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Recent Quant Links from Quantocracy as of 02/15/2025

This is a summary of links recently featured on Quantocracy as of Saturday, 02/15/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Dangers of Relying on OHLC Prices the Case of Overnight Drift in GDX ETF [Quantpedia]

    Can we truly rely on the opening price in OHLC data for backtesting? While the overnight drift effect is well-documented in a lot of asset classes, we investigated its presence in gold using the GLD ETF and then extended our analysis to the GDX Gold Miners ETF, where we observed an unusually strong overnight return exceeding 30% annualized. However, when we tested execution at 9:31 AM using
  • Modelling the yield curve of US government treasuries [OS Quant]

    The interest rate is a key input to pricing various instruments. For example, the price of an option depends on the risk free rate. The return earned holding bonds depends on the bond yield. A good model of interest rates means you can better price these interest rate derived products and have a beter idea of risk. The fiddly thing is that there is no one single interest rate. The yield that you
  • The Ability to NAV Time Interval Funds [Alpha Architect]

    Highly illiquid assets trade infrequently making it difficult to know their true market value. To address this issue, funds that invest in illiquid assets create fair valuation estimates at periodic intervals. These valuation estimates determine the share values at which interval and tender offer funds issue and redeem their shares. Because the true value is uncertain, wealth transfers can be
  • Research Review | 14 FEB 2025 | Rebalancing and Asset Allocation [Capital Spectator]

    The Unintended Consequences of Rebalancing Campbell R. Harvey (Duke University), et al. January 2025 Institutional investors engage in trillions of dollars of regular portfolio rebalancing, often based on calendar schedules or deviations from allocation targets. We document that such rebalancing has a market impact and generates predictable price patterns. When stocks are overweight, funds sell
  • The VIX of Crypto and How Options Data Predicts BTC Price Swings [Unravel Markets]

    The interactive version of this report can be found here; our previous report on exchange outflowss predictive power here. With investor sentiment and risk premiums encapsulated in its options data, Bitcoins implied volatility is becoming an interesting predictive factor with its dual role as a fear gauge and a proxy for speculative behavior. Implied Volatility (IV)a metric derived from
  • Hedging Efficiently: How Optimization Improves Tail Risk Protection [Relative Value Arbitrage]

    Tail risk hedging aims to protect portfolios from extreme market downturns by using strategies such as out-of-the-money options or volatility products. While effective in mitigating large losses, the challenge lies in balancing cost and long-term returns. In this post, well discuss tail risk hedging and whether it can be done at a reasonable cost. Tail Risk Hedging Strategies: Are They

Recent Quant Links from Quantocracy as of 02/11/2025

This is a summary of links recently featured on Quantocracy as of Tuesday, 02/11/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Can Miner Economics Predict Bitcoin Returns? [Unravel Markets]

    The Puell Multiple was invented by analyst David Puell, back in March 2019. He developed the metric as a way to quantify miner revenue in relation to historical averages: it compares the daily USD value of Bitcoin mined through block rewards to its 365-day moving average, directly meauring whether miners are earning above or below their annual average income. Unlike price-based technical
  • What’s the chance that a market effect is real? Monte Carlo permutation tests in Excel [Robot Wealth]

    Lets say you observe some effect in the market and quantify it with simple data analysis. A good question is, What are the chances Id see this effect solely due to chance? And using simple Excel tools, we can answer this question without doing any formal statistics. Before we get into it, its worth noting that while answering this can give you some insight into the effect, it relies
  • Data Visualization – the Momentum Map [Grzegorz Link]

    Dealing with multidimensional data in data visualization is tricky. You have to strike a balance between presenting a lot of useful information, but not cluttering charts too much. There are a lot of flashy and glimmery chart options. Yet it's easy to lose your audience by flooding them with too much information. In statistics, an analog is in multivariate variables. People come up with
  • Weekly Recap [Quant Seeker]

    Commodities Tail Risk Premium in the Crude Oil Market (Li and Li) While the variance risk premium has been widely studied in financial markets, this paper finds that the option-implied tail risk premium is a stronger predictor of crude oil futures returns. Short-term tail risks signal lower returns in the next month but higher returns two months later. A trading strategy based on these insights
  • Turn-of-the-Month Strategies: Do They Still Work? [Quant Seeker]

    Decades of research have uncovered numerous market anomalies, persistent patterns in asset returns that cannot be fully explained by traditional risk-based models. Among the most enduring and well-documented of these is the turn-of-the-month (TOM) effect, characterized by abnormally high stock returns during the days surrounding the turn of the calendar month. Unlike other seasonality effects that
  • Coding Trend Factor [Quantitativo]

    "Every great developer you know got there by solving problems they were unqualified to solve until they actually did it." Patrick McKenzie. Patrick McKenzie is a well-known software developer, entrepreneur, and writer, widely recognized for his work in the software industry, particularly in bootstrapped startups and software-as-a-service (SaaS) businesses. He built his entire career

Recent Quant Links from Quantocracy as of 02/07/2025

This is a summary of links recently featured on Quantocracy as of Friday, 02/07/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How much should we get paid for skew risk? Not as much as you think! [Investment Idiocy]

    A bit of a theme in my posts a few years ago was my 'battle' with the 'classic' trend followers, which can perhaps be summarised as: Me: Better Sharpe! Them: Yeah, but Skew!! My final post on the subject (when I realised it as a futile battle, as we were playing on different fields – me on the field of empirical evidence, them on …. a different field) was this one, in which
  • The Mathematics of Portfolio Return [Portfolio Optimizer]

    Whether we manage our own investment assets or choose to hire others to manage the assets on our behalf we are keen to know how well our [] portfolio of assets is performing1 and the calculation of portfolio return is the first step in [that] performance measurement process1. Now, while the matter of measuring the rate of return of [a portfolio] appears, on the surface, to be simple enough2,
  • Better Backtesting [Anton Vorobets]

    I recently wrote a post about naive backtesting of investment risk measures1, which is usually performed in the following way: Look at some historical data samples. Optimize portfolios using different risk measures. Crown the investment risk measure with the highest cumulative performance as the best investment risk measure. The above approach is usually performed by people who introduce a
  • The Low-Vol Effect in Crypto [Falkenblog]

    Thirty years ago, I wrote my dissertation on the low-vol effect, which was really bad timing. This was just after various anomalies highlighted in the 70s and 80s were exposed as the effects of measurement error and selection bias (the low-price effect, the January effect). The small-cap effect was perhaps the most well-known back then, but given it was initially identified as having a 20% annual
  • Taming Excessive “Timing Luck” in TAA by Tranching Strategies [Allocate Smartly]

    Fair warning: this article is intended for advanced DIY Tactical Asset Allocation investors, i.e. nerds like us. First, a bit of background knowledge youll need to understand this discussion Background knowledge: What is timing luck? Most Tactical Asset Allocation (TAA) strategies trade just once per month. Strategy developers almost always assume trades are executed on the last

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

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

  • US Stock Momentum Trading System for Retail Traders: Deep research [Cracking Markets]

    I recently tested ChatGPT Pros Deep Research functionality (released on Monday, February 3, 2025)currently priced at $200/monthusing the latest o3-mini-high model. My objective? To evaluate how effectively it can assist in developing a US Stock Momentum Trading System for retail traders. After about 10 minutes of AI-driven analysis, the results were quite impressive. The model pulled
  • Join the Race Once Again: Quantpedia Awards Competition Is Back! [Quantpedia]

    Hello everyone, Over the last few months, we have received numerous messages asking us if we plan to continue with our successful quant research competition in 2025. Last year, we promised our readers that the Quantpedia Awards would be back! And now its again time to unveil what we have prepared for you. For a quick recapitulation (for those who were not around in 2024, when we started this
  • ARTFIMA Model for Trading [Quant Insti]

    The ARFIMA model is well suited for capturing long-range memory in financial time series. However, its not always the case the time series exhibits long memory in their autocorrelation. The ARTFIMA model comes to the rescue to capture not only the long memory but also its short one and the relationships between them. Needless to say, this model cannot only help capture those effects but also
  • Momentum Strategies: Profitability, Predictability, and Risk Management [Relative Value Arbitrage]

    Momentum strategies have long been a cornerstone of investing, relying on the premise that past winners continue to outperform in the near future. This post explores the effectiveness of momentum strategies, analyzing their ability to generate abnormal returns and assessing their viability in different markets. While previous research has demonstrated the profitability of momentum strategies,
  • The Book is Out [Anton Vorobets]

    The PDF version of the Portfolio Construction and Risk Management book is now publicly available. You can find links to the book and its accompanying Python code at the bottom of this newsletter. It contains many new perspectives and results that are exclusive to the book, showing you that there are much better alternatives to Black-Litterman and mean-variance. I will continue editing the book in
  • The Predictive Power of Dividend Yield in Equity Markets [Relative Value Arbitrage]

    Dividend yield has long been a cornerstone of equity valuation. In this post, we explore how dividend yield predicts stock returns, its impact on stock volatility, and why it holds unique significance for mature, dividend-paying firms. Relationship Between Implied Volatility and Dividend Yield Reference [1] explores the relationship between implied volatility (IV) and dividend yield. It

Recent Quant Links from Quantocracy as of 01/30/2025

This is a summary of links recently featured on Quantocracy as of Thursday, 01/30/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Seasonality Patterns in the Crisis Hedge Portfolios [Quantpedia]

    Building upon the established research on market seasonality and the potential for front-running to boost associated profits, this article investigates the application of seasonal strategies within the context of crisis hedge portfolios. Unlike traditional asset allocation strategies that may falter during market stress, crisis hedge portfolios are designed to provide downside protection. We
  • Should You Buy A New Crypto Listing? [Quant Hedge]

    Every Monday, I get an email from CMC with the new Crypto listings and top trending coins. Heres the one from this past Monday. I usually give it a quick look and then delete it, but not so long ago, I started wondering: should I give a #@%! about this? It bugged me so I decided to look into it. I have data for over 8000 active coins painstakingly gathered from CMC (which I will be sharing with
  • Dr Ernest Chan – The Breakthrough Uses of Machine Learning in Risk Management [Algorithmic Advantage]

    Were back! Its 2025 and we are planning a cracker of a year! Stay tuned! It was strangely comforting talking to Ernie Chan. Whilst I was completely out of my depth talking about AI and Machine Learning, I came away broadly reinforced in my own belief that great trading still requires a human touch, and that the best niches in the market are best discovered by applying a certain kind of
  • Portfolio Construction and Risk Management Book [Anton Vorobets]

    The PDF version of the Portfolio Construction and Risk Management book is freely available online at the bottom of this post. The accompanying code to the book is available on GitHub at https://github.com/fortitudo-tech/pcrm-book1. The book has been written through a crowdfunding campaign that you can continue contributing to and getting perks for at https://igg.me/at/pcrm-book2. For example, you

Recent Quant Links from Quantocracy as of 01/21/2025

This is a summary of links recently featured on Quantocracy as of Tuesday, 01/21/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Iterative PSD Shrinkage (IPS) [CSS Analytics]

    In the previous post the framework for Generalized Downside Implied Correlations was introduced. You can use this correlation matrix derived from joint risk metrics to replace or augment/blend with traditional correlations for use in analysis or optimization. The challenge is that the resulting matrix may not be positive semi-definite (PSD) or well-conditioned. The solution is to find a reasonably
  • Monte Carlo Simulations: Pricing Weather Derivatives and Convertible Bonds [Relative Value Arbitrage]

    Monte Carlo simulations are widely used in science, engineering, and finance. They are an effective method capable of addressing a wide range of problems. In finance, they are applied to derivative pricing, risk management, and strategy design. In this post, we discuss the use of Monte Carlo simulations in pricing complex derivatives. Pricing of Weather Derivatives Using Monte Carlo Simulations
  • Artificial Intelligence and the Risks of Harking (Hypothesizing After-the-Fact) [Alpha Architect]

    Academics have long been aware of the risks of data miningtorturing the data until it confesses. The concern is that correlation of variables doesnt imply that the correlation is a result of causation. That is the reason that the prevailing academic standard for researchers is that they should first develop their hypothesis and predictions before testing them against the data. To minimize
  • Research Review | 17 January 2025 | Risk Premia [Capital Spectator]

    An Investigation into the Causes of Stock Market Return Deviations from Real Earnings Yields Austin Murphy (Oakland University), et al. December 2024 This research demonstrates that the simple difference between the current earnings yield on the S&P500 and the long-term real TIPS yield has significant forecasting power for excess returns on that stock market index over both short-term and
  • Intraday Momentum for ES and NQ [Quantitativo]

    "If I have seen further, it is by standing on the shoulders of giants. Sir Isaac Newton. First of all, Happy New Year! When I started Quantitativo a few months ago, I could never expect to gather such an amazing group of like-minded people in such a short time. Your enthusiasm, curiosity, and engagement have made this journey incredibly rewarding and inspiring. Reflecting on the many, many
  • Factor Investing Clearing the Air Datamining and the Antidotes [5th Horizon Research]

    Factor Investing Origins and Implications The roots of factor investing can be traced to work published in the early 1990s by two academics: Ken French and Eugene Fama. In two of their publications[1], they identified a set of risk factors priced to consistently and robustly provide a return premium. Three of these, Beta (market), Size (market capitalization), and Value (book-to-market) survived
  • Out-of-Sample Test of Formula Investing Strategies [Quantpedia]

    Can we simplify the complexities of the stock market and distill them into a simple set of quantifiable metrics? A lot of academic papers suggest this, and they offer formulas that should make the life of a stock picker easier. Some of the most compelling methodologies within this realm are the F-Score, Magic Formula, Acquirers Multiple, and the Conservative Formula. These quantitative

Recent Quant Links from Quantocracy as of 01/13/2025

This is a summary of links recently featured on Quantocracy as of Monday, 01/13/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Detecting Wash Trading in Major Crypto Exchanges [Quantpedia]

    The general acceptance of cryptocurrencies, especially Bitcoin, was a blessing from Wall Street, which institutionalized them as ETFs for comprehensive access by the general public and institutional investors. There is little to no denying now that this new asset class is becoming more traditional, often used as part of a diversified portfolio, and not taken as an alternative investment for
  • PCA in Action: From Commodity Derivatives to Dispersion Trading [Relative Value Arbitrage]

    Principal Component Analysis (PCA) is a dimensionality reduction technique used to simplify complex datasets. It transforms the original variables into a smaller set of uncorrelated variables called principal components, ranked in order of their contribution to the datasets total variance. In this post, well discuss various applications of PCA. Pricing Commodity Derivatives Using Principal
  • Training Machine Learning Models For Return Prediction [Alpha Architect]

    Machine learning models have proven effective in predicting stock returns using lagged stock characteristics, but their success is influenced by a wide range of modeling choices. One critical, yet often overlooked, choice is how stocks are weighted in the objective function during training, with equally weighted (EW) approaches being the norm. This paper investigates how such choices impact
  • Refining ETF Asset Momentum Strategy [Quantpedia]

    Todays research introduces a refined ETF asset momentum strategy by combining a correlation filter with selective shorting. While traditional long-short momentum strategies usually yield suboptimal results, the long leg proves effective on its own, and the correlation filter demonstrates significant value for improving the timing and performance of the short leg. We propose a final strategy of

Recent Quant Links from Quantocracy as of 01/09/2025

This is a summary of links recently featured on Quantocracy as of Thursday, 01/09/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Piard’s Annual Seasonality [Allocate Smartly]

    This is a test of two stock market seasonality strategies from Fred Piards book Quantitative Investing: Strategies to Exploit Stock Market Anomalies for All Investors. Strategy results from 1970 follow. Results are net of transaction costs see backtest assumptions. Learn about what we do and follow 90+ asset allocation strategies like this one in near real-time. Logarithmically-scaled.
  • Drawdown Implied Correlations Part 2: Generalized Downside Implied Correlations [CSS Analytics]

    In the previous post I introduced a Drawdown Implied Correlation (DIC) that is a joint time-series measurement which converts maximum drawdowns into a correlation coefficient using a simple formula derived from portfolio math. The DIC had some unique features such as a point-in-time reference to the exact point of maximum drawdown, and a triple reference which averages the calculation
  • CAPM, WACC, and Beyond: Beta s Application in Arbitrage [Relative Value Arbitrage]

    Beta is a measure of an assets sensitivity to market movements, indicating how much its price is expected to change in relation to the overall market. Beta is often used in CAPM and the calculation of WACC. However, it can also be applied in trading, specifically in arbitrage. In this post, Ill discuss beta arbitrage. Beta Arbitrage Around Macroeconomic Announcements The macroeconomic
  • Do less liquid assets trend better or is that they are just more diversified? [Investment Idiocy]

    As most of you know, one of the many projects / things I am involved with is the TTU Systematic Investor podcast series where I'm one of the rotating cast of co-hosts. On a recent episode (at 24:05) we discussed the reasons why 'alt' CTAs tend to do better than traditional CTAs. Examples of alt-CTAs mentioned in that segment are the Man-AHL Evolution fund which I was heavily
  • Stocks aren t always the best in the long-run [Alpha Architect]

    By examining data going back to 1792, McQuarries study comes up with a surprising observation : stocks are not as dominant as once thought. The variability of the performance of stocks vs. bonds across various time periods is dramatic. So buckle up, stocks do not invariably outperform bonds. Stocks for the long run, sometime yes, sometimes no Edward F. McQuarrie Financial Analysts Journal A

Recent Quant Links from Quantocracy as of 01/04/2025

This is a summary of links recently featured on Quantocracy as of Saturday, 01/04/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Hundreds Of Quant Papers From #QuantLinkADay In 2024 [Turnleaf Analytics]

    I tweet a lot (from @saeedamenfx and at BlueSky at @saeedamenfx.bsky.social)! In amongst, the tweets about burgers, I tweet out a quant paper or link every day under the hashtag of #QuantLinkDay, mostly around FX, rates, economics, machine learning etc. Some are directly relevant to what were doing at Turnleaf Analytics forecasting inflation and other economic variables (and if youre
  • Investigating Simple Formulaic Investing [Alpha Architect]

    Investing formulas are simple, easy-to-implement, systematic, stock screeners that provide instructions on how to outperform the total stock market. Marcel Schwartz and Matthias Hanauer, authors of the December 2024 study, Formula Investing, evaluated the effectiveness of four such popular investing formulas over the period 1963-2022: The Piotroski F-Score: The sum of nine binary (+ or 0)
  • What the last day of the year can teach us about research and trading [Quantifiable Edges]

    Overall, the last day of the year used to be consistently bullish for the market. But that has changed since the turn of the century. This is true across a number of indices. The most dramatic example is the NASDAQ, which I highlighted a few years ago. I have updated the chart below. NASDAQ last day of year historical returns Closing up 29 years in a row is fairly astounding. Just as astounding is
  • What the Index Effect s Disappearance means for Market Efficiency [Alpha Architect]

    This paper investigates the puzzling decline in the price impact of S&P 500 index additions and deletions over the past four decades, despite the rapid growth of passive investing. It explores potential explanations, including changes in market liquidity and efficiency, shifts in the composition of index changes, the role of migrations from related indices, and the predictability of index

Recent Quant Links from Quantocracy as of 12/30/2024

This is a summary of links recently featured on Quantocracy as of Monday, 12/30/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Top Ten Blog Posts on Quantpedia in 2024 [Quantpedia]

    The year 2024 is nearly behind us, so its an excellent time for a short recapitulation. In the previous 12 months, we have been busy again (as usual) and have published over 70 short analyses of academic papers and our own research articles. The end of the year is a good opportunity to summarize 10 of them, which were the most popular (based on the Google Analytics ranking). The top 10 is
  • A “New” Way to Smooth Price [Dekalog Blog]

    Rather than describe it, I'll just paste the "help" description below:- "This function takes an input series and smooths it by projecting a 5 bar rolling linear fit 3 bars into the future and using these 3 bars plus the last 3 bars of the rolling input to fit a FIR filter with a 2.5 bar lag from the last projected point, i.e. a 0.5 bar lead over the last actual rolling point in
  • From Gold to Bitcoin: Exploring the Oldest and Newest Asset Classes [Relative Value Arbitrage]

    Gold, one of the oldest and most enduring asset classes, had an exceptional run in 2024, capturing attention across financial markets. Its role in investment portfolios continues to spark interest, acting as a hedge against uncertainty. On the other end of the spectrum, cryptocurrencies represent the newest frontier in finance. While opinions remain divided, some are enthusiastic supporters, while
  • Linear Congruential Generators in Python [Quant Start]

    Some years ago we wrote a range of articles on random number generation (RNG) using C++. These techniques are primarily used for Monte Carlo simulations that underpin modern derivatives pricing methods. The articles included one that implemented a particular algorithm known as a Linear Congruential Generator (LCG). The LCG is an algorithm for generating random looking numbers, despite being