## Stata code to replicate the Amihud Illiquidity paper

Introduction The Amihud (2002) paper is a seminal work in the field of financial economics that introduced a new measure of illiquidity. This measure, often referred to as the “Amihud Illiquidity Ratio”, is calculated as the average ratio of absolute stock return to its trading volume. The paper demonstrated that this measure can effectively capture [...]

## Stata Code for the Average F-Test

Introduction We are excited to announce that we have developed a Stata code for the Average F-Test. This test was originally developed by Hwang and Satchell (2014). It averages the squared t-statistics on the estimated alphas of individual assets. A key advantage of the test is that it can be applied to a large number [...]

## Maximum Sharpe Ratio | Stata Code for Models Comparison

Introduction We are pleased to announce that we have developed a Stata code based on the methodologies proposed by Barillas, Robotti, and Shanken (2020). This code implements several statistical tests for comparing asset pricing models. These tests include the basic alpha test for nested model comparison, the direct test for comparing squared Sharpe ratios of [...]

## Stata Code for Stock Crash Risk

Introduction We are excited to introduce our Stata code designed to calculate the risk of stock market crashes. This code is based on the methodologies described in the paper “Forecasting Crashes: Trading Volume, Past Returns, and Conditional Skewness in Stock Prices” by Joseph Chen, Harrison Hong, and Jeremy C. Stein. The authors developed a series [...]

## Stata Code for Elton and Gruber’s “Are Investors Rational? Choices Among Index Funds”

Introduction In their seminal paper, Elton and Gruber examine the rationality of investors by analyzing their choices among index funds. They use regression analysis to examine the relationship between return measures and several variables that might predict return. Their findings suggest that both past returns and expenses are significant predictors of future performance, and this [...]

## Fastest regressions by groups in Stata with asreg

This is a quick overview of how to use asreg command for running regressions by groups. To know about the installation and other details of asreg, please click here. Regressions by groups in Stata A group is a subset of data that has a common identifier. Examples of groups can include families, industries, countries, regions, [...]