer holidays, Thanksgiving and Christmas, on financial markets. Using historical price data from 2004 to 2024, we analyze daily performance trends in the 10 trading days before and after each holiday to determine whether seasonal spending influences asset prices. Our findings suggest that seasonal consumer spending influences financial markets, with
  • ChatGPT in Systematic Investing – Enhancing Risk-Adjusted Returns with LLMs [Concretum Group]

    This paper investigates whether large language models (LLMs) can improve cross-sectional momentum strategies by extracting predictive signals from firm-specific news. We combine daily U.S. equity returns for S&P 500 constituents with high-frequency news data and use prompt-engineered queries to ChatGPT that inform the model when a stock is about to enter a momentum portfolio. The LLM evaluates
  • Value at Risk: Univariate Estimation Methods [Portfolio Optimizer]

    Value-at-Risk (VaR) is one of the most commonly used risk measures in the financial industry1 in part thanks to its simplicity – because VaR reduces the market risk associated with any portfolio to just one number2 – and in part due to regulatory requirements (Basel market risk frameworks34, SEC Rule 18f-45). Nevertheless, when it comes to actual computations, the above definition is by no

Recent Quant Links from Quantocracy as of 10/28/2025

This is a summary of links recently featured on Quantocracy as of Tuesday, 10/28/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Features Selection in the Age of Generative AI [Gatambook]

    Features are inputs to machine learning algorithms. Sometimes also called independent variables, covariates, or just X, they can be used for supervised or unsupervised learning, or for optimization. For example, at QTS, we use more than 100 of them as inputs to dynamically calibrate the allocation between our Tail Reaper strategy and E-mini S&P 500 futures. In general, modelers have no idea
  • Sensitivity Analysis 101 [Factor Research]

    SUMMARY Sensitivity analysis can highlight portfolio risks However, it is sensitive to data availability and assumptions Focusing on tail betas is sensible INTRODUCTION The U.S. stock market is currently being driven by a small group of stocks, creating a concentration risk not seen in decades. While comparisons to the 2000 tech bubble are common, a key difference is that todays leading
  • Weekly Research Recap [Quant Seeker]

    The Term Structure of Stock-Bond Covariances (Gandhi) After 2009, the term structure of stockbond covariances turned downward-sloping, with long-maturity bonds providing a stronger equity hedge than short-maturity bonds. A new stockbond factor based on these covariances predicts bond excess returns (R > 20%) after 2010. Key takeaway: Unconventional monetary policy post-GFC affected the
  • How does inflation impact trading? [Alpha Architect]

    A growing body of research suggests that inflation doesnt just erode purchasing power it reshapes how financial markets function. This paper shows that when inflation rises, trading behavior changes in systematic ways: liquidity deteriorates, bid-ask spreads widen, and investors trade less on fundamentals and more on short-term noise. These effects amplify volatility and reduce
  • Modeling Gold for Prediction and Portfolio Hedging [Relative Value Arbitrage]

    Gold prices have risen sharply in recent months, prompting renewed debate over whether the market has reached its peak. In this post, we examine quantitative models used to forecast gold prices and evaluate their effectiveness in capturing volatility and market dynamics. However, gold is not only a speculative vehicle, it also functions as an effective hedging instrument. We explore both aspects

Recent Quant Links from Quantocracy as of 10/26/2025

This is a summary of links recently featured on Quantocracy as of Sunday, 10/26/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Envision Your Financial Future and Plan How to Get There with a Portfolio of Portfolios [Engineered Portfolio]

    How much money do you aim to have by when and why? How will you achieve that goal? In this post I will share my vision and plan with specific investment strategies while posing questions to you to help you envision your own financial future and take steps to get there. At the age of 28 when I started this blog with my former boss, Chris, and lifelong friend, I aimed to find an optimal investment
  • Cryptocurrency as an Investable Asset Class 10 Lessons [Quantpedia]

    Cryptocurrencies have matured from experimental curiosities into a viable investable asset class whose return-generation and risk characteristics merit treatment within empirical asset pricing. A recent paper by Nicola Borri, Yukun Liu, Aleh Tsyvinski, Xi Wu summarizes ten facts from the literature that show cryptocurrencies share important similarities with traditional marketscomparable
  • Where Factors Speak Loudest: Why Size Matters in Factor Investing [Alpha Architect]

    The Curious Case of the Disappearing Size Premium The size effect was first documented by Rolf Banz in his 1981 paper The Relationship Between Return and Market Value of Common Stocks, which was published in the Journal of Financial Economics. After the 1992 publication of Eugene Fama and Kenneth Frenchs paper The Cross-Section of Expected Stock Returns, the size effect was
  • Research Review | 24 October 2025 | Risk Analysis [Capital Spectator]

    The case for low-risk equity investing: evidence from 2011-2025 Raul Leote de Carvalho (BNP Paribas), et al. July 2025 This paper investigates the performance of equity low-risk strategies since 2011, highlighting their ability to deliver strong risk-adjusted returns across diverse market conditions. We introduce a composite risk score that extends beyond volatility and demonstrate its

Recent Quant Links from Quantocracy as of 10/22/2025

This is a summary of links recently featured on Quantocracy as of Wednesday, 10/22/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The End-Of-Month Effect in Value Growth and Real Estate Equity Spreads [Quantpedia]

    The clustering of excess returns on the final trading days of the month constitutes a robust empirical regularity with significant implications for portfolio construction. We document a month-end premium that is both statistically and economically significant, distinct from the canonical turn-of-the-month (ToM) effect. Our strategy highlights systematic style rotationsparticularly shifts in
  • Optimization: Adaptive regret for regime-shifting markets [Trading the Breaking]

    In our preceding discourse, we talked about the features of parameter-free optimization, a methodology designed to liberate quantitative strategists from the sinister task of parameter tuning. The allure was undeniable: escape the perilous cycle of tweaking lookback windows, volatility thresholds, and rebalancing frequenciesa process that often culminates in overfitted models, brittle
  • Weekly Research Recap [Quant Seeker]

    Cryptocurrency as an Investable Asset Class: Coming of Age (Borri, Liu, Tsyvinski, and Wu) This paper describes 10 stylized facts about cryptocurrencies, including their 5 higher volatility but similar Sharpe ratios to equities, a rising correlation with stocks (2% to 37% post-2020) yet strong diversification benefits, frequent large jumps that complicate risk management, and the declining
  • Effectiveness of Covered Call Strategy in Developed and Emerging Markets [Relative Value Arbitrage]

    Covered call strategies are often promoted as an income-generation tool for investors seeking steady returns with reduced risk. But how effective are they in practice? In this post, we take a closer look at their real-world performance across different markets. Do Covered Calls Deliver Superior Returns? The covered call strategy is a popular and conservative options trading approach. It involves
  • Can Machine Learning Predict Factor Returns? [Alpha Architect]

    Nusret Cakici, Christian Fieberg, Carlos Osorio, Thorsten Poddig, and Adam Zaremba, authors of the study Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns, published in the April 2025 issue of The Journal of Portfolio Management, set out to answer a critical question: Can machine learning techniques improve the prediction of cross-sectional factor
  • Can Technology Sector Leadership Be Systematically Exploited? [Quantpedia]

    The U.S. equity market has periodically been dominated by a few technology-driven stocks, most recently the so-called Magnificent Seven. Historically, similar dominance occurred during the Nifty Fifty era in the 1960s1970s and the dot-com boom in the 1990s. These periods of concentrated leadership often led to temporary outperformance, but systematically capturing such gains has proven
  • The crucial difference in price momentum vs. earnings momentum [Klement on Investing]

    Sometimes, you dont know what you know until somebody spells it out crystal clear for you. At least thats how I felt when I read the analysis of Kewei Hou and his colleagues on price momentum and earnings momentum. Most investors know that both price momentum and earnings momentum are factors that can be employed to create outperformance vs. the market and to select attractive stocks. Price

Recent Quant Links from Quantocracy as of 10/14/2025

This is a summary of links recently featured on Quantocracy as of Tuesday, 10/14/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Why You Can t Tell if Your Strategy Stopped Working (Statistically Speaking) [Robot Wealth]

    Traders love the illusion of precision. A few bad weeks go by, and you think, Lets run a t-test and see if the strategy stopped working. It sounds rigorous. It isnt. Imagine a strategy that, in truth, earns 10% per year with 20% volatility roughly the S&Ps long-term profile. Well simulate five years of daily returns, about 1,260 observations, from a geometric Brownian
  • The Points-and-Line Chart [Financial Hacker]

    -and-Line Chart Traders like special bars on a chart, since they let the price curve appear smoother and more predictable as it really is. Some types of bars, such as Renko bars, even use fake prices for generating curves that appear to move straight upwards or downwards. In the TASC November issue, Mohamed Ashraf and Mohamed Meregy presented the Points and Line Chart that avoids this problem. At
  • Weekly Research Recap [Quant Seeker]

    Analyzing over 1,500 bond funds (20152024) shows that active returns, performance relative to benchmarks, are driven mainly by systematic risk exposures, not manager skill. For Aggregate and Corporate funds, about 55% of active returns come from underweighting duration and overweighting credit risk, while high-yield funds often reduce credit exposure. Key takeaway: Active return is not alpha;
  • Identifying and Characterizing Market Regimes Across Asset Classes [Relative Value Arbitrage]

    Identifying market regimes is essential for understanding how risk, return, and volatility evolve across financial assets. In this post, we examine two quantitative approaches to regime detection. Hedge Effectiveness Under a Four-State Regime Switching Model Identifying market regimes is important for understanding shifts in risk, return, and volatility across financial assets. With the

Recent Quant Links from Quantocracy as of 10/12/2025

This is a summary of links recently featured on Quantocracy as of Sunday, 10/12/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Asset Embeddings [Quantitativo]

    You shall know a word by the company it keeps. John Rupert Firth. John Rupert Firth was a British linguist and one of the pioneers of modern semantics. In the 1950s, he proposed a simple yet revolutionary idea: You shall know a word by the company it keeps. More than half a century later, this insight became the foundation for a breakthrough at Google, where Tomas Mikolov and his
  • Is the degradation of trend following performance a cohort effect, instrument decay or environmental problem? [Investment Idiocy]

    It's probably bad luck to say this, but the most recent poor performance of CTAs and trend following managers this year appears to have been reversed. My own system is up over 12% since the nadir of the summer drawdown, and is now up for year; admittedly by only by 5.5%. Nevetheless, it's true to say that trend following performance appears to have been degrading over the last few
  • Weekly Research Recap [Quant Seeker]

    Music as an Asset Class (Stoikov, Singla, Cetin, Cendra Villalobos) Analyzing 1,295 transactions from the Royalty Exchange, a marketplace for music royalties, the authors calibrate three discounted cash flow models to value music rights. The best-fitting model shows that Life of Rights assets earn 12.8% median annual net returns, comparable to the S&P 500s 12.2% annualized. 10-year

Recent Quant Links from Quantocracy as of 10/06/2025

This is a summary of links recently featured on Quantocracy as of Monday, 10/06/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Book and Workshop Introduction: Generative AI for Trading and Asset Management [EP Chan]

    The world of finance is no stranger to artificial intelligence. Most quantitative asset managers are already familiar with discriminative models, for example, given yesterdays return, what is the probability that todays return will be positive? Many are also familiar with reinforcement learning, used for tasks like optimizing order execution or figuring out how to set the best capital
  • Gold s Rally and the Gold Mining Stocks Trap [Quantpedia]

    Gold has been in the headlines lately as it climbs to new highs, prompting many investors to look for ways to benefit from the rally. However, many institutional investors such as mutual funds and pension funds face restrictions on buying physical gold or gold-backed ETFs. Instead, they often turn to gold mining stocks to gain indirect exposure to golds price. That approach seems
  • The Role of Data in Financial Modeling and Risk Management [Relative Value Arbitrage]

    Much emphasis has been placed on developing accurate and robust financial models, whether for pricing, trading, or risk management. However, a crucial yet often overlooked component of any quantitative system is the reliability of the underlying data. In this post, we explore some issues with financial data and how to address them. How to Deal with Missing Financial Data? In the financial
  • Employing volatility of volatility in long-term volatility forecasts [Outcast Beta]

    We demonstrate how simple long-term volatility forecasts can be improved by incorporating the volatility of short-term volatility into forecasting models. The theoretical framework for modelling volatility of short-term volatility, along with its role in long-term forecasts, will be outlined. Empirical tests will then illustrate the value of including volatility of volatility measures in practice.
  • Weekly Research Recap [Quant Seeker]

    State-Dependent Market (In)Efficiency in Cryptocurrency Markets (Barak, Razmi, and Mousavi) Cryptocurrency return predictability is regime-dependent. This paper tests strategies based on Directional Change events, where a new trend is confirmed only once price moves a fixed percentage from the last extreme. A machine-learning model adaptively selects the optimal threshold each day. From