Quantocracy

Quant Blog Mashup

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)

Recent Quant Links from Quantocracy as of 08/01/2025

This is a summary of links recently featured on Quantocracy as of Friday, 08/01/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 100 Papers an Hour: 10x’ing Your Strategy Research Speed With AI [Paper to Profit]

    As much as LLMs and AI seem to be writing our code, creating our art, and potentially replacing (or at least supplementing) our own artistic souls, they also still excel at pretty mundane tasks. When applied correctly, they can chew through hundreds of research papers at a time and give you deeper insights, inspiration, and clarity that you can use to apply to your own research process. So, if
  • Weekly Research Recap [Quant Seeker]

    The Actual Retail Price of Equity Trades (Schwarz, Barber, Huang, Jorion, and Odean) Contrary to conventional wisdom, payment for order flow (PFOF) isnt systematically linked to worse execution. This paper finds large cost differences across brokers for identical orders, not explained by PFOF or commissions. Market makers like Citadel treat each brokers flow differently, possibly due
  • First trading day of the month has generally been strong except August [Quantifiable Edges]

    Ive shown the chart below several times over the years. It breaks down by month the performance of the first trading day of the month. July has long had the strongest Day 1. But August is also notable for its lack of Day 1 performance. As you can see it is the only month with a negative Day 1 return. Below is a look at how it has played out over time. This doesnt appear to be a bearish

Recent Quant Links from Quantocracy as of 07/28/2025

This is a summary of links recently featured on Quantocracy as of Monday, 07/28/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Options: Iron Condor Strategy [Trading the Breaking]

    The iron condors appeal is statistically seductive: a high-probability, defined-risk structure promising steady income from time decay and volatility erosion. Yet beneath its deceptively flat payoff profile lies a quantitatively intricate realityone where theoretical win rates often mask a negative expected value. This isnt a flaw in the strategy itself, but a consequence of its dynamic
  • The Equity Overnight Anomaly ETFs [Falkenblog]

    TL;DR The overnight return anomaly became much less anomalous around 2009 The failed ETFs designed to capture it suffered from horrible timing, but also transaction costs Transaction costs are much greater than fees, and also greater than fees + (ask-bid)/2 The overnight equity anomaly is that most of the total equity returns are generated from the opening to the close. When I was an active
  • From Defense to Offense: A Tactical Model for All Seasons [Quantitativo]

    Basketball is a game of adjustments. Bob Knight. Bob Knight was the last coach to lead an NCAA team to a perfect season: 32 wins, zero losses. That record still stands nearly half a century later. His secret? He was a masterful tactician. Obsessed with preparation, relentless on fundamentals, and unmatched in making in-game adjustments. Knight believed basketball wasnt about memorizing
  • How to Identify Ponzi Funds? [Quantpedia]

    Can we spot a Ponzi scheme before it collapses? That question haunts regulators, investors, and journalists alike. But what if some modern investment funds operate on dynamics that, while not technically illegal, closely resemble Ponzi-like behavior? A new paper by Philippe van der Beck, Jean-Philippe Bouchaud, and Dario Villamaina examines whether certain actively managed funds inflate their own
  • The Risks of Passive Investing Dominance [Alpha Architect]

    Fueled by the persistent failure of active management (as evidenced, for example, by the annual SPIVA scorecards), passive investing now commands the majority of assets under management. This structural shift is not without consequence. Chris Brightman and Campbell Harveys May 2025 paper Passive Aggressive: The Risks of Passive Investing Dominance, along with recent academic and industry
  • Sentiment as Signal: Forecasting with Alternative Data and Generative AI [Relative Value Arbitrage]

    Quantitative trading based on market sentiment is a less developed area compared to traditional approaches. With the explosion of social media, advances in computing resources, and AI technology, sentiment-based trading is making progress. In this post, I will explore some aspects of sentiment trading. Using ChatGPT to Extract Market Sentiment for Commodity Trading A Large Language Model (LLM) is

Recent Quant Links from Quantocracy as of 07/23/2025

This is a summary of links recently featured on Quantocracy as of Wednesday, 07/23/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Validation framework [Trading the Breaking]

    Look, anyone can show you a backtest with a nice Sharp. I've seen a thousand of them. The question I always ask is simple: Is this real, or did you just get lucky? Did you find a genuine edge, or did you just curve-fit the hell out of the last ten years of data? A pretty equity curve from the past tells you what happened. It tells you nothing about whether it will keep happening. So, we stop
  • Why the Last Few Minutes of Trading Might Matter More Than You Think [Alpha Architect]

    This paper reveals a striking pattern in U.S. stock markets: the prices of individual stocks often reverse direction at the very end of the trading day. Using high-frequency data, the authors find that the last few minutesparticularly the closing auctionare dominated by large institutional flows that cause temporary price pressure. This is followed by a reversal the next day. This
  • Weekly Research Recap [Quant Seeker]

    News Sentiment and Commodity Futures Investing (Yeguang, El-Jahel, and Vu) While momentum and carry strategies are well-known in commodities, this paper shows that weekly news sentiment from financial media also predicts returns. Using Refinitivs MarketPsych indices, the authors construct long-short portfolios that deliver strong risk-adjusted returns, even after accounting for costs. Combining

Recent Quant Links from Quantocracy as of 07/20/2025

This is a summary of links recently featured on Quantocracy as of Sunday, 07/20/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Carlson’s “Defense First” [Allocate Smartly]

    This is a test of Thomas Carlsons Defense First strategy from his paper Defense First: A Multi-Asset Tactical Model for Adaptive Downside Protection. Strategy results from 1971 follow. Results are net of transaction costs see backtest assumptions. Learn about what we do and follow 90+ asset allocation strategies like this one in near real-time. Logarithmically-scaled. Click for
  • When your strategy works, is it just dumb luck? How to stack the odds in your favour [Robot Wealth]

    Recently, we had an excellent question on the Trade Like a Quant Discord server: How do you know if your strategy is working out of coincidence rather than actual edge? The strategy might work over a long period just because of blind luck. Damn good question. It hits right in the insecurity because the honest answer is: you can never know for sure. I remember when I first started trading. I
  • The Memorization Problem: Can We Trust LLMs Forecasts? [Quantpedia]

    Everyone is excited about the potential of large language models (LLMs) to assist with forecasting, research, and countless day-to-day tasks. However, as their use expands into sensitive areas like financial prediction, serious concerns are emergingparticularly around memory leaks. In the recent paper The Memorization Problem: Can We Trust LLMs Economic Forecasts?, the authors
  • Behavioral Biases and Retail Options Trading [Relative Value Arbitrage]

    Why Do Investors Lose Money? Behavioral finance is the study of how financial behavior affects economic decisions and market outcomes, and how those decisions and outcomes are affected by psychological, social, and cultural factors. Behavioral finance research has shown that people do not always make rational decisions when it comes to money. Factors such as emotion, social pressure, and cognitive
  • Do Smart Machines Make Smarter Trades? [Alpha Architect]

    Can machine learning models help us exploit stock market anomalies more effectively? This paper says yesbut with a few important caveats. By applying gradient boosting algorithms to a wide array of established anomalies (like value, momentum, and quality), the authors show that machine learning methods can significantly improve the performance of long-short strategies. These models capture