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Recent Quant Links from Quantocracy as of 06/14/2025

This is a summary of links recently featured on Quantocracy as of Saturday, 06/14/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Absolute Valuation Models for the Stock Market: Are Indexes Fairly Priced? [Quantpedia]

    Valuation models for equity indexes are essential tools for investors seeking to assess long-term market conditions. Traditional models like the CAPE ratio, introduced by Robert J. Shiller, or the Buffett Indicator often rely on macroeconomic variables such as corporate earnings or GDP. While informative, these models can be complex and dependent on data that may be revised or vary across regions.
  • 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
  • Generating Synthetic Equity Data with Realistic Correlation Structure [Quant Start]

    Recently on QuantStart we have begun looking at generating synthetic asset price paths using Stochastic Differential Equation models such as the Brownian Motion, Geometric Brownian Motion (GBM), Ornstein-Uhlenbeck and Vasicek Models. Historically, we have also considered more sophisticated models such as the Heston Stochastic Volatility Model. What we have not considered to any great extent in
  • Don’t Over-Engineer your Trading Business Make Money Instead [Robot Wealth]

    Someone sent me their trading technology blueprint. It was a thing of beauty: timeseries databases, Grafana dashboards, message queues, and all sorts of fancy architecture. My first question: What are you currently trading? Their answer: Nothing yet. But Im planning a medium frequency stat arb basket trade. I almost spat out my coffee. Look, I get it. If you come from a tech
  • Enhancing Momentum Strategies [Alpha Architect]

    Paul Calluzzo, Fabio Moneta, and Selim Topaloglu, authors of the April 2025 study Momentum at Long Holding Periods investigated a key aspect of how academic momentum strategies are typically constructed when forming a portfolio. Specifically, at the end of each month t1, the standard 12-2 momentum strategy sorts stocks based on their cumulative returns from month t12 to month t2 and
  • Research Review | 13 June 2025 | Analyzing And Monitoring Risk [Capital Spectator]

  • Short-Term Basis Reversal [Quantitativo]

    A single hair from the head of a woman is worth more than all the books of Galen and Avicenna. Paracelsus. Paracelsus (14931541) was one of the most radical and influential physicians and philosophers of the Renaissance. A restless traveler, alchemist, and fierce critic of medical orthodoxy, he believed that true knowledge came not from ancient books but from direct observation of nature
  • Weekly Research Recap [Quant Seeker]

    The Reaction of Corn Futures Markets to US and Brazilian Crop Reports (Silveira, Silva, Mattos, Junior, and Capitani) Crop reports from the US and Brazil inform markets about expected corn production, but not all reports have the same impact. The authors find that US reports prompt sharp price movements and trading spikes in both US and Brazilian futures markets while Brazilian reports, on the

Recent Quant Links from Quantocracy as of 06/09/2025

This is a summary of links recently featured on Quantocracy as of Monday, 06/09/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Supervised Portfolios: A Supervised Machine Learning Approach to Portfolio Optimization [Portfolio Optimizer]

    Standard portfolio allocation algorithms like Markowitz mean-variance optimization or Choueffati diversification ratio optimization usually take in input asset information (expected returns, estimated covariance matrix) as well investor constraints and preferences (maximum asset weights, risk aversion) to produce in output portfolio weights satisfying a selected mathematical objective like
  • Reduce Trading Costs and Boost Profits with the “No-Trade Region” Strategy [Robot Wealth]

    An easy, practical way to harness an edge in the face of trading costs is the no trade region technique. It nearly always improves after-cost performance. Heres a real example: And it does so with only one-tenth of the trading of the baseline strategy! Uncertain edge vs certain costs A good thing to remember is that your edge is uncertain not only do you usually not know how big it
  • Cross-Attention for Cross-Asset Applications [Gatambook]

    In the previous blog post, we saw how we can apply self-attention transformers to a matrix of time series features of a single stock. The output of that transformer is a transformed feature vector r of dimension 768 1. 768 is the result of 12 64: all the lagged features are concatenated / flattened into one vector. 12 is the number of lagged months, and 64 is the dimension of the embedding
  • Gold Ratios as Stock Market Predictors [Relative Value Arbitrage]

    The ratio of gold prices to other asset classes has been shown to be a useful predictor of stock market returns. In this post, we discussed several gold-based ratios and how they can be used to forecast equity market performance. Gold Oil Price Ratio As a Predictor of Stock Market Returns Analyzing intermarket relationships between assets can help identify trends and predict returns.
  • Pre-Announcement Drift for BoE, BoJ, SNB: Do Markets Move Before the Word Is Out? [Quantpedia]

    Weve previously examined how central bank policy decisionsparticularly those by the Federal Reserve and the European Central Bank (ECB)impact stock market behavior. The price drift in U.S. equities around the Federal Open Market Committee (FOMC) meetings is a well-documented phenomenon. Likewise, our research study of the ECB revealed a pre-announcement drift, underscoring the

Recent Quant Links from Quantocracy as of 06/03/2025

This is a summary of links recently featured on Quantocracy as of Tuesday, 06/03/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Off to the Races: A Universal Metastrategy [Paper to Profit]

    We often have baskets of assets that we turn into trading strategies. But also, we have baskets of trading strategies that we need to allocate our capital into. In my last post (here), I demonstrated how to use generative AI to create a theoretically limitless supply of trading strategies. But, this is no good unless those strategies actually make money. Backtests tell us what happened, but
  • Weekly Research Recap [Quant Seeker]

    Anomaly Persistence and Nonstandard Errors (Coqueret and Perignon) Many investing anomalies seem compelling, but their performance often depends on how they're tested. This paper demonstrates that overlapping design choices, such as holding periods and weighting, create strong correlations between results, making strategies appear more robust than they actually are. The authors propose a
  • Quickies #1: Overfitting and EWMAC forecast scalars [Investment Idiocy]

    I'm now in full book writing mode, so I don't have the time to do full blog posts. Instead I plan to do a series of quick posts where I share some research I did for the book. Cynically, there is also a chance it will encourage you to buy the book, as long as I don't overshare like one of those movie trailers that gives away the plot and includes all the best action scenes.
  • Data: Range, Renko, Filter and Volatility bars [Trading the Breaking]

    You are observing the markets in real-timethousands of price ticks cascading across your screen, each reflecting a momentary shift in supply, demand, and sentiment. At first glance, the data appears evenly spaced, structured, and regular. Yet beneath this surface lies a deeper asymmetry: the rhythm of market activity is not governed by the uniform cadence of the wall clock, but by the irregular
  • Explaining overnight returns in the US [Joachim Klement]

    Older people among my readers will remember the time when there was for a while a discussion about how the US stock market had significantly higher returns between yesterdays close and todays open (when there were no trades at all) than during the day. Those were the innocent days of an era long gone, aka 2018, when we were all nave and enthralled by a bull market that couldnt
  • Volatility of Volatility: Insights from VVIX [Relative Value Arbitrage]

    The volatility of volatility index, VVIX, is a measure of the expected volatility of the VIX index itself. In this post, we will discuss its dynamics, compare it with the VIX index, and explore how it can be used to characterize market regimes. Dynamics of the Volatility of Volatility Index, VVIX The VVIX, also known as the Volatility of Volatility Index, is a measure that tracks the expected

Recent Quant Links from Quantocracy as of 05/31/2025

This is a summary of links recently featured on Quantocracy as of Saturday, 05/31/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quantpedia Awards 2025 Winners Announcement [Quantpedia]

    Hello all, Welcome to the Quantpedia Awards 2025 winners announcement. This is the moment we all have been waiting for, and today, we would again like to acknowledge the accomplishments of the researchers behind innovative studies in quantitative trading. So, what do the top five look like, and what will the authors of the papers receive? 1st Place Harvey, Mazzoleni, Melone: The Unintended
  • 164 Profitable Trading Strategies [Paper to Profit]

    As mentioned in my last post (here), I designed and developed a way to quickly produce trading systems with the help of generative AI. And while this sounds like a recipe for disaster, because I constrained the problem to a very specific subset and I focused on only a few factors, the results were actually quite amazing. From 875 candidate strategies, I manually filtered out 259 contenders. Using
  • Can We Finally Use ChatGPT as a Quantitative Analyst? [Quantpedia]

    In two of our previous articles, we explored the idea of using artificial intelligence to backtest trading strategies. Since then, AI has continued to develop, with tools like ChatGPT evolving from simple Q&A assistants into more complex tools that may aid in developing and testing investment strategiesat least, according to some of the more optimistic voices in the field. Over a year has
  • 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! Bonds Book-to-Market, Mispricing, and the Cross Section of Corporate Bond Returns (Bartram, Grinblatt, and Nozawa) Many
  • Probabilistic Inferencing for Trading Strategies [Hanguk Quant]

    Previously, we have discussed classical non-parametric approaches to making probabilistic inferences on attributes of trading strategies based on typical artefacts available. In this post, we discuss and implement in Python a finite-sample probabilistic bounding method, a unique approach coined Rademacher Anti-Serum by Paleologo, in this new book; The Elements of Quantitative Investing. We show
  • I Asked 6 LLMs for Better Exit Strategies [Rogue Quant]

    You start writing a trading strategy. The entry? Solid. Sharp. Thought-out. The exit? Let me guess Fixed dollar profit target? A stop based on some ATR multiple? Maybe a hard-coded dollar loss? Or the classic: "Just close it after 7 bars I guess?" Same old, same old. What if thats your weakest link? Stop exiting like everyone else does. We obsess over entries. We optimize
  • Unlocking Cross-Asset Potential: A New Approach to Portfolio Construction [Alpha Architect]

    Christian Goulding and Campbell Harvey, authors of the study Investment Base Pairs, proposed a groundbreaking framework for portfolio construction that challenges traditional approaches in modern finance. Their research focused on leveraging cross-asset information to optimize investment strategies and improve returns across diverse asset classes. Heres an overview of their investigation,

Recent Quant Links from Quantocracy as of 05/26/2025

This is a summary of links recently featured on Quantocracy as of Monday, 05/26/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • What are your bars hiding from you? [Trading the Breaking]

    The electronic marketplace generates vast amount of databillions of timestamped trades, quotes, and cancellationsthat demand processing to extract actionable insights. For quantitative traders, the central challenge lies not in designing strategies but in constructing a robust framework to interpret this data. Raw tick data, while granular, is computationally intensive and often contains
  • Market Timing with Macro Surveys [Quant Seeker]

    Hi there. In recent months, there has been increased chatter about the possibility of a recession triggered by President Trumps tariff war. The recent pause in tariffs appears to have eased some of those concerns. For example, JP Morgan now sees the likelihood of a U.S. recession to be below 50%, and the recession odds on Polymarket currently hover around 40%. For investors, recessions are
  • Simplicity or Complexity? Rethinking Trading Models in the Age of AI and ML [Relative Value Arbitrage]

    When it comes to trading system design, there are two schools of thought: one advocates for simpler rules, while the other favors more complex ones. Which approach is better? This newsletter explores both perspectives through the lens of machine learning. Use of Machine Learning in Pairs Trading Machine learning has become an essential tool in modern finance, transforming the way financial

Recent Quant Links from Quantocracy as of 05/25/2025

This is a summary of links recently featured on Quantocracy as of Sunday, 05/25/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Taming OLMAR s 1222% Backtest into a Sustainable 106% CAGR [Paper to Profit]

    Often as traders, we equate complexity with profitability. A models edge comes from it doing something that no other person on Earth has tried yet. But the data shows that simple rules based on real market factors still outperform most models. Those that continue seeking complexity are headed towards a dead end. Today Im focusing on the Online Moving Average Reversion (OLMAR) system by Bin
  • The 1 AI Prompt I Use to Generate 20 Trading Ideas in Seconds [Rogue Quant]

    My kids love bedtime stories. Like most kids. But theyre not into fairy tales or superheroes. Theyre obsessed with one thing: Dad, can you tell a witch story? A mean witch, okay? Every night. Same request. So I lie next to their bed and say, Alright, buddies. A mean witch story it is. Now, I dont know about you But coming up with original witch plots every single night (for
  • No Magic Formulas: How I Actually Decide What to Trade [Robot Wealth]

    Someone recently asked me if I have a checklist for adopting new trading strategies. You know, a neat little formula like if backtested Sharpe > 1.8, trade it or if drawdown < 15%, green light. I get the appeal. We all want clear, objective criteria to make these decisions easier. But strategy adoption just doesnt work that way. The reality is messier. More nuanced. Your
  • Applying Transformers to Financial Time Series [Gatambook]

    In the previous blog post, we gave a very simple example of how traders can use self-attention transformers as a feature selection method: in this case, to select which previous returns of a stock to use for predictions or optimizations. To be precise, the transformer assigns weights on the different transformed features for downstream applications. In this post, we will discuss how traders can
  • I Used AI for 30 Minutes and Discovered 8 New Market-Beating Systems [Paper to Profit]

    Everyone either naively thinks that an LLM will find alpha for them, or equally naively thinks LLMs cannot develop their own systems with any sort of edge. The reality is quite the opposite. When used properly, LLMs can supercharge your strategy research process by at least 10x. Those who arent using AI in their development workflows are going to be easily smoked by those who are in the next
  • Macro-aware risk parity [Macrosynergy]

    Risk parity is an investment strategy that allocates risk exposure equally across asset types through volatility-based calibration and leverage. A most profitable risk parity strategy in the past decades has been the equity-duration long-long, which harvests combined equity and long fixed-income risk premia, while containing return volatility through diversification. Alas, this position is

Recent Quant Links from Quantocracy as of 05/21/2025

This is a summary of links recently featured on Quantocracy as of Wednesday, 05/21/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Cliff Smith’s BKLN Strategy [Allocate Smartly]

    Questions about this long-ago strategy from Cliff Smith land in our inbox periodically (heres another recent take). Smiths simple strategy trades senior loan (aka leveraged loan) ETFs like BKLN, and has continued to be effective at timing these ETFs in the 10+ years since it was published. Weve extended the authors original test back to 2007 using underlying index data (*). Important:
  • Comparing Affordable Intraday Data Sources: TradeStation vs. Polygon vs. Alpaca [Cracking Markets]

    When building an intraday systematic strategy, the quality and consistency of historical data can make or break your trading results. Cost, however, is also a critical factor for many traders. We conducted a comprehensive analysis comparing three popular data providers offering REST APIs for intraday minute-level data: TradeStation, Polygon.io, and Alpaca. These providers differ significantly in
  • Could data drift be silently sabotaging your PnL? [Trading the Breaking]

    In the day-to-day grind of systematic trading, volatility isnt just a market featureits the atmosphere we operate in. It drives the edge, defines the risk, and sets the tempo. But while volatility creates the conditions for profit, it also contains the seeds of our destruction. That tensionbetween opportunity and ruinsits at the heart of every execution cycle. And thats precisely
  • Is Machine Learning Better in Prediction of Direction or Value? [Quantpedia]

    Building machine learning models for trading is full of nuances, and one important but often overlooked question is: what exactly should we try to predictthe direction of the next market move or the actual value of the assets return? A recent paper by Cheng, Shang, and Zhao, titled Direction is More Important than Speed offers a clear and practical answer. Their research shows that

Recent Quant Links from Quantocracy as of 05/20/2025

This is a summary of links recently featured on Quantocracy as of Tuesday, 05/20/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Is your strategy built on distributional lies? [Trading the Breaking]

    During the previous optimization cycle, I was tasked with enhancing inventory management protocols for a legacy trading system operating under low-latency constraintsorder cycle times 500ms. While the academic corpus fixates on high-frequency trading paradigmsmicrosecond latency optimization, toxic flow mitigation, and continuous limit order book dynamicsthese constructs proved
  • Weekly Research Recap [Quant Seeker]

    Asset Allocation How Much Should You Pay for Alpha? Measuring the Value of Active Management with Utility Calculations (Ang and Basu) Many investors chase high-performing funds expecting them to beat the market, but rarely ask how much that outperformance is actually worth to them. Even when a fund delivers strong returns, the benefit to investors shrinks once risk, fees, and overlap with a
  • The Cybernetic Oscillator [Financial Hacker]

    Oscillator-type indicators swing around the zero line. They are often used for opening positions when oscillator exceeds a positive or negative threshold. In his article series about no-lag indicators, John Ehlers presents in the TASC June issue the Cybernetic Oscillator. It is built by applying a highpass and afterwards a lowpass filter to the price curve, then normalizing the result. We already
  • Low-Volatility Stocks: Reducing Risk Without Sacrificing Returns [Relative Value Arbitrage]

    The recent market turbulence highlights the need for improved risk management and strategies to reduce portfolio volatility. In this post, Ill explore how to enhance portfolio diversification using low-volatility stocks. Gold and Low-Volatility Stocks as Diversifiers Gold has long been regarded as a valuable diversification tool in investment portfolios due to its unique characteristics. As an

Recent Quant Links from Quantocracy as of 05/18/2025

This is a summary of links recently featured on Quantocracy as of Sunday, 05/18/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Poor Person’s Transformer: Transformer as a sample-specific feature selection method [EP Chan]

    For those of us who grew up before GenAI became a thing (e.g. Ernie), we often use tree-based algorithms for supervised learning. Trees work very well with heterogeneous and tabular feature sets, and by limiting the number of nodes or the depth of a branch, there is feature selection by default. With neural networks (NN), before deep learning comes around, it is quite common to perform feature
  • I Found a One-Hour Edge in the S&P, Then Three LLMs Made It Better [Rogue Quant]

    A friend of mine owns a Neapolitan-style pizzeria this is the real pizzeria When he first opened, he had one recurring headache: He could never guess how many pizzas hed sell each night. Some days he ran out of dough by 9pm. Other days he overprepared and ended up tossing dozens of unused bases. But a few months in, something clicked. He told me he started watching the playground. The
  • Research Review | 16 May 2025 | Asset Allocation [Capital Spectator]

    Rethinking the Stock-Bond Correlation Thierry Roncalli (Amundi Asset Management & University of Evry) February 2025 The stock-bond correlation is a basics of finance and is related to some of the fundamentals of asset management. However, understanding the stock-bond correlation is not easy. In this presentation, we answer the following questions What is the natural sign of the stock-bond
  • What Can We Expect from Long-Run Asset Returns? [Quantpedia]

    What can we realistically expect from investing across different asset classes over the long run? Thats the kind of big-picture question the Long-Run Asset Returns paper tacklesoffering a sweeping look at how stocks, bonds, real estate, and commodities have performed over the past 200 years. The paper goes beyond just listing historical returnsit explains how reliable (or not) those
  • Profitability Retrospective: Key Takeaways for Investors [Alpha Architect]

    In his 2013 paper The Other Side of Value: The Gross Profitability Premium, Robert Novy-Marx documented that profitability, broadly measured, has as much power as relative price in predicting cross-sectional differences in expected returns. With the publication of that paper, profitability quickly become a prominent theme in asset pricing research. For example, Dimensional began

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,