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Quantocracy’s Daily Wrap for 05/10/2024

This is a summary of links featured on Quantocracy on Friday, 05/10/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Design Patterns for Order Latency – Why You May Need to Implement Your Own Gateway for Trading [Hanguk Quant]

    Most recently, I announced some coming, exciting upcoming things for quantpylib: Exciting Additions to Quantpylib Exciting Additions to Quantpylib HangukQuant May 6 Read full story as well as a demontration of its powers: Dual Momentum in Cryptocurrencies? Dual Momentum in Cryptocurrencies? HangukQuant May 4 Read full story Today, I want to talk about design principles when order latency is
  • Building Better High Yield Portfolios – II [Finominal]

    The higher the yield, the lower the total return on average Combining the highest yielding strategies leads to risky portfolios Combining high yielding but uncorrelated strategies is more sensible INTRODUCTION We previously highlighted an almost linear inverse relationship between an investment strategys yield and its total return (read Building Better High Yield Portfolios). Investors,

Quantocracy’s Daily Wrap for 05/05/2024

This is a summary of links featured on Quantocracy on Sunday, 05/05/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Bootstrap Simulations with Exact Sample Mean Vector and Sample Covariance Matrix [Portfolio Optimizer]

    Bootstrapping is a statistical method which consists in sampling with replacement from an original data set to compute the distribution of a desired statistic, with plenty of possible variations depending on the exact context (non-dependent data, dependent data). Because bootstrap methods are not, in general, based on any particular assumption on the distribution of the data, they are well
  • Dual Momentum in Cryptocurrencies? [Hanguk Quant]

    Over the last few weeks, we talked about funding arbitrage, Crypto Arbitrage (1 Week Setup) Crypto Arbitrage (1 Week Setup) HangukQuant Apr 10 Read full story extended the quant library to incorporate crypto backtesting Announcing Crypto and Other Backtesting Logic ; Quantpylib Announcing Crypto and Other Backtesting Logic ; Quantpylib HangukQuant Apr 27 Read full story and introduced new
  • Inventory scores and metal futures returns [SR SV]

    Inventory scores are quantamental (point-in-time) indicators of the inventory states and dynamics of economies or commodity sectors. Inventory scores plausibly predict base metal futures returns due to two effects. First, they influence the convenience yield of a metal and the discount at which futures are trading relative to physical stock. Second, they predict demand changes for restocking by
  • Momentum and the Clarity of the Trend [Alpha Architect]

    Momentum continues to receive much attention from researchers because of the strong empirical evidence. Out of the hundreds of exhibits in the factor zoo, momentum (both cross-sectional [long-short] and absolute [trend]) was one of just five equity factors that met all the criteria (persistent, pervasive, robust, implementable, and intuitive) Andrew Berkin and I established in our book Your

Quantocracy’s Daily Wrap for 05/03/2024

This is a summary of links featured on Quantocracy on Friday, 05/03/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Make Things Easy on Yourself: Roll up Small Asset Positions [Allocate Smartly]

    Here are some things we know about Tactical Asset Allocation: (Learn more: What is TAA?) We shouldnt go 100% all in on just one TAA strategy. That introduces specification risk, or the risk that weve bet on an underperforming horse. Instead, we should combine multiple strategies together into what we call Model Portfolios. When trading a diversified combination of TAA strategies,

Quantocracy’s Daily Wrap for 04/30/2024

This is a summary of links featured on Quantocracy on Tuesday, 04/30/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Replicate Fama French 5-Factor Model from publicly available data sources [DileQuante]

    As an equity quantitative analyst, you have recurring positioning analysis tasks. Your most effective approach is to model your object of study (usually stocks, portfolios or indexes) and decompose its behavior into common risk factors. You can create your own factor model or use existing models. There are robust models generated by data providers like MSCI or Qontigo, which are quite

Quantocracy’s Daily Wrap for 04/29/2024

This is a summary of links featured on Quantocracy on Monday, 04/29/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Initial Test of Trading Forex News Announcements [Dekalog Blog]

    My first test of trading forex news announcements is to test the efficacy of breakouts immediately following a news announcement related to the US dollar, specifically, only the high impact news as shown on the forexfactory calendar in red. The intention would be to capture some of the profit available from the big movements resulting from surprise news or simply market manipulation around these
  • How to use autoencoders to create feature embeddings [PyQuant News]

    Embeddings are used in neural networks to transform large, sparse data into manageable, dense formats. In other words, they simplify complex data, making it easier to analyze. We can use embeddings to capture dense information about drivers of stock returns. This approach is a great way to select pairs and diversify portfolio risk. By the end of todays newsletter, youll have code to train an
  • Factor Investing Is Dead, Long Live Factor Investing! [Finominal]

    Market-neutral multi-factor products have reached all-time highs However, long-only multi-factor products have consistently underperformed Portfolio construction and implementation matters INTRODUCTION Investors have been style investing since the inception of stock markets. Some chase trends, while others focus on quality or cheap stocks. Factor investing is not new, but it did experience a
  • MLMs: do they work better than traditional approaches? [Alpha Architect]

    Can AI models improve on the failures in predicting returns strictly from a practical point of view? In this paper, the possibilities are tested with a battery of AI models including linear regression, dimensional reduction methods, regression trees and neural networks. These machine learning models may be better equipped to address the multidimensional nature of stock returns when compared to

Quantocracy’s Daily Wrap for 04/25/2024

This is a summary of links featured on Quantocracy on Thursday, 04/25/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Cluster Risk Parity: Equalizing Risk Contributions Between and Within Asset Classes [Portfolio Optimizer]

    The equal risk contribution (ERC) portfolio, introduced in Maillard et al.1, is a portfolio aiming to equalize the risk contributions from [its] different components1. Empirically, the ERC portfolio has been found to be a middle-ground alternative1 to an equally weighted portfolio and a minimum variance portfolio, balanced in risk and in weights1, which exhibits interesting performances in terms
  • A Simple Trick for Dealing with Overlapping Data [Robot Wealth]

    Last week, we looked at simple data analysis techniques to test for persistence. But we only looked at a feature that is measured over a single day the absolute range. Such a feature makes it easy to test persistence because you dont have the problem of overlapping data. Each data point is entirely self-contained and shares no information with the previous data point. Compare this with a
  • Portable alpha for all: Return stacked strategies for diversification without sacrifice [Invest Resolve]

    Diversification is the cornerstone of investing. This principle, fundamentally understood as not concentrating all resources into a single investment, inherently seeks to minimise risk while potentially smoothing out returns over the long term. By spreading investments across uncorrelated assets, investors may more reliably cushion against sector-specific downturns and geopolitical risks.

Quantocracy’s Daily Wrap for 04/22/2024

This is a summary of links featured on Quantocracy on Monday, 04/22/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Re-introduction To Quantitative Investing [Investing For A Living]

    Its been almost 11 years now since I posted my introduction to quantitative investing. Its kind of hard to believe as Im typing these words. A lot has happened since that summer day in 2013. And a lot has stayed the same. Learning about, implementing, and allocating to this type of investing has been the best investment decision Ive made. This type of algorithmic investing in
  • Macroeconomic data and systematic trading strategies [SR SV]

    While economic information undeniably wields a significant and widespread influence on financial markets, the systematic incorporation of macroeconomic data into trading strategies has thus far been limited. This reflects skepticism towards economic theory and serious data problems, such as revisions, distortions, calendar effects, and, generally, the lack of point-in-time formats. However, the
  • Quality versus Low Volatility ETFs [Finominal]

    Quality and low volatility stocks provide substantially different exposures Neither has outperformed the stock market, but both were less volatile Low volatility stocks have done poorly in recent years INTRODUCTION If you believe that the economy is entering a recession and can only invest in equities, what stocks should you select? Well, first we should probably acknowledge that we are terrible

Quantocracy’s Daily Wrap for 04/18/2024

This is a summary of links featured on Quantocracy on Thursday, 04/18/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Basic DSPy RAG tutorial on DataGrapple blog posts [Gautier Marti]

    This blog is more a note to self for experimenting further with DSPy (arXiv, GitHub) than a pedagogical or original intro to the framework. It essentially follows this weaviate tutorial with small adaptations, notably removing the weaviate part of it, and replacing their retrieval module by a very basic local search in the embeddings. I typically experiment against the jargon-heavy DataGrapple
  • Can Google Trends Sentiment Be Useful as a Predictor for Cryptocurrency Returns? [Quantpedia]

    In the fast-paced world of cryptocurrencies, understanding market sentiment can provide a crucial edge. As investors and traders seek to anticipate the volatile movements of Bitcoin, innovative approaches are continuously explored. One such method involves leveraging Google Trends data to gauge public interest and sentiment towards Bitcoin. This approach assumes that search volume on Google not
  • Is Sector Neutrality in Factor Investing a Mistake? [Alpha Architect]

    The justification for neutralizing sectors in factor strategies is a work in progress. To date, academic researchers havent had an empirical model to mimic the impact of removing sector effects on the measurement and performance of factor strategies. The authors develop and test a two-component model to address the question of, Is Sector Neutrality in Factor Investing a Mistake? Is

Quantocracy’s Daily Wrap for 04/13/2024

This is a summary of links featured on Quantocracy on Saturday, 04/13/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Use Markov models to detect regime changes [PyQuant News]

    Its rumored that Renaissance Technologies uses hidden Markov models in their trading. In fact, the Baum-Welch algorithm used with Markov models was partially invented by Leonard Baum who would help found RenTec. In todays newsletter, well look an example of using a Markov model to detect regime changes in the equities market. Lets go! Use Markov models to detect regime changes A Hidden
  • Research Review | 12 April 2024 | Equity Risk Premium [Capital Spectator]

    Macroeconomic Announcement Premium Hengjie Ai (University of Wisconsin-Madison), et al. November 2023 The paper reviews the evidence on the macroeconomic announcement premium and its implications on equilibrium asset pricing models. Empirically, a large fraction of the equity market risk premium is realized on a small number of trading days with significant macroeconomic announcements. We review
  • Minimizing the Risk of Cross-Sectional Momentum Crashes [Alpha Architect]

    While empirical research on cross-sectional (long-short) momentum has shown high returns, investors have also experienced huge drawdownsmomentum exhibits both high kurtosis and negative skewness. Since 1926, there have been several momentum crashes that featured short but persistent periods of highly negative returns. For example, from June to August 1932, the momentum portfolio lost about 91%,

Quantocracy’s Daily Wrap for 04/11/2024

This is a summary of links featured on Quantocracy on Thursday, 04/11/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trend Following with Return Stacking [Investing For A Living]

    After my last post on using trend following in Crypto markets I thought Id continue on that thread and look at applying trend following on a newish concept (similar concepts have existed at the institutional level for a long time) in creating diversified portfolios, called return stacking. First, what is return stacking? The term return stacking was coined by the team at Newfound research and
  • Factor Olympics Q1 2024 [Finominal]

    Some factor trends continued from 2023, but there were also two significant rotations Momentum performed the best, size the worst Most long-short multi-factor products have generated positive excess returns INTRODUCTION We present the performance of five well-known factors on an annual basis for the last 10 years. Specifically, we only present factors where academic research supports the existence