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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

Recent Quant Links from Quantocracy as of 03/02/2025

This is a summary of links recently featured on Quantocracy as of Sunday, 03/02/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Learning to Rank [Quantitativo]

    Give me a firm place to stand and a lever, and I can move the Earth. Archimedes. Archimedes, the brilliant Greek mathematician and engineer, was so fascinated by levers that he claimed he could move the Earth with one. His deep understanding of mechanics made him a legend, from designing war machines to discovering buoyancy while lounging in a bathtub. Just as he used levers to amplify
  • Can you trust the “Fear & Greed Index”? [Unravel Markets]

    In the last couple of days, X and other crypto social media is flooded with screenshots of the Fear & Greed Index printing Extreme Fear, usually with a Warren Buffet quote, or something similar. Lets dig into whether it really has any predictive power at all! The common assumption is that Extreme Fear cant last long, and this is the time to buy. What is it, at all? So
  • Confessions of a recovering engineer (or why engineers make bad traders) [Robot Wealth]

    Heres something that might shock you: Engineers make the worst traders. Im speaking from experience here. As an engineer who transitioned to trading, it wasnt until I stopped thinking like an engineer that I started to make progress. You can read all about this in my case study, by the way. Engineers like me are trained to solve implementation problems. We build bridges based on
  • Valuations Reflect U.S. Exceptionalism [Alpha Architect]

    Conventional wisdom can be defined as ideas that are so ingrained in our belief system that they go unchallenged. Unfortunately, much of the conventional wisdom about investing is wrong. One example of erroneous conventional wisdom is that investors seeking higher returns should invest in countries that are forecasted to have high rates of economic growth, such as India and China. It

Recent Quant Links from Quantocracy as of 02/26/2025

This is a summary of links recently featured on Quantocracy as of Wednesday, 02/26/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trading the Fed: The Pre-FOMC Drift is Alive [Quant Seeker]

    The pre-FOMC announcement drift is a well-documented anomaly where equities exhibit abnormal positive returns leading up to Federal Open Market Committee (FOMC) meetings, challenging traditional asset pricing models. In this blog post, I test the anomaly using data through December 2024 and find that it remains persistent despite being published over a decade ago. The results confirm that the
  • Sequential Entropy Pooling [Anton Vorobets]

    Entropy Pooling is a core method of the next generation investment framework, thoroughly presented in the Portfolio Construction and Risk Management book1. As a very oversimplified introduction to Entropy Pooling, you can think about it as a generalization of the Black-Litterman model without all the oversimplifying normal distribution and CAPM assumptions. Entropy Pooling works for fully general
  • How to evaluate leading indicators [Unravel Markets]

    I started this Substack, as I found close to zero investment research material that Id consider rigorous-enough predictive analytics also the reason why I co-founded Unravel. What I rather see a lot is: the price of the asset, and another metric plotted on the same chart, with someone proclaiming that This has been a leading indicator watch what will happen now!. So, this
  • Weekly Recap [Quant Seeker]

    Commodities Commodity dependence and optimal asset allocation (Dequiedt, Gomes, Pukthuanthong, and Williams) Investors often assume adding commodities to a portfolio enhances diversification. However, this paper finds that the benefits depend on a countrys economic structure. In nations heavily reliant on commodity exports, local stocks and bonds move in sync with commodity prices, limiting
  • Volatility Risk Premium: The Growing Importance of Overnight and Intraday Dynamics [Relative Value Arbitrage]

    The breakdown of the volatility risk premium into overnight and intraday sessions is an active and emerging area of research. It holds not only academic interest but also practical implications. ETF issuers are launching new ETFs to capitalize on the overnight risk premium, and the shift toward around-the-clock trading could impact the VRP and popular strategies such as covered call writing. In
  • Using Inflation Data for Systematic Gold and Treasury Investment Strategies [Quantpedia]

    Inflation significantly impacts the prices of gold and treasury bonds through various mechanisms. Gold is often viewed as a hedge against inflation, while treasury bonds exhibit a more complex relationship influenced by interest rates and investor behavior. This relationship between inflation, gold, and treasuries is well understood, but the real question is whether we can systematically

Recent Quant Links from Quantocracy as of 02/19/2025

This is a summary of links recently featured on Quantocracy as of Wednesday, 02/19/2025. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Time- and State-Dependent Resampling [Anton Vorobets]

    Generating realistic future paths for investment market time series is crucial for risk management, good backtesting1, fully general stress-testing2, and CVaR tail risk optimization, see the Portfolio Construction and Risk Management book3. The recent Time- and State-Dependent Resampling article4 presents a new class of resampling methods for high-dimensional investment market simulation and
  • Rob Carver – The Comprehensive Guide to a Diversified Futures Strategy [Algorithmic Advantage]

    In this episode, seasoned trader Rob Carver shared his nuanced approach to building and managing a diversified futures portfolioa methodology that appeals to advanced, technical traders, while we also covered off some of the 'basics' of futures trading, such as rolling, back-adjusting, and so on. I did my best to break down the key elements of his strategy, from market selection to
  • Weekly Recap [Quant Seeker]

    Welcome to this week's roundup of the latest investing research! Below is a carefully curated selection of highlights from the past week, with each title linking directly to its source for further reading. Thank you for reading and dont forget to hit the like button! Crypto Token Economics (Gregory and Mini) Blockchain networks rely on digital tokens to function effectively without a
  • Exploring Credit Risk: Its Influence on Equity Strategies and Risk Management [Relative Value Arbitrage]

    Credit risk, also known as default risk, is the likelihood of loss when a borrower or counterparty fails to meet its obligations. A lot of research has been conducted on credit risk, and an emerging line of study explores the connection between the equity and credit markets. In this post, well discuss how credit risk impacts investment strategies in the equity market and how equity options can

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