**Search completed Projects**

Our group, the *StataProfessor*, has successfully completed the following projects for national and international researchers. As per our privacy policy, we treat names and other details of our customers as confidential. Therefore, we disclose only those details in the following paragraphs that are generic in nature and are helpful to our potential customers in assessing our abilities.

**46. Backtesting VaR models**

The value at risk (VaR) measures the maximum loss a financial asset can experience in *n* days with a given probability **α**. There are several methods to calculate VaR. The reliability of these models is usually an empirical question. … Read more

**45. Replication of Amihud paper on liquidity**

. . . Read more

**44. Replication of Fama and French 5 factors paper**

**43. Comparison of asset pricing models with Sharp ratio**

. . . Read more

**42. Merton Distance to Default Model**

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. . . Read more

**41. Liquidity adjusted CAPM (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 . . . Read more

**40. Absolute strength momentum in stock returns**

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. They found that this measure is positively related to analyst following and forecast accuracy, and negatively related to analysts’ dispersion in earnings forecasts. These results suggest that financial statement comparability lowers the cost of acquiring information, and increases the overall quantity and quality of information available to analysts about the firm . . . Read more

**39. 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. They found that this measure is positively related to analyst following and forecast accuracy, and negatively related to analysts’ dispersion in earnings forecasts. These results suggest that financial statement comparability lowers the cost of acquiring information, and increases the overall quantity and quality of information available to analysts about the firm . . . Read more

**38. Trading frequency and asset pricing**

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 authors develop a new measure of stock’s liquidity, which they call “price impact ratio. This measure is presented as an alternative to the widely used new price impact ratio as an alternative to the widely used Amihud’s (2002) Return-to-Volume 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. . . Read more

**37. IPO performance in short- and long-run**

This project investigates the underpricing 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. . . Read more

**36. Conditional beta using MGARCH approach**

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. . . Read more

**35. Dynamic momentum strategy for avoiding market crash risk**

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. . Read more

**34. 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. Read more

**33. Stock price crash risk **

This project investigates the cross-sectional risk premiums of uncertainty risk factors in addition to traditional risk factors. The project uses 25 portfolios formed on size and book-to-market as test assets. The risk factors include RM-RF, SMB, HML, UMD, and economic policy uncertainty (EPU) indices from Baker, Bloom, and Davis (2016). The project examines the cross-sectional relation between the risk factors and expected stock returns using two-step Fama and MacBeth (1973) regressions. In the first step, time series regressions are used to estimate factor betas and in the second step, the cross-sectional regressions are used to estimate risk premiums. The estimation involves a 60-month rolling window estimation. Further details. Read more

**32. Uncertainty and downside risk using rolling window Fama MacBeth regressions**

This project investigates the cross-sectional risk premiums of uncertainty risk factors in addition to traditional risk factors. The project uses 25 portfolios formed on size and book-to-market as test assets. The risk factors include RM-RF, SMB, HML, UMD, and economic policy uncertainty (EPU) indices from Baker, Bloom, and Davis (2016). The project examines the cross-sectional relation between the risk factors and expected stock returns using two-step Fama and MacBeth (1973) regressions. In the first step, time series regressions are used to estimate factor betas and in the second step, the cross-sectional regressions are used to estimate risk premiums. The estimation involves a 60-month rolling window estimation. Further details. Read more

**31. HAR RV and volatility comparison models**

In this project, we used several measures of models comparison for testing the accuracy of volatility forecasts generated using the GARCH(1,1) model and the realized volatility (HAR – RV) model. Read more

**30. Patell z-test for event studies**

The Patell test is a widely used test statistic in event studies. In the first step Patell (1976, 1979) suggested to standardize each abnormal returns before calculating the test statistic by the forecast-error corrected standard deviation. Our code is fairly simple to use. One has to change just the varible names in the code to match those in the dataset. Read more

**29. Fama-MacBeth (1973) procedure with Shanken correction**

In applying standard OLS formulas to a cross-sectional regression, we assume that the right-hand variables β are fixed. The β in the cross-sectional regressions are not fixed, of course, but are estimated in the time-series regression. Therefore, there might be a sampling error in the estimates of β. Shanken (1992) suggested a correction to the standard errors of the estimates. Read more

**28. Three-way interactions**

This project required interactions of three variables to establish a causal relationship. We used margins, predictive margins / adjusted predictions,and margin plots to make the interpretation of the result intuitive and easy. Read more

**27. Earning management models**

In this project, we wrote codes for the widely used models of earning management. These models calculate discretionary accruals with varying assumptions. Four well-known models are Jones (1991); Dechow, Sloan, and Sweeney (1995); Kasznik (1999); and Kothari, Leone, and Wasley (2005). Read more

**26. Clause and Thomas code for ICC**

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. Read more

**25. Herding behavior in financial markets**

Herd behavior in financial markets is one example of the existence of inefficient markets. It is the tendency for individuals to mimic the actions (rational or irrational) of other investors. To model herd behavior, there are several models that empirical researchers have used. The most commonly used models are Christie and Huang (1995) and Change et al. (1999).. Read more

**24. CEO compensation, inside debt, and R&D expenditure**

In this project, we developed codes to model the effect of CEO compensation on the CEO career horizon problems ( CEOs that approach retirement to reduce R&D in order to preserve wealth). Our codes handle many aspects of the R&D and its association with career horizon, CEO’s age, CEO inside debt, etc. The codes calculate the required variables and then estimate a variety of regressions. Read more …

**23. Event study in mergers**

This project involved the event study methodology where economic impact of an event is ascertained in terms of changes in the stock prices. Read more …

**22. What drives stock returns of banks**

In this project, we investigated the factors that can explain variations in the market returns of banks. We wrote Stata codes to complete the following tasks: 1. Form 6 portfolios from two-size groups and three book-to-market ratio Read more …

**20. Returns to sentiment, disagreement, and breadth**

In this project, we developed codes in Stata for investigating the impact of investor sentiment on the relationship between disagreement among investors and future stock market returns. Several researchers have recently investigated this topic ( Antoniou, et al. (2015); Cen and Yang (2013); Kim et al. (2014)): Read more…

**19. KMV – Merton Distance to Default Model through an iterative process **

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 models in the following steps: Read more…

**18. Asset prices and stock liquidity**

The liquidity proxy developed by Amihud (2002) is one of the most widely used liquidity proxies in the finance literature. The Amihud measure has a simple construction that uses the absolute value of the daily return-to-volume ratio to capture price impact. The liquidity proxies existed even before Amihd’s measures, e.g, effective spreads of Roll (1984). Over the period of time, researchers extended the Amihud measure e.g (Goyenko, Holden, and Trzcinka, 2009). Other price impact rations include the Roll impact ratio, Pastor and Stambaugh (2003) ratio, and Amivest liquidity. Read more…

**17. Luck vs skill: mutual fund performance | bootstrap analysis**

Robert Kosowski, Allan Timmermann, Russ Wermers, and Hal White (2006) conducted an examination of mutual fund performance to know whether managers have superior stock picking skills or they just happen to perform better than others merely due to luck. They applied several different bootstrap approaches to analyze the significance of the alphas of extreme funds, that is, funds with large, positive estimated alphas. They conclude that the alphas of the top 10% of funds are more likely to be an outcome of managers’ superior skills to pick good stocks. Read more …

**16. Mutual fund performance in the crisis period**

Traditionally, mutual fund performance is evaluated with different measures that include Treynor measure, Sharpe measure, information ratio, etc. These measures express the portfolio returns as ratios of risk. One common factor in these measures is the use of a single measure of the risk factor. Another group of measures includes asset pricing models that use one or more than one risk factors to find risk-adjusted returns. These models include. Read more …

**15. Portfolio standard deviation from variance-covariance matrix**

Assume that you have a dataset of stocks for weekly or monthly frequency. Each stock is assigned to a given portfolio. If we were to find the portfolio standard deviation over the last 60 days, this will pose a considerable programming challenge. The solution can be decomposed into several steps. Specifically, I list these steps. Read more …

**14. Fama-MacBeth with Shanken Correction**

The Fama and MacBeth (1973) procedure is a two-step process. It involves the estimation of N cross-sectional regressions in the first step. And then in the second step, it requires the calculation of T time-series averages of the coefficients of the N-cross-sectional regressions. The standard errors are adjusted for cross-sectional. Read more …

**13. Misvaluing innovations**

Lauren Cohen, Karl Diether, and Christopher Malloy (2013), published their study in The Review of Financial Studies and showed that a firm’s ability to innovate is predictable, persistent, and relatively simple to compute. However, usually, investors misvalue stocks when they take past success as benchmarks for future success… Our Stata code. Read more …

**12. Investor attention and stock returns**

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 no-media premium. Similarly, Chen (2017) documents a significant decrease in index returns following an increase in investor attention. Our Stata code … Read more

**11. Volatility managed portfolios**

Alan Moreira and Tyler Muir (2017) show that volatility managed portfolios can produce large alphas, higher Sharpe ratios, and significant gains for investors who take investment decisions on the mean-variance frontier of modern portfolio theory. They use a number of well-known factors such as the market, value, momentum, proﬁtability, return on equity, and investment factors in equities, as well as the currency, carry trade. The underlying rationale behind volatility timing is that volatility timing increases Sharpe ratios because changes in factors’ volatilities are not fully offset by proportional changes in average returns … Read more

**10. Stata code for zero-leverage firms**

Ilya A. Strebulaev and Baozhong Yang (2013) report that almost 22% of public nonfinancial firms in the US have almost zero leverage. They report that these firms have unique characteristics such as dividend-paying zero-leverage firms pay substantially higher dividends, are more profitable, pay higher taxes, issue less equity, and have higher cash balances than control firms chosen by industry and size. Read more

**9. Testing one, three, and five-factor models in recession and boom**

We completed this project in two weeks after obtaining data from our customers. We received two separate data sets, one containing financial and other containing share prices data. We merged the two files and tested CAPM, Fama and French three-factor model, and Fama and French five-factor model under different economic conditions. For model testing, we used the methodology of Gibbson, Ross and Shanken (GRS) (1989) and other method suggested in Fama and French (2014). All the tests and data management activities were performed using Stata

**8. The persistence of risk-adjusted mutual fund performance**

We completed this project in three weeks time. This project involved the application of the methods as used in Elton, Grubber, and Black (2004). Specifically, we estimated risk-adjusted returns for each fund using factor models over a three and one year window, and then tested whether past returns predict future return; past expenses predict future returns, past betas and r-squared predict future returns. Next, we estimated how current risk-adjusted returns are related to one year and three years lagged differential returns, expenses ratio, alphas, dividend yield. This analysis further explored how betas, R-squared, and alphas are related to lagged measures of fund performance. As a next step, the analysis involved regressing mutual funds’ cash flows on lagged measures of funds performance such as alpha, beta, R-squared, expenses ratio, dividend yield ratio and other factors such as funds family sizes. And finally, we checked whether investors portfolios, past winners portfolio, and past losers portfolio perform better than others in the subsequent periods.

**7. Mutual fund size and performance**

We completed this project in two weeks’ time. This project had a focus on testing a mutual fund performance predictability in relation to the size of the mutual fund. We started by writing codes for finding the percentage of funds that were in a defined size range each year. Next, we applied the Jensen model, Carhart four-factor model, Fama, and French three-factor model to identify which of these models better fits the data. Selecting the best model, next we used risk-adjusted returns for each firm and estimated ranking- and evaluation-alphas from the best model and checked how evaluation alphas, fund size, expense ratios, and turnover are related to ranking-alphas’ deciles. To check the association between size and alphas, we used a rank correlation test. We also ranked funds by size and checked how evaluation alphas are related to fund size across size deciles. Next, we checked how funds evaluation alphas are related to fund size across ranking alphas’ deciles in the bottom-half of funds’ TNA and top-half of funds’ TNA. We also checked how the aggregate expense ratio and management fee moves across different sizes of funds. As a further step, we regressed changes in expense ratios on changes in fund size. Finally, evaluation alphas were regressed on ranking alphas, cash flows, fund size, expense ratio, turnover ratio, and fund family size.

**6. Implied cost of equity models**

This project was directed at finding implied cost of equity (ICC) using different models such as the Easton (2004) model, the Gordon and Gordon (1997) model, and the Ohlson and Juettner-Nauroth (2005) model. We developed Stata codes for these ICC models. Since ICC models require forecasting earning per share, we used the methodology of Hou, Dijk and Zhang (2012). The ICC estimates from different models were then checked in association with other explanatory variables. Read more….

**5. Momentum and contrarian strategies**

This project had an objective to investigate whether momentum or contrarian profits exist? Are such profits economically and statistically significant? Can these profits be explained by proxies of risks such as market beta? Are such profits different using different samples such as small stocks, stocks with little trading volume, and stocks with zero returns? Are such profits different using different weighting schemes in the holding period? We developed Stata codes for all of the above strategies based on well-regarded methodologies such as Jegadesh and Titman (1993).

**4. IPOs and abnormal returns**

We completed writing Stata codes for this project in two weeks time. The project had the objective of finding whether post-IPO significant underpricing occurs. For this project, Stata codes were developed using the famous event study methodology.

**3.Fama and French (1993) model in an emerging economy**

We completed writing Stata codes for this project in one weeks time. The project had the objective of testing Fama and French (1993) in an emerging economy. We developed the three factors in style of Fama and French. The SMB and HML factors were calculated from the returns of six-factor portfolios that are created from the intersection of two size and three value groups of firms. The LHS factors were based on 25 portfolios created at the intersection of 5 size and 5 value groups of firms. The model was finally tested using the Gibbson, Ross, and Shanken (GRS) (1989) method.

**2. Tax changes and asset pricing**

This project involved writing codes to replicate the methodology of Sialm (2009), “Tax Changes and Asset Pricing”. The paper first calculates abnormal returns with well-known asset pricing models such as CAPM, Fama and French, and Carhart models, and then relates those abnormal returns with portfolios that are formed on lagged dividend yields.

**1. Trading Frequency and Asset Pricing**

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.