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The case intends to achieve the following objectives with the audience: Apply the knowledge of fundamental analysis to select good companies for investment; analyse companies for investment through the contrarian strategy; and appreciate the need of own research before investing.

This case study provides an insight into the general process around investment for a retail investor. The protagonist of the case, Rajdeep Sharma, is a middle-class, salaried IT consultant from India. He has taken inspiration from Rakesh Jhunjhunwala, a renowned investor in India, and started his journey on the road to generate wealth. He decides to adopt the contrarian strategy and identifies Jaiprakash Associates Limited (JPA) to invest his savings. JPA is a large conglomerate with decades of experience, which has diversified business across multiple sectors. However, JPA’s pursuit of growth and expansion has created financial issues because of a large amount of debt it has amassed over the past few years. The company has been unable to repay its interest and principal obligations to the debt holders, making it consider a restructuring plan to meet these responsibilities. Meanwhile, Rajdeep has been optimistic about his investments but must experience excessive turmoil due to the high volatility in the company’s share prices. The case highlights the value of sound analysis, along with the impact of information on retail investors’ decision-making process. It will help prospective investors appreciate the importance of economic, sectoral and financial statement analysis before making any investments.

Fundamental analysis for MBA students majoring in Finance.

Teaching notes are available for educators only.

CSS 1: Accounting and Finance.

For the past two and half decades, there has been a marked shift in the corporate governance regulations around the world. The change is more remarkable in developing countries where countries with little or no corporate governance regime have adopted “world class” standards. While there can be a debate on whether law in books actually translates into law in action, in the meantime it might be interesting to analyse the law in books to understand how the corporate governance regime has evolved in the past 20 years. This paper quantitatively tracks 21 countries, most of them being developing and emerging economies, over a period of 20 years. The period covers 1995 to 2014; thus, it traverses the pre and post crisis period in 1999 and 2008. Thus, the paper also provides a snapshot of the macrolegal changes that the countries engage in hoping to stave off the next crisis. The paper uses over 50 parameters modelled on the OECD Principles of Corporate Governance. The paper confirms the suspicion that corporate governance norms around the developing economies are converging on shareholder primacy end of the continuum. The rate of convergence was highest just before the financial crisis of 2008 and has since then slowed down.

The paper uses data collected from experts. They filled up detailed questionnaire which quizzed them on the rules relating to corporate governance norms in their country and asked them to retrospectively check their data every five years for the past 20 years. This provided an excellent overview as to how the law has evolved in the past two decades on corporate governance. The data were then tabulated using a scoring sheet and then was put together using item response theory (IRT) which is a Bayesian method similar to factor analysis. The paper then follows a comparative approach using heatmaps to analyse the evolution of corporate governance in developing countries.

Corporate governance norms around the developing economies are converging on shareholder primacy end of the continuum. The rate of convergence was highest just before the financial crisis of 2008 and has since then slowed down.

This is the first time that corporate governance panel data analysis has been carried out on top developing countries across so many parameters for such a long period. This paper also uses Bayesian IRT modelling to analyse the evolution which is novel in its approach especially in the corporate governance literature. The paper thus provides a clear view on the evolution of corporate governance norms and how they are converging on a particular ideology.

The purpose of this study is to compare the predictive performance of the hedonic multivariate regression model with the probabilistic neural network (PNN)-based general regression neural network (GRNN) model of housing prices in “Pune-India.”

Data on 211 properties across “Pune city-India” is collected. The price per square feet is considered as a dependent variable whereas distances from important landmarks such as railway station, fort, university, airport, hospital, temple, parks, solid waste site and stadium are considered as independent variables along with a dummy for amenities. The data is analyzed using a hedonic type multivariate regression model and GRNN. The GRNN divides the entire data set into two sets, namely, training set and testing set and establishes a functional relationship between the dependent and target variables based on the probability density function of the training data (Alomair and Garrouch, 2016).

While comparing the performance of the hedonic multivariate regression model and PNN-based GRNN, the study finds that the output variable (i.e. price) has been accurately predicted by the GRNN model. All the 42 observations of the testing set are correctly classified giving an accuracy rate of 100%. According to Cortez (2015), a value close to 100% indicates that the model can correctly classify the test data set. Further, the root mean square error (RMSE) value for the final testing for the GRNN model is 0.089 compared to 0.146 for the hedonic multivariate regression model. A lesser value of RMSE indicates that the model contains smaller errors and is a better fit. Therefore, it is concluded that GRNN is a better model to predict the housing price functions. The distance from the solid waste site has the highest degree of variable senstivity impact on the housing prices (22.59%) followed by distance from university (17.78%) and fort (17.73%).

The study being a “case” is restricted to a particular geographic location hence, the findings of the study cannot be generalized. Further, as the objective of the study is restricted to just to compare the predictive performance of two models, it is felt appropriate to restrict the scope of work by focusing only on “location specific hedonic factors,” as determinants of housing prices.

The study opens up a new dimension for scholars working in the field of housing prices/valuation. Authors do not rule out the use of traditional statistical techniques such as ordinary least square regression but strongly recommend that it is high time scholars use advanced statistical methods to develop the domain. The application of GRNN, artificial intelligence or other techniques such as auto regressive integrated moving average and vector auto regression modeling helps analyze the data in a much more sophisticated manner and help come up with more robust and conclusive evidence.

To the best of the author’s knowledge, it is the first case study that compares the predictive performance of the hedonic multivariate regression model with the PNN-based GRNN model for housing prices in India.