Quantocracy

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Quantocracy’s Daily Wrap for 10/10/2024

This is a summary of links featured on Quantocracy on Thursday, 10/10/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How to Improve ETF Sector Momentum [Quantpedia]

    In this article, we explore the historical performance of sector momentum strategies and examine how their alpha has diminished over time. By analyzing the underlying causes behind this decline, we identify key factors contributing to the underperformance. Most importantly, we introduce an enhanced approach to sector momentum, demonstrating how this solution significantly improves the performance
  • How I Automated My Trading Strategy Using AWS Cloud for Free (Part 1) [Black Arbs]

    This year I launched a strategy subscription service for a long-only ETF strategy developed in house. I learned a lot through this process but I made several mistakes that pushed me to learn new skills and improve the product offering. In this series I will discuss my initial mistakes, and how correcting them led me to automate the system using AWS cloud and how you can too. Mistake #1 First

Quantocracy’s Daily Wrap for 10/08/2024

This is a summary of links featured on Quantocracy on Tuesday, 10/08/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Reading the WSJ May Make You a Better Economist [Alpha Architect]

    What are the Research Questions? The research questions are as follows: How can textual analysis of business news, specifically The Wall Street Journal (WSJ), be used to measure the state of the economy? What is the structure of news coverage related to economic events, and how do these topics evolve over time? Does news attention contain information that is distinct from standard numerical
  • The Hidden Cost of Index Replication [Alpha Architect]

    As the annual SPIVA studies demonstrate, index funds persistently outperform the vast majority of actively managed funds, even before considering taxes. With that said, most investors are unaware that there are weaknesses of index funds that result from their strategy to replicate the return of an index. Those weaknesses, which result from the desire to minimize what is called tracking error

Quantocracy’s Daily Wrap for 09/29/2024

This is a summary of links featured on Quantocracy on Sunday, 09/29/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A different indicator [Quantitativo]

    "Mathematical reasoning may be regarded rather schematically as the exercise of a combination of two facilities, which we may call intuition and ingenuity. Alan Turing. It's hard to find anyone in Computer Science who doesn't hold Alan Turing in deep admiration. Widely regarded as the father of modern computing, Turing's groundbreaking work during World War II on breaking
  • Vasicek Model Simulation with Python [Quant Start]

    Recently on QuantStart we wrote a tutorial article that discussed the mean-reverting Ornstein-Uhlenbeck process, outlining some of its applications as well as providing some Python snippets to generate sample paths. In this article we are going to introduce the Vasicek Model, which is example of a one-factor short rate model used to model interest rate behaviour for interest rate derivatives
  • Can Skewness Identify Future Outperforming Mutual Funds [Alpha Architect]

    The annual SPIVA has documented that retail mutual funds underperform with great persistence, with any persistence of outperformance not significantly greater than would be randomly expected. The large body of research on the failure of active management led Charles Ellis to famously call it a losers gameone that is possible to win, though the odds of doing so are so poor the surest

Quantocracy’s Daily Wrap for 09/25/2024

This is a summary of links featured on Quantocracy on Wednesday, 09/25/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

Quantocracy’s Daily Wrap for 09/23/2024

This is a summary of links featured on Quantocracy on Monday, 09/23/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Replicating Pandas exponentially weighted variance [OS Quant]

    You are most likely familiar with the idea of calculating averages with an exponential weighting. The idea is that you have a higher weight to more recent information. The weights for an exponentially weighted average look like: for . And the exponentially weighted average of a series looks like: You can easily calculate an exponentially weighted moving average in Pandas with:
  • Ornstein-Uhlenbeck Simulation with Python [Quant Start]

    Some time ago on QuantStart we wrote an article on generating Brownian Motion paths for simulating stock price assets. In this tutorial article we are going to consider a more advanced stochastic process model known as the Ornstein-Uhlenbeck (OU) process that can be used to model time series that exhibit mean reverting behaviour. This is particularly useful for interest rate modelling in
  • Data-driven Approach to Clustering Similar Macroeconomic Regimes [Alpha Architect]

    The research team at Verdad does some of the most interesting and innovative empirical financial research that is consistently rigorous and based on systematic approaches that are implementable and replicable, providing confidence in the findings. In a recent piece, Analogous Market Moments, they focused on how macroeconomic signals can help predict expected returns across asset classes.

Quantocracy’s Daily Wrap for 09/17/2024

This is a summary of links featured on Quantocracy on Tuesday, 09/17/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trend-Following Filters Part 8 [Alpha Architect]

    Regression analysis is a statistical method used to estimate and model the relation between a dependent variable and one or more independent variables. The dependent variable, also called the observation, is the variable being explained or predicted. The Independent variables are used to explain or predict the dependent variable. In economics, regression analysis is used, for example, to measure
  • How to Improve Commodity Momentum Using Intra-Market Correlation [Quantpedia]

    Momentum is one of the most researched market anomalies, well-known and widely accepted in both public and academic sectors. Its concept is straightforward: buy an asset when its price rises and sell it when it falls. The goal is to take advantage of these trends to achieve better returns than a simple buy-and-hold strategy. Unfortunately, over the last decades, we have been observers of the

Quantocracy’s Daily Wrap for 09/14/2024

This is a summary of links featured on Quantocracy on Saturday, 09/14/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Revisiting Trend-following and Mean-reversion Strategies in Bitcoin [Quantpedia]

    Over the past few years, significant shifts in the financial landscape have reshaped the dynamics of global markets, including the cryptocurrency sector. Events such as the ongoing war in Ukraine, rising inflation rates, the soft landing scenario in the US economy, and the recent Bitcoin halving have all profoundly impacted market sentiment and price movements. Given these developments, we decided
  • The devil is in the details [Quantitativo]

    The group coined a name for the difference between the prices they were getting and the theoretical trades their model made without the pesky costs. They called it The Devil. Gregory Zuckerman. The quote above is from the great book The Man Who Solved the Market. In it, Gregory Zuckerman tells the story of Jim Simons, a brilliant mathematician who revolutionized the world of finance with his
  • Investors trade Cryptos and Trad-Fi Differently [Alpha Architect]

    The paper examines several key questions related to how retail investors trading behaviors in cryptocurrencies differ from their behaviors in traditional asset classes like stocks and commodities. Are cryptos different? Evidence from retail trading Shimon Kogan, Igor Makarov, Marina Niessner, Antoinette Schoar Journal of Financial Economics, 2024 A version of this paper can be found here Want

Quantocracy’s Daily Wrap for 09/08/2024

This is a summary of links featured on Quantocracy on Sunday, 09/08/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Exploring Bond Tax Efficiency: Futures or Bond ETFs? [Alpha Architect]

    Bond futures are often assumed to be more tax-efficient than bond ETFs. My analysis indicates that this assumption is frequently incorrect. Although investors might view the 60/40 tax treatment of futures as advantageous, a futures strategy faces several challenges compared to a bond ETF, including frequent taxable events, potential tax drag from cash collateral, and additional state taxation. My
  • Adding Leveraged, Long-Short Factor Strategies to Improve Tax Alpha [Alpha Architect]

    Empirical research, including the 2020 study An Empirical Evaluation of Tax-Loss Harvesting Alpha and the 2023 study Expected Loss Harvest from Tax-Loss Harvesting with Direct Indexing, has found that tax-loss harvesting strategies in separately managed accounts (SMAs) can improve the post-tax returns of an investment portfolio by employing a strategy of selling positions in securities
  • Research Review | 6 September 2024 | Portfolio Risk Management [Capital Spectator]

    Semivolatility-managed portfolios Daniel Batista da Silva (U. of Geneva) and M. Fernandes (Getulio Vargas Fnd.) July 2024 There is ample evidence that volatility management helps improve the risk-adjusted performance of momentum portfolios. However, it is less clear that it works for other factors and anomaly portfolios. We show that controlling by the upside and downside components of volatility

Quantocracy’s Daily Wrap for 09/04/2024

This is a summary of links featured on Quantocracy on Wednesday, 09/04/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Python Libraries for Quantitative Trading [Quant Start]

    For anyone looking to dive into the world of quantitative finance and systematic trading, Python is an indispensable tool. As the go-to programming language for many quant developers, Python offers a vast ecosystem of libraries that streamline everything from data analysis to strategy execution. Whether you're just starting out or looking to sharpen your skills, understanding the right Python

Quantocracy’s Daily Wrap for 09/03/2024

This is a summary of links featured on Quantocracy on Tuesday, 09/03/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Insights from the Geopolitical Sentiment Index made with Google Trends [Quantpedia]

    Throughout history, geopolitical stress and tension has been ever-present. From ancient civilizations to todays world, global dynamics have been largely shaped by wars, terrorism, and trade disputes. Financial markets, as always, have keenly observed and been significantly influenced as a result. Our article delves into understanding this relation between geopolitical stress and financial
  • Book Reviews and Reading List [Mark Best]

    How do you eat an elephant? I have wanted to write a reading list but I have been apprehensive since I didnt want to include too much and wanted also to explain why the books were in the list. If you want to trade crypto there is no point reading the Hull interest rate model book. This list likely will be a work in progress so keep that in mind. As I write this I realise I have a deep problem
  • Can smart rebalancing improve factor portfolios? [Alpha Achitect]

    This paper aims to test an effective rebalancing method that prioritizes trades with the strongest signals to capture more of the factor premium while reducing turnover and trading costs. The authors coin the term smart rebalancing to capture the essence of their ideas. The empirical tests include widely used factor strategies, including long-short factors and long-only factor-based