3162MG114 -CORPORATE FINANCE
Module I: Broad Themes:
FA
Corporate Finance and its principles - Meaning, Importance and Scope of Corporate Finance, Laws governing
Corporations – Goal of Corporate Finance: Why Value maximization is a legitimate goal? – Objective in decision making
– Corporate Governance Mechanisms: Clause 49 - Profit Maximization - Wealth Maximization - Functions of Finance
Manager
Module II: Understanding hurdle rates:
SAPM
Define risk – Models to measure risk – Risk free rates – Risk premium – Understanding Betas - Discounted Cash Flow
Model
Module III: Capital Budgeting:
FM
Irreversible nature of Investment Decisions – Methods of Capital Budgeting: Pay-back, ARR, Net Present Value and
Internal Rate of Return – Mutually exclusive projects – NPV vs. IRR
Module IV: Understanding the Financing mix:
FM-Corporate Ac
Equity Finance - Public Issue a) Initial Public Offer (IPO) b) Further Public Offer (FPO) - Rights Issue - Bonus Issue Prospectus – Information and Disclosure Requirements
Debt Finance: Debentures - Nature, Issue and Class - Creation of charge: fixed and floating charges - Non-Convertible
Debentures - Venture Capital/Private Equity
Module V: Dividend Policy:
FM -MA
Dividend trends of Indian Companies – Dividend policy: Trade off – Reasons behind initiating and changing dividends –
cash dividend – Bonus shares – Stock Splits
TOTAL: 45 periods
7. TEXT BOOKS
1. Van Horne and Sanjay Dhamija, Financial Management and Policy 12th edition, Pearson
8. REFERENCE BOOKS
1. Damodharan, Corporate Finance: Theory & Finance, 4th Ed, WILEY
2. Jonathan Berk and Thampy, Financial Management (Latest) Pearson
LOGISTICS MANAGEMENT & SHIPPING
ELECTIVE 3
3162MG176 -MULTI-MODAL TRANSPORT AND RISK MANAGEMENT
MODULE I INTRODUCTION TO MULTI MODAL
Multi modal transportation - Introduction, history & growth of multimodal transportation, Physical multi modal
operations – Inter relationship of transport mode, specialized container equipment – FCL (Full Container Loads), LCL
(Less than Container loads) and Customs facilitation.
MODULE II MULTIMODAL TRADE ROUTES
Multimodal trade routes – factors affecting Mode and Route choices, Multimodal transport operators –Vessel
Operators – Importance - Types of vessel operators - other provisions through Transport services.
MODULE III MULTIMODAL SYSTEM AND PRICING
Corporate structures in Multimodal Transport, System required by the Transport Operator, Transport Pricing-Modern
Freight Tariffs, Meeting the Demand-Tracking the Container Fleet.
MODULE IV DIFFERENT MODES OF TRANSPORT
Rail Transport- Role of Rail transport-Significance of Rail Transport, Railway networks, Air Transport- Role of Air
Transport, Significance of Air Transport, Airline Scheduler-Air Line Schedule Planning, IATA, Maritime industries.
MODULE V RISK MANAGEMENT
Introduction – Risk Management- Meaning and Definition- Many Sources of Risks- Managing the Unknown Factors –
Introduction to Global Risks-Global Risks-Managing Global Risks.
TOTAL: 45 Periods
TEXT BOOKS:
1. Dr. Hariharan K. V. ,Container & Multimodal Transport Management, Shroff Publishers & Distributers Private
Limited - Mumbai; 1 edition (2002)
2. MekkiKsouri and Slim Hammadi Multimodal Transport Systems,Nov 2013.
REFERENCES:
1. Hertz and Alfredson 2003 Hertz, Susanne; Monica Alfredsson (February2003). “Strategic development of third party
logistics providers”. Industrial Marketing Management (Elsevier Science)
2. CALM Supply Chain & Logistics Journal, “Fourth Party Logistics: The Evolution of Supply Chain Outsourcing”, DN
Bauknight, JR Miller, 1999.“4PL”. Toolbox for IT. Juillet 2
3. The Economist Intelligence Unit, “SCMO - The Next Generation”, China Hand November 2006 –Chapter 11:
Distribution, November 2006.
Elective – Business Analytics
20PM06 Marketing Analytics
Course Code: Credits: 3
Total No of Sessions: 45
Course Objectives:
1. Develop an understanding of the importance and need for marketing analytics and data driven decision making.
2. Build exposure to the use of appropriate and popular statisfical software such as MS Excel/ SPSS/ R/ SAS/ Any other
for modelling analysis for marketing related applications. Make use of analytics methods - descriptive, predictive
and prescriptive analytics models for solutions to marketing problems
3. Illustrate 4P's and STP
of
marketing
through
mathematical
models
Master the ability to
communicate to senior executives through data.
Module 1: Introduction to Marketing Analytics
Learning Outcomes:
 Develop an understanding of the importance of analytics in business decision making.
 Make use of prescriptive models for allocation of marketing resources.
 Build exposure to the use of statistical software package in marketing analytics. Introduction to Marketing
Analytics. Evolution and Scope of Analytics. Decision Models - Descriptive, Predictive and Prescriptive Models.
Problem Solving and Decision making process., Models for customer value analysis.
 Developing Spread Sheet Models. Art of developing Spread sheet models - Guidelines to develop an adequate
spread sheet model. Application of Resource allocation models. Optimization using Excel solver.
 Basic statistical software skills. Using statistical software functions Introduction to MS Excel/SPSS/R/SAS/Any Other
software application. Data input, coding recoding and data reshaping in the statistical software packages. Split file,
Group, Subset, merge file options.
Module 2: Descriptive Analytics
Learning Outcomes:
Demonstrate characteristics of data through visualization and its interpretation. Make use of parametric and
non-parametric tests for single, two and multiple group comparison. Descriptive Analytics using any statistical software
package. Visualization, exploration and extracting data summary statistics and their interpretation. Important
parametric and non-parametric tests for single, two and multiple group comparison (chi square, t-test and one-way
ANOVA)
Module3: Predictive Analytics Methods in Marketing
Learning Outcomes:
 Make use of analytical modelling approach for a group of correlated variables. (Factor Analysis)
 Predicting a dependent variable using single and multiple independent variables Predicting a dependent variable
using continuous and categorical variables. (Dummy Regression)
Principal Component Analysis, Eigen values Communalities, Rotation of factors, Kaiser- Meyer-OlkinIndex, Bartlett's
test of sphericity.
 Regression Modelling - fitting, model fit measures, hypothesis testing, prediction and validation model assumptions
by residual analysis. Regression model building - Stepwise, forward selection, backward elimination and optimum
sub set methods. Regression models with categorical predictor variables (Dummy Regression). Regression analysis
any modelling with any statistical software package.
 Understanding Pricing Sales Promotion and advertising, Sales forecasting through Regression analysis
Module 4: Customer Segmentation and Classification Methods
Learning Outcomes:
•Identify homogeneous group of customers called clusters which are similar to members in the same cluster but
different from those of other cluster.
•Identify predictor variables which impact the probability of an event e.g. customer choice. Analytics for Segmentation Introduction to Cluster analysis multivariate method, Estimation, Model performance and validation of cluster analysis
results. Assumptions for Cluster analysis.
Analytics methods for Classification - Introduction to Logistic regression and Discriminant analysis models. Assumptions,
estimation, model performance and model building for logistic regression discriminant analysis models.
Module 5: Marketing Models for Positioning & Product Design - Perceptual Maps and Conjoint Analysis
Learning Outcomes:
•Draw positioning
maps using the
attribute
model Identify the desired features in product design
(New product development or modifying existing product)
•Multidimensional scaling - Objectives, decision framework for MDS. Assumptions of MDS Deriving the MDS solution
and assessment of overall fit. Selecting the dimensionality of the perceptual map. Interpreting the MDS results Identifying the dimensions. Validating the MDS results
Conjoint Analysis - objectives of conjoint analysis. Steps in conducting conjoint analysis. Examples of evaluating product
design options using results from conjoint analysis. Strengths and limitations of conjoint analysis.
(* These topics are categorized as ‘Self-learning’ topics and are subjected to testing)
Basic Texts:
Rao, P. H (2013). Business Analytics an Application Focus. New Delhi Prentice Hall
Reference Books:
Lilien, G. L., Rangaswamy A., and Bruyn. A D. (2013) Principles of Marketing Engineering Pearson Education Inc.
First I thank the MTNC to arrange the two days SPSS workshop. Before starting the session I have got some ideas y
because the materials which u have been send us is very clear and most worthy The workshop was really elevate and
help me academically in spurs optimum level. I would convey my heartfelt thanks to the Resource person Karthikeyan
sir as well as the organisers for making us enlightened.