Qualification: 
Ph.D, MSc, BE
biplab@am.amrita.edu
Phone: 
0476 – 2808119

Dr. Biplab Bhattacharjee holds a PhD in Systems and Business Analytics from School of Management Studies, National Institute of Technology Calicut. He has completed his Bachelors in Engineering from Visvesvaraya Technological University. He has done a research-based post-graduation degree, M.Sc. Engineering by Research from Visvesvaraya Technological University. He started his career with an entrepreneurship journey in 2007, where he founded an education and research focussed organization in Bangalore. After four years of entrepreneurship, he worked in multiple research and academic setups in Bangalore. He was involved in multiple roles in this period, namely researcher, academician, and corporate trainer. He also had a brief career in Data Science based start-up in Bangalore. Post to his PhD, he worked as a Faculty in School of Management Studies, National Institute of Technology, Calicut. He works as a consultant for various data science companies and involves himself in grooming data science teams of several IT companies. He also has worked as Visiting Faculty for several B-schools in India handling subjects on Data Sciences, Machine Learning, Business Analytics and Statistical computing. He is also a resource person in several National Level Workshops in Data Science and Statistical computing in R.

He has been a recipient of several Best Paper Research awards in multiple conferences in India and abroad. He is an active researcher in the field of Data Sciences, Business Analytics, Network Science, Bioinformatics, and Financial Network Analysis. He has a research output of twenty six research papers in peer-reviewed journals.

Education

  • 2018: Ph. D. 
    NIT Calicut
  • 2014: M. Sc. Engg by Research
    Visvesvaraya Technological University, Karnataka
  • 2006: B. E. 
  • Visvesvaraya Technological University, Karnataka 

Teaching Interest

Data Sciences, Management Information Systems, Network Science, Business Analytics, Enterprise Resource Planning, Computational Finance, Statistical Computing

Publications

Publication Type: Book Chapter

Year of Publication Title

2018

Biplab Bhattacharjee, T. Radha Ramanan, Muhammad Shafi, and Animesh Acharjee, “Elucidation of the Backbone Structure of Cross-Market Dependency Network of World Market Indices: A Global Threshold Filtering Approach”, in Frontiers in Artificial Intelligence and Applications, vol. 309: Fuzzy Systems and Data Mining IV, 2018, pp. 189-195.[Abstract]


The multi-tiered cross-market dependency structures among capital market worldwide have a highly complex architecture which makes international portfolio management a challenging exercise. The challenge in recent times has been magnified given that the strengths of linkage structures have increased over the past decades. Understanding the complex interdependencies in a temporal scale and identifying the backbone structure in this interdependent system will aid in unearthing the avenues from where diversification benefits may possibly arise. With this objective in mind, the current study attempts to mathematically formulate the cross-market connectivity into weighted network models and elucidate the backbone structures by deploying a global threshold filtering approach. The present work investigates the dependency structure based on weekly-data series belonging to forty-three global markets. The weighted networks depicting cross-market relationships are filtered and visually inspected to decipher the significant connectivity structures. The study identifies that average cross-market linkage strengths increased during market stress conditions. The study also identified a disjointed set of markets wherein one can direct the investments to cushion oneself from systemic risk impacts.

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Publication Type: Journal Article

Year of Publication Title

2017

Biplab Bhattacharjee, Muhammad Shafi, and Animesh Acharjee, “Network mining based elucidation of the dynamics of cross-market clustering and connectedness in Asian region: An MST and hierarchical clustering approach”, Journal of King Saud University - Computer and Information Sciences (Elsevier), 2017.[Abstract]


We investigate the dynamics of cross-market clustering and connectedness of the Asian capital markets in this study. We perform the cross-correlation structure analysis of the daily return data of 14 global indices belonging to the major Asian capital markets by using the sub-dominant ultrametric distance based MST and Hierarchical Clustering techniques. The study dataset is for fourteen years duration (2002–2016). A rolling window approach is used to generate 151 temporally synchronous observations. We generate MSTs and Hierarchical Clustering plots (based on average linkage distance) for these temporally synchronous observations, and visually comprehend them to decipher the cross-market cluster formation, hub node formation, and connectivity structure with hub nodes. To identify those set of Asian markets having close connectivity with India, we employed a weighted hop count method and based on its scorings the Asian indices are subsequently ranked. We also investigate the influence of the 2008 financial crisis on the connectivity and clustering patterns in the Asian indices network. We also compute the key network topological parameters to decipher the dynamically varying topological properties, and with a particular reference during financial crisis periods.

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2017

Biplab Bhattacharjee, Muhammad Shafi, and Animesh Acharjee, “Investigating the Evolution of Linkage Dynamics among Equity Markets Using Network Models and Measures: The Case of Asian Equity Market Integration”, Data, vol. 2, no. 4, p. 41, 2017.[Abstract]


The state of cross-market linkage structures and its stability over varying time-periods play a key role in the performance of international diversified portfolios. There has been an increasing interest of global investors in emerging capital markets in the Asian region. In this setting, an investigation into the temporal dynamics of cross-market linkage structures becomes significant for the selection and optimal allocation of securities in an internationally-diversified portfolio. In the quest for this, in the current study, weighted network models along with network metrics are employed to decipher the underlying cross-market linkage structures among Asian markets. The study analyses the daily return data of fourteen major Asian indices for a period of 14 years (2002–2016). The topological properties of the network are computed using centrality measures and measures of influence strength and are investigated over temporal scales. In particular, the overall influence strengths and India-specific influence strengths are computed and examined over a temporal scale. Threshold filtering is also performed to characterize the dynamics related to the linkage structure of these networks. The impacts of the 2008 financial crisis on the linkage structural patterns of these equity networks are also investigated. The key findings of this study include: a set of central and peripheral indices, the evolution of the linkage structures over the 2002–2016 period and the linkage dynamics during times of market stress. Mainly, the set of indices possessing influence over the Asian region in general and the Indian market in particular is also identified. The findings of this study can be utilized in effective systemic risk management and for the selection of an optimally-diversified portfolio, resilient to system-level shocks. View Full-Text

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2017

Biplab Bhattacharjee, Amulyashree Sridhar, and Anirban Dutta, “Identifying the causal relationship between social media content of a Bollywood movie and its box-office success - a text mining approach”, International Journal of Business Information Systems, vol. 24, no. 3, pp. 344 - 368, 2017.[Abstract]


Movie marketing strategies have undergone a rapid metamorphosis over the years with the progress in technological innovations and advent of social media. Social media gives a two way interacting platform and such interactions generate voluminous textual content which can be a source for deriving new insights into the customer behavioural dynamics and can also act as a handy tool for revenue enhancement. This study is designed to understand whether the polarity of the social media content of Bollywood movies can essentially reveal any insights about the potential box office revenues. The initial steps involved data collection from social media, followed by text mining to identify the sentiments about a movie. Furthermore, the relationship between the sentiments captured from social media and total revenue generated was explored in both pre-release and post-release scenarios and linear regression models were built. The model can be further improved by incorporating additional metrics.

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2016

Biplab Bhattacharjee, Muhammad Shafi, and Animesh Acharjee, “Investigating the Influence Relationship Models for Stocks in Indian Equity Market: A Weighted Network Modelling Study”, PLOS ONE, vol. 11, no. 11, p. e0166087, 2016.[Abstract]


The socio-economic systems today possess high levels of both interconnectedness and interdependencies, and such system-level relationships behave very dynamically. In such situations, it is all around perceived that influence is a perplexing power that has an overseeing part in affecting the dynamics and behaviours of involved ones. As a result of the force & direction of influence, the transformative change of one entity has a cogent aftereffect on the other entities in the system. The current study employs directed weighted networks for investigating the influential relationship patterns existent in a typical equity market as an outcome of inter-stock interactions happening at the market level, the sectorial level and the industrial level. The study dataset is derived from 335 constituent stocks of ‘Standard & Poor Bombay Stock Exchange 500 index’ and study period is 1st June 2005 to 30th June 2015. The study identifies the set of most dynamically influential stocks & their respective temporal pattern at three hierarchical levels: the complete equity market, different sectors, and constituting industry segments of those sectors. A detailed influence relationship analysis is performed for the sectorial level network of the construction sector, and it was found that stocks belonging to the cement industry possessed high influence within this sector. Also, the detailed network analysis of construction sector revealed that it follows scale-free characteristics and power law distribution. In the industry specific influence relationship analysis for cement industry, methods based on threshold filtering and minimum spanning tree were employed to derive a set of sub-graphs having temporally stable high-correlation structure over this ten years period.

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