This is a summary of links featured on Quantocracy on Tuesday, 07/27/2021. To see our most recent links, visit the Quant Mashup. Read on readers!
-
Intro to Partial Sample Regression [Hudson and Thames]Ordinary least squares (OLS) regression is probably the most commonly used statistical method in quantitative finance (and likely in other quantitative fields). It is very fast to compute, and the results are often quite interpretable. Due to its simplicity, it serves as the cornerstone for many more complex statistical or machine learning models. Also, it has been studied so thoroughly
-
Residualization of Risk Factors: Examples and Pitfalls [Portfolio Optimizer]The most common approach to measuring portfolio (risk) factor exposures is linear regression analysis, which describes the relationship between a dependent variable – portfolio returns – and explanatory variables – factors – as linear. One of the outputs of this analysis are the partial regression coefficients, also known as the betas ( ). Each one of them measures the expected change in the
-
“Low-effort Trading Strategies” with Cesar Alvarez (@AlvarezQuant) [Better System Trader]Algorithmic trader Cesar Alvarez from Alvarez Quant Trading joins us to discuss low effort trading strategies, including: An explanation of rotational trading and the benefits/challenges of using rotational strategies, Why rotational trading is a fantastic way to diversify time (and also get to trade lazy), How often to rebalance and the impacts of the day you choose to rebalance, Ranking