PhD Students

M.Tech Students

MBA Special Studies / Capstone Projects

Abstract

This study aims to understand consumer preferences, the choices that consumers make and the premium that they are willing to pay for different attributes and brands in the consumer durable market, specifically for refrigerators. Primary data was collected by questionnaires administered to a sample of 82 respondents in the campus to understand the preferences of consumers for different attributes and brands of refrigerators. This data was analyzed using conjoint analysis and part worth utilities were calculated for each level of attributes and for different brands. We found that brand was the most important attribute in consumer preferences (with Samsung being most preferred and Godrej being least preferred), followed by price and then capacity. Next, consumer choice behaviour was studied using primary data.

A conditional logit model of consumer choices showed that price had a statistically significant and negative impact on the choice. Godrej had a negative impact on choice irrespective of changes in price with respect to Samsung. For the other brands (LG and Whirlpool), the cross price elasticity was positive while the own price elasticity was negative as expected. Finally, using secondary data on prices of refrigerators of different brands and different models (with varying attributes) available on websites, a hedonic regression was performed that showed that consumers are willing to pay a positive and high premium for auto-frost feature, a positive premium for certain brands like LG, a negative premium for brands like Godrej and a small premium for high volumes.

Abstract

The objective of this thesis is to empirically evaluate efficacy of technical analysis by analysing the stock recommendations given by the technical analyst over a period of around 4 years (9-Mar-2011 to 22-Sept-2014). 213 "Buy" recommendations made in this period were randomly selected and were analysed separately for short-term (time period of 1week) and medium-term return (time period of 5-6-months) on investments.

We find that in the short-term, volume traded (proxy measure for liquidity of stock) was a statistically significant factor that had a positive effect on the probability of success of recommendation. The other factors considered such as price aggressiveness of the recommender (degree of optimism of the analyst), beta of the stock and market trend (bullish or bearish) during this time were not statistically significant. In the medium-term, we identified that a bullish market trend and beta of the stock were statistically significant factors that positively affect the probability of success in achieving target price. However, we did not find any significance of volume traded (liquidity), price aggressiveness and industry sector with the success of recommendation in achieving target price. Our study also suggests that no particular recommender had an edge over the other recommenders as far as the probability of target being achieve was concerned.

This study confirms previous studies by Ratner and Leal (1999) and Sehgal and Gupta (2007) who had also found that technical analysis was not very effective in the Indian stock market. Our study also finds the factors that might affect the efficacy in the short term and medium term.

Abstract

The objective of this thesis is to empirically determine the efficacy of Fundamental Analysis, by analysing stock recommendations, archived on web portals like "indiainfoline.com", by the fundamental analyst, in the period of Jan 2011 - April 2015. A total number of 211 "BUY" and "SELL" recommendations made in this period were analysed, 3 months prior to and 9 months post to the day of recommendation.

We find that in the medium term return on investment scenario, the analyst's optimism or price aggressiveness, the market sentiment and the average volume of the stock traded over the past three months are positively and significantly associated with the target price accuracy of the recommended stocks. On the other hand, the volume of the stock traded on the day of the recommendation and the shareholding pattern or the promoter holding of the recommended company are negatively and significantly associated with the target price accuracy of the recommended stock.

Analysing the success of the recommendations from a temporal aspect, we found that again the targeted return emerges as a highly statistically significant variable that increases the time to fulfilment of the recommendations. On the other hand, the market sentiment, and the promoter holding in the recommended company expedites the process of achieving the target price within the prescribed time window. We also find from Kaplan Meier curves that 48.38% of "BUY" recommendation and 44% of "SELL" recommendation have not reached their recommended target prices, even after the expiry of the prescribed 9 months window. Our findings confirm previous studies by Gupta and Singla (2008), Choudhary and Bajaj (2011) and Jegadeesh et al. (2004), that analyst recommendations do not add value in the Indian stock market.

Abstract

Stock market investors are concerned not only about the returns they get from their investments but also the liquidity of the assets in their portfolio. Ownership concentration leads to information asymmetry and may affect the liquidity of the stocks of a company as has been noted by studies in other countries.

Using panel data for 130 stocks over 9 years from 2007 to 2015 from the CMIE database, we study the relationship between ownership concentration and liquidity of stocks in the Indian stock market. The variable of interest in this study is an aggregate measure of liquidity viz. the stock liquidity ratio, computed as the ratio of the value of shares traded annually to the total market capitalisation of the company. Several stock related characteristics are included as control variables. Entity and time fixed effects are included to avoid omitted variable bias. The results show that ownership concentration has a statistically significant negative impact on stock liquidity. A 1% increase in ownership concentration reduces the LR by about 0.179. The negative effect of ownership concentration is moderated by high growth or dividend yield of the company.

We also investigate the effect of different kinds of ownership structures, such as family holdings, institutional holdings, foreign holdings and non-promoter institutional holdings affect the liquidity of a stock. Shares owned by individuals/HUF has the greatest negative effect on liquidity. Shareholding by government, financial institutions and foreign promoters also has a statistically significant though much smaller negative effect on liquidity. This is supported by adverse selection hypothesis. Shareholding by non promoter institutional investors has a significant positive impact on stock liquidity which is supported by signalling theory and trading hypothesis.

Abstract

In this work, we set out to try and find the main underlying traits of financial personality among Indians. While there have been several studies in Western countries that have tried to measure money attitude and financial personality, there has been no such attempt for the Indian population in the academic literature. Western societies differ from the Indian society in terms of average socio-economic conditions, access to credit, access to financial institutions as well as a culture that is more individualistic and materialistic. The theoretical basis of developing this scale was the tri-component model viz., the affective, behavioural and cognitive components of attitude formation (Eagly and Chaiken, 1993). The scale was developed in both English and Hindi. It was administered using an online survey as well as through collection of data in the field. The final data included 625 respondents from 20 villages in North India and more than 20 cities across India.

After collection of data exploratory factor analysis was conducted which yielded a factor structure with 6 factors which outlined the following aspects of financial behaviour: (i) planning and being financially careful, (ii) being extravagant, (iii) knowledge about financial markets, (iv) worrying about the future, (v) the extent of importance given to money and (vi) financial support network. Exploratory factor analysis (EFA) was performed on the English questionnaires and this was validated on the Hindi questionnaires using Confirmatory Factor Analysis (CFA). The statistical fit of the model in the CFA was observed to be good. This study allowed us to understand the underlying financial behavioural traits of the Indian consumer. The findings of this study are of potential interest to social policy makers, NGOs engaged in helping people achieve financial well-being as well as agents of financial institutions trying to market insurance and investment products to the Indian customer.

Abstract

Understanding the financial behaviour of Indians is key to communicating, educating, and improving their financial wellbeing. While there have been several studies in Western countries to study financial behaviour, there has been no such attempt in the Indian context. Due to differences in socio-economic conditions as well as cultural aspects, such scales have low applicability in the Indian context (Khare, 2014). In an effort to understand the underlying dimensions of financial behaviour, primary data were collected from 625 respondents across several cities and villages in different parts of India. Six financial behavioural traits were identified by Keswani (2017) using exploratory factor analysis which were named "Financial Prudence", "Extravagance", "Financial Knowledge", "Financial Worry", "Money as measure of Success" and "Financial Support Network".

In the present thesis, we use the same data to identify segments of the population that scored similarly on each of these six dimensions of financial behaviour. Cluster analysis was performed and six clusters were identified. Based on the levels of the financial behaviour that were present in a particular cluster, we named the clusters as "Financially Driven", "Prudent Strivers", "Money not Important", "Live for Today", "No Financial Responsibilities" and "Financially Inactive".

Further, we wanted to understand the demographic and socio-economic factors that could be used to predict the presence or absence of each of the six behavioural traits. While the regression models did not have high explanatory power, some variables were statistically significant which followed the researchers' intuition. This study further consolidates our understanding of the financial behaviour of Indians.

Abstract

India has a diverse culture which differs across states and geographical regions. This cultural diversity is reflected in differences in individual behaviour in every aspect of life, in rituals, in dress, in food and in music and arts. These cultural differences may also be reflected in the financial behaviour of individuals. The primary aim of this study was to understand the financial attitudes and behaviors of people from a southern state of India, Kerala. Kerala stands apart from the rest of India in terms of its matriarchal structure and higher levels of literacy and human development index. In prior work, Keswani (2017) had developed a financial behaviour scale used to understand the main financial behavioural traits of individuals from northern states of U.P, Rajasthan and Uttarakhand. We were interested in understanding the similarities and differences in financial behavioural traits of individuals from north and south India.

Using the same questionnaire that was used in Keswani's Financial Behaviour Scale, we collected primary data from 511 respondents in Kerala. Exploratory factor analysis was performed to uncover the underlying common financial traits. Six factors were identified in the Kerala sample which were named "Financial Anxiety & Money as Priority", "Financial Prudence", "Taking Financial Responsibility", "Financial Knowledge", "Extravagance" and "Financial Support Network". Of these six, four were identical with the factors uncovered by Keswani (2017); viz. "Financial Prudence", "Financial Knowledge", "Extravagance" and "Financial Support Network". "Financial Anxiety" and "Money as Priority" were two separate factors identified by Keswani. However, in the Kerala sample, these factors combined into a single factor. One factor that was completely new in the Kerala sample was named "Taking Financial Responsibility".

The six common financial traits identified in the first part were used to segment the Kerala respondents into homogeneous clusters that were similar in terms of their financial behaviour. The segments identified were as follows: "Responsible but Low Financial Aptitude", "Prudent Strivers", "Independent, no financial burdens", "No Financial Responsibilities", "Financially Driven", "Financially responsible working mothers". While there were some similarities with the sample from northern India, the segments from the Kerala sample identified women who were taking financial responsibilities as some of the major consumer segments. This has implication for marketing of financial services which may be targeted to this segment.
The final question addressed in this thesis is as follows: What are the factors that affect the level of importance that individuals attach to money? We used a structural equation modelling approach to answer these questions. Several models were conceptualized, with socio-economic and demographic variables as well as financial behaviour related constructs being used as independent variables. We found that anxiety was the most important predictor of the priority given to money for all decisions. The overall fit of the final model was good.

This thesis aims to gain a better understanding of several aspects of the financial behaviours of Indians, especially from the southern state of Kerala. Our results show that while there are some differences, significant commonalities also exist with other parts of India.

Abstract

Happiness and subjective well-being have been studied extensively in the last few decades. One of the main factors that contribute to a greater sense of well-being in one's life is financial wellbeing. There have been several studies that have tried to define financial well-being and understand what contributes to such a state among individuals. Several factors may affect an individual's financial well-being. There may be "situational" or external factors such as absolute income, relative income, social security, family or parental support, the amount of financial burden on an individual and the macroeconomic conditions such as economic growth, and inflation. There may also be "dispositional" or internal factors such as the personality, motivations, education, attitudes, cognitive and affective abilities. In this study, we are primarily interested in understanding how different kinds of "situational factors" such as financial responsibilities, income levels as well as income security affect financial well-being in the Indian context. We are also interested in understanding how financial management behaviours can affect the financial well-being of individuals, given that they are faced with certain external financial circumstances.

We use the existing IFDFW (In Charge Financial Distress/Financial Well-being) scale developed by Prawitz et al. (2006) for measuring financial well-being. We also created our own sub-scales for measurement of the level of financial responsibilities that the respondent has in his life and another sub-scale for measurement of the level of income security enjoyed by the respondent. We also used a scale for measurement of the kind of financial management behaviour developed by Joo & Grable (2004). These scales were combined into a single questionnaire which was administered to 329 respondents. Demographic and socio-economic data was also included.

Confirmatory factor analysis as well as structural equation modeling was used to test the model that was specified. The results of the SEM models indicated that income security had a large, positive and statistically significant effect on the financial wellbeing of Indian adults. Having greater levels of financial responsibilities had a large, negative and statistically significant effect on financial well-being. Good financial management behaviours had a statistically significant positive effect on FWB. However, the effect size was small compared to income security and financial responsibilities. Among demographic factors, we find that males and older individuals were likely to have to take on greater levels of financial responsibilities but exhibit lower levels of good financial management. Income had a direct positive effect on financial well-being as well as an indirect positive effect when mediated via income security.

The primary contribution of this study is the finding that income security rather than the level of income itself was the primary factor that affected financial well-being among Indian individuals. In a country with a large population, few jobs and uncertainty in agricultural incomes, assurance of a steady income stream is more important to the overall financial well-being than the level of income per capita. Government policies regarding employment generation and unemployment insurance may lead to a better sense of wellbeing among Indian citizens.

Abstract

The purpose of this study is to understand the effect of materialistic values, socially driven aspirations and income security motives in determining the financial well-being of Indian adults. A questionnaire was designed using available scales for financial well-being and materialism. Upward social comparison motives and income security questions were incorporated. A structural equation model using data from 329 respondents was used to understand how different personal traits and socioeconomic variables affected financial well-being.

Exploratory factor analysis revealed four latent traits, here designated as socially motivated aspirations, overt materialism, non materialistic values and income security. The path coefficients in the structural model indicated that overt materialism negatively affects financial well-being. Socially motivated aspirations have an indirect negative effect mediated by overt materialism, but a direct positive effect. Income security has the largest effect on subjective financial well-being, and it is positive.

Income security is found to be an important determinant of financial well-being, outweighing the effects of materialism, social comparisons or income. Socially motivated aspirations can, via materialism, lower financial well-being; but they may directly improve financial well-being too. Our findings are aligned with similar results reported by other authors (Sheldon et al., 2004; Garđarsdóttir et al., 2009) but are new in the Indian context. The critical role of income security for financial well-being is an important insight that will help policy makers, and financial service providers.

Abstract

The Graduate Aptitude Test in Engineering (GATE) is administered annually to assess the scientific and engineering aptitude of bachelor’s degree holders in India. GATE questions are either multiple choice or numerical answer type, with three possible outcomes: omitted, attempted-incorrect, attempted-correct, with a pre-announced scoring formula. There exist alternative methods for evaluation of candidate ability other than traditional formula scoring. Item Response Theory (IRT) is one such method based on statistical modeling of test data, where the probability of getting an answer correct is modeled as a function of both item parameters and the candidate’s latent ability traits. This method has certain advantages over traditional formula scoring methods; for instance it does not constrain the ability estimate of a candidate to be based on pre-determined difficulty parameter of a question as set by the examiner. IRT is widely used in international assessments such as the SAT (Scholastic Assessment Test) and the GRE (Graduate Record Examinations).

In this thesis, we have performed a post-facto analysis on the item response dataset of the GATE examination conducted in 2015. We analyze data for 15 out of 23 subjects and our sample consists of 66,084 candidates. Our aim in this work was to identify the models that have the best fit for the GATE 2015 data. Initially, we implemented two variations of traditional IRT models with a single latent trait. Then we implemented four variations of an advanced Tree-based Item response (IRTree) model, which uses a tree structure to replicate the process of attempting a question. Finally, we compare three possible tree structures that represent alternative decision processes of the test taker, even though one of the tree structures is intuitively most pleasing.

Based on three different measures of fit, we found that the linear-tree multidimensional IRTree model having two latent traits (propensity to attempt a question and the ability to answer correctly if attempted) per candidate and two item difficulties per question (intensity of omission induction and easiness) provided the best fit to the dataset for all the 15 subjects. We used the freely available statistical software R and inbuilt packages for the analysis.

We recommend the model with the best fit that can be used to generate ability estimates of candidates, which can be used either as an alternative or as a secondary decision criterion to the current GATE formula score. The models generated by the IRTree methodology in this study provide a statistically robust alternative to traditional formula scoring in estimating candidate ability.

Abstract

The Graduate Aptitude Test in Engineering (GATE) is a national exam conducted annually in India, and the score obtained by a candidate is used simultaneously for two purposes: (i) recruitment in public sector undertaking (PSU) companies, and (ii) admission to post-graduate programs in Science, Technology, Engineering, and Mathematics (STEM) disciplines in nationally reputed institutions in India. Annually, several lakh candidates appear for the examination. Among test takers, typically employment is a more valued outcome than higher education. The dual purpose of GATE leads to some conflicts that motivate the present work.

In this work, we conduct a post-facto analysis of GATE 2015 data. The data used pertains to 66,084 candidates and covers 15 out of a total of 23 disciplines included in GATE 2015. The question papers consist of either multiple choice type or numerical answer type questions with three possible outcomes- omitted, attempted correct, and attempted incorrectly with a pre-declared scoring formula. One of the statistically valid alternatives to the formula scoring method currently used which is based on the Classical Test Theory (CTT) is the Item Response Theory (IRT). Here, the probability of answering a question correctly is modelled as a function of question parameter (item parameter) and latent trait (propensity parameter) or candidate’s ability.

We implement a tree-based IRT model with two parameters per question and two traits per candidate that provides the best fit among some other alternatives. We compare IRTree traits or propensities with the currently used formula score. We combine IRTree traits and propose a composite score which is found to have a high correlation with GATE formula score for all 15 subjects. Our analysis provides a two-dimensional assessment policy of candidate’s performances that: (i) is potentially acceptable to GATE stakeholders, (ii) is academically defensible, (iii) partially separates two conflicting uses of the same exam, and (iv) introduces an additional criterion for subsequent sorting and tie-breaking among top percentile of candidates.

Abstract

The concept of over tourism has gained extensive academic attention in recent years. Several researchers have studied over-tourism in the context of a given destination using primary data that measure the perceptions and attitudes of local residents and tourists. Such latent measures for perceived over-tourism based on primary data may not be generalizable to other destinations. Further, while these attitudinal scales may accurately capture local sentiment, they are costly to administer and difficult to replicate across several destinations.

In order to monitor over-tourism on a real time basis, there is a need for a simpler, objective and numerical measure that is easy to compute based on available data. Several different numerical measures have been suggested in literature to measure the level of tourist activity. These include the Schneider's Tourism Intensity Ratio, Tourist Penetration Ratio, Defert Tourist Function Index (DTFI); Baretje-Defert's indicator, Charvat's index and the Tourism Concentration Index. Using data for 22 European countries from Eurostat and Worldbank (data.worldbank), we compute these indicators for each country. We conduct exploratory factor analysis with these computed indicators to uncover two underlying dimensions: tourism activity, and supply-demand imbalance. Further, we have clustered the 22 countries based on the factor loadings obtained from EFA. We were able to clearly identify the countries which are experiencing overtourism and countries that are not.

Thus we have succeeded in our primary goal of developing a more straightforward, objective, and numerical measure, to monitor overtourism, which is externally valid.

Abstract

Tourism is a multidimensional and complex phenomenon. Tourism has several positive impacts on destination countries, viz. economic growth, employment generation and improvement in the foreign exchange supplies of the destination countries. Excessive levels of tourism leads to environmental degradation and destruction of the local ecosystem that leads to eventual decay of tourism.

In this thesis, we use panel data from 22 European countries between the years 2000-2018 to measure the impact of tourism on the environment. We arrive at a composite index for overtourism that consists of two primary dimensions that we named "tourism demand intensity ratio" and "tourism supply intensity ratio". Next, we specified panel regression models for identifying the factors that affect the level of pollutants in the country. Prior research has found that population pressure along with affluence and technological growth affect levels of pollution. We considered four primary pollutants - these were carbon dioxide, nitrous oxide, methane and particulate material PM2.5. Independent variables included the tourism ratios (proxy for tourist population) along with GDP per capita (affluence) and electric power consumption (technological growth).

Entity fixed effect panel models were built to see the effect of tourism intensity on environmental variables. One percent increases in Tourist demand intensity, Tourist Supply intensity, and Tourist accommodation density lead to 0.0428%, 0.0452%, and 0.0253% decreases in CO2, respectively. The impact of tourism activity on Pm2.5 was found to be statistically insignificant. Country wise regression analysis allowed us to group countries based on the impact of tourism activity on the environment. These two groups of countries clustered are i) Eco-tourism countries: In richer and more developed countries, increases in tourism activity leads to improvement of environment. ii) Non-Eco-Tourism countries: in countries that are typically less developed, increase in tourism leads to environmental degradation. This result confirms similar findings in prior literature and provides further evidence of the Environmental Kuznets curve.

Abstract

India has witnessed a steady drop in Child Sex Ratio for girls in the age of 0-6-year-old in the last few decades. CSR fell from 97.6 to 91.9 girls for 100 boys between 1961 and 1991. It dropped even more in 2001 and 2011, to 90.5 percent and 89.5 percent, respectively. This is a human rights crisis with serious social, economic and cultural implications for India. The Government of India introduced a policy called the "Beti Bachao, Beti Padhao"(BBBP) policy in 2015 with the vision to reduce gender inequality and improve the sex ratio at birth.

The aim of this study was to understand whether the BBPP policy was successful in improving the child sex ratio at birth (SRB). We used panel data from 19 states of India over the years 2000- 2018 to measure the impact of BBBP Policy on child sex ratio. We use an event study method, with a dummy variable for BBBP. Years prior to the introduction of BBBP marked as "0", and those after marked as "1". We included economic variables such as net state domestic product (NSDP), female literacy rate and cultural variables including crimes against women. Several statistical tests were performed, and entity fixed model with binary variables for each state had the best fit.

The states of Punjab and Himachal Pradesh showed an improvement in sex ratio at birth after implementation of the BBBP policy. The state of Delhi had a negative coefficient, which indicates that SRB has worsened after the policy implementation. Among control variables, higher income per capita has a positive impact on SRB. However, some of the wealthier states such as Haryana, Maharashtra, Uttarakhand, Gujarat, and Himachal Pradesh report decrease in SRB over the years. Increase in molestation cases worsen the SRB. Female literacy rate did not have a statistically significant effect on SRB. We conclude that BBBP policy has been successful only in a few states in improving sex ratio at birth. This may be due to deeply entrenched cultural attitudes related to son preference and the prevalence of the dowry system. Implementation of BBBP will be successful only if there is a greater involvement at a regional level. Education and awareness programs along with efforts to change cultural mores may improve sex ratio at birth.

Abstract

Access to education and improvement in literacy rates leadsto higher economic growth, improvements in public health outcomes, and poverty eradication in developing countries. In programs aimed at raising literacy rates and educating the masses, it is necessary to include men as well as women. Education empowers women to make decisions that benefit the entire family, which has a downstream effect on societal wellbeing. The Government of India has introduced several policies and schemes that aim to reduce gender inequality and improve the lives of women. One such policy introduced in 2015 was the "Beti Bachao, Beti Padhao" Policy.

In this study, we use panel data from19 Indian states over the years 2004-2017 to measure the impact of the "Beti Bachao, Beti Padhao" (BBBP) Policy. Outcome variables include female literacy rate and gross enrolment ratio (GER) of girls at primary, upper primary, secondary, and higher secondary levels. Descriptive analysis shows the variation of these indicators across both states and time. Several statistical tests indicated that entity fixed effects specification was appropriate. We use an event study method with a dummy variable for BBBP. Years prior to the introduction of BBBP were marked as "0", and those after were marked as "1". We included several other variables to remove omitted variable bias. These include economic and infrastructural factors such as net state domestic product (NSDP) per capita, road length, and number of school educational institutions. For cultural factors that reflect gender inequality, we used proxies such as crimes against women and sex ratio at birth.

We find that BBBP Policy has a positive impact on the gross enrolment ratio of girls at primary, upper primary, and secondary levels, but a negative impact on the gross enrolment ratio at the higher secondary level. BBBP Policy has a statistically significant, positive impact on the female literacy rate. Economic factors positively affect female literacy and school enrollment, while cultural factors such as crimes against women had a negative effect. Our results indicate that the BBBP Policy has been effective for improving educational outcomes for women in India, especially for younger girls. This study provides empirical evidence for the success of BBBP Policy at the state level, providing insights that may be useful for policymakers.

Abstract

COVID-19 pandemic was one of the most severe and extensive health shocks in the twentieth century, both in terms of geographical dispersion and mortality rates. One of the important issues that arose was the affordability of medical and paramedical services, leading to a perceived need for health insurance. Our aim in this study was to see whether individuals who directly experience a COVID health shock would be more likely to demand health insurance. We were also interested in understanding whether financial literacy, financial inclusion, and financial resilience influenced the chance of health insurance consumption.

We collected primary data from 338 individuals, of whom 131 did not have health insurance previously. We built several SEM models and found that individuals who had experienced COVID, and were financially resilient were more likely to buy health insurance. Financial literacy and financial inclusion are statistically insignificant. Furthermore, we discovered that health insurance is not purchased by low-income families.

Our results shows an interesting insight into the features that might impact the intention to purchase health insurance in the post COVID era. Given that more disease-prone individuals may seek out insurance, the insurance industry may face a problem of adverse selection leading to an overall increase in insurance premiums.