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Recent Quant Links from Quantocracy as of 05/15/2025

This is a summary of links recently featured on Quantocracy as of Thursday, 05/15/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Are you blind to the tail risks lurking in calm markets? [Trading the Breaking]

    Algorithmic trading systems can give you this sleek, high-tech confidencelike the robots have everything under control. Theyre fast, precise, and backtested to death, right? But thats where the trap snaps shut. When your risk metrics are built on things like standard deviation or recent drawdowns, youre basically judging a hurricane by the breeze in your backyard. Sure, those stats
  • Are Sector-Specific Machine Learning Models Better Than Generalists? [Quantpedia]

    Can machine learning models better predict stock returns if they are tailored to specific industries, or is a one-size-fits-all (generalist) approach sufficient? This question lies at the heart of a recent research paper by Matthias Hanauer, Amar Soebhag, Marc Stam, and Tobias Hoogteijling. Their findings suggest that the optimal solution lies somewhere in between: a Hybrid machine learning
  • The Virtue of Complexity in Return Prediction [Alpha Architect]

    In the realm of investment strategies, simplicity has long been favored. Traditional models with a limited number of parameters are prized for their interpretability and ease of use. However, recent research challenges this convention, suggesting that embracing complexity can lead to more accurate return predictions. The study The Virtue of Complexity in Return Prediction by Bryan T. Kelly,

Recent Quant Links from Quantocracy as of 05/13/2025

This is a summary of links recently featured on Quantocracy as of Tuesday, 05/13/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How I Fused Momentum and Mean-Reversion to Achieve 20% CAGR on ETFs Since 2000 [Paper to Profit]

    We think of momentum and mean reversion as opposing forcespick one or the other. Yet, data from 2000 shows that blending both via a local adaptive learning filter produces 20% CAGR on liquid equities versus 8% buy-and-hold. Traders ignoring this hybrid edge are leaving significant extra returns on the table. In todays article, Ill walk you through how I built the LOAD strategya clean,
  • Bias-Variance Decomposition for Trading: ML Pipeline with PCA, VIF & Evaluation [Quant Insti]

    Welcome to the second part of this two-part blog series on the bias-variance tradeoff and its application to trading in financial markets. In the first part, we attempted to develop an intuition for bias-variance decomposition. In this part, well extend what we learned and develop a trading strategy. Prerequisites A reader with some basic knowledge of Python and ML should be able to read and
  • Weekly Research Recap [Quant Seeker]

    Time for another round of the latest 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-Bond Momentum Spillovers (Wang, Wu, and Yang) A large body of research has studied the

Recent Quant Links from Quantocracy as of 05/11/2025

This is a summary of links recently featured on Quantocracy as of Sunday, 05/11/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Beta hedging [Quantitativo]

    "If you're not thinking about risk, then you're not thinking." William Sharpe. William Sharpe is a Nobel Prize-winning economist renowned for his work on the Capital Asset Pricing Model (CAPM) and the Sharpe Ratio, both of which highlight the central role of risk in pricing and evaluating assets. While the quote If you're not thinking about risk, then you're not
  • Equity trend-following with market and macro data [Macrosynergy]

    The popularity of trend-following bears the risk of market excesses. Medium-term market price trends often fuel economic trends that eventually oppose them (macro headwinds). Fortunately, relevant point-in-time economic indicators can provide critical information on the sustainability of medium-term market movements and are a natural complement to standard trend signals. This post
  • The Calendar Effects in Volatility Risk Premium [Relative Value Arbitrage]

    I recently covered calendar anomalies in the stock markets. Interestingly, patterns over time also appear in the volatility space. In this post, Ill discuss the seasonality of volatility risk premium (VRP) in more detail. Breaking Down the Volatility Risk Premium: Overnight vs. Intraday Returns The decomposition of the volatility risk premium (VRP) into overnight and intraday components is an
  • Weekly Research Recap [Quant Seeker]

    Bitcoin Arbitrage: The Role of a Single Exchange (Flowerday, Gandal, Halaburda, Olson, and Ardel) Cross-exchange arbitrage has historically been common in crypto markets. This paper analyzes Bitcoin price differences across major exchanges from 2017 to 2020 and finds that Bitfinex was responsible for most discrepancies, driven by withdrawal restrictions, reliance on Tether, and regulatory
  • Andrea Unger – 672% Returns? Sure! Would You Like Some Risk with That? [Algorithmic Advantage]

    Finishing our little mini-series on shorter-term futures trading we talk to Andrea Unger and happily inject some click-bait in the form of gloating about his 672% return in a single year when he won the World Trading Competition. Naturally, we know that this kind of return is generated by specifically trying to win the comp, and taking on the associated risks! If you've been asleep the first

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

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

  • Can I build a scalping bot? A blogpost with numerous double digit SR [Investment Idiocy]

    Two minute to 30 minute horizon: Mean reversion works, and is most effective at the 4-8 minute horizon from a predictive perspective; although from a Sharpe Ratio angle it's likely the benefits of speeding up to a two minute trade window would overcome the slight loss in predictability. There is no possibility that you would be able to overcome trading costs unless you were passively filled,
  • The Aggregated Equity Risk Premium [Alpha Architect]

    This article explores how researchers forecast market returns by aggregating expected returns from individual stocks. Using machine learning, they improve accuracy over traditional methods. The approach helps identify when to increase or reduce market exposure. This can lead to better-informed investment decisions and improved performance. To explore how aggregate signals can aid in market timing,
  • Stock-Bond Correlation: What Drives It and How to Predict It [Relative Value Arbitrage]

    The correlation between stocks and bonds plays a crucial role in portfolio allocation and diversification strategies. In this issue, I discuss stock-bond relationships, the factors that influence their correlation, and techniques for forecasting it. What Influences Stock-Bond Correlation? Correlation between stocks and bonds is crucial for portfolio allocation and diversification, but this

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