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Recent Quant Links from Quantocracy as of 05/04/2025

This is a summary of links recently featured on Quantocracy as of Sunday, 05/04/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Correlation-Based Clustering: Spectral Clustering Methods [Portfolio Optimizer]

    Clustering consists in trying to identify groups of similar behavior1 – called clusters – from a dataset, according to some chosen characteristics. An example of such a characteristic in finance is the correlation coefficient between two time series of asset returns, whose usage to partition a universe of assets into groups of close and distant assets thanks to a hierarchical
  • A New Approach to Regime Detection and Factor Timing [Alpha Architect]

    The financial research literature has found that the performance of assets (and factors) can vary substantially across regimes (for example, see here and here)factor premiums can be regime dependent. Unfortunately, the real-time identification of the current economic regime is one of the biggest challenges in finance. Amara Mulliner, Campbell Harvey, Chao Xia, and Ed Fang, authors of the March
  • Why data mining risks your trading career [Robot Wealth]

    I was recently talking to someone about data mining as an approach to finding edges to trade. I get the appeal. Feed enough data into a computer, run enough tests, and surely something profitable will emerge, right? Maybe. But almost certainly not. But the worst thing about this approach is that it risks your entire trading career. Sound extreme? Ill explain. Two types of traders Let me tell
  • Revisiting Pragmatic Asset Allocation: Simple Rules for Complex Times [Quantpedia]

    Pragmatic Asset Allocation (PAA) represents a portfolio construction approach that seeks to balance the benefits of systematic trend-following with the realities faced by semi-active investors (mainly taxes and lack of time to manage positions). Building upon the insights presented in Quantpedias initial overview and later in the follow-up, we see clear potential in this frameworkespecially
  • Weekly Research Recap [Quant Seeker]

    Time for another batch of top-tier investing research. Below is a carefully curated list of great papers from last week, each linked to the original source for easy access. If youre enjoying these posts, a like or subscribe is always appreciated, thank you for your support! Asset Allocation Global Tactical Asset Allocation: Updated Results and Real-Market Implementation Using Python and IBKR

Recent Quant Links from Quantocracy as of 04/29/2025

This is a summary of links recently featured on Quantocracy as of Tuesday, 04/29/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Front-Running Seasonality in Country ETFs: An Extended Test [Allocate Smartly]

    This is a test of a dynamic seasonality strategy from Quantpedia that selects from 23 individual country ETFs. Weve extended the authors test by 30+ years using MSCI index data. Backtested results from 1971 follow versus an equal-weight benchmark of those 23 country ETFs (1). Learn more about what we do and follow 90+ asset allocation strategies in near real-time. Note: for reasons we
  • Quantpedia Awards 2025 Countdown [Quantpedia]

    Hello all, Just little over 24 hours remain until the end of the deadline for QUANTPEDIA AWARDS 2025 April 30th, 2025, at 23:59 UTC. Join the competition now, and dont miss out on this chance to showcase your skills! Alternatively, if you cant (or dont want) to join, then please help us spread the word and let people in your professional network know!
  • Finding an Edge in IPOs: Research and a Backtested Mechanical Trading System [Cracking Markets]

    Ever heard the term "IPO" thrown around in financial news? Let's break down what it means and why it might be interesting for systematic traders. What Exactly is an IPO? "IPO" stands for "Initial Public Offering." It's the very first time a private company offers its shares (stock) to the public on a stock exchange. Think of it as a company's big debut
  • How Speculative Money Flows into Crypto [Unexpected Correlations]

    Compared to traditional futures or equities, crypto markets offer greater transparencythanks primarily to the public blockchain and also to the unique culture that shaped the industry. This opens up new opportunities for investors and traders to monitor and measure liquidity dynamics that are otherwise hidden in conventional markets. Weve outlined a base framework to systematically identify
  • How Tiny Price Differences Help Track Small Investors Trades [Alpha Architect]

    This article explains how researchers studied small investors trading habits by looking at tiny price differences, called subpennies, in stock trades. They found that the current method to identify these trades isnt very accurate. By using a new approach, they improved the accuracy, helping to better understand how small investors buy and sell stocks. A (Sub)penny for Your Thoughts: Tracking
  • Short-Term Correlated Stress Reversal Trading [Quantpedia]

    Short-term reversal strategies in U.S. large-cap equity indexes, such as the S&P 500, are well-documented and widely followed. These reversals often occur in response to brief periods of market stress, where sharp declines are followed by quick recoveries (as we have experienced in the last few weeks). Traditional approaches typically identify such stress periods using only the price action of
  • Boosting macro trading signals [Macrosynergy]

    Boosting is a machine learning ensemble method that combines the predictions of a chain of basic models, whereby each model seeks to address the shortcomings of the previous one. This post applies adaptive boosting (Adaboost) to trading signal optimisation. Signals are constructed with macro factors to guide positioning in a broad range of global FX forwards. Boosting is beneficial for learning

Recent Quant Links from Quantocracy as of 04/24/2025

This is a summary of links recently featured on Quantocracy as of Thursday, 04/24/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Weekly Research Insights [Quant Seeker]

    Geopolitical tensions, such as wars, threats, or major international conflicts, are known to affect economies and markets. In recent years, several papers have examined the impact of geopolitical risk on stock returns. For example, Sheng et al. (2025) construct a risk index based on news articles from 1984 to 2025 and find that geopolitical risk predicts returns both in the time series and the
  • Kevin Davey II – Selecting Optimal Strategies for Peak Performance [Algorithmic Advantage]

    In Part II with Kevin, he delves into the intricate mechanics behind his systematic futures trading approach, offering advanced quantitative traders a window into the finer points of strategy design, walk forward analysis, robustness testing, and portfolio construction. Drawing on decades of experience and a background in aerospace, he emphasizes practical best practices, highlights common
  • The Bitter Lesson [Quantitativo]

    The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. Richard Sutton. Richard Sutton is one of the greatest minds of our time. He is a founding figure in modern AI and a pioneer of reinforcement learning, the framework behind many of todays most advanced intelligent

Recent Quant Links from Quantocracy as of 04/22/2025

This is a summary of links recently featured on Quantocracy as of Tuesday, 04/22/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Uncovering the Pre-ECB Drift and Its Trading Strategy Applications [Quantpedia]

    As the worlds attention shifts from the US-centric equity markets to international equity markets (which strongly outperform on the YTD basis), we could review some interesting anomalies and patterns that exist outside of the United States. In the world of monetary policy, traders have long observed a notable positive drift in U.S. equities on days surrounding Federal Reserve (FOMC) meetings.
  • New Feature: Walked-Forward Optimal Strategy Combinations (aka “Meta Walk-Forwards”) [Allocate Smartly]

    Members: See the complete list of Meta Walk-Forwards In our previous post, we introduced this concept of walking forward optimal strategy combinations. In other words, were finding the optimal combination of strategies, in real-time, based only on data available at that moment in time. We call these Meta Walk-Forwards. For members who dont want the complication of handcrafting
  • The unreasonable effectiveness of volatility targeting – and where it falls short [Unexpected Correlations]

    This is part 1 of our in-depth investigation of how quantitative risk management could help improve risk-adjusted returns: I'll explain what volatility targeting is, explore a seemingly paradoxical phenomenon, and highlight its blindspots. Volatility targetings goal is to keep an asset or portfolios volatility within reasonable boundaries, by actively adjusting exposure – its among
  • Weekly Research Recap [Quant Seeker]

    Its time for another round of great investing research. Below is a curated selection of last weeks highlights, each linked to the original source for easy reading. If youre enjoying these posts, a like or subscribe is always appreciated, thank you for your support! And may I kindly ask you to take a moment to fill out the short poll below? Your input helps shape future editions.
  • Making Factor Strategies Work for Everyone [Alpha Architect]

    This article explores the difference between tradable and on-paper (theoretical) risk factors in investing. Risk factors are strategies that help explain stock market returns, but many work only in theory and not in real life. Researchers developed ways to make these factors tradable by using mutual funds and ETFs, making them accessible to both large institutions and everyday investors. However,
  • Machine Learning in Financial Markets: When It Works and When It Doesn t [Relative Value Arbitrage]

    Machine learning (ML) has made a lot of progress in recent years. However, there are still skeptics, especially when it comes to its application in finance. In this post, I will feature articles that discuss the pros and cons of ML. In future editions, Ill explore specific techniques. How Accurate is Machine Learning Prediction in Finance? Machine Learning has many applications in finance, such
  • What Works Below the 200-Day Moving Average? [Quant Seeker]

    Given the recent market downturn, marked by the S&P 500 and the Nasdaq trading well below their 200-day moving averages, the familiar adage Nothing good happens below the 200-day moving average has once again gained traction in financial media. The 200-day simple moving average (200SMA) is among the most widely followed indicators of long-term market trends, and a sustained breach below

Recent Quant Links from Quantocracy as of 04/18/2025

This is a summary of links recently featured on Quantocracy as of Friday, 04/18/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Building a Survivorship Bias-Free Crypto Dataset with CoinMarketCap API [Concretum Group]

    When you look at a chart of Bitcoins price from 2010 to today, it tells a story of volatility, resilience, and long-term gains. But what about the thousands of coins that launched, pumped, and then disappeared along the way? Most commonly used crypto datasets, especially those tied to current exchange listings or public dashboards, tend to highlight tokens that are still actively trading. This
  • Weekly Research Insights [Quant Seeker]

    In this weeks edition of Research Insights, I break down three recent papers. The first examines look-ahead bias in large language models. The second introduces a new approach to enhancing momentum strategies. The third explores how sensitive many cross-country return anomalies are to investors design choices. Thanks for reading! If you enjoyed the post, feel free to like it, and consider
  • The Least-Amount of Assumptions Backtest [Unexpected Correlations]

    Theres this Neumann quote: "With four parameters I can fit an elephant, and with five I can make him wiggle his trunk." Funny, but also true. Its very fitting (haha) to our job at Unravel where we scan tens of thousands of time series in order to identify the ones that can be used as predictive risk factors. Sooo, how do we avoid false positives, really? By definition, well
  • Trump s Executive Orders and Their Impact on Financial Markets [Quantpedia]

    In recent months, financial markets have experienced heightened volatility as Donald Trump, in his second term as President of the United States, increasingly uses executive orders to steer economic policy. While he also made use of this presidential power during his first term (20172021), the volume and impact of executive actions have notably intensified. In this analysis, we explore how
  • 036 – Kevin Davey Part I – It’s All About Process in Algo Trading [Algorithmic Advantage]

    I trust everyone is having a relaxing Passover week and is ready to devour some trading wisdom from Kevin Davey, an algorithmic trader with over 30 years of experience and a background in aerospace engineering and quality assurance, who exemplifies the importance of a disciplined process in trading. We spoke for 2 hours so Ive broken the show up into two parts. His expertise lies in trading
  • Enhancing Industry Momentum Strategies: Finding Hidden Neighbors [Alpha Architect]

    Momentum is a financial anomaly in which buying stocks with positive past returns and selling the negative yielding ones has delivered positive returns. After Jegadeesh and Titman (1993)s seminal paper Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency was released, empirical research has identified various forms of momentum profits over the years
  • Fear, Not Risk, Explains Asset Pricing [Quantpedia]

    With financial markets increasingly whipsawed by geopolitical tensions and unpredictable policy shifts from the Trump administrationinvestors are once again questioning how to understand risk, fear, and the true drivers of returns. A recent and compelling paper dives into this debate with a provocative thesis: in Fear, Not Risk, Explains Asset Pricing, authors Rob Arnott and Edward

Recent Quant Links from Quantocracy as of 04/15/2025

This is a summary of links recently featured on Quantocracy as of Tuesday, 04/15/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Researching trading ideas in Excel [Robot Wealth]

    In this webinar, James explores a simple seasonality effect and finds that theres more to the story than an upwardly sloping equity curve. Watch the video to see how you can use Excel to explore market phenomena efficiently and gather evidence that you can use to make practical trading decisions. If youd like to master trading research in Excel through case studies like this one, join us for
  • 97 Years of Death Crosses [Quantifiable Edges]

    The SPX is going to experience a Death Cross today at the close. Ive written many times in the past about Death Crosses. A Death Cross is when the 50ma crosses below the 200ma. It is confirmation of a downtrend. Some people view it as a bearish signal. As youll see, it is not a great signal. My Norgate data goes back to 1928 for SPX (this includes its predecessor, the S&P 90,
  • Weekly Research Recap [Quant Seeker]

    Time for another round of great investing research. Below is a curated list of last weeks highlights, each linked to the original source for easy access. Appreciate your continued support! If youre finding value in these posts, feel free to like and subscribe if you havent already. Bonds Cross-Asset Trend Spillover: A Novel Factor for Corporate Bond Returns (Fieberg, Liedtke, Schlag, and
  • Do Calendar Anomalies Still Work? Evidence and Strategies [Relative Value Arbitrage]

    nd Strategies Subscribe to newsletter Calendar anomalies in the stock market refer to recurring patterns or anomalies that occur at specific times of the year, month, or week, which cannot be explained by traditional financial theories. These anomalies often defy the efficient market hypothesis and provide opportunities for investors to exploit market inefficiencies. In this post, I will feature

Recent Quant Links from Quantocracy as of 04/14/2025

This is a summary of links recently featured on Quantocracy as of Monday, 04/14/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Annual performance update returneth – year 11 [Investment Idiocy]

    Mad out there isn't it? Tarrifs on/off/on/partially off/on… USD/SP500/Gold/US10/Bitcoin all yoyoing like crazy. Seems a good moment to be slightly reflective. I skipped my annual performance update last year, a little sad given it was my tenth anniversary. Mainly this is because it had become a lot of work, covering my entire portfolio. The long only stuff is especially hard, as all my
  • Quantamental economic surprise indicators: a primer [Macrosynergy]

    Quantamental economic surprises are point-in-time measures of deviations of economic indicators from expected values. There are two types of surprises: first-print events and pure revisions. First-print events feature new observation periods, and the surprise element depends on market expectations of the indicator. Market surveys can approximate such expectations, but only for a limited number of
  • Catastrophe Bonds: Modeling Rare Events and Pricing Risk [Relative Value Arbitrage]

    A catastrophe (CAT) bond is a debt instrument designed to transfer extreme event risks from insurers to capital market investors. Theyre important for financial institutions, especially insurers and reinsurers, because they offer a way to manage large, low-probability. In this post, I feature research on CAT bonds, how theyre priced, and why they matter more than ever in a world of rising
  • Weekly Research Insights [Quant Seeker]

    In this weeks Research Insights, I cover three interesting papers. The first examines the performance of crypto breakout strategies. The second questions the reliability of the 4% withdrawal rule amid todays market turmoil and inflation concerns, while the third explores how commodity tail risks may help forecast bond returns. Thank you for your continued interest. If you enjoyed the
  • Trading the Channel [Financial Hacker]

    One of the simplest form of trend trading opens positions when the price crosses its moving average, and closes or reverses them when the price crosses back. In the latest TASC issue, Perry Kaufman suggested an alternative. He is using a linear regression line with an upper and lower band for trend trading. Such a band indicator can be used to trigger long or short positions when the price crosses

Recent Quant Links from Quantocracy as of 04/08/2025

This is a summary of links recently featured on Quantocracy as of Tuesday, 04/08/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Resampled Portfolio Stacking [Anton Vorobets]

    This post gives a high-level introduction to Resampled Portfolio Stacking, which is a method for portfolio optimization with fully general parameter uncertainty introduced in Chapter 6 of the Portfolio Construction and Risk Management book1. The fundamental perspectives for the Resampled Portfolio Stacking approach were originally introduced in the Portfolio Optimization and Parameter Uncertainty
  • Weekly Research Recap [Quant Seeker]

    Its time for another roundup of the latest investing research. Below is a carefully curated selection of last weeks highlights, with each title linking directly to its source for further reading. Thanks for your ongoing support! If you enjoy this content, please consider hitting the like button and subscribing if you havent yet. Behavioral Finance FoMO in Investment: A Critical Literature
  • Understanding What Drives Momentum in Global Stock Markets [Alpha Architect]

    This article explores why stocks that have been performing well tend to continue doing so, a phenomenon known as momentum. Researchers analyzed data from various countries to see if explanations found in U.S. markets also apply internationally. They discovered that when information about a company comes out gradually, investors might not react strongly, leading to momentum. Other factors,

Recent Quant Links from Quantocracy as of 04/06/2025

This is a summary of links recently featured on Quantocracy as of Sunday, 04/06/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Turning on-chain data into a profitable, systematic strategy (with code) [Unravel Markets]

    The usual things people first look at when designing new trading systems is trend following / mean reversion while in practice there are a wide range of other: liquidity, macroeconomic & sentiment factors that also heavily influence an assets returns (sometimes even cross-asset lead-lag relationships!). You may hear sometimes talking heads on CNBC reciting their favorite macro metric
  • Forecasting Current Market Turbulence with the GJR-GARCH Model [Sitmo Machine Learning]

    Last week, global stock markets faced a sharp and sudden correction. The S&P 500 dropped 10% in just two trading days, its worst weekly since the Covid crash 5 years ago. Big drops like this remind us that market volatility isnt random, it tends to stick around once it starts. When markets fall sharply, that volatility often continues for days or even weeks. And importantly, negative
  • Weekly Research Insights [Quant Seeker]

    In this weeks Research Insights, I cover three interesting papers. The first is a timely study on how tariffs impact exchange rates. The second explores how volatility scaling can improve Sharpe ratios in crypto strategies. The third studies whether simple pairs trading in U.S. stocks remains profitable. Thank you for your continued interest. If you enjoyed the post, consider liking it

Recent Quant Links from Quantocracy as of 04/02/2025

This is a summary of links recently featured on Quantocracy as of Wednesday, 04/02/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Walking Forward Optimal Strategy Combinations [Allocate Smartly]

    The key takeaway: The Portfolio Optimizer is effective at selecting optimal strategy combinations, even when walked-forward (i.e. when limited to data it would have had at that moment in time). First, a bit of background knowledge youll need to understand this analysis Background Knowledge: Model Portfolios and the Portfolio Optimizer We track 90+ asset allocation strategies. Members
  • Front Running in Country ETFs, or How to Spot and Leverage Seasonality [Quantpedia]

    Understanding seasonality in financial markets requires recognizing how predictable return patterns can be influenced by investor behavior. One underexplored aspect of this is the impact of front-runningwhere traders anticipate seasonal trends and act early, shifting returns forward in time. We have already explored seasonality front-running in commodities, stock sectors, and crisis hedge
  • Weekly Research Recap [Quant Seeker]

    It's time once again to explore some of the most compelling investing research from the past week. Below, you'll find a hand-picked selection of recent papers, each linked directly to the original source for further reading. Thanks for your ongoing support! If you enjoy this content, please consider hitting the like button and subscribing if you havent yet. Behavioral Finance The
  • Breaking Down Volatility: Diffusive vs. Jump Components [Relative Value Arbitrage]

    Implied volatility is an important concept in finance and trading. In this post, I further discuss its breakdown into diffusive volatility and jump risk components. Decomposing Implied Volatility: Diffusive and Jump Risks Implied volatility is an estimation of the future volatility of a securitys price. It is calculated using an option-pricing model, such as the Black-Scholes-Merton model.