## Stata code | Boone Indicator for Market Competition

Boone Indicator for Market Competition The Boone indicator is a new measure of competition based on the theoretical assumption that, in a more eﬃcient or competitive industry, ﬁrms are punished severely for being ineﬃcient (Boone et al., 2005, 2007; Boone, 2008). Hence, for an industry with a high level of competition, it is expected that an increase in marginal cost leads to a drastic fall in variable proﬁts. Therefore, the Boone indicator is measured by estimating the following regression: where VROAit is the variable proﬁt (measured as sales revenue less cost of goods [...]

## Stata Codes for Dynamic Momentum Strategy | Avoiding Market Crash Risk

Why a dynamic momentum strategy? Momentum returns are usually negatively skewed and subject to occasional severe crashes. Several research papers have recently explored the timing of momentum crashes and show that momentum strategies tend to crash in 1–3 months after the overall market crash. To exploit this fact, researchers propose a dynamic trading strategy which coincides with the standard momentum strategy in calm times, but switches to the opposite contrarian strategy after a market crash and keeps the contrarian position for the following months, after which it reverts back to the momentum position. [...]

## Stata Codes for Conditional Beta using MGARCH Approach

Time-Varying Beta estimated from Bivariate GARCH This project estimates time-varying betas and conditional betas using the method outlined in "Forecasting Ability of GARCH vs Kalman Filter Method". The code uses daily data. The interesting part of the project is that it contained data for a panel of 3000 companies, however, the estimation technique required time-series analysis. Therefore, we developed the code to fit the project requirements. detail of this project: Pricing The code is available for $ $99 / per model with some example data. If the data is not in [...]

## Returns to New IPOs : Stata Codes

This project investigates the under-pricing phenomenon of initial public offering (IPO) both in the short- and long-run. The project uses a variety of empirical methods used in IPO research. Following are the detail of this project: Importing different files from Excel

## Stata Codes for Trading frequency and asset pricing | Price Impact Ratio

This project involved writing codes to replicate the methodology of Florackis et al. (2011), “Trading frequency and asset pricing on the London Stock Exchange: Evidence from a new price impact ratio”. The paper forms portfolios on price impact ratios using different approaches, and then finds risk-adjusted residual returns of these portfolios using a variety of asset pricing models.

## P608 – Stata Codes for Measuring Financial Statement Comparability

In this project, we have developed Stata codes for the measures of Financial Statement Comparability as defined by DeFranco, Kothari and Verdi (2011). The authors first develop this measure and then test it empirically to find its determinants.

## Stata Codes for Expected Idiosyncratic Skewness and Stock Returns – P603

In this project, we have developed Stata Codes for A Skewness Prediction Model. Boyer, Mitton, and Vorkink (2010) developed a model of expected skewness that incorporates past returns and trading volume as well as known ﬁrm characteristics. In this asset pricing model, they try to explain excess stock returns using trading volume, lagged skewness, and a set of control variables such as firm size, exchange dummy, stock momentum, and industry dummies as explanatory variables. What is included in our code?

## Stata Codes | Absolute Strength Momentum in Stock Returns

We document a new pattern in stock returns that we call absolute strength momentum. Stocks that have signiﬁcantly increased in value in the recent past (absolute strength winners) continue to gain, and stocks that have signiﬁcantly decreased in value (absolute strength losers) continue to lose in the near future

## Stata Code for Liquidity Adjusted (LCAPM) with Application of MGARCH

Liquidity Adjusted (LCAPM) Acharya and Pedersen (2005) presented a theoretical model to explain asset prices with four types of liquidity risks, thereby modifying the single-factor capital asset pricing model (CAPM) into a liquidity adjusted capital asset pricing model (LCAPM). They argue that expected return on a security is based on: Return sensitivity of a portfolio or an asset with the market return Commonality in liquidity of a portfolio or an asset with the market liquidity, Return sensitivity of a portfolio or an asset to market liquidity Liquidity sensitivity of a portfolio or an [...]

## P31 – HAR RV (Realized) volatility and GARCH(1,1) comparison models | Stata

Project's Overview In this project, we compared volatility model that included Heterogeneous Autoregressive model of Realized Volatility (HAR RV) and GARCH(1,1). Following is the list of main coding activities of this project. Importing different files from Excel Reshaping the data to a long format Merging different datasets Making business calendar to account for non-trading days Finding stock returns Estimating volatility with GARCH(1,1) model Fit the HAR-RV model and create realized variance Using several measures for model comparison to see whether GARCH(1,1) or the HAR-RV model perform well Our Stata Code We have [...]

## KMV – Merton Distance to Default Model through an iterative process in Stata

Our Stata | Mata code implements the Merton distance to default or Merton DD model using the iterative process used by Crosbie and Bohn (2003), Vassalou and Xing (2004), and Bharath and Shumway (2008). Specifically, our code implements the model in the following steps: 1.Generate log returns from stock prices 2. Find volatility for each stock in each year from the daily stock returns and convert it to yearly volatility 3. Use the above stock volatility as a proxy for asset volatility in the first iteration and use the following equation to [...]

## Implied cost of equity(ICC) with Claus and Thomas (2001) in Stata | Excel

Clause and Thomas (2001) estimate the equity premium from the discount rate that equates market valuations with prevailing expectations of future flows. Thier model uses forecasted earnings and market share prices and solves for the implied cost of equity. Usually, all ICC models have lengthy equations and unknowns that call for deep root functions. With the exception of Gordon and Gorden (1997) model, all other models require complex algebra to solve for the unknown i.e. the required rate of return. The good news is that we have developed an efficient and [...]

## Investors attention and stock returns | Stata Codes

Project Details Investors' attention is a topic of behavioral finance. Merton (1987) presented the investors' recognition hypothesis, which later was tested in a variety of empirical designs. For example, Fang and Peress (2009) found a no-media premium. Similarly, Chen (2017) documents a significant decrease in index returns following an increase in investor attention. The Google Search Volume (SVI) data provides an excellent measure of investor attention. Search is a revealed attention measure for retail investors: if a retail investor searches for a stock ticker in Google, he is undoubtedly paying attention to [...]