Devlina Chatterjee
Associate Professor
Industrial and Management Engineering
IIT Kanpur, 208016

Student Projects/ Theses Guidance

PhD Students

Year Student Name of Thesis
2018 Manohar Giri A study of consumer behavior in the life insurance industry
Ongoing Mahfuzuar Rahman Sustainable Tourism in India: A Resilience View
Ongoing Shakti Chaturvedi Topic to be decided

M.Tech Students

Year Student Thesis Current Organization
1 2015 Pankaj Bisht Study of Consumer Preferences in the Refrigerator Market in India
2 2015 Saurabh Kumar Efficacy of Technical Analysis in the Indian Stock Market Fidelity Investments
3 2016 Purba Chatterjee Efficacy of Fundamental Analysis in the Indian Stock Market KPMG
4 2016 Kamal Kumar Gupta Ownership Concentration and Stock Liquidity: A Panel Analysis of Indian Stocks IIT Kanpur
5 2017 Tanvi Keswani Development of a Financial Behaviour Scale for a Cross-section of Indian Adults Core Compete, Hyderabad
6 2017 Sanjay Kumar Gupta Socioeconomic and Demographic Factors that affect Financial Behaviour of Indian Adults PBO Plus, Delhi
7 2018 Sruthy Sreekumar Financial Behavioral Traits and Money Centrality: Insights from Kerala Alaska, USA
8 2018 Kapil Dayma Effect of Financial Circumstances on Financial Well-being of Individuals: A Study from India Accenture, Gurgaon
9 2018 Mahendra Kumar The Role of Socially Motivated Aspirations, Materialism and Income Security as Drivers of Financial Well Being: A Study in India Accenture, Bombay
10 2019 Arijit Ganguly Post Facto Analysis of GATE Data: Item Response Theory (IRT) and IRTree Models
11 2019 Rhit Sanyal IRTree Modeling of Gate Data: A New Composite Score With Policy Implications
12 2020 Danish Nawaz A Simple Robust Two Dimensional Measure for Overtourism: Longitudinal Study of 22 EU Countries
13 2020 Karthik Ramakrishnan How does overtourism affect environmental quality parameters in EU nations?

MBA Special Studies / Capstone Projects

Year Student Name of SS / Capstone Project
1 2013 Alok Jain Factors Affecting the Decision to Purchase a Tractor in Northern India
2 2014 Anupam Naskar Demand Estimation and Forecasting of Petroleum Products in India
3 2014 Pramod Rathi Pricing of TRAI Spectrum Licenses
4 2014 Nishu Navneet Relationship between Stock Prices and Exchange rates
5 2014 Sushil Panigrahi Factors Affecting Food Inflation in India
6 2014 Ramakrishna Majumdar Factors affecting differences in Gender Ratio across Different States in India
7 2015 Nitin Agarwal Factors Affecting Road Accidents in India: A Poisson Model
8 2015 Saurabh Prasad Willingness to Transact Online (WTO) using Item Response Theory
9 2015 Aditya Raghu Nandan Willingness to Transact Online (WTO) using Item Response Theory
10 2015 Saibal Gupta Time Varying Betas using GARCH Models
11 2015 Prashant The Indian Commercial Vehicles Industry
12 2016 Raghav Sharma Consumer Motivation for Online Product/Service Purchase
13 2016 Rajan Chouhan Will People Shift to Mobile Shopping? If Yes What Kind of People?
14 2016 Gourav Agarwal Online Grocery Shopping In India
15 2017 Ishaan Singh Nostalgia Marketing - Expatriates vs. Residents
16 2017 Sapna Tuteja Nostalgia Marketing - in the Tourism Industry
17 2017 Deepti Sekhri Forecasting Mango Prices across mandis in india
18 2017 Abhilash Chandra Automobile Market in India
19 2018 Deep Shah Automated Cryptocurrency Portfolio Optimization Tool
20 2018 Himanshu Dubey Go-to market strategies for Water - purifier - Tata Swachh
21 2018 Deepak Harjai Association of Financial Behavior with Culture
22 2018 Shruti Dhadwal Text Analytics of Online Customer Reviews about Insurance Companies
23 2018 Surbhi Jindal Association of Financial Behaviour with Culture
24 2019 Aditya Mitra Psychographic factors affecting purchase intentions towards Airbnb: The Indian context
25 2019 Bahul Dandona Psychographic factors affecting purchase intentions towards Airbnb: The Indian context
26 2019 Ankit Chawla Cross Generation Financial Personality Assessment for Indian Population
27 2019 Rishabh Gupta Cross Generation Financial Personality Assessment for Indian Population
28 2020 Milind Dev Shukla Factors that affect intention to participate in Coffee Tourism in the Indian context
29 2020 Dhananjay Mobile AID - Theory of _Planned Behavior in Adoption of M-AID
30 2020 Isvorya Cultural Value Orientation and Stock Trading Behavior: A Study of Individual Investors from India
31 2020 Yash Sikri Cultural Value Orientation and Stock Trading Behavior: A Study of Individual Investors from India

Abstracts of M.Tech theses guided

1.Study of Consumer Preferences in the Refrigerator Market in India (Pankaj Bisht, 2015)


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.

2.Efficacy of Technical Analysis in the Indian Stock Market (Saurabh Kumar, 2015)


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.

3.Efficacy of Fundamental Analysis in the Indian Stock Market (Purba Chatterjee, 2016)


The objective of this thesis is to empirically determine the efficacy of Fundamental Analysis, by analysing stock recommendations, archived on web portals like "", 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.

4.Ownership Concentration and Stock Liquidity: A Panel Analysis of Indian Stocks (Kamal Kumar, 2016)


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.

5.Development of a Financial Behaviour Scale for a Cross-section of Indian Adults (Tanvi Keswani, 2017)


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.

6.Socioeconomic and Demographic Factors that affect Financial Behaviour of Indian Adults (Sanjay Gupta, 2017)


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.

7.Financial Behavioral Traits and Money Centrality: Insights from Kerala (Sruthy Sreekumar, 2018)


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.

8.Effect of Financial Circumstances on Financial Well-being of Individuals: A Study from India (Kapil Dayma, 2018)


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.

9.The Role of Socially Driven Aspirations, Materialism and Income Security as Drivers of Financial Well Being in India (Mahendra Kumar, 2018)


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.

10.Post Facto Analysis of GATE Data: Item Response Theory (IRT) and IRTree Models (Arijit Ganguly, 2019)


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.

11.IRTree Modeling of Gate Data: A New Composite Score with Policy Implications (Rhit Sanyal, 2019)


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.