Advanced Data Analysis, Business Analytics and Intelligence

Size: px
Start display at page:

Download "Advanced Data Analysis, Business Analytics and Intelligence"

Transcription

1 3rd IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence April 13-14, 2013, Ahmedabad, India ABSTRACT BOOKLET

2

3 3rd IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence April 13-14, 2013 Indian Institute of Management Ahmedabad, India

4

5 ICADABAI-2013 The 3 rd IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence (ICADABAI-2013) is being organized with the purpose of exploring the frontiers of theory and applications of Data Analysis, Business Analytics and Business Intelligence in the context of rapidly changing economic and business environment. The conference brings together leading academic researchers and practitioners from universities, research institutions and industries from India and abroad to a common platform with a view to facilitate the sharing of research based knowledge and cutting edge applications. Dr Vikram Sarabhai and a few other public spirited industrialists founded the Indian Institute of Management, Ahmedabad (IIMA) in 1961 as an autonomous body with the active collaboration of the Government of India, Government of Gujarat, and industry. The Institute had initial collaboration with Harvard Business School which greatly influenced the Institute s approach to education. Gradually it emerged as a confluence of the best of eastern and western management approaches having strong ties with both industry and government. The 1 st IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence (ICADABAI- 2009), was held on 6-7 June 2009 and was attended by about 120 participants from academia and industry. A total of 116 research papers and case studies were presented in this conference. The second conference in this series ICADABAI-2011 was held at IIM, Ahmedabad on 8-9 January, 2011 and was attended by about 140 participants from academia and industry. The two day conference had three key-note speeches delivered by eminent academicians and practitioners and two panel discussions on special topics aligned to the theme of the conference. One panel discussion addressed the topic of Business Intelligence while the other addressed the issues relating to Data Quality. This conference saw academicians and practitioners present a total of 100 research papers and case studies. The present conference, which is the third in this series, is being attended by more than 140 participants from academia and industry. It would see six key-note speeches by eminent academicians and business leaders and presentation of about 110 research papers and case studies. As a Knowledge management initiative both the ICADABAI-2009 and ICADABAI-2011 were video recorded. The conference documentations (in the form of interactive DVDs) of these two conferences are available for reference of academicians and practitioners. The ICADABAI-2013 conference would also be video recorded which will later be turned into interactive DVDs. The conference participants can acquire a copy of the documentation by contacting the Conference Convener. Conference Convener: Conference Team: Prof. Arnab Kumar Laha, IIM Ahmedabad Ms. Pravida Raja, IIM Ahmedabad Prof. Mahesh K. C., SLIMS-Ahmedabad

6 Members of International Programme Committee: Neeraj Arora Wisconsin School of Business, Madison, USA Sankarshan Basu Indian Institute of Management, Bangalore Sudip Bhattacharjee University of Connecticut, USA Anand Bodapati University of California, Los Angeles, USA Smarajit Bose Indian Statistical Institute Anil Kumar Ghosh Indian Statistical Institute Mrinal K Ghosh Indian Institute of Science Sujit K Ghosh North Carolina State University, USA Ravindra Khattree Oakland University, USA Ranjan Maitra Iowa State University, USA Chiranjit Mukhopadhyay Indian Institute of Science Ganapati Panda Indian Institute of Technology, Bhubaneswar Nityananda Sarkar Indian Statistical Institute Ashis SenGupta Indian Statistical Institute Vishal Singh New York University, USA N. Balakrishna Cochin Institute of Science and Technology, India Ashok Banerjee Indian Institute of Management, Calcutta Sumanta Basu Indian Institute of Management, Calcutta Atanu Biswas Indian Statistical Institute Sharad W Borle Rice University, USA Samarjit Das Indian Statistical Institute Aurobindo Ghosh Singapore Management University Pulak Ghosh Indian Institute of Management, Bangalore Sandeep Juneja Tata Institute of Fundamental Research, India Debasis Kundu Indian Institute of Technology, Kanpur Neeraj Misra Indian Institute of Technology, Kanpur Harikesh Nair Stanford University, USA Indrajit Ray University of Birmingham, UK Subrata Sarkar Indira Gandhi Institute of Development Research, India Janat Shah Indian Institute of Management, Udaipur Anshuman Tripathy Indian Institute of Management, Bangalore Session Organizers: Atanu Biswas Indian Statistical Institute Anil Kumar Ghosh Indian Statistical Institute Debdutta Pal Indian Institute of Management, Indore Ranjan Maitra Iowa State University, USA Kousik Guhathakurta Indian Institute of Management, Kozhikode Nityananada Sarkar Indian Statistical Institute 04 3rd IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence

7 Referees: Sharad W Borle Rice U. Vineet Virmani IIM-Ahmedabad Prahalad Venkateshan IIM-Ahmedabad Sachin Jayaswal IIM-Ahmedabad V.V.Rao IIM-Ahmedabad Kavitha Ranganathan IIM-Ahmedabad Anand Kumar Jaiswal IIM-Ahmedabad Dhiman Bhadra IIM-Ahmedabad Shobhesh Agrawal IIM-Ahmedabad Subrata Sarkar IGIDR Brajesh Kumar JGBS-NCR Biju Varkkey IIM-Ahmedabad Debdatta Pal IIM-Indore Anshuman Tripathy IIM-Bangalore Joshy Jacob IIM-Ahmedabad Ramani K V IIM-Ahmedabad Saral Mukherjee IIM-Ahmedabad Abhishek IIM-Ahmedabad Rudra P Pradhan IIT-Kharagpur Sanjay Verma IIM-Ahmedabad Viswanath Pingali IIM-Ahmedabad Himadri Roy Chaudhuri IMI-Kolkata Neharika Vohra IIM-Ahmedabad Sankarshan Basu IIM-Bangalore Mahesh K.C SLIMS-Ahmedabad Debjit Roy IIM-Ahmedabad Ajay Pandey IIM-Ahmedabad Dheeraj Sharma IIM-Ahmedabad Kartik D IIM-Ahmedabad Sumanta Basu IIM-Calcutta Arnab K Laha IIM-Ahmedabad B.H.Jajoo IIM-Ahmedabad Sanjeev Tripathi IIM-Ahmedabad Samarjit Das ISI-Kolkata Ramanathan S IIM-Ahmedabad Vijaya Sherry Chand IIM-Ahmedabad Nityananda Sarkar ISI-Kolkata Indrajit Ray U. Birmingham Arvind Sahay IIM-Ahmedabad Ganapati Panda IIT-Bhubhneswar Chetan Soman IIM-Ahmedabad Preeta Vyas AIIM-Ahmedabad N. Venkiteswaran IIM-Ahmedabad Arijit Laha Infosys Jayant R Varma IIM-Ahmedabad Apratim Guha IIM-Ahmedabad 3rd IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence 05

8

9 Contents KN-1 Big Data : The challenges to official statistics...13 TCA Anant KN-2 KN-3 Online Early Detection of Change for High Volatility Multivariate Portfolios and Updating VaR...13 Ashis SenGupta Art, Science and the Secret Sauce - A recipe for building world class analytics capability...15 Srikanth Velamakanni S-028 Role of Inclusive Financial Services in Empowering the MSME sector for a Green Economy: An Indian Perspective...15 Raka Banerji, Sudipti Banerjea S-043 Commodity Indices: Defining their Role in Indian Market-Understanding Dynamic Conditional Correlation with Stock Index in Multi-resolution Set-up...16 Rahul Deora, Brajesh Kumar S-018 Devising the Usp in Sporadic Markets - A Study Done on Wedding Planners in Tamilnadu...16 P.Baba Gnanakumar S-139 Applications of Bayesian Networks in Business Intelligence...17 Heena Timani S-181 Improvements to be introduced in the algorithms of CVRP and VRPB when solved over instances involving asymmetric distances and heterogeneous fleet Kaviti Keshav Kumar, Sunil Agrawal S-081 Determinants of Lapsation of Policies: An Investigation in Indian Insurance Sector...18 Sunita Mall, Seshadev Sahoo S-083 On Testing Exponentiality against NBAFR Alternatives...18 Aditi Pal, Murari Mitra, M.Z. Anis S-117 A Cognitive Business Intelligence System for Dynamic Portfolio Design...19 Shyam A V, Swain A K S-150 Signals of Initial Public Offering (IPO) Underpricing: Indian IPO Market Smitha V Shenoy, K Srinivasan S-112 Rainfall Insurance in India...20 Aditya Bansal, Girish Singhal, Sudhir Dutt S-075 Predictive Modeling of Non-compliance Detection...21 Venugopal Jarugumalli, Suresh Venkata, Sathyanarayana Ramani, KV Nathan S-118 Design of Marketing Promotions of Low-Ticket sized Consumer Goods and Services using Probabilistic Simulation Modelling...21 Sachin Sapte S-099 Business simulations: Building uncertainty into learning and strategy...22 Debashish Banerjee S-177 Using Analytics to pitch the right product to customers at inbound channels...22 Subhankar Mukherjee S-175 Modelling of Indian Stock Prices using Nonhomogeneous Poisson Processes with Time Trends...24 Rupal Shah, K. Muralidharan S-056 A Non Markovian Time Dependent Point Process Approach To Estimate Cumulative Loss Due To Defaults...24 KSS Iyer, Abhijit Chirputkar 3rd IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence 07

10 S-113 Time Series Analysis Using Wavelet Filters Improved Instance Based Learning For Multi-Step Predictions...25 Pushpalatha M P, Nalini N S-141 Forecasting inflation expectation using financial market data...25 Shreyes Upadhyay, Madhav Kumar S-035 On Nonparametric Phase-II Joint Monitoring of Location and Scale based on a Single Chart...26 Shovan Chowdhury, Amitava Mukherjee, Subha Chakraborti S-084 Economic Measurement and Empirical Analysis of Health Inequality in Odisha-Application of Econometric & Statistical Methods...26 Usha Kamilla, Divya Gupta S-180 Modeling and Simulation of Glaucoma Risk Appraisal (GRA) Based on Clinical Data and Automated Early Nerve Fiber Layer Defects Detection using Feature Extraction in Retinal Colored Stereo Fundus Images...27 Jyotika Pruthi, Saurabh Mukherjee S-024 Alternative Goodness of Fit for Continuous Dependent Variable...27 Sandeep Das S-069 Hierarchical Models in Marketing Mix and Price Promotion Analysis...28 Zaki Ashraf, Lakshmi Prasad V S-077 Retailer Pricing Impact of Cross Channel Price and Store on Sales Lakshmi Prasad V, Priya Viswanathan S-147 Advance Analytics Shaping The Marketing Strategy of Insurance Organizations - An Approach...29 P. H. A. Desik, Ravi Babu, Sureshkumar Dubagunta, Samarendra B S-071 Scientific Business Forecasting: Case of Vehicle Demand Forecasting in Indian Market...30 Nilmadhab Mandal S-202 Hellinger Type Distance using Probability Generating Functions in Parameter Estimation for MultivariateDiscrete Distributions...30 C. M. Ng, S. H. Ong, H. M. Srivastava S-193 Statistical Analysis for the Discrete Charlier Series Distribution...31 Tan ZM, Chua KC, Ong SH S-167 Zero-inflated integer-valued time series processes...31 Raju Maiti, Atanu Biswas, Apratim Guha, Seng Huat Ong S-096 SB-robustness of Performance Measures of Control Chart...31 Arnab Kumar Laha, Pravida Raja A.C S-199 Purchase Intention of Extended Warranty - An Integrated Model...32 Jithesh Kumar K. S-197 A Study on Coordination Mechanism for Return Policy Contracts with Warranty...32 Shirsendu Nandi S-203 Patterns of PED Test Sanctions in Professional Sports Baseline and Implications for Research...32 Deepak Dhayanithy S-195 Sectoral Choice of Credit in Rural India...33 Debdatta Pal, Arnab K. Laha S-046 Application of Data Mining techniques for energy loss Estimation with partial Data...33 Atul Pratap Singh S-068 A Combined Structured and Unstructured Data Mining Approach to Identify the Opportunity in SMB Unified Communication Space...34 Aswinraj Govindaraj, Paromita Sen, Karen Zhang, Michael Glander, Daryl Berry, Jacob Chi 08 3rd IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence

11 S-145 Improving Employee s Learning Performance by Prediction using Decision Tree Algorithm...34 Vandana Sharma, Solomon Manuelraj S-130 Optimizing Agency Efficiency for Aadhaar enrolments using Data Analytics...35 K Vinay Kumar S-033 Study on Effectiveness of Mobile Telephone Services in Assam and the North East...35 Niranjan Agarwal, K.M.Date S-072 VIRTUAL ADDICTION-a terrific Mania...36 Sangeeta Trott S 187 Modeling Sales of Automobiles With Customer Engagement in Facebook...37 Tuhin Chattopadhyay S-019 Identifying the factors influencing purchasing decision of consumer of a new product-a study in Consumer Psychology...37 Madhuchhanda Karmakar S-013 Key Determinants of Mortgage Default: A Cross Sectional Analysis...38 Sanjay Kr. Mishra, Arti Devi S-047 Impact of Operating Efficiency on Valuation of Firms...39 Dyal Bhatnagar, Pritpal Singh Bhullar S-085 Value Implication of Analysts Recommendations: Empirical Evidence from Indian IPO Market...39 Seshadev Sahoo S-189 Change point detection in Gamma distributions with economic applications...40 K.Sanath S-041 Data Quality and Health Management Tool...40 Anuja A Kokrady, Anup Merkap S-093 Dynamic Insurance Pricing Analytics A GLM Perspective...40 P.H.A. Desik, Suresh Kumar Dubagunta, Bhishma Gajavelli, Vivek Rathi S-098 Comparison of linear and logistic regression for segmentation...41 Debashish Banerjee, Kranthi Ram Nekkalapu S-123 Data Mining: Discover Hidden Value...42 Sachchidanand Singh S-133 E-Commerce verses M-Commerce: The future of Online Marketing, A comparative study...42 Madhusmita Choudhury, I.G.Srikanth S-190 Ra.One Success or Failure?...43 Indu Mehta, Richard Suman Halder S-116 Data Envelopment Analysis Approach for Analyzing Human Competency and Enhancing Service Quality...43 Reshmi Manna, Ravi Shankar S-040 Greedy Search and Genetic Algorithm - Combined approach in 3PL Optimization...44 Vivekanandhan.P, Paramasivam.A, Anand.S, Vasanthavanan.T, Gopinath.B S-191 Prioritization of Barriers Faced By Independent Power Producers in Wind Power Industry A Multi Criteria Approach...45 Mallikarjune Gowda M.C., Bharath Repaka, Puttabore Gowda, Rajakumar D G, Chandrashekar R S-020 Analytical Network Process (ANP) Based Modeling For Analysing The Risks In Traditional, Agile And Lean Supply Chain Mahesh Chand, Tilak Raj, Ravi Shankar S-030 Mobility Mining Techniques for Big Data Analysis in Supply Chain Traffic...46 Sajimon Abraham, Siby Zacharias, P. Sojan Lal S-031 Multi-Echelon Efficiency Decomposition of Serial Supply Chains...46 Mithun J. Sharma, Yu, Song Jin 3rd IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence 09

12 S-110 Impact of World IT Stock Indices on Indian IT Stock Indices- A Linear Regression Modelling Approach...47 Dharmender Jhamb S-160 An Empirical Analysis of Governance Practices of Indian Firms...47 Arunima Haldar, S.V.D. Nageswara Rao S-170 Macroeconomic Factors and FDI determining impact on India s growth and development: a Cointegration Analysis...48 Preeti Flora, Gaurav Agrawal, Manoj Kumar Dash S-183 Interpreting Financial Datasets by Time Series Data mining Techniques: A Search for Similarities and Features...48 Anushree Goutam Ringne, Durga Toshniwal S-036 Board Independence and Firm Performance in India...48 Pranati Mohapatra S-149 Structural Equation Modeling: A study on Cyber Banking in India...49 Divya Gupta, Usha Kamilla S-188 Combining Expert Opinions for Probabilistic Risk Analysis...50 Sriram Bharadwaj R, Arnab K Laha S-025 An Experiment on Role of Anchoring Bias in Financial and Economic Decision Making. 50 Gautam Bandyopadhyay, Arindam Banerjee, Prithiraj Banerjee, Parijat Upadhyay S-205 Donor profiling and donation enhancement strategy: A project completed for Save the Children...51 Uma Venkataraman S-206 Improving court case tracking efficiency for Delhi High Court...51 Uma Venkataraman S-207 Data Quality Improvement for Automated Data Flow Project implementation of Allahabad Bank...52 Uma Venkataraman KN-4 Analytics Journey to ROI...52 Amit Khanna KN-5 Big Data and Marketing Analytics...53 Arvind Sahay KN-6 Big Data Analytics: Transforming big data into big value...54 Sudipta K. Sen S-200 Discrete-Valued Time Series Using Categorical ARMA Models...54 Peter X.-K. Song, R. Keith Freeland, Atanu Biswas, Shulin Zhang S-194 Optimal sample proportion for a two-treatment clinical trial in presence of surrogate endpoints...55 Buddhananda Banerjee, Atanu Biswas, Saumen Mandal S-201 Vital roles of generating functions in Lie-theoretic approach...55 Manik C Mukherjee S-196 Bootstrapping for Significance in Clustering Ranjan Maitra, Soumen Lahiri, Volodymyr Melnykov S-049 On the use of K-means algorithm with Mahalanobis distances...56 Igor Melnykov, Volodymyr Melnykov S-198 A Method of Combing Mixture Components for Colour Estimation Problems...56 Subhra Sankar Dhar, Kajsa Mollersen, Fred Godtliebsen S-159 Robustness of tests for the concentration parameter of circular normal distribution: A breakdown approach...57 Arnab Kumar Laha, Mahesh K.C S-059 Social Media Analytics...57 Suresh Chakravarthy, Sandeep Kumar Sharma, Sandeep Kumar Sethi, Latesh Joshi, Jitesh Dhupar, Pavan Kumar Vedam 10 3rd IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence

13 S-140 Developing Advertising Response Models through two Level Regression...58 Priya Viswanathan, Lakshmi Prasad V S-143 Method for Improving Customer Insights Using Unstructured Data in Retail...59 Suresh Veluchamy, Gopal Govindasamy S-097 Logistic Regression or Neural Network for Risk Assessment...59 Vandita Bansal, Subarna Roy S-109 Empirical Investigation of Indian Currency Dynamics: Pricing, Volatility and Forecasting...59 Sanjay K Singh S-135 A Simplified Algorithm of X-11 Census Method II Seasonal Adjustment Program and An Alternative software of Proc X11/X12 in SAS for Monthly and Quarterly Data..60 Ariful Islam Mondal, Saran Ishika Maiti S-142 Performance Evaluation of the Listed Companies in Indian IT Industry based on Factor Analysis...60 Manisha Sharma, Prashant Gupta S-154 Analysis of the Effect of Service Broker Policy on the Costing of Cloud Computing Based Services...61 Vikas Kumar, Jitendra Singh S-086 Risk Analysis of FDI in Multi-Brand Retail Sector in India Piyush Nawathe, Sumit Kumar S-103 Attribution Modeling for online Companies: A study of different approaches...62 Zubin Joy Saini, Pratyush Kumar, Vishal Aggarwal, Ramneesh Singla S-162 Assessing Response Towards Internet Advertisements Using RCE Scale (With Special Reference To Banking Products And Services)...62 Deepak Jaroliya, Pragya Jaroliya S-073 Business Analytics and Business Intelligence: A boon or bane for Public Sector Enterprises...63 Shaheen, Mishra R K, Hamendra Kumar Dangi S-204 Quality in Management Education- Analytics and Rankings...64 Kalika Bansal, Arnab Kumar Laha S-027 A restricted r-k class estimator in the mixed regression model with non-spherical disturbances...64 Shalini Chandra, Nityananda Sarkar S-011 Finance-Social Development and Economic Growth: The Panel VAR Application...65 Rudra P. Pradhan, Bele Samadhan, Sasikanta Tripathy, Subhanish Dey S-063 Financial Development and Economic Growth in Emerging Asian Countries: A Panel Co integration Approach...65 Ved Pal Sheera, Ashwani Bishnoi S-088 Causal Relationship between the major worlds financial markets with particular attention on United States & European Union Using Cross Correlation Arima, Sas/Ets...66 Changani Jagdish, Amit Saraswat, Dinesh Thapak S-171 Comparing nonlinear dynamics of emerging and developed stock markets using Empirical Mode Decomposition...66 Kousik Guhathakurta, Soumyajit Panigrahi, Shana Vijay Gawande S-126 Does Investors Risk Appetite affect Value-at-Risk (VaR)? - A Study on Selected Indian Stocks...67 Piyali Dutta Chowdhury, Basabi Bhattacharya S-185 Simultaneous Modelling of Skewness and Sparse Time- Varying Jumps in Asset Return with Stochastic Volatility...67 Sujay K Mukhoti, Pulak Ghosh 3rd IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence 11

14 S-029 Solving Sales Force Allocation problem using a Differential Evolution algorithm...68 Debayan Bose, Bindu Narayan S-070 Adapting to emerging visualization techniques for advanced data analytics...68 Yugandhar Chodagam, Panini Jannabhatla, Raghu Nemani, Anoop Nambiar S-125 Data Visualization: Techniques and Applications...69 Priyam Banerjee, Geetanjali Chakraborty, Abhimanyu Dasgupta S-080 Themes and Sentiment Classification Using Support Vector Machines...69 Subhamitra Chatterjee S-166 Building an ensemble of machine learners to predict the US Census mail return rates...70 Shashishekhar Godbole, Madhav Kumar, Shreyes Upadhyay S-067 VaR for a generalized IGARCH type non-stationary data using extreme value theory Arabin Kumar Dey, Shyam Sundar Soumitra Josyula S-066 Use of Forced Distribution System in Appraising Employee s Performance: Its Problem and Solution...70 Rachana Chattopadhyay, Anil Kumar Ghosh S-064 Some strategic aspects of supply chain configurations...71 Debapriya Sen S-065 Statistical simulation using Markov Chain Monte Carlo (MCMC) method and its adaptations G K Basak, Arunangshu Biswas S-153 Campaign Effectiveness and Design of Letter Campaign...73 Vineeta Nair, Milind Kokate, Siddhartha Roy, Girdhar Agarwal S-155 Propensity to Pay Model...73 Milind Kokate, Vineeta Nair Prashant Shinde, Agarwal G. G., Siddhartha Roy S-156 Time Series Analysis for Call Volume Forecasting in Contact Centre used for Manpower Planning and Scheduling...74 Prashant Anant Shinde, Siddhartha Roy S-078 Warranty Analytics...75 Geetanjali Chakraborty, Abhimanyu Dasgupta S-178 Enhancing the value of Predictive in-silico Models in Pre-Clinical Pharmaceutical Research Projects, by providing enriched information to researchers, using data analysis and visualization approaches...75 Sanjay Srivastava, Pierre Bonneau S-100 Advanced Market Mix Modeling Techniques: Evaluation and Comparison...76 Rajat Narang, Anika Mahajan, Rohan Aggarwal S-102 Deriving Optimal Bundle Use of Combination of Anchored MaxDiff and TURF...76 Supriya Suri, Jayant Rajpurohit, Manish Mittal S-106 Package Optimization Through Conjoint and TURF Analysis Rajat Narang, Raj Ganla, Kanika Malik, Anant Prakash S-208 Analyzing alternate storage strategies in mobile rack-based order-pick Systems...78 Shobhit Nigam, Debjit Roy S-114 Estimating the Likelihood of a Customer Purchase...78 Susan Mani 12 3rd IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence

15 KN-1 Big Data : The challenges to official statistics TCA Anant Chief Statistician of India, and Secretary, Ministry of Statistics and Programme Implementation. Official Statistics in the last 60 years or so has evolved enormously. In the period before the second world war, the work of National Statistical Office may have been limited to giving a simple descriptive statistics of the nation based on relatively few sources of administrative records. The period after the war saw rapid growth in the range and scope of statistics put out by National Statistics Offices. These now include at one end measures of economic activity such as National Income to a range of measures of social development and human well-being on the other end. But these developments are overwhelmed by the sheer size and scope of information which has become available in the last 20 years through the growth of communication, internet and the widespread availability of computing resources. The rise of the internet has made available wide range of data. The data arrives from the fact that individuals, Governments and businesses are using the new technology to maintain records and manage their affairs. Thus it is in principle possible to observe a individual on where he goes from his mobile, what he buys from his credit card so on. On the one hand these raise the possibility of detailed descriptions of our society on the other the fears of loss of privacy. KN-2 Online Early Detection of Change for High Volatility Multivariate Portfolios and Updating VaR Ashis SenGupta Applied Statistics Unit, Indian Statistical Institute, Kolkata, INDIA Due to the initiation of the open market and the emergence of international players in our domestic financial horizon, prices of shares, stocks and even bonds have been experiencing high volatility previously not encountered. The term volatility refers to the variability of the financial returns, which may change from time to time. Financial data exhibit certain patterns, which are crucial to be identified in order to achieve satisfactory model specification. Of special concern among these patterns is the display of fat tails, which translates to high volatility or infinite variance. Asymmetry in the distribution is another such important feature. In the modelling and forecasting of returns from financial investments or for exchange rates, volatility plays a key role. It relates to risk management, derivative pricing and hedging, market making, market timing and portfolio selection. Its impact on the change in price is profound. An option trader would like to know the volatility that is expected over the future life of the contract. To hedge his contract, he may be interested in knowing how volatile the stock market is in future. More generally, an investor seeks an early determination of the possible change in his investment portfolio during the course of trading. Gaussian assumption has been the fundamental key underlying the theory of modelling the modern portfolios. Usually, efficient portfolios are given by the traditional mean-variance trade-off consideration, where the investor has to maximize the expected return (profit) for a given variance (volatility). In the portfolio analysis framework, the variance corresponds to the risk measure. Thus, for high volatility non-gaussian, specially fat-tailed distributions with infinite variance, alternate measures must be defined. 3rd IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence 13

16 Given the high volatility exhibited in various price markets, the early detection of change-point in price distributions is of crucial current interest. We first present methods for detecting change retrospectively. Next, online optimal decision rules are presented for such early detection as using distributions in the exponential family. It is proven that a well designed multivariate portfolio consisting of suitably correlated profiles can achieve this detection at an earlier stage compared to univariate decision rules applied independently. This important feature is shown to persist even for low shift-to- noise ratio environments. It is then recalled that unfortunately high volatility distributions are seldom, if at all, members of the exponential family. Cauchy and double exponential distributions are some such popular models. We thus study the performance of our rules for sensitivity and robustness with respect to these distributions. The work of Mandelbrot has shown the appropriateness of more general families, which include these two distributions, for price distributions. However, in general these families do not possess any analytical closed form for their probability density functions. This leads to the complexity of inference involving the parameters of such distributions. In this era of emerging complex problems, multidisciplinary research in mathematical sciences has become indispensable. Directional statistics is one such scientific innovation as which on one hand is developed from the conglomeration of the inductive logic of statistics, objective rigor of mathematics and the skills of numerical analysis of computer science. On the other hand, it possesses the richness to handle the need for providing statistical inference to a wide and emerging arena of applied sciences. Directional data (DD) in general refer to multivariate observations on variables with possibly circular ones. Circular random variables are usually those which pertain to observations on directions, orientations, etc. Data on periodic occurrences can also be cast in the arena of DD. Analysis of such data sets differs markedly from those for linear ones due to the disparate topologies between the line and the circle. We overcome the aforementioned problem of modelling high volatility data by appealing to the area of probability distributions for directional data. First, methods of construction of probability distributions for such data are presented. This is a challenging problem leading to that of deriving distributions on smooth manifolds, such as those on the torus and the hypertorus. Then, it is shown how methods for DD can be exploited to provide solutions to obtaining crucial inference for high volatility distributions. The use of DDSTAP, the statistical package for DD, developed by the speaker is demonstrated for detection of change-point retrospectively. Online detection methods are then proposed for such distributions. While the modelling problem may be overcome by a judicious choice from the family of distributions enhanced for DD, non-trivial difficulties are encountered in deriving optimal decision rules with them. Once the change-point is detected, characteristics of the portfolio affected by it must be evaluated. The most prominent characteristic in terms of the risk measure certainly is Value at Risk (VaR). It refers to the question of how much a portfolio position can fall in value over a certain time period with a a priori chosen probability. VaR is the crucial measure in the financial sectors for determining market risk and mandatory capital reserves. The estimation of VaR is hence of substantial importance. We briefly discuss this aspect also. The above methods are exemplified through several real-life financial data sets. Finally, several interesting and important problems in this context for future research are exposed. 14 3rd IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence

17 KN-3 Art, Science and the Secret Sauce - A recipe for building world class analytics capability Srikanth Velamakanni Fractal Analytics Inc. Analytics has become critical to better decision making for organizations around the world. While the concepts surrounding analytics have been around for 50+ years, recent developments in AI/Machine learning (science), understanding human behavior (art) and the organizational environment (secret sauce) have made it challenging for companies to create world class analytics capability to deliver innovation, insight and impact. The complexities of Big data, need for sophisticated techniques, lack of sufficient consumer insight and challenges in hiring/building a team make it hard for organizations to realize the promise of analytics to drive competitive advantage. S-028 Role of Inclusive Financial Services in Empowering the MSME sector for a Green Economy: An Indian Perspective Raka Banerji Army Institute of Management, Kolkata Sudipti Banerjea University of Calcutta, Kolkata T his research paper attempts to do an impact assessment of the role of financial services in entrepreneurship development within the MSME sector in India. Access to well-functioning and efficient financial services can empower the micro and small enterprises, allowing them to better integrate into the country s productive processes and contribute to the more resourceefficient and sustainable economic growth, thus, addressing the issues of economic and social equity. Financial inclusion in this particular sector can facilitate the up scaling of innovations for nurturing the energy-efficient technology necessary for sustaining the Green Economy. The study focuses on a strongly positive link between the total outstanding credit of all the scheduled commercial banks to the MSME sector and growth of the total product of the MSME sector during , using the Regression Analysis and Time Series model like NLS and Autoregressive Moving Average (ARMA) approach. The results highlight that the enhancement of financial assistance clearly generates a positive and statistically significant impact on the growth of the MSME sector which is capable of contributing in big way towards the self-sustaining economic growth in India. This study also highlights several related issues on which more exploratory research is needed in the context of channelizing financial assistance to the MSME sector for effectively taking the economy on the path of green growth. 3rd IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence 15

18 S-043 Commodity Indices: Defining their Role in Indian Market- Understanding Dynamic Conditional Correlation with Stock Index in Multi-resolution Set-up Rahul Deora Indian Institute of Technology Kharagpur Brajesh Kumar O P Jindal Global University, Recently commodity products have been widely recognised as an important asset class. Financial investors regard commodity investing very effective and important as it helps in diversifying portfolio risk, improving the risk/return profile of the portfolio and works as hedging tool against inflation. In India, commodity future trading is relatively new and facing restrictions/oppositions in terms of less innovative products and limited participation. Commodity indices in India are not traded, however, exchanges and investors are advocating the need of commodity indices as important investment vehicle. Hence, this study proposes a wavelet-based multi-resolution DCC-GARCH model to investigate dynamic conditional correlation between Indian stock index and commodity indices. We consider NIFTY as equity index and for commodity indices, MCXCOMDEX, MCXAGRI, MCXENERGY, MCXMETAL of MCX are used. We decompose the daily data into different frequencies/resolutions (2, 4, 8, and 16 days) using wavelet analysis to understand the characteristics of conditional correlation structure between NIFTY and commodity indices. The data rages from 1st August 2005 to 5th January We find that direction and magnitude of conditional correlations significantly vary with their time scales. MCXMETAL is relatively less correlated (though very less on absolute basis) with NIFTY in shortest time horizon (2 and 4 days) than other indices, gives a signal to daily traders to accommodate MCXMETAL in their portfolio. However, in longest time horizon (8 and 16 days), MCXAGRI shows lower correlation giving similar signals to investors who trade on monthly or bi-monthly basis. S-018 Devising the Usp in Sporadic Markets - A Study Done on Wedding Planners in Tamilnadu P.Baba Gnanakumar Sri Krishna Arts and Science College, Coimbatore This research explores the unique selling proposition (USP) in wedding planner market and aim to position it at various service levels. We explored the mismatch between the customer expectations and wedding planner services, and discovered the common factor that discriminates both of them in the state of Tamilnadu. Based on the common factor, the service level leap for the wedding planners has been identified. This research identified the gap among customer expectation and wedding planners service level weights by extorting multivariate analysis; determine the USP by employing Box Ljung Statistic, and recursive positioning of USP with MDD analysis. We conclude that an integrated time-driven network is essential to satisfy the customers. The additional cost involved in the time-driven network is to be considered as an opportunity cost. Time-driven network can be implemented using MDD analysis. The research enables to identify a non-linear growth tools necessary while marketing to different types of customers following different cultures. Keywords: Service level leaps, Fragmented Marketing, Extreme Marketing 16 3rd IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence

19 S-139 Applications of Bayesian Networks in Business Intelligence Heena Timani Ahmedabad University Bayesian network is a kind of probabilistic graphical model to represent the relationships A between variables. It provides an effective and natural way to represent causal relationships. Bayesian networks have the ability of capturing both qualitative knowledge through their network structure, and quantitative knowledge through their parameters. While expert knowledge from practitioners is mostly qualitative, it can be used directly for building the structure of a Bayesian network. In addition, data mining algorithms can encode both qualitative and quantitative knowledge and encode both forms simultaneously in a Bayesian network. As a result, Bayesian networks can bridge the gap between different types of knowledge and serve to unify all available knowledge into a single form of representation. Within many domains, the amount of data available is so large that learning directly on the data is intractable. In order to deal with this intractability, learning is applied to features extracted from the data. The method for extracting features is generally specific,. In this paper applications related to data mining in Electronic commerce are discussed using Bayesian Classifiers and Bayesian Networks techniques for extracting and encoding knowledge from data. Keywords: Bayesian Classifiers, Probabilistic graphical model, Electronic Commerce, Data mining S-181 Improvements to be introduced in the algorithms of CVRP and VRPB when solved over instances involving asymmetric distances and heterogeneous fleet. Kaviti Keshav Kumar, Sunil Agrawal PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur T his paper shows the modifications to be introduced in the models of Capacitated Vehicle Routing problem (CVRP) and Vehicle Routing Problem with backhauls (VRPB). In the paper Ubeda et al. (2011) CVRP and VRPB models have been proposed over instances involving symmetric distances between the nodes which follow the triangular inequality. The fleet has been considered to be homogeneous. Our work is based on instances in which the distances between the nodes are asymmetric and do not follow the triangle inequality and the fleet is considered to be heterogeneous. The CVRP and VRPB models given by Ubeda et al. (2011) are good enough to solve over the instances in our work and provide an optimal solution. However, in our work we have proposed two modifications in the constraints of the existing models and have shown with the help of a numerical example that these modifications lead to the improvement in the value of the objective function obtained. Three different cases have been considered to show the effects of these two modifications. Case I consists of the model with the first modification only which gives the flexibility to choose only those vehicles in the fleet that are good enough to satisfy the demand of all the customer nodes instead of using all the available vehicles. Case II consists of the model with the second modification which allows every node to be visited by more than one vehicle instead of restricting every node to be visited by a single vehicle. Case III consists of the model with both the modifications 3rd IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence 17

20 considered simultaneously. In the numerical problem the results obtained by the application of the above mentioned cases in the respective models have been compared with the results obtained by the unmodified models of CVRP and VRPB (given by Ubeda et al. (2011)) when solved over the instances mentioned above. The results obtained in the former case were better compared to the latter and they have been explained in a detailed manner in the paper. S-081 Determinants of Lapsation of Policies: An Investigation in Indian Insurance Sector Sunita Mall School of Science, NMIMS Seshadev Sahoo IIM Lucknow In this Research paper, we study the impact of fourteen explanatory variables related to product and policy holder characteristics on lapsation of Indian Life Insurance Company. Our study has a distinguished contribution to the existing literature. The data is obtained from a large Indian life insurance company and includes product categories like Traditional and Unit-linked products which is a consumer database of 2967 contracts. The analyzed time period taken is from We extend the existing literature by considering some new explanatory variables related to product and policyholder characteristics like dependency, occupation, gender, education, marital status and outstanding premium. Lapse of a contract is defined as a binary event as the contract can either be valid or lapsed and we use logistic regression model to analyze the lapse rates with respect to the specific explanatory variables. The findings of the paper show that product characteristics like sum insure, product type, outstanding premium, mode of payment, policy duration and outstanding policy duration and policy holder characteristics like age of the policy holder, occupation, dependency, marital status are important drivers of lapsation. For policy duration, our result contradicted the existing literature. This paper displays a better picture of the lapse drivers and will surely help the Insurance Company to minimize the lapse. Keywords: Lapsation, Dependency, Outstanding premium, outstanding duration, Rider S-083 On Testing Exponentiality against NBAFR Alternatives Aditi Pal, Murari Mitra Bengal Engineering and Science University, Shibpur M.Z. Anis Indian Statistical Institute, Kolkata In this paper, we propose an interesting approach for testing exponentiality against NBAFR alternatives based on a technique involving density estimation. Thus our testing problem can be formulated as: H 0 : F is exponential vs. H 1 : F is NBAFR and not exponential A measure of deviation from exponentiality has been derived on the basis of an inequality which we have proved. A test procedure has been suggested and a test statistic has been constructed using density estimators. The asymptotic normality of the test statistic has been 18 3rd IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence

Master of Mathematical Finance: Course Descriptions

Master of Mathematical Finance: Course Descriptions Master of Mathematical Finance: Course Descriptions CS 522 Data Mining Computer Science This course provides continued exploration of data mining algorithms. More sophisticated algorithms such as support

More information

Predictive modelling around the world 28.11.13

Predictive modelling around the world 28.11.13 Predictive modelling around the world 28.11.13 Agenda Why this presentation is really interesting Introduction to predictive modelling Case studies Conclusions Why this presentation is really interesting

More information

DATA MINING TECHNIQUES AND APPLICATIONS

DATA MINING TECHNIQUES AND APPLICATIONS DATA MINING TECHNIQUES AND APPLICATIONS Mrs. Bharati M. Ramageri, Lecturer Modern Institute of Information Technology and Research, Department of Computer Application, Yamunanagar, Nigdi Pune, Maharashtra,

More information

ElegantJ BI. White Paper. The Competitive Advantage of Business Intelligence (BI) Forecasting and Predictive Analysis

ElegantJ BI. White Paper. The Competitive Advantage of Business Intelligence (BI) Forecasting and Predictive Analysis ElegantJ BI White Paper The Competitive Advantage of Business Intelligence (BI) Forecasting and Predictive Analysis Integrated Business Intelligence and Reporting for Performance Management, Operational

More information

Contents. List of Figures. List of Tables. List of Examples. Preface to Volume IV

Contents. List of Figures. List of Tables. List of Examples. Preface to Volume IV Contents List of Figures List of Tables List of Examples Foreword Preface to Volume IV xiii xvi xxi xxv xxix IV.1 Value at Risk and Other Risk Metrics 1 IV.1.1 Introduction 1 IV.1.2 An Overview of Market

More information

Better decision making under uncertain conditions using Monte Carlo Simulation

Better decision making under uncertain conditions using Monte Carlo Simulation IBM Software Business Analytics IBM SPSS Statistics Better decision making under uncertain conditions using Monte Carlo Simulation Monte Carlo simulation and risk analysis techniques in IBM SPSS Statistics

More information

Admission Criteria Minimum GPA of 3.0 in a Bachelor s degree (or equivalent from an overseas institution) in a quantitative discipline.

Admission Criteria Minimum GPA of 3.0 in a Bachelor s degree (or equivalent from an overseas institution) in a quantitative discipline. Overview Offered by the Mona School of Business in conjunction with the Department of Mathematics, Faculty of Science & Technology, The University of the West Indies. The MSc. ERM degree programme is designed

More information

Course Descriptions Master of Science in Finance Program University of Macau

Course Descriptions Master of Science in Finance Program University of Macau Course Descriptions Master of Science in Finance Program University of Macau Principles of Economics This course provides the foundation in economics. The major topics include microeconomics, macroeconomics

More information

Get to Know the IBM SPSS Product Portfolio

Get to Know the IBM SPSS Product Portfolio IBM Software Business Analytics Product portfolio Get to Know the IBM SPSS Product Portfolio Offering integrated analytical capabilities that help organizations use data to drive improved outcomes 123

More information

Algorithmic Trading Session 1 Introduction. Oliver Steinki, CFA, FRM

Algorithmic Trading Session 1 Introduction. Oliver Steinki, CFA, FRM Algorithmic Trading Session 1 Introduction Oliver Steinki, CFA, FRM Outline An Introduction to Algorithmic Trading Definition, Research Areas, Relevance and Applications General Trading Overview Goals

More information

DecisionCraft Proposition

DecisionCraft Proposition Ltd. DecisionCraft Proposition Background Consulting firm with focus on quantitative modeling & analytical techniques Founded by thought leaders with vast experience in academia & industry Teams with ideal

More information

ANALYTICS CENTER LEARNING PROGRAM

ANALYTICS CENTER LEARNING PROGRAM Overview of Curriculum ANALYTICS CENTER LEARNING PROGRAM The following courses are offered by Analytics Center as part of its learning program: Course Duration Prerequisites 1- Math and Theory 101 - Fundamentals

More information

Course Syllabus For Operations Management. Management Information Systems

Course Syllabus For Operations Management. Management Information Systems For Operations Management and Management Information Systems Department School Year First Year First Year First Year Second year Second year Second year Third year Third year Third year Third year Third

More information

A STUDY OF DATA MINING ACTIVITIES FOR MARKET RESEARCH

A STUDY OF DATA MINING ACTIVITIES FOR MARKET RESEARCH 205 A STUDY OF DATA MINING ACTIVITIES FOR MARKET RESEARCH ABSTRACT MR. HEMANT KUMAR*; DR. SARMISTHA SARMA** *Assistant Professor, Department of Information Technology (IT), Institute of Innovation in Technology

More information

Role of Social Networking in Marketing using Data Mining

Role of Social Networking in Marketing using Data Mining Role of Social Networking in Marketing using Data Mining Mrs. Saroj Junghare Astt. Professor, Department of Computer Science and Application St. Aloysius College, Jabalpur, Madhya Pradesh, India Abstract:

More information

INTERNATIONAL MASTER IN BUSINESS ANALYTICS AND BIG DATA

INTERNATIONAL MASTER IN BUSINESS ANALYTICS AND BIG DATA POLITECNICO DI MILANO GRADUATE SCHOOL OF BUSINESS BABD INTERNATIONAL MASTER IN BUSINESS ANALYTICS AND BIG DATA Courses Description A JOINT PROGRAM WITH POLITECNICO DI MILANO SCHOOL OF MANAGEMENT PRE-COURSES

More information

Principles of Data Mining by Hand&Mannila&Smyth

Principles of Data Mining by Hand&Mannila&Smyth Principles of Data Mining by Hand&Mannila&Smyth Slides for Textbook Ari Visa,, Institute of Signal Processing Tampere University of Technology October 4, 2010 Data Mining: Concepts and Techniques 1 Differences

More information

Auto Days 2011 Predictive Analytics in Auto Finance

Auto Days 2011 Predictive Analytics in Auto Finance Auto Days 2011 Predictive Analytics in Auto Finance Vick Panwar SAS Risk Practice Copyright 2010 SAS Institute Inc. All rights reserved. Agenda Introduction Changing Risk Landscape - Key Drivers and Challenges

More information

Master of Science in Marketing Analytics (MSMA)

Master of Science in Marketing Analytics (MSMA) Master of Science in Marketing Analytics (MSMA) COURSE DESCRIPTION The Master of Science in Marketing Analytics program teaches students how to become more engaged with consumers, how to design and deliver

More information

Sanjeev Kumar. contribute

Sanjeev Kumar. contribute RESEARCH ISSUES IN DATAA MINING Sanjeev Kumar I.A.S.R.I., Library Avenue, Pusa, New Delhi-110012 sanjeevk@iasri.res.in 1. Introduction The field of data mining and knowledgee discovery is emerging as a

More information

DEPARTMENT OF MANAGEMENT STUDIES MBA Master of Business Administration

DEPARTMENT OF MANAGEMENT STUDIES MBA Master of Business Administration DEPARTMENT OF MANAGEMENT STUDIES MBA Master of Business Administration Course No Course Title L T E P O TH C MS 5003 Basics of Probability and Statistics 2 0 0 0 4 6 3 MS 5004 Basics of Accounting and

More information

KIE SQUARE PERSPECTIVE

KIE SQUARE PERSPECTIVE KIE SQUARE PERSPECTIVE ANALYTICS DRIVEN COMPETITIVE EDGE IN SHOPPER MARKETING Analytics Driven Competitive Edge in Shopper Marketing Introduction For leading Consumer Packaged Goods (CPG) companies across

More information

Executive Program in Managing Business Decisions: A Quantitative Approach ( EPMBD) Batch 03

Executive Program in Managing Business Decisions: A Quantitative Approach ( EPMBD) Batch 03 Executive Program in Managing Business Decisions: A Quantitative Approach ( EPMBD) Batch 03 Calcutta Ver 1.0 Contents Broad Contours Who Should Attend Unique Features of Program Program Modules Detailed

More information

A constant volatility framework for managing tail risk

A constant volatility framework for managing tail risk A constant volatility framework for managing tail risk Alexandre Hocquard, Sunny Ng and Nicolas Papageorgiou 1 Brockhouse Cooper and HEC Montreal September 2010 1 Alexandre Hocquard is Portfolio Manager,

More information

Data Mining Applications in Higher Education

Data Mining Applications in Higher Education Executive report Data Mining Applications in Higher Education Jing Luan, PhD Chief Planning and Research Officer, Cabrillo College Founder, Knowledge Discovery Laboratories Table of contents Introduction..............................................................2

More information

Learning outcomes. Knowledge and understanding. Competence and skills

Learning outcomes. Knowledge and understanding. Competence and skills Syllabus Master s Programme in Statistics and Data Mining 120 ECTS Credits Aim The rapid growth of databases provides scientists and business people with vast new resources. This programme meets the challenges

More information

8.1 Summary and conclusions 8.2 Implications

8.1 Summary and conclusions 8.2 Implications Conclusion and Implication V{tÑàxÜ CONCLUSION AND IMPLICATION 8 Contents 8.1 Summary and conclusions 8.2 Implications Having done the selection of macroeconomic variables, forecasting the series and construction

More information

Marketing Mix Modelling and Big Data P. M Cain

Marketing Mix Modelling and Big Data P. M Cain 1) Introduction Marketing Mix Modelling and Big Data P. M Cain Big data is generally defined in terms of the volume and variety of structured and unstructured information. Whereas structured data is stored

More information

Analytics in Action. What do Jeopardy, Pampers, and Major League Baseball all have in common? October 24, 2012

Analytics in Action. What do Jeopardy, Pampers, and Major League Baseball all have in common? October 24, 2012 Analytics in Action What do Jeopardy, Pampers, and Major League Baseball all have in common? October 24, 2012 University of Cincinnati Tangeman University Center Theater Sponsored by LUCRUM, Inc. ABOUT

More information

International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, May-Jun 2014

International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, May-Jun 2014 RESEARCH ARTICLE OPEN ACCESS A Survey of Data Mining: Concepts with Applications and its Future Scope Dr. Zubair Khan 1, Ashish Kumar 2, Sunny Kumar 3 M.Tech Research Scholar 2. Department of Computer

More information

INDIAN STATISTICAL INSTITUTE

INDIAN STATISTICAL INSTITUTE Organized by SQC & OR Unit, ISI, Kolkata INDIAN STATISTICAL INSTITUTE Symposium on Business Analytics December 17-18, 2014 Scope and Objective of the Symposium: With the advent of time, human decision

More information

Business Process Services. White Paper. Predictive Analytics in HR: A Primer

Business Process Services. White Paper. Predictive Analytics in HR: A Primer Business Process Services White Paper Predictive Analytics in HR: A Primer About the Authors Tuhin Subhra Dey Tuhin is a member of the Analytics and Insights team at Tata Consultancy Services (TCS), where

More information

Optimization applications in finance, securities, banking and insurance

Optimization applications in finance, securities, banking and insurance IBM Software IBM ILOG Optimization and Analytical Decision Support Solutions White Paper Optimization applications in finance, securities, banking and insurance 2 Optimization applications in finance,

More information

Quantitative Methods for Finance

Quantitative Methods for Finance Quantitative Methods for Finance Module 1: The Time Value of Money 1 Learning how to interpret interest rates as required rates of return, discount rates, or opportunity costs. 2 Learning how to explain

More information

International Journal of World Research, Vol: I Issue XIII, December 2008, Print ISSN: 2347-937X DATA MINING TECHNIQUES AND STOCK MARKET

International Journal of World Research, Vol: I Issue XIII, December 2008, Print ISSN: 2347-937X DATA MINING TECHNIQUES AND STOCK MARKET DATA MINING TECHNIQUES AND STOCK MARKET Mr. Rahul Thakkar, Lecturer and HOD, Naran Lala College of Professional & Applied Sciences, Navsari ABSTRACT Without trading in a stock market we can t understand

More information

ICT Perspectives on Big Data: Well Sorted Materials

ICT Perspectives on Big Data: Well Sorted Materials ICT Perspectives on Big Data: Well Sorted Materials 3 March 2015 Contents Introduction 1 Dendrogram 2 Tree Map 3 Heat Map 4 Raw Group Data 5 For an online, interactive version of the visualisations in

More information

CoolaData Predictive Analytics

CoolaData Predictive Analytics CoolaData Predictive Analytics 9 3 6 About CoolaData CoolaData empowers online companies to become proactive and predictive without having to develop, store, manage or monitor data themselves. It is an

More information

Multichannel Attribution

Multichannel Attribution Accenture Interactive Point of View Series Multichannel Attribution Measuring Marketing ROI in the Digital Era Multichannel Attribution Measuring Marketing ROI in the Digital Era Digital technologies have

More information

Masters in Financial Economics (MFE)

Masters in Financial Economics (MFE) Masters in Financial Economics (MFE) Admission Requirements Candidates must submit the following to the Office of Admissions and Registration: 1. Official Transcripts of previous academic record 2. Two

More information

LEVERAGING DATA ANALYTICS TO ACQUIRE AND RETAIN LIFE INSURANCE CUSTOMERS

LEVERAGING DATA ANALYTICS TO ACQUIRE AND RETAIN LIFE INSURANCE CUSTOMERS Tactful Management Research Journal ISSN: 2319-7943 Impact Factor : 2.1632(UIF) LEVERAGING DATA ANALYTICS TO ACQUIRE AND RETAIN LIFE INSURANCE CUSTOMERS Ms. Archana V. Rao Assistant Professor, Model College

More information

Statistics Graduate Courses

Statistics Graduate Courses Statistics Graduate Courses STAT 7002--Topics in Statistics-Biological/Physical/Mathematics (cr.arr.).organized study of selected topics. Subjects and earnable credit may vary from semester to semester.

More information

MSCA 31000 Introduction to Statistical Concepts

MSCA 31000 Introduction to Statistical Concepts MSCA 31000 Introduction to Statistical Concepts This course provides general exposure to basic statistical concepts that are necessary for students to understand the content presented in more advanced

More information

Invited Applications Paper

Invited Applications Paper Invited Applications Paper - - Thore Graepel Joaquin Quiñonero Candela Thomas Borchert Ralf Herbrich Microsoft Research Ltd., 7 J J Thomson Avenue, Cambridge CB3 0FB, UK THOREG@MICROSOFT.COM JOAQUINC@MICROSOFT.COM

More information

ADVANCED FORECASTING MODELS USING SAS SOFTWARE

ADVANCED FORECASTING MODELS USING SAS SOFTWARE ADVANCED FORECASTING MODELS USING SAS SOFTWARE Girish Kumar Jha IARI, Pusa, New Delhi 110 012 gjha_eco@iari.res.in 1. Transfer Function Model Univariate ARIMA models are useful for analysis and forecasting

More information

Gonzaga MBA Electives

Gonzaga MBA Electives Gonzaga MBA Electives MBA & MACC PROGRAMS Gonzaga MBA students complete a third of their program, 11 credits, in elective coursework, allowing them the flexibility to tailor the program based on personal

More information

Financial Trading System using Combination of Textual and Numerical Data

Financial Trading System using Combination of Textual and Numerical Data Financial Trading System using Combination of Textual and Numerical Data Shital N. Dange Computer Science Department, Walchand Institute of Rajesh V. Argiddi Assistant Prof. Computer Science Department,

More information

The University of North Carolina at Pembroke 2015-2016 Academic Catalog COMMON BODY OF KNOWLEDGE OR FOUNDATION REQUIREMENTS:

The University of North Carolina at Pembroke 2015-2016 Academic Catalog COMMON BODY OF KNOWLEDGE OR FOUNDATION REQUIREMENTS: Graduate Studies and Research 452 The University of North Carolina at Pembroke 2015-2016 Academic Catalog MASTER OF BUSINESS ADMINISTRATION (M.B.A.) Director: Nick Arena The Master of Business Administration

More information

DEPARTMENT OF BANKING AND FINANCE

DEPARTMENT OF BANKING AND FINANCE 202 COLLEGE OF BUSINESS DEPARTMENT OF BANKING AND FINANCE Degrees Offered: B.B., E.M.B.A., M.B., Ph.D. Chair: Chiu, Chien-liang ( 邱 建 良 ) The Department The Department of Banking and Finance was established

More information

Easily Identify Your Best Customers

Easily Identify Your Best Customers IBM SPSS Statistics Easily Identify Your Best Customers Use IBM SPSS predictive analytics software to gain insight from your customer database Contents: 1 Introduction 2 Exploring customer data Where do

More information

The Data Mining Process

The Data Mining Process Sequence for Determining Necessary Data. Wrong: Catalog everything you have, and decide what data is important. Right: Work backward from the solution, define the problem explicitly, and map out the data

More information

Operations and Supply Chain Management Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology, Madras

Operations and Supply Chain Management Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology, Madras Operations and Supply Chain Management Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology, Madras Lecture - 36 Location Problems In this lecture, we continue the discussion

More information

Think. Marketing Consulting and Research

Think. Marketing Consulting and Research Think. Marketing Consulting and Research 1 Simply put, because of big data, managers can measure, and hence know, radically more about their businesses, and directly translate that knowledge into improved

More information

Discovering, Not Finding. Practical Data Mining for Practitioners: Level II. Advanced Data Mining for Researchers : Level III

Discovering, Not Finding. Practical Data Mining for Practitioners: Level II. Advanced Data Mining for Researchers : Level III www.cognitro.com/training Predicitve DATA EMPOWERING DECISIONS Data Mining & Predicitve Training (DMPA) is a set of multi-level intensive courses and workshops developed by Cognitro team. it is designed

More information

Effective downside risk management

Effective downside risk management Effective downside risk management Aymeric Forest, Fund Manager, Multi-Asset Investments November 2012 Since 2008, the desire to avoid significant portfolio losses has, more than ever, been at the front

More information

Informational Content of Trading Volume and Open Interest An Empirical Study of Stock Option Market In India. Sandeep Srivastava

Informational Content of Trading Volume and Open Interest An Empirical Study of Stock Option Market In India. Sandeep Srivastava I. INTRODUCTION Informational Content of Trading Volume and Open Interest An Empirical Study of Stock Option Market In India Sandeep Srivastava Over the past three decades, option contract defined as a

More information

Dr. U. Devi Prasad Associate Professor Hyderabad Business School GITAM University, Hyderabad Email: Prasad_vungarala@yahoo.co.in

Dr. U. Devi Prasad Associate Professor Hyderabad Business School GITAM University, Hyderabad Email: Prasad_vungarala@yahoo.co.in 96 Business Intelligence Journal January PREDICTION OF CHURN BEHAVIOR OF BANK CUSTOMERS USING DATA MINING TOOLS Dr. U. Devi Prasad Associate Professor Hyderabad Business School GITAM University, Hyderabad

More information

Nine Common Types of Data Mining Techniques Used in Predictive Analytics

Nine Common Types of Data Mining Techniques Used in Predictive Analytics 1 Nine Common Types of Data Mining Techniques Used in Predictive Analytics By Laura Patterson, President, VisionEdge Marketing Predictive analytics enable you to develop mathematical models to help better

More information

Segmentation: Foundation of Marketing Strategy

Segmentation: Foundation of Marketing Strategy Gelb Consulting Group, Inc. 1011 Highway 6 South P + 281.759.3600 Suite 120 F + 281.759.3607 Houston, Texas 77077 www.gelbconsulting.com An Endeavor Management Company Overview One purpose of marketing

More information

STOCHASTIC ANALYTICS: increasing confidence in business decisions

STOCHASTIC ANALYTICS: increasing confidence in business decisions CROSSINGS: The Journal of Business Transformation STOCHASTIC ANALYTICS: increasing confidence in business decisions With the increasing complexity of the energy supply chain and markets, it is becoming

More information

How Organisations Are Using Data Mining Techniques To Gain a Competitive Advantage John Spooner SAS UK

How Organisations Are Using Data Mining Techniques To Gain a Competitive Advantage John Spooner SAS UK How Organisations Are Using Data Mining Techniques To Gain a Competitive Advantage John Spooner SAS UK Agenda Analytics why now? The process around data and text mining Case Studies The Value of Information

More information

Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization. Learning Goals. GENOME 560, Spring 2012

Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization. Learning Goals. GENOME 560, Spring 2012 Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization GENOME 560, Spring 2012 Data are interesting because they help us understand the world Genomics: Massive Amounts

More information

Make the Leap from ecommerce to Omni- Channel

Make the Leap from ecommerce to Omni- Channel Iaodesign/Shutterstock An ecommerce platform is the foundation for a successful Omni- Channel business model arvato Systems North America 6 East 32nd Street, New York, New York 10016 United States All

More information

IS MORE INFORMATION BETTER? THE EFFECT OF TRADERS IRRATIONAL BEHAVIOR ON AN ARTIFICIAL STOCK MARKET

IS MORE INFORMATION BETTER? THE EFFECT OF TRADERS IRRATIONAL BEHAVIOR ON AN ARTIFICIAL STOCK MARKET IS MORE INFORMATION BETTER? THE EFFECT OF TRADERS IRRATIONAL BEHAVIOR ON AN ARTIFICIAL STOCK MARKET Wei T. Yue Alok R. Chaturvedi Shailendra Mehta Krannert Graduate School of Management Purdue University

More information

Master of Business Administration COMMON BODY OF KNOWLEDGE OR FOUNDATION REQUIREMENTS: CBK OR FOUNDATION Principles of Accounting, 6 hours or

Master of Business Administration COMMON BODY OF KNOWLEDGE OR FOUNDATION REQUIREMENTS: CBK OR FOUNDATION Principles of Accounting, 6 hours or 473 MASTER OF BUSINESS ADMINISTRATION (M.B.A.) Director: Nick Arena The Master of Business Administration (MBA) is a professional degree program designed to accelerate entrepreneurial career development

More information

Data Mining + Business Intelligence. Integration, Design and Implementation

Data Mining + Business Intelligence. Integration, Design and Implementation Data Mining + Business Intelligence Integration, Design and Implementation ABOUT ME Vijay Kotu Data, Business, Technology, Statistics BUSINESS INTELLIGENCE - Result Making data accessible Wider distribution

More information

A Big Data Analytical Framework For Portfolio Optimization Abstract. Keywords. 1. Introduction

A Big Data Analytical Framework For Portfolio Optimization Abstract. Keywords. 1. Introduction A Big Data Analytical Framework For Portfolio Optimization Dhanya Jothimani, Ravi Shankar and Surendra S. Yadav Department of Management Studies, Indian Institute of Technology Delhi {dhanya.jothimani,

More information

Building a Database to Predict Customer Needs

Building a Database to Predict Customer Needs INFORMATION TECHNOLOGY TopicalNet, Inc (formerly Continuum Software, Inc.) Building a Database to Predict Customer Needs Since the early 1990s, organizations have used data warehouses and data-mining tools

More information

harpreet@utdallas.edu, {ram.gopal, xinxin.li}@business.uconn.edu

harpreet@utdallas.edu, {ram.gopal, xinxin.li}@business.uconn.edu Risk and Return of Investments in Online Peer-to-Peer Lending (Extended Abstract) Harpreet Singh a, Ram Gopal b, Xinxin Li b a School of Management, University of Texas at Dallas, Richardson, Texas 75083-0688

More information

Chapter: IV. IV: Research Methodology. Research Methodology

Chapter: IV. IV: Research Methodology. Research Methodology Chapter: IV IV: Research Methodology Research Methodology 4.1 Rationale of the study 4.2 Statement of Problem 4.3 Problem identification 4.4 Motivation for the research 4.5 Comprehensive Objective of study

More information

Vinay Parisa 1, Biswajit Mohapatra 2 ;

Vinay Parisa 1, Biswajit Mohapatra 2 ; Predictive Analytics for Enterprise Modernization Vinay Parisa 1, Biswajit Mohapatra 2 ; IBM Global Business Services, IBM India Pvt Ltd 1, IBM Global Business Services, IBM India Pvt Ltd 2 vinay.parisa@in.ibm.com

More information

UNIVERSITY OF DELHI -------------------

UNIVERSITY OF DELHI ------------------- S.NO.AER/2015/106 MASTER OF BUSINESS ADMINISTRATION (EXECUTIVE) SECOND YEAR (IV SEMESTER) EXAMINATION, 2015 ----------- The following candidates after having passed the Second Year (IV Semester) Examination

More information

Data Analytical Framework for Customer Centric Solutions

Data Analytical Framework for Customer Centric Solutions Data Analytical Framework for Customer Centric Solutions Customer Savviness Index Low Medium High Data Management Descriptive Analytics Diagnostic Analytics Predictive Analytics Prescriptive Analytics

More information

Use of Data Mining in Banking

Use of Data Mining in Banking Use of Data Mining in Banking Kazi Imran Moin*, Dr. Qazi Baseer Ahmed** *(Department of Computer Science, College of Computer Science & Information Technology, Latur, (M.S), India ** (Department of Commerce

More information

Prescriptive Analytics. A business guide

Prescriptive Analytics. A business guide Prescriptive Analytics A business guide May 2014 Contents 3 The Business Value of Prescriptive Analytics 4 What is Prescriptive Analytics? 6 Prescriptive Analytics Methods 7 Integration 8 Business Applications

More information

Chapter 6. The stacking ensemble approach

Chapter 6. The stacking ensemble approach 82 This chapter proposes the stacking ensemble approach for combining different data mining classifiers to get better performance. Other combination techniques like voting, bagging etc are also described

More information

Review on Financial Forecasting using Neural Network and Data Mining Technique

Review on Financial Forecasting using Neural Network and Data Mining Technique ORIENTAL JOURNAL OF COMPUTER SCIENCE & TECHNOLOGY An International Open Free Access, Peer Reviewed Research Journal Published By: Oriental Scientific Publishing Co., India. www.computerscijournal.org ISSN:

More information

TRANSACTIONAL DATA MINING AT LLOYDS BANKING GROUP

TRANSACTIONAL DATA MINING AT LLOYDS BANKING GROUP TRANSACTIONAL DATA MINING AT LLOYDS BANKING GROUP Csaba Főző csaba.fozo@lloydsbanking.com 15 October 2015 CONTENTS Introduction 04 Random Forest Methodology 06 Transactional Data Mining Project 17 Conclusions

More information

Better planning and forecasting with IBM Predictive Analytics

Better planning and forecasting with IBM Predictive Analytics IBM Software Business Analytics SPSS Predictive Analytics Better planning and forecasting with IBM Predictive Analytics Using IBM Cognos TM1 with IBM SPSS Predictive Analytics to build better plans and

More information

Driving Insurance World through Science - 1 - Murli D. Buluswar Chief Science Officer

Driving Insurance World through Science - 1 - Murli D. Buluswar Chief Science Officer Driving Insurance World through Science - 1 - Murli D. Buluswar Chief Science Officer What is The Science Team s Mission? 2 What Gap Do We Aspire to Address? ü The insurance industry is data rich but ü

More information

Data Mining Solutions for the Business Environment

Data Mining Solutions for the Business Environment Database Systems Journal vol. IV, no. 4/2013 21 Data Mining Solutions for the Business Environment Ruxandra PETRE University of Economic Studies, Bucharest, Romania ruxandra_stefania.petre@yahoo.com Over

More information

I D C A N A L Y S T C O N N E C T I O N. C o g n i t i ve C o m m e r c e i n B2B M a rketing a n d S a l e s

I D C A N A L Y S T C O N N E C T I O N. C o g n i t i ve C o m m e r c e i n B2B M a rketing a n d S a l e s I D C A N A L Y S T C O N N E C T I O N Dave Schubmehl Research Director, Cognitive Systems and Content Analytics Greg Girard Program Director, Omni-Channel Retail Analytics Strategies C o g n i t i ve

More information

MSc Finance and Economics detailed module information

MSc Finance and Economics detailed module information MSc Finance and Economics detailed module information Example timetable Please note that information regarding modules is subject to change. TERM 1 TERM 2 TERM 3 INDUCTION WEEK EXAM PERIOD Week 1 EXAM

More information

Major Trends in the Insurance Industry

Major Trends in the Insurance Industry To survive in today s volatile marketplace? Information or more precisely, Actionable Information is the key factor. For no other industry is it as important as for the Insurance Industry, which is almost

More information

Data Mining Algorithms Part 1. Dejan Sarka

Data Mining Algorithms Part 1. Dejan Sarka Data Mining Algorithms Part 1 Dejan Sarka Join the conversation on Twitter: @DevWeek #DW2015 Instructor Bio Dejan Sarka (dsarka@solidq.com) 30 years of experience SQL Server MVP, MCT, 13 books 7+ courses

More information

Business Process Services. White Paper. Optimizing Extended Warranty Processes by Embracing Analytics

Business Process Services. White Paper. Optimizing Extended Warranty Processes by Embracing Analytics Business Process Services White Paper Optimizing Extended Warranty Processes by Embracing Analytics About the Author Dr. Anuj Prakash Anuj Prakash is a part of the TCS Analytics and Insights Practice,

More information

Central Mechanical Engineering Research Institute (Council of Scientific & Industrial Research) Durgapur 713 209 NOTICE

Central Mechanical Engineering Research Institute (Council of Scientific & Industrial Research) Durgapur 713 209 NOTICE Central Mechanical Engineering Research Institute (Council of Scientific & Industrial Research) Durgapur 713 209 NOTICE No. 8/2/2007-Rct Dated : 14.07.2008 Based on their performance in the Competitive

More information

A Cloud Based Solution with IT Convergence for Eliminating Manufacturing Wastes

A Cloud Based Solution with IT Convergence for Eliminating Manufacturing Wastes A Cloud Based Solution with IT Convergence for Eliminating Manufacturing Wastes Ravi Anand', Subramaniam Ganesan', and Vijayan Sugumaran 2 ' 3 1 Department of Electrical and Computer Engineering, Oakland

More information

Data Isn't Everything

Data Isn't Everything June 17, 2015 Innovate Forward Data Isn't Everything The Challenges of Big Data, Advanced Analytics, and Advance Computation Devices for Transportation Agencies. Using Data to Support Mission, Administration,

More information

IMPORTANCE OF QUANTITATIVE TECHNIQUES IN MANAGERIAL DECISIONS

IMPORTANCE OF QUANTITATIVE TECHNIQUES IN MANAGERIAL DECISIONS IMPORTANCE OF QUANTITATIVE TECHNIQUES IN MANAGERIAL DECISIONS Abstract The term Quantitative techniques refers to the methods used to quantify the variables in any discipline. It means the application

More information

CUSTOMER RELATIONSHIP MANAGEMENT OF SELECT LIFE INSURANCE COMPANIES

CUSTOMER RELATIONSHIP MANAGEMENT OF SELECT LIFE INSURANCE COMPANIES I n t e r n a t i o n a l J o u r n a l o f M a n a g e m e n t F o c u s 1 CUSTOMER RELATIONSHIP MANAGEMENT OF SELECT LIFE INSURANCE COMPANIES G. RAJU Asst. Professor of Business Administration, St. Thomas

More information

Total Credits: 30 credits are required for master s program graduates and 51 credits for undergraduate program.

Total Credits: 30 credits are required for master s program graduates and 51 credits for undergraduate program. Middle East Technical University Graduate School of Social Sciences Doctor of Philosophy in Business Administration In the Field of Accounting-Finance Aims: The aim of Doctor of Philosphy in Business Administration

More information

An Overview of Knowledge Discovery Database and Data mining Techniques

An Overview of Knowledge Discovery Database and Data mining Techniques An Overview of Knowledge Discovery Database and Data mining Techniques Priyadharsini.C 1, Dr. Antony Selvadoss Thanamani 2 M.Phil, Department of Computer Science, NGM College, Pollachi, Coimbatore, Tamilnadu,

More information

Declared as Deemed-to-be University u/s 3 of the UGC Act,1956. For enrolment, send in your request to connect@analyticsindia.org

Declared as Deemed-to-be University u/s 3 of the UGC Act,1956. For enrolment, send in your request to connect@analyticsindia.org & JAIN UNIVERSITY Declared as Deemed-to-be University u/s 3 of the UGC Act,1956 Presents FACULTY DEVELOPMENT PROGRAM ON BUSINESS ANALYTICS Date : May 26 & 27, 2016 Last date for registration : 14th May

More information

Social Business Intelligence For Retail Industry

Social Business Intelligence For Retail Industry Actionable Social Intelligence SOCIAL BUSINESS INTELLIGENCE FOR RETAIL INDUSTRY Leverage Voice of Customers, Competitors, and Competitor s Customers to Drive ROI Abstract Conversations on social media

More information

WebFOCUS RStat. RStat. Predict the Future and Make Effective Decisions Today. WebFOCUS RStat

WebFOCUS RStat. RStat. Predict the Future and Make Effective Decisions Today. WebFOCUS RStat Information Builders enables agile information solutions with business intelligence (BI) and integration technologies. WebFOCUS the most widely utilized business intelligence platform connects to any enterprise

More information

NICE MULTI-CHANNEL INTERACTION ANALYTICS

NICE MULTI-CHANNEL INTERACTION ANALYTICS NICE MULTI-CHANNEL INTERACTION ANALYTICS Revealing Customer Intent in Contact Center Communications CUSTOMER INTERACTIONS: The LIVE Voice of the Customer Every day, customer service departments handle

More information

Service courses for graduate students in degree programs other than the MS or PhD programs in Biostatistics.

Service courses for graduate students in degree programs other than the MS or PhD programs in Biostatistics. Course Catalog In order to be assured that all prerequisites are met, students must acquire a permission number from the education coordinator prior to enrolling in any Biostatistics course. Courses are

More information

Integrated Marketing Performance Using Analytic Controls and Simulation (IMPACS SM )

Integrated Marketing Performance Using Analytic Controls and Simulation (IMPACS SM ) WHITEPAPER Integrated Performance Using Analytic Controls and Simulation (IMPACS SM ) MAY 2007 Don Ryan Senior Partner 35 CORPORATE DRIVE, SUITE 100, BURLINGTON, MA 01803 T 781 494 9989 F 781 494 9766

More information

Technology to Control Hybrid Computer Systems

Technology to Control Hybrid Computer Systems INFORMATION TECHNOLOGY Hynomics (formerly HyBrithms Corporation, formerly Sagent Corporation) Technology to Control Hybrid Computer Systems Businesses and industries, both large and small, increasingly

More information

Genetic algorithm evolved agent-based equity trading using Technical Analysis and the Capital Asset Pricing Model

Genetic algorithm evolved agent-based equity trading using Technical Analysis and the Capital Asset Pricing Model Genetic algorithm evolved agent-based equity trading using Technical Analysis and the Capital Asset Pricing Model Cyril Schoreels and Jonathan M. Garibaldi Automated Scheduling, Optimisation and Planning

More information