Quantocracy

Quant Blog Mashup

Recent Quant Links from Quantocracy as of 09/03/2025

This is a summary of links recently featured on Quantocracy as of Wednesday, 09/03/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Bitcoin ETFs in Conventional Multi-Asset Portfolios [Quantpedia]

    Understanding how Bitcoin-related instruments can fit into traditional portfolios is increasingly relevant for investors. Some risk-averse investors do not like to hold cryptocurrencies in their portfolios strategically; however, they may be open to investing in crypto-linked assets on a tactical level. In this context, our goal is to explore how we can provide short-term Bitcoin exposure while
  • Weekly Research Recap [Quant Seeker]

    Global News Networks and Return Predictability (Freire, Moin, Quaini, and Soebhag) News sentiment, extracted from a massive global article dataset, predicts daily equity index returns across 14 developed markets. Local sentiment strategies nearly double buy-and-hold Sharpe ratios (e.g., U.S. 1.34 vs. 0.62), with net alphas of about 16% after trading costs and one-third smaller drawdowns. Adding
  • Stochastic Volatility Models for Capturing ETF Dynamics and Option Term Structures [Relative Value Arbitrage]

    The standard Black-Scholes-Merton model is valuable in both theory and practice. However, in certain situations, more advanced models are preferable. In this post, I explore stochastic volatility models. Stock and Volatility Simulation: A Comparative Study of Stochastic Models Stochastic volatility models, unlike constant volatility models, which assume a fixed level of volatility, allow

Recent Quant Links from Quantocracy as of 09/01/2025

This is a summary of links recently featured on Quantocracy as of Monday, 09/01/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Combinatorial Purged Cross Validation for Optimization [Trading the Breaking]

    Traditional grid or Bayesian searches conducted on a single path reward parameters that overfit to this specific historical path. This inflates performance metrics through selection bias and temporal leakage. Combinatorial Purged Cross-Validation (CPCV) addresses this flaw by generating a multitude of chronology-respecting train-test partitions. Crucially, it purges any overlapping information,
  • New open-source library: Conditional Gaussian Mixture Models (CGMM) [Sitmo]

    Ive released a small, lightweight Python library that learns conditional distributions and turns them e.g. into scenarios, fan charts, and risk bands with just a few lines of code. Its built on top of scikit-learn (fits naturally into sklearn-style workflows and tooling). Example usage: In the figure below, a non-parametric model is fit on VIX conditioned on the VIX level, so it naturally
  • The Reversal Tendency of Labor Day Week [Quantifiable Edges]

    In the subscriber letter over the last several years I have demonstrated that the performance during the week of Labor Day has been impacted by the performance in the month leading up to it. Interestingly, is has been somewhat of a momentum reversal week. When SPX has rallied up to Labor Day, then it has struggled that week. And declines into Labor Day have seen positive performance. Below is an

Recent Quant Links from Quantocracy as of 08/31/2025

This is a summary of links recently featured on Quantocracy as of Sunday, 08/31/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Volume Shocks and Overnight Returns [Quantitativo]

    Albert Einstein had a way of capturing deep truths in simple words. His quote is a reminder, especially relevant to us when building models. Stripping away unnecessary complexity is vital, but going too far risks oversimplification: a model that looks neat but fails to capture reality. This week, we will implement the idea from the paper Volume Shocks and Overnight Returns, by lvaro Cartea,
  • The 5 Point Trade Quality Scoring System [Paper to Profit]

    Often we have a trading system with a countless number of trades (in my case 70,000,000) with little to no way to understand actually what is going on. Sure, we get massive printouts and tear sheets with a ton of figures that quantify our strategy. But, what about on a trade-by-trade basis? What we really need is to understand the quality of our trading systems on a trade-by-trade basis. Its
  • DataFrame Rec Tests with Recx [OS Quant]

    Code changes. Data changes. Outputs change. Somewhere between the first analysis and an odd position in production, little mismatches creep in: a misstated value, off-by-one date ranges, rounding shifts, subtle drift in calculations, missing IDs. The most reliable way to catch them is to compare a new DataFrame to a previously validated onea reconciliation, or rec, test. recx is a lightweight
  • The (hidden) trading value of central bank liquidity information [Macrosynergy]

    Central banks regularly adjust the economys monetary base through foreign exchange interventions and open market operations. Point-in-time information on such intervention-based liquidity expansion has predictive power for asset returns. That is because such operations often come in longer-term trends, and there are lagged effects, for example, through private sector portfolio rebalancing.
  • Finding Edges [Robot Wealth]

    How do we find edges? First, we must be clear about what constitutes a good idea. It isnt as simple as it having to make money. The risk profile must also be tolerable. This is a personal preference. Next, we need to be able to trade it. Robinhood wont let you sell naked options. You cant trade the Indian markets or crypto derivatives from the US. Retail cant trade OTC. These

Recent Quant Links from Quantocracy as of 08/29/2025

This is a summary of links recently featured on Quantocracy as of Friday, 08/29/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Neural Nets and Factor Models [Falkenblog]

    Gu, Kelly, and Xiu (2020) – "Empirical Asset Pricing via Machine Learning" and Chen, Pelger, and Zhu (2019) – "Deep Learning in Asset Pricing" examine various machine learning and neural net algorithms. Both find significant improvements to standard factor models. Several hidden parameter choices are not directly learned during training but significantly impact model
  • How Can We Explain the Low-Risk Anomaly? [Quantpedia]

    The low-risk anomaly in financial markets has puzzled researchers and investors, challenging the traditional risk-return paradigm (higher risk->higher return). This phenomenon, where low-risk assets outperform their high-risk counterparts on a risk-adjusted basis, has been observed across various asset classes, including stocks and mutual funds. What may be the possible explanation?
  • Cross-Sectional Momentum: Results from Commodities and Equities [Relative Value Arbitrage]

    Momentum strategies can be divided into two categories: time series and cross-sectional. In a previous newsletter, I discussed time series momentum. In this post, I focus on cross-sectional momentum strategies. Cross-Sectional Momentum in the Commodity Market Momentum trading is often divided into 2 categories: time-series momentum and cross-sectional momentum. Time-series based trading strategies
  • Weekly Research Recap [Quant Seeker]

    Asymmetry and Crude Oil Returns (Liu, Zhang, and Bouri) This paper introduces a new distribution-based asymmetry factor (OIS) for crude oil that strongly predicts WTI futures returns. A one-standard-deviation rise in OIS, signaling right-tail clustering, forecasts a 3.15% drop in next-month returns (t3.1, R=4.1%). Out-of-sample, OIS achieves an R of 4.2%, far exceeding standard

Recent Quant Links from Quantocracy as of 08/24/2025

This is a summary of links recently featured on Quantocracy as of Sunday, 08/24/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Walk-Forward optimization [Trading the Breaking]

    I want to start by saying that the key is in the data, not in the model or its parameters. Therefore, if your data is garbage, no matter how much you parameterize it, the results will still be garbage. If you parameterize a model, it's to fine-tune something that already works. Period. Knowing that, we can proceed. The genesis of a strategy is often an elegant, compelling concept. This
  • Laurens Bensdorp – Building Strategies with Purpose [Algorithmic Advantage]

    Theres a special place in trading graveyards reserved for the back-test that looked gorgeous on paper and then detonated in production. Ive been there. If you trade long enough, you will too. We all know the over-fittings issues, and Ill get into that, but theres another reason why back-tests can fail: the initial purpose is not matched to the right method. If we ask the wrong thing of
  • The Best Strategies for FX Hedging [Quantpedia]

    Foreign exchange (FX) markets are a cornerstone of global finance, offering investors and corporations opportunities to manage currency risk, enhance returns, and optimize portfolio performance. Among the most critical challenges in FX is the design of robust hedging strategies to mitigate exposure to volatile currency movements. How does the financial industry deal with this task? We can draw
  • Unlocking REIT Returns: Real Estate Investment Factors [Alpha Architect]

    As of 2024, real estate investment trusts (REITs) have cemented their role as a $1.5 trillion segment within global capital markets, offering investors a liquid and regulated gateway to commercial real estate. With robust dividend mandates, leverage restrictions, and transparent operations, REITs continue to attract both institutional and individual investors seeking diversification and steady
  • Cesar Alvarez – A Novel Way to Combine Trend, Reversion, ETFs, Volatility & More [Algorithmic Advantage]

    When I sat down recently with Cesar Alvarez of Alvarez Quant Trading, I knew I'd be tapping into a deep reservoir of quantitative trading wisdom. Cesars journey into systematic trading began similarly to many of usstarting with discretionary trades, dabbling in mutual funds, and eventually stumbling into the quant world. From his early days at Connors Research to managing sophisticated

Recent Quant Links from Quantocracy as of 08/19/2025

This is a summary of links recently featured on Quantocracy as of Tuesday, 08/19/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quantifying Global Real Estate Returns Over Centuries [Quantpedia]

    In the realm of quantitative finance, understanding the dynamics of real estate returns over extended periods is often overlooked, which is not good, as real estate constitutes a significant portion of investors portfolios. The article titled Global Housing Returns, Discount Rates, and the Emergence of the Safe Asset, 1465-2024 fills the gap and provides a comprehensive historical overview of
  • Correlation Matrix Generation using Object Oriented Python [Quant Start]

    In the last article Generating Synthetic Equity Data with Realistic Correlation Structure we discussed how to generate synthetic structured correlation matrices for the purposes of generating synthetic correlated equities data. This has a number of uses within systematic trading backtesting validation and machine learning model training. We mentioned in the Next Steps section that we would explore
  • Weekly Research Recap [Quant Seeker]

    Is Gold an Inflation Hedge? (Baur) Gold is not a consistent hedge against average inflation. Between 1971 and 2025, realized inflation explains less than 3% of golds price variation, and the hedge effect evident in the 1970s80s largely disappears thereafter. Gold does, however, respond strongly to extreme inflation shocks and especially to changes in inflation expectations: 1-year and 5-year
  • Predictive Information of Options Volume in Equity Markets [Relative Value Arbitrage]

    A lot of research in options literature has been devoted to the volatility risk premia and developing advanced pricing models. Much less attention has been given to volume. In this post, Ill discuss some aspects of options volume. Can Options Volume Predict Market Returns? Most of the research in equity and index options has been devoted to volatility and the volatility risk premium. Relatively

Recent Quant Links from Quantocracy as of 08/16/2025

This is a summary of links recently featured on Quantocracy as of Saturday, 08/16/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Cross-Sectional Alpha Factors in Crypto: 2+ Sharpe Ratio Without Overfitting [Unexpected Correlations]

    In the early 90s, the quant forefathers (Fama and French) introduced their now-canonical factor models: first three, then five, and eventually seven, explaining much of the variation in US equity returns. Today, these models are used to understand what easy-to-replicate risk factors managers are being exposed to. Allocators will want to pay for only the truly unique return sources that cant
  • Trading Signals in High Definition [Robot Wealth]

    Weve all used on/off type trading signals at some point. But you can nearly always extract more insight with a simple adjustment that focuses on using data efficiently. Let me show you how using a crypto trend example. The problem with binary signals Youve seen them everywhere. If price is above the 20-day moving average, be long. If its below, be short. Thats a binary signal.
  • Python Tooling in 2025 [OS Quant]

    Today, Pythons ecosystem offers an abundance of tooling to support every aspect of the development workflow. From dependency management to static analysis, from linting to environment setup, there are more options than ever. This article presents a modern, opinionated toolchain for Python development in quantitative research and development. The focus is code quality, ensuring that your
  • Research Review | 15 August 2025 | Forecasting [Capital Spectator]

    Partisan Bias in Professional Macroeconomic Forecasts Benjamin S. Kay (Federal Reserve), et al. June 2025 Using a novel dataset linking professional forecasters in the Wall Street Journal Economic Forecasting Survey to their political affiliations, we document a partisan bias in GDP growth forecasts. Republican-affiliated forecasters project 0.3-0.4 percentage points higher growth when Republicans
  • Systematic equity allocation across countries for dollar-based investors [Macrosynergy]

    This post demonstrates that country allocation with macroeconomic factors can materially enhance the returns on international equity portfolios in dollar terms. We identify a range of economic developments that, according to standard theory and in conjunction with market inattention, should predict the outperformance of countries either through exchange rate appreciation or higher local-currency

Recent Quant Links from Quantocracy as of 08/12/2025

This is a summary of links recently featured on Quantocracy as of Tuesday, 08/12/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Retrospective Simulation in Trading: Testing Strategies Beyond Realized Price Paths [Quant Insti]

    This blog introduces retrospective simulation, inspired by Talebs "Fooled by Randomness," to simulate 1,000 alternate historical price paths using a non-parametric Brownian bridge method. Using SENSEX data (20002020) as in-sample data, the author optimises an EMA crossover strategy across the in-sample data first, and then applies it to the out-of-sample data using the optimum
  • Robeco’s One-Legged Vol Factor [Falkenblog]

    Two months ago, Robecos Amar Soehbag, Guido Baltussen, and Pim van Vliet posted a new empirical paper, Factoring in the Low-Volatility Factor. I consider Pim a good friend, and he is one of the initial low-vol portfolio managers, as he started his conservative fund at Robeco around 2006 (the others were Analytic Investors, Acadian, and Unigestion). He says he was introduced to the low-vol
  • Understanding “why” beats statistical significance [Robot Wealth]

    Do you find yourself obsessing over p-values and t-stats when evaluating trading ideas? I get it. If you come from an academic or scientific background, statistical significance feels like the gold standard for determining whether something is real or just random noise. And in many fields, thats exactly right. But trading is different. Statistical tests arent useless in trading. I use
  • Weekly Research Recap [Quant Seeker]

    Is Social Media Information Noise or Fundamentals? Evidence from the Crude Oil Market (Ma, Tourani-Rad, Xu, and Zhou) Social media sentiment from Thomson Reuters MarketPsych Indices predicts crude oil returns, with a one-standard-deviation rise implying a next-day gain of roughly 21 bps. Positive sentiment reflects fundamentals, persisting for months and forecasting inventory changes, while
  • The Impact of Market Regimes on Stop Loss Performance [Relative Value Arbitrage]

    Stop loss is a risk management technique. It has been advocated as a way to control portfolio risk, but how effective is it? In this post, I will discuss certain aspects of stop loss. When Are Stop Losses Effective? A stop loss serves as a risk management tool, helping investors limit potential losses by automatically triggering the sale of a security when its price reaches a predetermined level.
  • New Contributor: GLD Put-Write Strategy [Deltaray]

    Exploring alternative assets like GLD ETF options enhances portfolio diversification by tapping into distinct volatility profiles and correlation patterns, especially beneficial during volatile market environments. In this post, we examine a simple, yet effective Put-Write strategy applied to GLD ETF Options, demonstrating how precious metals can serve as source of options income. GLD for
  • Options: Iron Butterfly [Trading the Breaking]

    In the previous article, we deconstructed the Iron Condor, a robust strategy for harvesting the variance risk premium in markets characterized by range-bound behavior. The Condor, with its constituent out-of-the-money credit spreads, offers a wide plateau of profitability, making it a forgiving instrument for general forecasts of contained volatility. It is, in many ways, the workhorse of
  • Overnight Returns: Risk or Conspiracy? [Falkenblog]

    TL;DR Virtually all of crypto returns come outside of NYSE trading hours, more so for coins pulled from the top 100, more so than for ETH & BTC Overnight returns dominate the WallStreetBets meme stock pumps of 2021 This pattern could be a signature of a conspiratorial pump or the nature of risky asset returns The equity overnight puzzle refers to the fact that, since we had good data on
  • Step-by-Step Python Guide for Regime-Specific Trading Using HMM and Random Forest [Quant Insti]

    Most trading strategies fail because they assume the market behaves the same all the time. But real markets shift between calm and chaotic, and strategies must adapt accordingly. This project builds a Python-based adaptive trading strategy that: Detects current market regime using a Hidden Markov Model (HMM) Trains specialist ML models (Random Forests) for each regime Uses the most relevant model

Recent Quant Links from Quantocracy as of 08/05/2025

This is a summary of links recently featured on Quantocracy as of Tuesday, 08/05/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quantamental Catch-Up [Anton Vorobets]

    Many of you have undoubtedly enjoyed the summer holidays, so you might have missed out on the first five lectures of the Applied Quantitative Investment Management course. So far, we have been through the first four chapters of the Portfolio Construction and Risk Management book, reaching a point where we understand stylized market facts, the investment simulation framework, and multi-asset
  • Cultural Calendars and the Gold Drift: Are Holidays Moving GLD ETF? [Quantpedia]

    Financial markets exhibit persistent calendar anomalies, which often defy the efficientmarket hypothesis by generating predictable return patterns tied to institutional or cultural events. In this paper, we document a novel, globally pervasive drift in gold prices surrounding major wealth-oriented festivals across the four principal cultural and religious domains: Christianity, Islam, Hinduism,
  • Weekly Research Recap [Quant Seeker]

    Commodities and Conundrums: Decoding Behavioural Finance in Market Dynamics (Till) Investors often underestimate the influence of psychological biases in trading, particularly in commodity markets. This paper examines real-world cases, such as the collapse of MF Global, where overconfidence, loss aversion, and confirmation bias led to significant failures. It also explores common commodity trading
  • The Limits of Out-of-Sample Testing [Relative Value Arbitrage]

    In trading system design, out-of-sample (OOS) testing is a critical step to assess robustness. It is a necessary step, but not sufficient. In this post, Ill explore some issues with OOS testing. How Well Overfitted Trading Systems Perform Out-of-Sample? In-sample overfitting is a serious problem when designing trading strategies. This is because a strategy that worked well in the past may not

Recent Quant Links from Quantocracy as of 08/03/2025

This is a summary of links recently featured on Quantocracy as of Sunday, 08/03/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • We interrupt this service for an important message [Klement on Investing]

    Hi everyone Usually, I dont comment too much on current affairs on this substack. Still, Trump firing the Head of the BLS, Erika McEntarfer, because he didnt like the labour market data, is extremely dangerous for investors everywhere. If you have investments in the US, you should be highly concerned about this because having truthful data about the state of the US economy is the foundation
  • A Quant’s Guide to Covariance Matrix Estimation [OS Quant]

    In this article, we explore three techniques to improve covariance matrix estimation: evaluating estimates independently of backtests, decoupling variance and correlation, and applying shrinkage for more robust outputs. Author Adrian Letchford Published 2 August 2025 Length 12 minutes Like what you see? Follow Adrian on Twitter to be notified of new content. Follow Estimating a covariance matrix
  • Overnight Crypto Returns [Falkenblog]

    On Monday, I examined the flaw in capturing the overnight equity return anomaly. The basic issue was that the anomaly shrank considerably after the 2008 bear market, and given that one has to turn over the entire portfolio twice a day, the minuscule transaction costs eliminate any alpha. The guys who created the overnight ETFs were also plagued by incredibly bad luck, but that just did them a
  • A Different Way of Looking at Returns [Mark Best]

    It would be nice if it were possible to trade a moving average cross. The problem with this is always that the data lags. Its not possible to trade the current value of a moving average since it requires trading prices in the past. The advantage to doing so is that, due to the smoothing, forecasts are less noisy. The good thing is than many moving averages are finite impulse response (FIR)