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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.

Recent Quant Links from Quantocracy as of 03/30/2025

This is a summary of links recently featured on Quantocracy as of Sunday, 03/30/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Informational Edge [Quantitativo]

    The idea We don't have better algorithms; we just have more data. Peter Norvig. Peter Norvig is one of the greatest computer scientists of all time and a leading figure in artificial intelligence. As the former Director of Research at Google, he played a key role in shaping the technologies behind Google Search the flagship product of one of the most transformative companies of our
  • Bias-Variance Tradeoff in Machine Learning for Trading [Quant Insti]

    Prerequisites To fully grasp the bias-variance tradeoff and its role in trading, it is essential first to build a strong foundation in mathematics, machine learning, and programming. Start with the fundamental mathematical concepts necessary for algorithmic trading by reading Stock Market Math: Essential Concepts for Algorithmic Trading. This will help you develop a strong understanding of
  • How to Download Multiple Stocks Data at Once Using Python Multithreading [Quant Insti]

    Imagine you have to backtest a strategy on 50 stocks and for that you have to download price data of 50 stocks. But traditionally you have to download ticker by ticker. This sequential download process can be painfully slow, especially when each API call requires waiting for external servers to respond. What if you could download multiple stock data simultaneously? "Multithreading does
  • How Mega Tech Stocks Impact Factor Strategies [Quantpedia]

    The dominance of mega-tech stocks, particularly the Magnificent 7, in both U.S. and global equity indexes has a profound impact on factor portfolios. When constructing value-weighted smart beta strategies, these portfolios often end up heavily concentrated in a few individual stocks. This concentration introduces idiosyncratic risk, skewing the risk profiles of factor strategies. While no
  • Bob Pardo – Building Trading Strategies that Work with Walk Forward Analysis – Part 2 of 2 [Algorithmic Advantage]

    I had a thought this week about what constitutes my "trading edge". You know, the question every trader is expected to be able to answer. It's supposed to constitute some kind of evidence that you can out-perform the market, your peers, or whatever. Something Bob Pardo mentioned made me think differently about this when he reminded me that when trading pits were around, every trader
  • EM sovereign bond allocation with macro risk premium scores [Macro Synergy]

    Macro risk premium scores are differences between market-implied risk and point-in-time quantified macroeconomic risk. Two principal types of scores can be calculated for credit markets: spread-based risk premium scores and rating-based risk premium scores. This post proposes a small set of these scores for EM foreign-currency sovereign debt, targeting 24 country sub-indices of the EMBI Global.
  • Easy games vs hard games in trading [Robot Wealth]

    In Trade Like a Quant Bootcamp, we talk about win-win risk premia harvesting. Its a game where no ones really competing for the edge. Think about VTI (Vanguards Total Stock Market ETF). You expect to make more than implied by the stock markets cash flows (a risk premium) because holding these stocks is uncomfortable. Theyre sensitive to all kinds of nasty surprises. When you buy
  • Weekly Research Recap [Quant Seeker]

    Cross-Asset Momentum Capturing Time-Varying Return Predictability: The Multi-Asset Time Series Momentum Strategy (Harris, Taylor, and Wang) While standard time-series momentum strategies rely only on each asset's own return history, research shows that incorporating cross-asset predictability can be beneficial. This paper explores that idea by building a dynamic strategy that trades equities,
  • Crypto Market Arbitrage: Profitability and Risk Management [Relative Value Arbitrage]

    Cryptocurrencies are becoming mainstream. In this post, I feature some strategies for trading and managing risks in cryptocurrencies. Arbitrage Trading in the Cryptocurrency Market Arbitrage trading takes advantage of price differences in different markets and/or instruments. Reference [1] examined some common and unique arbitrage trading opportunities in cryptocurrency exchanges that are not

Recent Quant Links from Quantocracy as of 03/23/2025

This is a summary of links recently featured on Quantocracy as of Sunday, 03/23/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Autoregressive Drift Detection Method (ADDM) in Trading [Quant Insti]

    Imagine yourself, a great retail trader with an algorithm that flawlessly predicts stock movements for monthsuntil a surprise Fed rate hike sends markets into chaos. Overnight, the models accuracy plummets. Why? Concept drift: your model no longer finds patterns in historical data and now underperforms its predictions. For machine-learning-based traders, this is a latent enemy. But, what if
  • Yield Curve Interpolation with Gaussian Processes: A Probabilistic Perspective [Sitmo Machine Learning]

    Here we present a yield curve interpolation method, one thats based on conditioning a stochastic model on a set of market yields. The concept is closely related to a Brownian bridge where you generate scenario according to an SDE, but with the extra condition that the start and end of the scenarios must have certain values. In this paper we use Gaussian process regression to generalization
  • Historical Market Data Sources [Quant Insti]

    A good trading or investment strategy is only as good as the data behind it. High-quality data is essential if you are backtesting a quant model, analyzing market trends, or building an algorithmic trading system. Prerequisites: To make the most of this blog, it is essential to have a strong foundation in market data sources, data handling techniques, and financial data processing. Start with
  • Research Review | 21 MAR 2025 | Models and Forecasts [Capital Spectator]

    ChatGPT and Deepseek: Can They Predict the Stock Market and Macroeconomy? Jian Chen (Xiamen University), et al. February 2025 We study whether ChatGPT and DeepSeek can extract information from the Wall Street Journal to predict the stock market and the macroeconomy. We find that ChatGPT has predictive power. DeepSeek underperforms ChatGPT, which is trained more extensively in English. Other large
  • Optimizing Portfolios: Simple vs. Sophisticated Allocation Strategies [Relative Value Arbitrage]

    Portfolio allocation is an important research area. In this issue, we explore not only asset allocation but also the allocation of strategies. Specifically, I discuss tactical asset and trend-following strategy allocation. Tactical Asset Allocation: From Simple to Advanced Strategies Tactical Asset Allocation (TAA) is an active investment strategy that involves adjusting the allocation of assets
  • How Global Neutral Rates Impact Currency Carry Strategies? [Quantpedia]

    Market practitioners often rely on experience-based wisdom to navigate currency markets, and one such widely held belief is that low dispersion in global bond yields signals weak future returns for carry trades (and high dispersion implies high future carry returns). While this intuition makes sensewhen yield differentials are compressed, the incentive to exploit them diminishesa recent
  • Adverse Effects of Index Replication [Alpha Architect]

    Mutual funds and ETFs whose main directive is index replication incur adverse selection costs from responding to changes in the composition of the stock market because indices rebalance in response to composition changes (due to IPOs, delistings, additions, deletions, new seasoned issuance, and buybacks) to maintain a value-weighted portfolio. While this approach successfully tracks the index, it
  • The Growth and Inflation Sector Timing Model [CSS Analytics]

    big forces to worry about: growth and inflation. Each could either be rising or falling, so I saw that by finding four different investment strategieseach one of which would do well in a particular environment (rising growth with rising inflation, rising growth with falling inflation, and so on)I could construct an asset-allocation mix that was balanced to do well over time while being
  • Trading the Spread: Bitcoin ETFs vs. Cryptocurrencies Infrastructure ETFs [Quantpedia]

    In this study, we explore the application of simple spread trading strategies using Bitcoin ETFs and cryptocurrency infrastructure ETFstwo highly correlated asset classes due to the broader influence of cryptocurrency market movements. Given their strong relationship, this setup provides a compelling case for implementing pair trading strategies based on mean reversion principles. Building on

Recent Quant Links from Quantocracy as of 03/18/2025

This is a summary of links recently featured on Quantocracy as of Tuesday, 03/18/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • On inflation and stock returns [Outcast Beta]

    Are stocks an inflation hedge? At least in the long run? We find the answer to both questions is no. While fundamental returnscomprising dividend return and earnings growthdo help hedge inflation, valuation changes undermine this hedge. This holds true whether we examine yearly returns, ten-year returns, or anything in between. Stocks do help protect our purchasing power from inflation, but
  • Anti-Dividend Investing: Yield Matters – But Not How You Think! [Alpha Architect]

    Dividends are the comfort food of investing. Who wouldnt love feeling like theyre getting a seemingly free payout just for holding onto a stock? Its no wonder so many investors are drawn to the siren call of yield. As with all good things, theres a little moreperhaps a whole lot moreto the story. Heres why: its possible that even in a tax-free setting, selling stocks
  • 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. Thank you for reading and dont forget to hit the like button. Crypto Including Cryptos in Equity Portfolios: Trend or Opportunity? (Cesarone, Figa-Talamanca, and Luciani) Cryptocurrencies have
  • The 30% Selloff Signal: What History Tells Us About Market Recoveries [Alvarez Quant Trading]

    I was talking to my trading buddy and he mentioned that he read that 40% of Russell 3000 stocks are 30% or more off their 52-week high. To us that sounded really bad. But as usual, we asked is it? Or is this normal when we finally cross under the 200-day moving average after a long time being above it. Going into this, I had lots of questions. How is this stat on the S&P500 stocks? How has the
  • Building Correlation Matrices with Controlled Eigenvalues: A Simple Algorithm [Sitmo Machine Learning]

    In some cases, we need to construct a correlation matrix with a predefined set of eigenvalues, which is not trivial since arbitrary symmetric matrices with a given set of eigenvalues may not satisfy correlation constraints (e.g., unit diagonal elements). A practical method to generate such matrices is based on the Method of Alternating Projections (MAP), as introduced by Waller (2018). This
  • Ehlers Ultimate Oscillator [Financial Hacker]

    In his TASC article series about no-lag indicators, John Ehlers presented last month the Ultimate Oscillator. Whats so ultimate about it? Unlike other oscillators, it is supposed to indicate the current market direction with almost no lag. The Ultimate Oscillator is built from the difference of two highpass filters. The highpass function below is a straightforward conversion of Ehlers

Recent Quant Links from Quantocracy as of 03/15/2025

This is a summary of links recently featured on Quantocracy as of Saturday, 03/15/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Impact of the Inflation on the Performance of the US Dollar [Quantpedia]

    Inflation is one of the key macroeconomic forces shaping financial markets, influencing asset prices across the board. In our previous analysis, we examined how gold and Treasury prices react to changes in the inflation rate, uncovering patterns that suggested inflation dynamics also impact the US Dollar. In this follow-up, we shift our focus entirely to the dollar, analyzing how it responds to
  • Finding the Nearest Valid Correlation Matrix with Higham s Algorithm [Sitmo Machine Learning]

    In quantitative finance, correlation matrices are essential for portfolio optimization, risk management, and asset allocation. However, real-world data often results in correlation matrices that are invalid due to various issues: Merging Non-Overlapping Datasets: If correlations are estimated separately for different periods or asset subsets and then stitched together, the resulting matrix may
  • Macro trading signal optimization: basic statistical learning methods [Macro Synergy]

    A key task of macro strategy development is condensing candidate factors into a single positioning signal. Statistical learning offers methods for selecting factors, combining them to a return prediction, and classifying the market state. These methods efficiently incorporate diverse information sets and allow running realistic backtests. This post applies sequential statistical learning to
  • Variance for Intuition, CVaR for Optimization [Anton Vorobets]

    While everyone understand that investment risk is characterized by large losses or drawdowns, mainstream finance and economics academics still continue to promote mean-variance analysis. Even Harry Markowitz understood that risk should be measured by the downside, but in 1950s the computational burden was unimaginably large. Estimating a low-dimensional covariance matrix was considered
  • Weekly Research Insights [Quant Seeker]

    Many readers have asked for a richer discussion of useful and interesting papers, alongside the most recent research I cover in my weekly recap. So, Im testing a new Thursday post: Weekly Research Insights. In this format, Ill highlight a few noteworthy papers and discuss their key findings and practical takeaways. Let me know what you think. In This Post: Generating Alpha from Analysts
  • Backtesting the Opening Range Breakout (ORB) Strategy using Polygon.io [Concretum Group]

    In this article, we will show you how to run, customize, and analyze a backtest for the Opening Range Breakout (ORB) strategy. Instead of explaining every line of code, well focus on how to execute the backtest, adjust key parameters, and interpret the results. By the end, youll be able to: ???? Run the backtest in Google Colab with minimal setup. ???? Modify strategy settings to test
  • Weekly Recap [Quant Seeker]

    Behavioral Finance How Costly are Trading Heuristics? (Han, He, and Weagley) Retail investors often rely on simple decision-making shortcuts when picking stocks, but these habits can be costly. By analyzing decades of research and actual trading data, the paper finds that traders frequently use heuristics like chasing lottery-like stocks (betting on extreme past winners) or herding (following the
  • Capturing Volatility Risk Premium Using Butterfly Option Strategies [Relative Value Arbitrage]

    The volatility risk premium is a well-researched topic in the literature. However, less attention has been given to specific techniques for capturing it. In this post, Ill highlight strategies for harvesting the volatility risk premium. Long-Term Strategies for Harvesting Volatility Risk Premium Reference [1] discusses long-term trading strategies for harvesting the volatility risk premium in

Recent Quant Links from Quantocracy as of 03/10/2025

This is a summary of links recently featured on Quantocracy as of Monday, 03/10/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Volatility Forecasting: HExp Model [Portfolio Optimizer]

    In this series on volatility forecasting, I previously detailed the Heterogeneous AutoRegressive (HAR) volatility forecasting model that has become the workhorse of the volatility forecasting literature1 since its introduction by Corsi2. I will now describe an extension of that model due to Bollerslev et al.3, called the Heterogeneous Exponential (HExp) volatility forecasting model, in which the
  • Efficient Rolling Median with the Two-Heaps Algorithm. O(log n) [Sitmo Machine Learning]

    Calculating the median of data points within a moving window is a common task in fields like finance, real-time analytics and signal processing. The main applications are anomal- and outlier-detection / removal. Fig 1. A slow-moving signal with outlier-spikes (blue) and the rolling median filter (orange). A naive implementation based on sorting is costlyespecially for large window sizes. An
  • Fast Rolling Regression: An O(1) Sliding Window Implementation [Sitmo Machine Learning]

    In finance and signal processing, detecting trends or smoothing noisy data streams efficiently is crucial. A popular tool for this task is a linear regression applied to a sliding (rolling) window of data points. This approach can serve as a low-pass filter or a trend detector, removing short-term fluctuations while preserving longer-term trends. However, naive methods for sliding-window
  • Does gold belong in a risk premia portfolio? [Robot Wealth]

    With GLD up 40-something percent since early 2024, Ive been thinking about golds place in a risk premia harvesting portfolio. Its a fascinating rabbit hole and theres plenty of disagreement. Lets break this down from two perspectives the academic one (yawn) and the practical one (which is what actually matters if you want to make money). Academically speaking, gold shouldnt
  • How Bond ETFs Make Trading Easier and Cheaper [Alpha Architect]

    Bond Exchange-Traded Funds (ETFs) help people invest in bonds without having to buy them one by one. Instead, they let investors buy a mix of bonds all at once, making it easier and cheaper to trade. This is especially helpful for bonds that are usually harder to buy or sell. Because of bond ETFs, more people can invest in bonds, and they can do it faster and at lower costs. Market Accessibility,
  • Can Margin Debt Help Predict SPY s Growth & Bear Markets? [Quantpedia]

    Navigating the financial markets requires a keen understanding of risk sentiment, and one often-overlooked dataset that provides valuable insights is FINRAs margin debt statistics. Reported monthly, these figures track the total debit balances in customers securities margin accountsa key proxy for speculative activity in the market. Since margin accounts are heavily used for leveraged

Recent Quant Links from Quantocracy as of 03/04/2025

This is a summary of links recently featured on Quantocracy as of Tuesday, 03/04/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Very… slow… mean reversion, and some thoughts on trading at different speeds [Investment Idiocy]

    Bit of a mixed bag post today. The golden thread connecting them is the idea that markets trend and mean revert at different frequencies. – A review of the discussion around timeframes for momentum and mean reversion in 'Advanced Futures Trading Strategies', in light of this excellent recent paper (which I also discussed on the TTU podcast, here from 1:02:12 onwards). – A mea culpa on
  • Weekly Recap [Quant Seeker]

    Commodities Macroeconomic Conditions, Speculation, and Commodity Futures Returns (Adhikari and Putnam) This paper tests the predictability of weekly commodity returns using a range of macroeconomic variables and measures of speculation derived from the Commitment of Traders report. The predictive power of speculation varies across time and commodity sectors, while the St. Louis Fed Financial
  • What is Trend Following? A Painful Journey to Smarter Investing [Alpha Architect]

    When it comes to choosing an investment strategy, most investorswhether they realize it or notare looking for something that: Beats the benchmark Never loses money Works all the time And heres the harsh reality: this unicorn of a strategy doesnt exist. Anyone promising you all three is either blissfully ignorant or straight-up lying. Trend following is no exception. Trend following is
  • Batch Linear Regression via Bayesian Estimation [Quant Start]

    In previous articles we have discussed the theory of state space models and Kalman Filters as well as their application to estimating a dynamic hedging ratio between a pair of cointegrating ETFs. The articles were relatively light on theory and did not explore the much broader field of Bayesian Filtering and Smoothing, which the Kalman Filter is a part of. In this new series of articles we are
  • Understanding Mean Reversion to Enhance Portfolio Performance [Relative Value Arbitrage]

    In a previous newsletter, I discussed momentum strategies. In this edition, Ill explore mean-reverting strategies. Mean reversion is a natural force observed in various areas of life, including sports performance, portfolio performance, volatility, asset prices, etc. In this issue, I specifically examine the mean reversion characteristics of individual stocks and indices. Long-Run Variances of
  • Understanding the Stock Bond Correlation [Alpha Architect]

    This study looks at how stocks and bonds move together over time, using data from 1875 to 2023. The authors find that inflation, interest rates, and government stability affect this relationship. When inflation and interest rates go up, stocks and bonds tend to move in the same direction, making diversification less effective. This means investors may need other assets, like commodities or