financial engineering syllabus

KERALA TECHNOLOGICAL UNIVERSITY

Master of Technology

Curriculum, Syllabus and Course Plan

Cluster : 01

Branch : Mechanical Engineering

Stream : Financial Engineering

Year : 2015

No. of Credits : 67

Kerala Technological University Master of Technology – Curriculum, Syllabus & Course Plan

Cluster: 1 Branch: Mechanical Engineering Stream: Financial Engineering

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SEMESTER 1

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A 01MA6017 Probability and Stochastic Processes 3-0-0 40 60 3 3

B 01ME6601 Statistics for Engineering Applications 3-1-0 40 60 3 4

C 01ME6603 Financial Time Series Analysis 3-1-0 40 60 3 4

D 01ME6605 Financial Reporting and Analysis 3-0-0 40 60 3 3

E Elective I 3-0-0 40 60 3 3

S 01ME6999 Research Methodology 0-2-0 100 2

T 01ME6691 SeminarI 0-0-2 50 2

U 01ME6693 Data AnalysisLaboratory 0-0-2 50 1

TOTAL 15-4-4 400 300 - 22

TOTAL CONTACT HOURS : 23 TOTAL CREDITS : 22

Elective I

01ME6611 Corporate Finance and Portfolio Management

01ME6613 Numerical Methods for Finance

01ME6615 Object Oriented Programming for Financial Engineers



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SEMESTER 2

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A 01ME6602 Optimization in Finance 3-1-0 40 60 3 4

B 01ME6604 Equity and Fixed Income 3-0-0 40 60 3 3

C 01ME6406 System Analysis and Design 3-0-0 40 60 3 3

D Elective II 3-0-0 40 60 3 3

E Elective III 3-0-0 40 60 3 3

V 01ME6692 Mini Project 0-0-4 100 2

U 01ME6694 Optimization and Simulation

Laboratory 0-0-2 50 1

TOTAL 15-1-6 350 300 - 19


Elective II

01ME6612 Derivatives and Alternative Investments

01ME6414 Data Analytics using R and Python

01ME6616 Financial Markets

Elective III

01ME6618 Quantitative Trading Strategies

01ME6422 Enterprise Resource Planning

01ME6624 Big Data Analytics



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SEMESTER 3

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A Elective IV 3-0-0 40 60 3 3

B Elective V 3-0-0 40 60 3 3

T 01ME7691 Seminar II 0-0-2 50 2

W 01ME7693 Project (Phase 1) 0-0-12 50 6

TOTAL 6-0-14 180 120 - 14


Elective IV

01ME7611 Asset Pricing

01ME7613 Financial Business Intelligence

01ME7415 Heuristic Solution Methods

Elective V

01ME7617 Predictive Modeling

01ME7419 Managerial Economics

01ME7621 Financial Modeling



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SEMESTER 4

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W 01ME7694 Project (Phase 2) 0-0-23 70 30 12

TOTAL 0-0-23 70 30 - 12


TOTAL NUMBER OF CREDITS: 67



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SEMESTER - I

Syllabus and Course Plan



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Course No. Course Name L-T-P Credits Year of Introduction

01MA6017 PROBABILITY AND

STOCHASTIC PROCESSES 3-0-0 3 2015

Course Objectives The objective of this course is to reinforce basic ideas of probability distributions they may already

have learned, from a modern point of view. The basic ideas of stochastic processes are also

introduced, preparing the students with the necessary tools for its diverse applications in applied

sciences and engineering. This course provides a strong background of some basic mathematical

methods which will be essential for higher studies and research in engineering

Syllabus

Techniques for theorem proving, Principle of mathematical induction, principle of complete

Multiple random variables, Conditional distributions, limit theorems, Discrete time Markov chains,

Continuous time Markov chains, Poisson Process, Renewal process, Brownian motion.

Expected Outcome

On completion of the course, the students will have acquired knowledge and practical skills in the

modeling and analysis of probabilistic and stochastic systems which has applications in diverse

areas of engineering. This will also prepare them with some of the most important mathematical

tools essential for higher studies and research.

References

1. Saeed Ghahramani , ” Fundamentals of Probability with Stochastic process”, Pearson.

2. V G Kulkarni, “Introduction to Modeling and Analysis of Stochastic Systems”, Springer

3. S.M.Ross, ”Introduction to probability models”, Elsevier

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I Multiple random variables: Joint and Marginal distributions, 4

15 Independence of random variables, Covariance, Correlation 3



8

II

Conditional probability distributions and Conditional expectations.

Distributions of sum of two random variables. 4

15 Limit theorems: Central limit theorem and Law of large numbers

(without proof). 3

FIRST INTERNAL EXAM

III

Stochastic process and their classifications

Discrete time Markov chains: Transition probability matrix, Chapman-

Kolmogorov Equation, classification of states, Ergodic chains, Steady

State Probabilities. First passage times, computation of expected first

passage times.

7 15

IV

Continuous - time Markov chains: Transition probability matrix,

Chapman, Kolmogorov Equations, transition rates and rate matrix and

generator matrix.

4 15

Steady State Probabilities and flow balance equations, Birth - death

processes, First passage times. 3

SECOND INTERNAL EXAM

V Poisson processes- Inter-arrival distribution, Reproductive properties.

Renewal processes-basic properties 4 20

Renewal Reward process, Limit theorems(without proof) 3

VI

Standard Brownian motion (Wiener processes), basic properties, First

passage times of standard Brownian motion 4

20 Brownian motion with drift, Geometric Brownian motion(ideas and

computations without proof) 3

END SEMESTER EXAM



9


01ME6601 STATISTICS FOR ENGINEERING APPLICATIONS

3-1-0 4 2015

Course Objectives

1. To provide an introduction to statistical techniques and their applications in the context of

business and management problems.

2. To utilize single and multi-variable measures to make decisions.

3. To perform and interpret elementary statistical procedures (such as confidence intervals

and hypothesis tests).

4. To develop decision making and analytical skills.

Syllabus

Data collection, classification and tabulation – Measures of Central Tendency – Measures of

Dispersion – Sampling and Sampling Distributions – Estimation and Confidence Intervals –

Hypothesis Testing – Non Parametric Tests – Analysis of Variance – Correlation Analysis –

Regression Analysis – Introduction to Multivariate Analysis.

Expected Outcome

1. The student will be able to apply techniques for analyzing and interpreting data to real-world datasets relevant to varied fields of business and industry.

2. The student will be able to critically evaluate reports presenting statistical data and translate and communicate the results of statistical analyses.

References

1. P. E. Green, D. S. Tull, G. Albaum, “Research for Marketing decisions”, Prentice- hall of India Pvt. Ltd

2. Thomas C. Kinnear, James R. Taylor, “Marketing Research: An Applied approach”, McGraw-Hill Inc

3. A. B. Bowker and G. J. Liberman, “Engineering Statistics”, Asia, 1972. 4. F. E. Brown, “Marketing Research: A structure for decision making”,Addison-Wesley

publishing Co., California. 5. J.K. Sharma, “Business Statistics”, Pearson Education. 6. R. Panneerselvam, “Research Methodology”, Prentice Hall India. 7. Amir D Aczel and Jayavel Sounderpandian, “Complete Business Statistics”, Tata

McGraw-Hill 8. Richard I Levin and David S Rubin, “Statistics for Management”, Pearson Education 9. Hair et al., “Multivariate Data Analysis”, Pearson Education



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Data Collection, Classification and Tabulation: Need for data, Types of Data, Scale of measurement, Sources of data, Basis of classification, Methods of data classification, Tabulation of data, Presentation of data, Exploratory data analysis- Stem and Leaf displays.

5 10

II

Measures of Central Tendency: Significance, Classification - Arithmetic mean (Grouped and Ungrouped data), Geometric mean, Harmonic mean, Median, Mode, Quartiles, Deciles and Percentiles.

5

15 Measures of Dispersion: Significance, Classification – Range, Interquartile range, Mean Absolute Deviation, Variance and Standard deviation, Coefficient of variation, Chebyshev’s theorem, Skewness, Moments and Kurtosis.

6

FIRST INTERNAL EXAM

III Sampling and Sampling Distributions: Population parameters and Sample statistics, Sampling methods, Sampling distribution of sample mean, Sampling distribution of sample proportions.

5 10

IV

Estimation and Confidence Intervals: Point estimation, Confidence Interval estimation – Interval estimation of population means (σ known and σ unknown).

4

15 Hypothesis Testing: Procedure, Hypothesis testing for population parameters with large samples and small samples. Hypothesis testing based on F- Distribution.

7


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Non Parametric tests: One sample tests- Chi-square tests, K-S Test, Two Sample tests – Sign test, Median test, Mann-Whitney U-test, K-Samples test – Median test, Kruskal-Waliis test.

6

25 Design of Experiments: Analysis of Variance, Completely randomized design, Randomized complete block design, Latin square design, Factorial design, 2n Factorial experiment, Yate’s algorithm.

7

VI

Correlation Analysis: Karl Pearson’s correlation, Spearman’s rank correlation, Auto correlation.

3

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Regression Analysis: Simple and Multiple Regression models, Determination of regression coefficients, Coefficient of determination, Significance test of Regression model.

6

Introduction to Multivariate Analysis: Overview of Discriminant Analysis, Factor Analysis, Cluster Analysis, Multidimensional scaling and Conjoint Analysis.

2

END SEMESTER EXAM



11


01ME6603 FINANCIAL TIME SERIES

ANALYSIS 3-1-0 4 2015

Course Objectives

1. To introduce a variety of statistical models for time series and main methods for analyzing these models.

Syllabus

Characteristics of Financial Time Series; Linear Time Series Analysis; Conditional Heteroscedastic

Models; Nonlinear Models; Continuous-Time Models; Multivariate Time Series Analysis; Factor

Models.

Expected Outcome

At the end of the course, the student should be able to

1. Choose an appropriate time series model for a given set of data

2. Compute forecasts for a variety of linear and nonlinear methods and models.

References

1. Ruey S. Tsay, “Analysis of Financial Time Series”, John Wiley & Sons 2. A C Harvey, “Time Series Models”,Pearson; 2/e.

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Characteristics of Financial Time Series; Linear Time Series Analysis-

Simple AR models, MA models, ARMA models; Unit-Root Non-

stationarity; Seasonal Models; Regression Models with Time Series

Errors; Consistent Covariance Matrix Estimation ;Long-Memory

Models; Applications.

9 15



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II

Conditional Heteroscedastic Models- ARCH model, GARCH model,

GARCH-M Model, EGARCH Model, Threshold GARCH Model,

CHARMA Model, Random Coefficient Autoregressive Models,

Stochastic Volatility Model, Long-Memory Stochastic Volatility Model,

Applications.

9 15

FIRST INTERNAL EXAM

III

Nonlinear Models- Bilinear Model, Threshold Autoregressive (TAR) Model, Smooth Transition AR (STAR) Model, Markov Switching Model, Nonparametric Methods, Functional Coefficient AR Model, Nonlinear Additive AR Model, Nonlinear State-Space Model, Neural Networks; Nonlinearity Tests, Modeling, Forecasting, Applications. High-Frequency Data Analysis.

10 15

IV

Continuous-Time Models- Wiener Process, Generalized Wiener Process,

Ito Process, Ito’s Lemma, Distributions of Stock Prices and Log Returns,

Derivation of Black–Scholes Differential Equation, Black–Scholes Pricing

Formulas, Extension of Ito’s Lemma, Stochastic Integral, Jump Diffusion

Models.

10 15


V

Multivariate Time Series Analysis: Weak Stationarity and Cross-

Correlation Matrices, Vector Autoregressive Models, Vector Moving-

Average Models, Vector ARMA Models, Unit-Root Nonstationarity and

Cointegration, Cointegrated VAR Models, Threshold Cointegration and

Arbitrage, Pairs Trading.

9 20

VI

Factor Models- Macro econometric Factor Models, Fundamental Factor

Models, Principal Component Analysis. Multivariate Volatility Models-

Multivariate GARCH Models, GARCH Models for Bivariate Returns,

Higher Dimensional Volatility Models, Factor–Volatility Models,

Multivariate t Distribution.

9 20

END SEMESTER EXAM



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01ME6605 FINANCIAL REPORTING

AND ANALYSIS 3-0-0 3 2015

Course Objectives

1. The course aims to provide students with an introduction to financial statements, their analyses and applications in financial performance measurement.

Syllabus

Financial Statement Analysis: Scope, Major financial statements and other information sources;

Financial Reporting Standards; Income Statements and Balance Sheet; Cash Flow Statements;

Financial Analysis Techniques; Inventories; Long Lived Assets; Income Taxes; Applications.

Expected Outcome

After successful completion of the course, the students will be able to:

1. Describe the roles of financial reporting and analysis 2. Describe the roles of key financial statements in evaluating a company’s performance and

financial position. 3. Describe the components of income statements, Balance Sheet and Cash Flow statements and

to use them to evaluate a company’s financial performance. 4. Describe the tools and techniques used in financial analysis, including their uses and

limitations.

5. Apply the tools and techniques to evaluate past and future financial performance, credit risk, equity investments etc.

References

1. Benninga and Sarig, “Corporate Finance: A Valuation Approach”, McGraw-Hill Series in Finance.

2. Pinto, Jerald E.,Elaine Henry, Thomas R. Robinson, and John D. Stowe, “Equity Asset Valuation”, 2nd edition. Hoboken, NJ: John Wiley & Sons, 2010.

3. Van Greuning, Hennie, and Sonja Brajovic Bratanovic, “Analyzing and Managing Banking Risk: A Framework for Assessing Corporate Governance and Financial Risk”, Washington, DC: World Bank, 2003.

4. International Auditing and Assurance Standards Board (IAASB),“Handbook of International Quality Control, Auditing, Review, Other Assurance, and Related Services Pronouncements”, Standard 200, available at www.ifac.org/IAASB.

5. Stowe, J.D., T.R. Robinson, J.E. Pinto, and D.W.McLeavey,“Analysis of Equity Investments: Valuation”, Charlottesville, VA:. CFA Institute, 2002.



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Financial Statement Analysis: Scope, Major financial statements and other information sources; Financial Statement Analysis framework. Financial Reporting Mechanics: Accounts and financial statements; Accounting Process; Accruals and valuation adjustments; Accounting Systems.

4

15

Financial Reporting Standards: Standard Setting Bodies and Regulatory Authorities; International Financial Reporting Standards Framework; Effective Financial Reporting.

3

II

Income Statements- Components and format of the Income Statement; Revenue Recognition; Expense Recognition; Non-recurring items and non-operating items; Earnings per Share; Analysis of the Income Statement.

4

15

Balance Sheet- Components and format of the Balance Sheet: Current Assets and Current Liabilities, Non-current Assets, Non-current Liabilities, Equity; Analysis of the Balance Sheet.

3

FIRST INTERNAL EXAM

III

Cash Flow Statements- Components and format of Cash Flow Statements; Linkages of the Cash Flow Statement with the Income Statement and Balance Sheet; Analysis of Cash Flow Statements-Evaluation of the Sources and uses of Cash, Common size analysis of the statement of Cash Flows, Free Cash Flow to the Firm and Free Cash Flow to Equity, Cash Flow Ratios.

7 15

IV

Financial Analysis Techniques: Financial Analysis Process; Analytical Tools and Techniques; Common Ratios used in Financial Analysis- Activity Ratios, Liquidity Ratios, Solvency Ratios, Profitability Ratios; Integrated Financial Ratio Analysis; Equity Analysis; Credit Analysis; Business and Geographic Segments-Segment reporting Requirements, Segment Ratios; Model Building and Forecasting.

7 15


V

Inventories: Cost of Inventories; Inventory Valuation Methods; Measurement of Inventory Value; Evaluation of Inventory Management-Inventory ratios.

4

20 Long Lived Assets: Acquisition of Long Lived Assets; Depreciation and Amortisation of Long Lived Assets; The Revaluation model; Impairment of Assets; De-recognition; Investment Property.

3

VI Income Taxes: Accounting Profit and Taxable Income; Recognition and Measurement of Current and Deferred Tax. Non-current Liabilities. Financial Reporting Quality; Accounting Shenanigans on the Cash Flow

4 20



15

Statement.

Applications of Financial Statement Analysis: Evaluating Past Financial Performance, Projecting Future Financial Performance, Assessing Credit Risk, Screening for Potential Equity Investments, Adjustments to Reported Financials.

3

END SEMESTER EXAM



16


01ME6611 CORPORATE FINANCE AND PORTFOLIO MANAGEMENT

3-0-0 3 2015

Course Objectives

1. The course aims to provide students with an introduction to corporate finance and portfolio management process.

Syllabus

Capital Budgeting; Cost of Capital; Measures of Leverage; DividendsandShareRepurchases;

Working-CapitalManagement; CorporateGovernance; Portfolio Management; Portfolio Risk

and Return; Portfolio Planning and Construction; Mergers and Acquisitions.

Expected Outcome

After successful completion of the course, the students will be able to:

1. Analyse capital budgeting problems 2. Estimate a company's cost of capital and evaluate working capital, and corporate governance

policies

3. Describe the mechanics of dividends and share repurchases, portfolio approach to investment and portfolio management.

References

1. Calverley, John P., Alan M. Meder, Brian D. Singer, and Renato Staub, “Capital Market Expectations” Managing Investment Portfolios; A Dynamic Process. 3rd ed. Wliey, 2007.

2. Sharpe William F., Peng Chen, Jerald E. Pinto, and Dennis W. McLeavey "Asset Allocation.", 2007.

3. Ibbotson Stocks, Bonds, Bills, and Inflation (SBBI) Classic Yearbook. 2009. Chicago, IL: Morningstar.

4. Dimson, Elroy, Paul Marsh, and Mike Staunton, “Credit Suisse Global Investment Returns Sourcebook”, Zurich, Switzerland: Credit Suisse Research Institute, 2009.

5. Taleb, Nassim N.,“The Black Swan: The Impact of the Highly Improbable”.New York:Random House Inc., 2007.



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Capital Budgeting: Basic Principles of Capital Budgeting; Investment DecisionCriteria-Net Present Value, Internal Rate of Return, Payback Period, Discounted Payback Period, Average Accounting Rate of Return, Profitability Index, NPV Profile, The Multiple IRR Problem and the NoIRR; Cash Flow Projections; Project Analysis and Evaluation.

6 15

II

Cost of Capital: Costs of the Different Sources of Capital- Cost of Debt, Cost of Preferred Stock, Cost of Common Equity; Cost of Capital Estimation-Estimating Beta and Determining a Project Beta, Country Risk, Marginal Cost of Capital Schedule, Flotation Costs.

4

15 Measures of Leverage: Leverage; Business Risk and Financial Risk. Capital Structure-Capital Structure Decision, Practical Issues in Capital Structure Policy.

Dividends and Share Repurchases: Dividends-Forms, Payment Chronology, Dividend Policy and Company Value, Factors Affecting Dividend Policy, Payout Policies, Analysis of Dividend Safety; Share Repurchases- Methods, Effects of Repurchases, Valuation Equivalence of Cash Dividends and Share Repurchases.

4

FIRST INTERNAL EXAM

III

Working Capital Management: Managing and Measuring Liquidity, Managing the Cash Position, Investing Short-Term Funds, Managing Accounts Receivable, Managing Inventory, Managing Accounts Payable, Managing Short-Term Financing.

6 15

IV

Corporate Governance: Importance, Definitions, Corporate Governance Considerations- The Board, Management, Shareowner Rights; Forms of Business and Conflicts of Interest, Sources of Conflict, Corporate Governance Evaluation.

4

20 Portfolio Management: Overview; Portfolio Perspective on Investing; Investment Clients; Steps in the Portfolio Management Process; Pooled Investments. Portfolio Concepts- Mean-Variance Analysis, Multifactor Models, Active Portfolio Management.

4



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V

Portfolio Risk and Return: Investment Characteristics of Assets; Risk Aversion and Portfolio Selection; Portfolio Risk; Efficient Frontier and Investor's Optimal Portfolio; Capital Market Theory; Pricing of Risk and Computation of Expected Return; The Capital Asset Pricing Model(CAPM).

8 20

VI

Portfolio Planning and Construction: Portfolio Planning- The Investment Policy Statement, Major Components of an IPS, Gathering Client Information; Portfolio Construction- Capital Market Expectations, The Strategic Asset Allocation, Steps Toward an Actual Portfolio, Additional Portfolio Organizing Principles.

3

15

Mergers and Acquisitions: Definitions and Classifications, Motives for Merger, Transaction Characteristics, Takeovers, Regulation, Merger Analysis, Corporate Restructuring.

3

END SEMESTER EXAM



19


01ME6613 NUMERICAL METHODS

FOR FINANCE 3-0-0 3 2015

Course Objectives

1. To introduce a general survey of significant numerical methods any practitioner should know, and a detailed study of certain numerical methods specific to finance

Syllabus

Solving systems of linear equations; Solving non-linear equations; Curve fitting; Interpolation;

Numerical Integration; Finite Difference Methods for Partial Differential Equations; Convex

Optimization; Methods for constrained optimization; Applications.

Expected Outcome

1. After successful completion of the course, the students shall demonstrate skills in the application of numerical methods to solve practical problems in mathematical finance.

References

1. Paolo Brandimarte, “Numerical Methods in Finance and Economics “, John Wiley & Sons. 2. Gerald & Wheatley,“Applied Numerical Analysis”, Addison-Wesley 3. V.Rajaraman , “Computer Oriented Numerical Methods”. 4. M.K.Jain , S.R.K.Iyengar and R.K.Jain, “Numerical Methods for Scientific and Engineering

Computations”. 5. S.S.Sastry,“Introductory methods of Numerical Analysis”.

6. S. Rajasekharan,“Numerical Methods in Science and Engg”.

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Nature of numerical computation; Errors and approximations, floating point arithmetic, sources of errors, control of errors, propagation of errors, Condition and stability, Rate of convergence.

3

15 Solving systems of linear equations; Function approximation and interpolation- Ad hoc approximation, Elementary polynomial interpolation, Interpolation by cubic splines, Theory of function approximation by least squares.

4



20

II

Solving non-linear equations- Bisection method, Newton’s method, Optimization- based solution of non-linear equations, Solving a functional equation by a collocation method, Homotopy continuation methods

7 15

FIRST INTERNAL EXAM

III

Curve fitting: Method of least squares, non-linear relationships, Correlation and Regression – Linear correlation, Measures of correlation, Standard error of estimate, Coefficient of correlation.

3

15 Interpolation – Newton’s divided difference, Lagrange, Aitken, Hermite and spline techniques – Inverse Interpolation – Numerical differentiation.

3

IV

Numerical Integration: Deterministic quadrature- Classical interpolatory formulas, Gaussian quadrature, Extensions and product rules; Monte Carlo integration; Generating pseudorandom variates; Variance reduction techniques; Quasi-Monte Carlo simulation.

4

15 Finite Difference Methods for Partial Differential Equations: Numerical solution by finite difference methods, Explicit and implicit methods for the heat equation, Solving the bi-dimensional heat equation, Convergence, consistency, and stability.

4


V

Convex Optimization: Elements of convex analysis- Convexity in optimization, Convex polyhedra and polytopes, Classification of optimization problems; Numerical methods for unconstrained optimization- Steepest descent method, The subgradient method, Newton and the trust region methods, No-derivatives algorithms: quasi-Newton method and simplex search.

5

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Methods for constrained optimization: Penalty function approach, Duality theory, Kuhn-Tucker conditions, Kelley's cutting plane algorithm, Active set method.

3

VI

Option Pricing by Binomial and Trinomial Lattices; Option Pricing by Monte Carlo Methods; Option Pricing by Finite Difference Methods- Applying finite difference methods to the Black- Scholes equation, Pricing a vanilla European option by an explicit method, Pricing a vanilla European option by a fully implicit method Pricing a barrier option by the Crank-Nicolson method

6 20

END SEMESTER EXAM



21


01ME6615 OBJECT ORIENTED

PROGRAMMING FOR FINANCIAL ENGINEERS

3-0-0 3 2015

Course Objectives 1. To give a rigorous introduction to computer programming and software engineering with

special emphasis on applications to financial engineering

Syllabus

Introduction to C++ and Quantitative Finance;Generic Data Structures and Standard Template

Library; Interfaces to STL for QF Applications; Design Patterns; Binomial Method; Implementation

of One-Factor Black Scholes; Two-Factor Option Pricing; C++ Classes for Numerical Analysis

Applications in Finance; Monte Carlo Method Theory and C++ Frameworks.

Expected Outcome

1. After successful completion of the course, the students shall have the knowledge of advanced complex techniques in C++ and real-life applications in financial engineering.

References 1. Daniel J. Duffy, “Introduction to C++ for Financial Engineers: An object-oriented

approach”, John Wiley & Sons Ltd. 2. Ashok M. Kamthane, “Object oriented Programming with ANSI & Turbo C++”,

Pearson 3. Education. 4. Nagler, “Learning C++, A Hands on Approach”, Jaico publications. 5. Stanley B. Lippman and Josee Lajoie, “C++ Primer”, Pearson Education. 6. Balaguruswamy, “Object Oriented Programming with C++”, TataMcgraw Hill. 7. Nabajyothi barkakati, “Object Oriented Programming in C++” , Prentice Hall. 8. Balaguruswamy, “Numerical Methods”, TataMcgraw Hill. 9. C.F. Gerald and P.O.Wheatley, “Applied Numerical Analysis” , Pearson Education.

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I Introduction to C++ and Quantitative Finance; C++ fundamentals, Classes, Operator overloading, Memory Management 6 15



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II Functions, Namespaces, Inheritance, Run-Time Behaviour, C++ Templates; Generic Data Structures and Standard Template Library 8 15

FIRST INTERNAL EXAM

III

Interfaces to STL for QF Applications, Data Structures for Financial Engineering Applications- Property sets and data modelling for quantitative finance, Lattice structures.

7 15

IV

Introduction to Design Patterns.Programming the Binomial Method; Implementation of One-Factor Black Scholes; Two-Factor Option Pricing: Basket and Other Multi-Asset Options- Modelling basket option PDE in UML and C++, The finite difference method for two-factor problems, Discrete boundary and initial conditions, Assembling the system of equations.

7 15


V

C++ Classes for Numerical Analysis Applications in Finance- Solving tridiagonal systems, The trinomial method for assets, Lattice data structures, Trinomial tree for the short rate , The multidimensional binomial method, Generic lattice structures, Approximating exponential functions.

7 20

VI

The Monte Carlo Method Theory and C++ Frameworks: The Monte Carlo method in quantitative finance, Software architecture for the Monte Carlo method, Examples and test cases- Plain options, Barrier options, Asian options.

7 20

END SEMESTER EXAM



23


01ME6999 RESEARCH METHODOLOGY 0-2-0 2 2015

Course Objectives

1. To prepare the student to do the M. Tech project work with a research bias. 2. To formulate a viable research question.

3. To develop skill in the critical analysis of research articles and reports. 4. To analyze the benefits and drawbacks of different methodologies. 5. To understand how to write a technical paper based on research findings.

Syllabus

Introduction to Research Methodology-Types of research- Ethical issues- Copy right-royalty-

Intellectual property rights and patent law-Copyleft- Openacess-

Analysis of sample research papers to understand various aspects of research methodology:

Defining and formulating the research problem-Literature review-Development of working

hypothesis-Research design and methods- Data Collection and analysis- Technical writing- Project

work on a simple research problem

Approach

Course focuses on students' application of the course content to their unique research interests. The

various topics will be addressed through hands on sessions.

Expected Outcome

Upon successful completion of this course, students will be able to 1. Understand research concepts in terms of identifying the research problem

2. Propose possible solutions based on research 3. Write a technical paper based on the findings.

4. Get a good exposure to a domain of interest. 5. Get a good domain and experience to pursue future research activities.

References 1. C. R. Kothari, Research Methodology, New Age International, 2004 2. Panneerselvam, Research Methodology, Prentice Hall of India, New Delhi, 2012. 3. J. W. Bames, Statistical Analysis for Engineers and Scientists, Tata McGraw-Hill, New York. 4. Donald Cooper, Business Research Methods, Tata McGraw-Hill, New Delhi. 5. Leedy P. D., Practical Research: Planning and Design, McMillan Publishing Co. 6. Day R. A., How to Write and Publish a Scientific Paper, Cambridge University Press, 1989. 7. Manna, Chakraborti, Values and Ethics in Business Profession, Prentice Hall of India, New

Delhi, 2012.

8. Sople, Managing Intellectual Property: The Strategic Imperative, Prentice Hall ofIndia, New Delhi, 2012.

END SEMESTER EXAM



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Introduction to Research Methodology: Motivation towards research -

Types of research: Find examples from literature.

Professional ethics in research - Ethical issues-ethical committees. Copy

right - royalty - Intellectual property rights and patent law - Copyleft-

Openacess -Reproduction of published material - Plagiarism - Citation

and acknowledgement.

Impact factor. Identifying major conferences and important

journals in the concerned area. Collection of at least 4 papers in the

area.

5

II

Defining and formulating the research problem - Literature Survey-

Analyze the chosen papers and understand how the authors have

undertaken literature review, identified the research gaps, arrived at

their objectives, formulated their problem and developed a hypothesis.

4

FIRST ASSESSMENT

III

Research design and methods: Analyze the chosen papers to

understand formulation of research methods and analytical and

experimental methods used. Study of how different it is from

previous works.

4 No end

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Data Collection and analysis. Analyze the chosen papers and study the

methods of data collection used. - Data Processing and Analysis

5



25

strategies used – Study the tools used for analyzing the data.

SECOND ASSESSMENT

V

Technical writing - Structure and components, contents of a typical

technical paper, difference between abstract and conclusion, layout,

illustrations and tables, bibliography, referencing and footnotes- use of

tools like Latex.

5

VI

Identification of a simple research problem – Literature survey-

Research design- Methodology –paper writing based on a hypothetical

result.

5

END SEMESTER ASSESSMENT



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01ME6691 SEMINAR I 0-0-2 2 2015

Course Objectives To make students

1. Identify the current topics in the specific stream. 2. Collect the recent publications related to the identified topics. 3. Do a detailed study of a selected topic based on current journals, published papers

and books. 4. Present a seminar on the selected topic on which a detailed study has been done. 5. Improve the writing and presentation skills.

Approach

Students shall make a presentation for 20-25 minutes based on the detailed study of the topic and submit a report based on the study.

Expected Outcome

Upon successful completion of the seminar, the student should be able to 1. Get good exposure in the current topics in the specific stream. 2. Improve the writing and presentation skills.

3. Explore domains of interest so as to pursue the course project



27


01ME6693 DATA ANALYSIS LABORATORY

0-0-2 1 2015

Course Objectives

1. Should acquire knowledge on working of data analysis software packages.

Syllabus

Experiments on conducting data analysis tasks using software packages.

Expected Outcome

1. Have working knowledge of data analysis software packages.

List of Experiments 1. Data Analysis using SPSS / Excel /R /Python /SAS / Systat/EViews etc(free and open

source, trial or free academic version of the software package may be used). Exercises shall be given on

Data input

Descriptive statistics and Tabulation

Fitting Probability Distribution

Data Munging

Hypothesis Testing

Graphical Analysis

t tests

ANOVA

Regression Analysis

Time Series and Autocorrelation

Clusteringetc.



28

SEMESTER – II




29


01ME6602 OPTIMIZATION IN

FINANCE 3-1-0 4 2015

Course Objectives

1. To introduce mathematical modelling and optimization techniques. 2. To apply these techniques for financial optimization.

3. To experiment with real-life problems and promote decision making skills.

Syllabus

Linear Programming; Sensitivity Analysis; Non-linear programming;Quadratic programming;

Conic Optimization Tools; Integer Programming; Dynamic Programming; Stochastic Programming;

Robust Optimization and Case studies in Financial Optimization.

Expected Outcome

1. The students will be able to model real life problems. 2. The students will have the knowledge to select and applysuitable optimization techniques in

financial optimization problems.

References

1. Gerard Cornuejols et al., Optimization Methods in Finance, Cambridge University press.

2. Stavros Zenios, Andrea Consiglio and Søren S. Nielsen, Practical Financial Optimization, John Wiley and Sons, Ltd.

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I Overview of optimization concepts; Linear programming: theory and algorithms, Sensitivity Analysis; 3 15



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LP models: asset/liability cash-flow matching- Short-term financing; asset pricing and arbitrage- Derivative securities and the fundamental theorem of asset pricing, Arbitrage detection using linear programming

5

II

Nonlinear programming: Univariate optimization, Unconstrained optimization, Constrained optimization, Non-smooth optimization: sub-gradient methods

6

15 NLP models: volatility estimation-Volatility estimation with GARCH models, Estimating a volatility surface. 2

FIRST INTERNAL EXAM

III

Quadratic programming: theory and algorithms; QP models: portfolio optimization- Mean-variance optimization, Maximizing the Sharpe ratio, Returns-based style analysis, Recovering risk-neutral probabilities from options prices.

9 15

IV

Conic optimization tools- Second-order cone programming, Semi-definite programming, Algorithms; Conic optimization models in finance- Tracking error and volatility constraints, Approximating covariance matrices, Recovering risk-neutral probabilities fromoptions prices, Arbitrage bounds for forward start options

9 15


V

Integer programming: theory and algorithms; Integer programming models: Constructing index fund- Combinatorial auctions, the lockbox problem, constructing an index fund, Portfolio optimization with minimum transaction levels

7

20

Dynamic programming; DP models: option pricing- A model for American options, Binomial lattice; structuring asset-backed securities 4

VI

Stochastic programming: theory and algorithms- Two-stage problems with recourse, Multi-stage problems, Decomposition, Scenario generation; Stochastic programming models: Value-at-Risk and Conditional Value-at-Risk- Risk measures, Minimizing CVaR; asset/liability management.

5

20 Robust optimization: theory and tools; Robust optimization models in finance, Robust multi-period portfolio selection, Robust profit opportunities in risky portfolios, Robust portfolio selection, Relative robustness in portfolio selection, Moment bounds for option prices. Case Studies in Financial Optimization- Applications in International Asset Allocation, Corporate Bond Portfolio Management, Insurance Policies with Guarantees, Personal Financial Planning.

6

END SEMESTER EXAM



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01ME6604 EQUITY AND FIXED

INCOME 3-0-0 3 2015

Course Objectives

1. To introduce equity investments, security markets and indices, and equity valuation models. 2. To introduce the basic features and characteristics of fixed income securities and associated

risks. 3. Describe the primary issuers, sectors and types of bonds 4. To introduce yields and spreads and the effect of monetary policy on financial markets.

Syllabus

Market Organization and Structure; Security Market Indices; Market Efficiency; Equity Securities;

Industry and Company Analysis; Equity Valuation; Fixed Income; Risks Associated with Investing

in Bonds; Bond Sectors and Instruments; Yield Spreads; Valuation of Debt Securities; Yield

Measures; Spot Rates; Forward Rates; Measurement of Interest Rate Risk; Credit Analysis.

Expected Outcome

After the completion of the course, the students shall be able

1. To describe the characteristics of equity investments, security markets and indices. 2. To analyze industries, companies and equity securities and to describe and demonstrate the

use of equity valuation models. 3. To describe the basic features of bonds, the various coupon rate structures, and the structure

of various floating rate securities and the risks associated with investing in bonds.

4. To describe features, credit risk characteristics and distribution methods for government securities, mortgage-backed securities etc.

References

1. Sharpe, W, G. Alexander, and J. Bailey, “Investments”. New Jersey: Prentice Hall, lnc, 1999.

2. Dreman, D.,”Psychology of the Stock Market”. New York: AMACOM, 1977. 3. O'Shaughnessy, J., “What Works on Wall Street”. New York: McGraw-Hill, 2005. 4. Hill, Charles, and Gareth Jones, "External Analysis: The Identification of

Opportunities and Threats:' Strategic Management: An Integrated Approach.”Boston, MA: Houghton Mifflin Co, 2008.

5. Porter, Michael E., “The Five Competitive Forces that Shape Strategy”. Harvard Business Review, vol. 86, no. 1:78-93, 2008.

6. “Equity and Fixed Income”. CFA Institute, 2012.



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Market Organization and Structure: The Functions of the Financial System, Assets and Contracts, Financial intermediaries, Positions, Orders, Primary Security Markets, Secondary Security Market and Contract Market Structures, Market Regulation.

4

15 Security Market Indices: Index Definition and Calculations of Value and Returns, Index Construction and Management, Uses of Market Indices, Equity Indices, Fixed-Income Indices, Indices for Alternative Investments.

3

II

Market Efficiency- Concept; Forms; Market Pricing Anomalies; Behavioral Finance. 3

15 Equity Securities: Types and Characteristics of Equity Securities, Private versus Public Equity Securities, Risk and Return Characteristics of Equity Securities.

4

FIRST INTERNAL EXAM

III

Introduction to Industry and Company Analysis-Uses, Industry Classification Systems, Describing and Analyzing an industry, Company Analysis.

3

15

Equity Valuation- Valuation Concepts, Return Concepts, Equity Valuation Models: Discounted Dividend Valuation- Dividend Discount Model, Gordon Growth Model, Multistage Dividend Discount Models, Financial Determinants of Growth Rates; Free Cash Flow Valuation; Market-Based Valuation, Residual Income Valuation, Private Company Valuation.

4

IV

Fixed Income: Features of Debt Securities- Coupon Rate; Provisions for Paying off Bonds; Put Provision; Currency Denomination; Embedded Options.

2

15 Risks Associated with Investing in Bonds

2

Bond Sectors and Instruments-Sectors; Sovereign Bonds; Semi-Government/ Agency Bonds; Corporate Debt Securities; Asset Backed Securities; Primary Market and Secondary Market for Bonds

3


V Yield Spreads; Valuation of Debt Securities- General Principles of Valuation, Traditional Approach to Valuation, Valuation Models. 5 15



33

VI

Yield Measures, Spot Rates, and Forward Rates; Measurement of Interest Rate Risk. 4

25 Fundamentals of Credit Analysis- Credit risk, Capital Structure, Seniority Ranking, and Recovery Rates, Ratings Agencies, Credit Ratings, and their Role in the Debt Markets.

5

END SEMESTER EXAM



34


01ME6406 SYSTEM ANALYSIS AND

DESIGN 3-0-0 3 2015

Course Objectives

1. To apply system concepts to solve problems in industrial and business organizations. 2. To model and simulate discrete event systems.

3. To study the tools for modeling and simulating dynamic systems.

Syllabus

Introduction to System Simulation – Random Numbers – Random number generators – Generation

of Random deviates – Input modeling – Verification and validation of simulation models - Analysis

of Simulation outputs - Structure and Behavior of Dynamic systems – Tools for systems thinking –

Elements of system dynamics modeling – Steps in SD modeling – Overview of computer simulation

languages and packages.

Expected Outcome

1. The student will have an understanding of real life systems with interacting components, elements and sub-systems, modeling and analysis of these interacting components and elements in a system and the system as a whole.

2. The student will be able to conduct experiments on the system models and to predict the system behavior at different environments and input states and parameter settings and to find out the best suited system parameter settings to meet the predefined objectives.

References

1. Geoffrey Gordon, “System Simulation”, PHI. 2. Narsingh Deo, “System Simulation with Digital Computer”, PHI. 3. J. Banks, “Discrete Event System Simulation”, Pearson Education. 4. Fishman – John, “Concepts and Methods in Discrete Event Digital; Simulation”,

Willey & Sons. 5. Sterman, “Business Dynamics”, McGraw Hill. 6. Mohapatra, “System Dynamics”, PHI. 7. Ogata, “System Dynamics”, Pearson Education.



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I Introduction to System Simulation: System approach to problem solving, Steps in simulation study, Comparison of simulation and numerical methods, Monte Carlo simulation.

5 10

II

Random Numbers: Properties, Generation of Pseudo-Random numbers – Random number generators, Tests for random numbers – Frequency, Run, Gap, Autocorrelation and Poker tests.

9 20

FIRST INTERNAL EXAM

III

Generation of Random Deviates: Inverse Transformation method - Exponential, Uniform, Weibull, Triangular, and discrete distributions, Direct transformation method for the Normal and Lognormal distributions, Acceptance-rejection technique - Poisson and Gamma distributions.

7 15

IV

Input modeling - data collection, identifying the distribution with the collected data, goodness of fit tests, Verification and Validation of simulation models, Analysis of simulation Outputs. Discrete event simulation techniques - Next-Event approach and Fixed Time Increment methods.

7 15


V

Structure and Behavior of Dynamic systems: Fundamental modes of dynamic behavior – Exponential growth, goal seeking, oscillation and process point, Interactions of fundamental modes. Tools for Systems thinking - Causal loop diagramming, Behavior of low order systems - Analytical approach.

7 20

VI

Elements of System Dynamics Modeling: Physical flows, Information flows, Level & Rate variables, Flow diagrams, Delays, Information smoothing, Table functions and Table function multipliers, First order positive and negative feedback systems, Second order systems. Steps in system dynamics modeling: Problem identification/conceptualization, fixing model aggregates and boundary, principles of simulation modeling, Developing model equations. Overview of computer simulation languages and packages.

7 20

END SEMESTER EXAM



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01ME6612 DERIVATIVES AND

ALTERNATIVE INVESTMENTS

3-0-0 3 2015

Course Objectives

1. To introduce the students to derivative markets and instruments, alternative investments and various methods of analysis.

Syllabus

Derivative Markets and Instruments; Forward Markets and Contracts; Futures Markets and

Contracts; Option Markets and Contracts; Swap Markets and Contracts; Interest Rate Derivative

Instruments; Risk Management Applications of Option Strategies; Alternative Investments:

Categories; Investment in Hedge Funds, Commodities, Real Estate; Private Equity.

Expected Outcome

1. The student will be able to demonstrate a working knowledge of the analysis of derivative investments, including forwards, futures, options, and swaps as well as the analysis of alternative investments, including hedge funds, private equity, real estate, and commodities.

References

1. “Derivatives and Alternative Investments”, CFA Institute, 2012.

2. Lionel Martellini, Philippe Priaulet, “Fixed-Income Securities: Valuation, Risk Management and Portfolio Strategies”, Wiley.

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Derivative Markets and Instruments: Types of Derivatives, Elementary Principles of Derivative Pricing. 3

15 Forward Markets and Contracts: Global Forward Markets, Types of Forward Contracts, Pricing and Valuation of Forward Contracts, Credit Risk and Forward Contracts

4

II

Futures Markets and Contracts: Futures Trading, the Clearinghouse, Margins, and Price Limits, Futures Exchanges, Types of Futures Contracts, Pricing and Valuation of Futures Contracts.

5 15



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FIRST INTERNAL EXAM

III

Option Markets and Contracts: Basic Definitions and Illustrations of Options Contracts, the Structure of Global Options Markets, Types of Options, Principles of Option Pricing, Discrete-Time Option Pricing: The Binomial Model, Continuous-Time Option Pricing: The Black-Scholes-Merton Model, Pricing Options on Forward and Futures Contracts and an Application to Interest Rate Option Pricing.

5 15

IV

Swap Markets and Contracts: Global Swap Markets, Types of Swaps, Pricing and Valuation of Swaps, Variations of Swaps, Swaptions, Credit Risk and Swaps.

4

20

Interest Rate Derivative Instruments: Interest Rate Futures, Interest Rate Options, Interest Rate Swaps, Interest Rate Caps and Floors. Overview of Credit Derivatives. 4

Risk Management Applications of Option Strategies: Option Strategies for Equity Portfolios. 3


V

Alternative Investments: Categories; Investment in Hedge Funds: Fee Structures, Hedge Fund Strategies, Hedge Fund Databases and Performance Biases, Factor Models for Hedge Fund Returns, Non-Normality of Hedge Fund Returns, Liquidity, Complexity, and Valuation Risks, Hedge Fund Replication, Hedge Fund Portfolio Analysis, Performance Persistence of Hedge Funds

2

15

Commodities: Basics, Controversies, Commodities in a Portfolio, Implementation of Commodity Strategies.

2

Real Estate: Private Real Estate Investments; Valuation approaches for Real Estate Investments

3

VI

Publicly Traded Real Estate Securities-Types; Valuation methods 3

20 Private Equity: Introduction to Valuation Techniques in Private Equity Transactions, Private Equity Fund Structures and Valuation 4

END SEMESTER EXAM



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01ME6414 DATA ANALYTICS USING R

AND PYTHON 3-0-0 3 2015

Course Objectives

1. Learn about what it’s like to be a Data Scientist. 2. Learn R and Python for Data Analytics.

Syllabus

Introduction to R; R and Rstudio; Basics of R; Advanced Data Structures; Reading Data into R;

Statistical Graphics; R programming; Data Munging; String Manipulation; Basic Statistics; Linear

Models; Predictive Modeling; Time Series Analysis; Clustering; Association Rules; Text Mining;

Sentiment Analysis; Social Network Analysis; Reports and Slideshows; R Package Building.

Introduction to Python; Python Programming; NumPy; Pandas; Data Loading, Storage , File

formats, Data Wrangling; Plotting and Visualization; Data Aggregation and Group Operations;

Time Series Analysis; Financial and Economic Data Applications

Expected Outcome

After Completion of course, the students will be able to use R and Python to:

1. Manipulate and extract information from data 2. Make informative plots 3. Construct and apply statistical learning methods for predictive modeling, 4. Properly select, tune, and assess models

5. Reproduce and present results from data analysis

References

1. Jarad Lander, “R for Everyone: Advanced Analytics and Graphics”, Addison Wesley. 2. Mark Gardener, “R The Statistical Programming” , Wiley. 3. James, Witten, Hastie and Tibshirani,“An Introduction to Statistical Learning: with

Applications in R”, free electronic version of this book available at http://www-bcf.usc.edu/~gareth/ISL/.

4. Johannes Ledolter, “Data mining and business analytics with R”, John Wiley & Sons. 5. Torgo, Luís, “Data mining with R : learning with case studies”, CRC Press 6. Dirk Eddelbuettel, “Seamless R and C++ Integration with Rcpp”, Springer 7. http://www.rdatamining.com/ 8. Wes McKinney, “Python for Data Analysis”, O’Reilly.

9. Peter Wang and Aron Ahmadia, “Fundamentals of Data Analytics in Python”, Addison Wesley Live Lessons



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Introduction to R; Installation of R and R Studio; Installing and loading R packages

1

10 Basic building blocks in R; Advanced Data Structures in R; Reading data into R; Statistical Graphs in R

3

R Programming 3

II Data Munching-Group manipulation, Reshaping; String Manipulation 3

20 Basic Statistics; Linear Models 4

FIRST INTERNAL EXAM

III

Predictive Modeling: Generalized Linear Models; Model Diagnostics; Regularization and Shrinkage

3

20 Nonlinear Models; Time Series and Autocorrelation; Multivariate data exploration and discrimination. 3

IV

Clustering;Association Rules; Text Mining; Sentiment Analysis; Social Network Analysis; Reports and Slideshows 4

10 R Package Building, Introduction to Rcpp, Data structures, Using Rcpp in package, Modules, Operators, Functions, Applications. 4


V

Introduction to Python: Python Libraries, Installation and Setup; Python Programming: Data Types and Variables, Python input and output, If statements, while loops, for loops, Iterators, Lists, Functions , Modules, Object Oriented Programming, Inheritance, Exception Handling, Using Data Structures.

7 20

VI

Basic Analytics with Python; Numerical Analysis with NumPy 2

20 Advanced Analytics with Sci-Py and sci-kit learn 2

Tabular Data Analysis with Pandas; Python Visualization Tools; Financial and Economic Data Applications

3

END SEMESTER EXAM



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01ME6616 FINANCIAL MARKETS 3-0-0 3 2015

Course Objectives

1. To learn the business and the economics of money and capital markets 2. To become familiar with the various types of financing available to a firm

3. To analyze the structural interrelationships among the important participants in the financial markets

Syllabus

Money and Capital Markets; Financial Institutions; Financial Markets; Risk Management in

Financial Institutions; Indian Financial Markets.

Expected Outcome

At the end of this course students should be able to:

1. Explain financial markets and institutions and how firms obtain funds in the financial markets.

2. Explain how the financial services component industries interact.

References

1. Frederic S. Mishkin and Stanley G. Eakins,“Financial Markets and Institutions”, Pearson, 8/e.

2. Rakesh Sahani, “Financial Markets in India”, Anamika Publishers and Distributors,2008.

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Money and Capital Markets; Overview of the Financial System;

Fundamentals of Financial markets-Interest rates and their role in

Valuation; Interest rate change; Risk and Term Structure; Efficiency of

Financial Markets.

7 15

II

Financial Institutions: Banks and Monetary Policy; Banking and the

Management of Financial Institutions ; Financial Regulation ; Banking

Industry: Structure and Competition ; The Mutual Fund Industry

;Insurance Companies and Pension Funds ; Investment Banks, Security

7 15



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Brokers and Dealers, and Venture Capital Firms

FIRST INTERNAL EXAM

III

Financial Markets: The Money Markets ; The Bond Market ; The Stock

Market ; The Mortgage Markets ; The Foreign Exchange Market ; The

International Financial System 7 15

IV

Risk Management in Financial Institutions; Hedging with Financial

Derivatives.

Nature and Role of Financial Markets in India; Behavioural Finance and

the Role of the Psychology; Primary Market for Industrial Securities in

India

7 15


V

Role of Investment and Merchant Banking as Intermediaries in the field

of Financial Markets; Stock Market Volatility and Securities Trading In

India 5 20

VI

Indian Stock Market Indices; Futures, Options and other financial

derivatives; Collective Investment Vehicles; Money Market in India;

Market for Government Securities and Foreign Investment in India.

Liquidity, Turnover and Impact Costs on Indian Exchanges; Listing and

Delisting of Securities; Major Financial Services Operating in the Indian

Financial Markets.

Brief Introduction into Corporate Governance; Non-Performing Assets

(NPA) in Banking Sector; Insider Trading; Buy Back of Shares by the

Company; Benchmark Prime Lending Rate.

9 20

END SEMESTER EXAM



42


01ME6618 QUANTITATIVE TRADING

STRATEGIES 3-0-0 3 2015

Course Objectives

1. To introduce the students to financial trading strategies which will focus on stock and equity markets as well as the use of statistical arbitrage methods

2. To develop, automate and evaluate models that reflect market and behavioral patterns.

Syllabus

Foundations of Securities Trading and Market Microstructure; Information- and Inventory-Based

Microstructure Models ; Trading Costs and Optimal Trading Strategies; High Frequency Trading

Strategies

Expected Outcome

1. The students completing this course will develop the knowledge to apply theory for practical application to realistic trading and strategy problems.

References

1. Lars N. Kestner,“Quantitative Trading Strategies: Harnessing the Power of

Quantitative Techniques to Create a Winning Trading Program”, McGraw Hill Professional publication.

2. Morton Glant, “Optimal Trading Strategies: Quantitative Approaches for Managing Market Impact and Trading Risk”.

3. Joel Hasbrouck, “Empirical Market Microstructure – The Institutions, Economics, and Econometrics of Securities Trading”, Oxford University Press 2007

4. Maureen O’Hara, “Market Microstructure Theory”, Blackwell Publishing 1995 5. Irene Aldridge, “High Frequency Trading – A Practical Guide to Algorithmic

Strategies and Trading Systems”, Wiley, 2010.

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I Foundations of Securities Trading and Market Microstructure : Nature of Markets and Prices; Trading Mechanisms, Markets and Market Making; Univariate Time Series Analysis

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II Information- and Inventory-Based Microstructure Models Information-Based Model;Sequential Trade Model;Strategic Trade Models

7 15

FIRST INTERNAL EXAM

III

Generalized Roll Model; Multivariate Linear Microstructure Models- Modeling Vector Time Series, Structural Model of Prices and Trades, Forecasts and Impulse Response Functions, Random Walk Decomposition in Multivariate Models , Other Structural Models

6 15

IV

Multiple Securities and Multiple Prices; Inventory Models- Order Arrival and Market Making , Market Maker’s Ruin Problem , Risk Aversion and Dealer’s Problem , Empirical Analysis of Dealer , Inventories, Dynamics of Prices, Trades, and Inventories; Market Depth

8 15


V

Trading Costs and Optimal Trading Strategies Transaction Costs- Component Transaction Costs , Implementation Shortfall , Market Impact, Timing Risk and Opportunity Costs, Optimal Trading Strategies ; Trading Benchmarks- Benchmark Prices, BAM and VWAP , VWAP Trading Strategies , Models of Order Slicing and Timing

7 20

VI

High Frequency Trading Strategies Evolution of High Frequency Trading-Comparison with Traditional Approaches to Trading , Evaluating Performance of High Frequency Strategies , Market Efficiency and Trading Opportunities at Different Frequencies;High Frequency Data;High Frequency Strategies-Trading on Microstructure: Inventory Based and Information Based , Event Arbitrage Strategies , Statistical Arbitrage Strategies , Managing Portfolios of High Frequency Strategies

7 20

END SEMESTER EXAM



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01ME6422 ENTERPRISE RESOURCE

PLANNING 3-0-0 3 2015

Course Objectives

1. The student should be able to acquire knowledge in ERP architecture and different packages. 2. Should have exposure to latest trends in ERP. 3. Ability to identify important issues pertaining to implementation of ERP software in an

industrial scenario

Syllabus

Introduction to ERP and Enterprise Applications; Risks and Benefits of ERP; ERP and Related

Technologies; ERP Manufacturing Perspective; Business Process Reengineering (BPR); ERP

Implementation—Life Cycle, Methodologies, Issues; Business Modules in an ERP Package; ERP

Market, ERP and eBusiness,ERP II, Future Directions and Trends in ERP, ERP Resources on the

Web, ERP Case studies.

Expected Outcome

After Completion of course, students should be able to

1. Understand the architecture of ERP systems. 2. Understand the working of different modules in ERP. 3. Understand the correct choice of an ERP package for the selected industry.

References

1. Alexis Leon, “ERP Demystified”, McGraw-Hill Education India Pvt. Ltd.,3/e. 2. Rajesh Ray, “Enterprise Resource Planning”, TMH,2011. 3. Mary Sumner, “Enterprise Resource Planning”, Pearson Education, 2010. 4. Bradford M., “Modern ERP Systems: Select Implement and Use Today’s Advanced

Business Systems”,H&M Books,2010.

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I Introduction to ERP and Enterprise Applications: Overview, Need, History, Risks and Benefits, Enterprise Applications

3 15

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ERP Manufacturing Perspective. 2

II

Business Process Reengineering; Business Process Modeling and Business Modeling 3

15 ERP software packages and selection of ERP package-various approaches to ERP selection;Procurement process for ERP package, Features of various modules of ERP.

3

FIRST INTERNAL EXAM

III

ERP Implementation—Life Cycle, Methodologies, Issues, Hidden costs, Vendors, Consultants and Users; ERP Project Management;ERP Security; ERP Training; Change Management; Application Support. 6 15

IV

ERP Functional Modules: Human Capital Management; Financial Management; Procurement and Inventory Management; Supplier Relationship Management; Production Planning and Execution; Supply Chain Planning; Sales and Service; Warehouse and Transport Management; Customer Relationship Management; Quality Management; Maintenance Management and Enterprise Asset Management; Product Lifecycle Management

9 20


V

ERP Market: SAP AG, Baan company, People soft, Oracle corporation, Microsoft Dynamics, JD Edwards world solution company, QUAD system software associates, Epicor ERP, Lawson ERP etc. Open source ERP packages.

8 20

VI

ERP and eBusiness, ERP II, Future Directions and Trends in ERP, ERP Resources on the Web

3

15 ERP Case studies: HRM, finance, production, materials, sales and distribution.

3

END SEMESTER EXAM



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01ME6624 BIG DATA ANALYTICS 3-0-0 3 2015

Course Objectives

1. To bring together several key technologies used in manipulating, storing, and analyzing big data.

2. To make the student understand details of Hadoop.

3. To introduce tools that provides SQL-like access to unstructured data.

Syllabus

Big Data: Importance, Applications, Data Analysis, Mining Data Streams, Frequent Item Sets and

Clustering, Big Data Frameworks, Visualization.

Expected Outcome

Students who complete this course will be able to

1. Categorize and Summarize Big Data and its importance. 2. Manage Big Data and analyze Big Data.

3. Apply tools and techniques to analyze Big Data.

References

1. Michael Berthold, David J. Hand, “Intelligent Data Analysis”, Springer, 2007. 2. AnandRajaraman and Jeffrey David Ullman, “Mining of Massive Datasets”,

Cambridge University Press, 2012. 3. Bill Franks, “Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data

Streams with Advanced Analytics”, John Wiley & sons, 2012. 4. Glenn J. Myatt, “Making Sense of Data”, John Wiley & Sons, 2007 5. Pete Warden, “Big Data Glossary”, O’Reilly, 2011. 6. Jiawei Han, MichelineKamber “Data Mining Concepts and Techniques”, Second

Edition, Elsevier, Reprinted 2008.

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Introduction to BigData Platform – Traits of Big data -Challenges of Conventional Systems - Web Data – Evolution Of Analytic Scalability - Analytic Processes and Tools - Analysis vs Reporting - Modern Data Analytic Tools - Statistical Concepts: Sampling Distributions – Re-

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Sampling - Statistical Inference - Prediction Error.

II

Regression Modeling - Multivariate Analysis - Bayesian Modeling - Inference and Bayesian Networks - Support Vector and Kernel Methods - Analysis of Time Series: Linear Systems Analysis - Nonlinear Dynamics - Rule Induction - Neural Networks: Learning And Generalization - Competitive Learning - Principal Component Analysis and Neural Networks - Fuzzy Logic: Extracting Fuzzy Models from Data - Fuzzy Decision Trees - Stochastic Search Methods.

7 15

FIRST INTERNAL EXAM

III

Introduction To Streams Concepts – Stream Data Model and Architecture - Stream Computing - Sampling Data in a Stream – Filtering Streams – Counting Distinct Elements in a Stream – Estimating Moments – Counting Oneness in a Window – Decaying Window - Real time Analytics Platform(RTAP) Applications - Case Studies - Real Time Sentiment Analysis, Stock Market Predictions.

7 15

IV

Mining Frequent Itemsets - Market Based Model – Apriori Algorithm – Handling Large Data Sets in Main Memory – Limited Pass Algorithm – Counting Frequent Itemsets in a Stream – Clustering Techniques – Hierarchical – K-Means – Clustering High Dimensional Data – CLIQUE And PROCLUS – Frequent Pattern based Clustering Methods – Clustering in Non-Euclidean Space – Clustering for Streams and Parallelism.

7 20


V

MapReduce – Hadoop, Pig, Hive, MapR – Sharding – NoSQL Databases - S3 - Hadoop Distributed File Systems –Oracle Big Data- Visualizations - Visual Data Analysis Techniques - Interaction Techniques

6 20

VI

Systems and Analytics Applications - Analytics using Statistical packages-Approaches to modeling in Analytics – correlation, regression, decision trees, classification, association Intelligence from unstructured information-Text analytics-Understanding of emerging trends and technologies-Industry challenges and application of Analytics.

8 20

END SEMESTER EXAM



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01ME6692 MINI PROJECT 0-0-4 2 2015


Design and develop a system or application in the area of their specialization.

Approach

The student shall present two seminars and submit a report.The first seminar shall highlight the topic, objectives, methodology, design and expected results. The second seminar is the presentation of the work / hardwareimplementation.

Expected Outcome

Upon successful completion of the miniproject, the student should be able to 1. Identify and solve various problems associated with designing and implementing a system or

application. 2. Test the designed system or application.



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01ME6694 OPTIMIZATION AND

SIMULATION LABORATORY 0-0-2 1 2015

Course Objectives

1. Should acquire knowledge on simulation model building and simulation through software packages.

2. Should have working knowledge of optimization software packages.

Syllabus

Simulation Modeling and Optimization with emphasis on Financial Applications.

Expected Outcome

1. Understand simulation model building and simulation through software packages

2. Use optimization software packages to solve optimization problems in Finance.

List of Experiments

1. Exercises on solving optimization problems using IBM ILOG CPLEX /AIMMS/GAMS/Lindo/Lingo etc (free and open source, trial or free academic version of the software package may be used). Exercises shall be on:

Linear and Nonlinear Programming Problems

Integer Programming Problems

Quadratic Programming Problems

Robust Optimization etc. 2. Simulation model building and conducting simulation experiments using Simio

/Arena / AnyLogic / Vensim /NetLogo etc. (free and open source/ trial / free academic version of the software package may be used)

Exercises shall be on:

Discrete Event Modeling

System Dynamics

Agent Based Modeling



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01ME7611 ASSET PRICING 3-0-0 3 2015

Course Objectives

1. To endow students with the foundations of financial economics and to expose them to the classic and modern models of asset pricing.

2. To develop technical skills and become comfortable in modeling an economic problem of their choice.

Syllabus

Introduction to Continuous-Time Stochastic Models: Diffusions & Diffusion Models; Equity

Premium and Risk, Time Varying Risk Premium, the Cross Section of Stock Returns, Asset Pricing

Theory; Classic issues in Finance; Equilibrium, Contingent Claims, Risk-Neutral Probabilities: Risk

Free Rate and Macroeconomics, Consumption and Risk Premiums; Contingent Claims, State Prices,

Risk-Neutral Probabilities; Mean-Variance Frontier; Implications of the Existence and Equivalence

Theorems; Factor Pricing Models, Value Premium, the Fama-French model; Term Structure

Models; Portfolio Theory.

Expected Outcome

1. The students completing this course will have a “Big Picture” conceptually and with applicable tools in Asset Pricing.

References

1. John H. Cochrane,“Asset Pricing”, (Revised), Princeton University Press, 2003; http://faculty.chicagobooth.edu/john.cochrane/teaching/35904_Asset_Pricing/

2. Rajnish Mehra, “Handbook of the Equity Risk Premium”, Elsevier,2008.

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I Introduction to Continuous-Time Stochastic Models: Diffusions &

Diffusion Models; Ito's Lemma; Stochastic Differential Equations. 3 20

http://faculty.chicagobooth.edu/john.cochrane/teaching/35904_Asset_Pricing/



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Equity Premium and Risk, Time Varying Risk Premium, the Cross

Section of Stock Returns, Asset Pricing Theory. 3

II

Classic issues in Finance; Equilibrium, Contingent Claims, Risk-Neutral

Probabilities: Risk Free Rate and Macroeconomics, Consumption and

Risk Premiums, Risk Premiums & Betas, Mean Variance Frontier and

Roll Theorem, Random Walks & Time-Varying Risk Premiums, General

Equilibrium and Causality

6 15

FIRST INTERNAL EXAM

III

Contingent Claims, State Prices, Risk-Neutral Probabilities: States &

Complete Markets, Discount Factor in Complete Markets, Risk-Neutral

Probabilities in Complete Markets, Investors in Complete Markets, Risk-

Sharing in Complete Markets, State-Space Geometry. Incomplete

Markets: Discount Factor in Incomplete Markets. Positive M &

Arbitrage.

Mean-Variance Frontier: Classic Approach, State-Space [Hansen-

Richard] Approach, Comparing Frontiers, Roll Theorem.

8 20

IV

Implications of the Existence and Equivalence Theorems: History and

Representation, Fishing, Mimicking Portfolio Theorem & Fishing.

Conditioning Information: Conditioning Down, Instruments &

Managed Portfolios, Conditional & Unconditional Models

3

15

Factor Pricing Models, Value Premium, the Fama-French model:

Introduction/Overview, CAPM / Simple 2-Period Quadratic, CAPM:

Derivation with Log Utility or IID Consumption Growth, ICAPM /

State Variables, Multifactor Models - Outside Income, Multifactor

Models - Portfolio Intuition, Multifactor Models – U’ Intuition, Macro,

Mimicking Portfolios, Arbitrage Pricing Theory (APT), APT vs

Equilibrium Models.

5


V

The Fama-French Model, The Fama/French 3-Factor Model, The

Fama/French Model, Using the 3-Factor Model, Momentum & Reversal.

Option Pricing: Payoffs, Arbitrage Bounds, Black-Scholes, Other

Method, Spanning, State Prices, and Current Models, Date, Smile,

Models.

7 15



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VI

Term Structure Models: Introduction, Definitions, Expectation

Hypothesis, Risk Premium Introduction, Facts - Fama/Bliss, Statistical

Factor Models, Term Structure Model with Expectations Hypothesis,

Discrete-Time Vasicek, Other Approaches, Continuous-Time Term

Structure. Portfolio Theory: Classic Approach, Mean-Variance, Merton,

Merton Examples.

7 15

END SEMESTER EXAM



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01ME7613 FINANCIAL BUSINESS

INTELLIGENCE 3-0-0 3 2015

Course Objectives The course aims at

1. Exposing the field of business intelligence systems.

2. Providing a practical understanding of the business intelligence life cycle and the

techniques used in it.

3. Helping the students to decide on appropriate technique.

Syllabus

Evolution, History and Power of Financial Business Intelligence; Business intelligence

architecturesBusiness focussed data analysisUser Types Visualization; Efficiency measures; BI

software packages; Financial Data Modelling.;Marketing models; Logistic and Production models;

Case studies.

Expected Outcome


1. Explain the fundamentals of business intelligence. 2. Link data mining with business intelligence. 3. Explain the data analysis and knowledge delivery stages.

4. Apply Business intelligence methods to decision making in finance.

References

1. Larissa T. Moss, S. Atre , “Business Intelligence Roadmap: The Complete Project Lifecycle for Decision Making”, 1st Edition, Addison Wesley, 2003.

2. Carlo Vercellis, “Business Intelligence: Data Mining and Optimization for Decision Making”, 1st Edition, Wiley Publications, 2009.

3. David Loshin Morgan, Kaufman, “Business Intelligence: The Savvy Manager's Guide”, 2nd Edition, 2012.

4. Cindi Howson, “Successful Business Intelligence: Secrets to Making BI a Killer App”, 1st Edition, McGraw-Hill, 2007.

5. Nils H. Rasmussen, Paul S. Goldy, Per O. Solli, Financial Business Intelligence: Trends, Technology, Software Selection, and Implementation”, John Wiley and Sons.



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I Evolution of Financial Business Intelligence: History and Power of BI

tools. Financial and Non-financially focussed tools, Data storage

methodology, Data warehousing, Front end analytic tools. 5 15

II

Effective and timely decisions - Data, information and knowledge - Role

of mathematical models - Business intelligence architectures: Cycle of a

business intelligence analysis - Enabling factors in business intelligence

projects - Development of a business intelligence system - Ethics and

business intelligence.

5

15

Business focussed data analysis – Top down logical data modelling –

Bottom up source data analysis – Data cleansing – Deliverables of data

analysis – Importance of data analysis 4

FIRST INTERNAL EXAM

III

The Business Intelligence User Types - Standard Reports - Interactive

Analysis and Ad Hoc Querying - Parameterized Reports and Self-

Service Reporting- Dimensional analysis - Alerts/Notifications 5

20 Visualization: Charts, Graphs, Widgets, Scorecards and Dashboards, Geographic Visualization- Integrated Analytics- Considerations: Optimizing the Presentation for the Right Message.

4

IV Efficiency measures – The CCR model: Definition of target objectives –

Peer groups – Identification of good operating practices: cross efficiency

analysis – Virtual inputs and outputs – Other models. 5 15


V

Overview of Business Intelligence Software, Major software companies

in BI, Software Evaluation. Implementation of BI system: project

planning, Multidimensional model definition and maintenance,

financial data modelling.

6 15

VI

Marketing models – Logistic and Production models – Case studies.

Future of business intelligence-Emerging Technologies, Predicting the

Future, BI Search & Text Analytics-Advanced Visualization- Rich

Report, Future beyond Technology.

8 20

END SEMESTER EXAM



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01ME7415 HEURISTIC SOLUTION

METHODS 3-0-0 3 2015

Course Objectives

The main objectives of this course are:-

1. To introduce the students to various metaheuristic solution algorithms. 2. To demonstrate the applications of these algorithms for solving large real life

problems

Syllabus

Introduction to Non-traditional optimization; Computational Complexity; Classification of

heuristic solution techniques; Metaheuristics; Introduction to evolutionary computation; Genetic

Algorithms: Concepts, Algorithm, Binary GA, Continuous GA, Hybrid GA, Parallel GA. Scatter

Search-Components, Algorithm, Applications. Multi objective evolutionary optimization; Greedy

Randomized Adaptive Search Procedure, Ant Colony Algorithms: Overview, Basic algorithm,

Variants; Particle Swarm Optimization; Lagrangean Relaxation; Local Search Algorithms; Tabu

Search; Simulated Annealing, Components, Variants of Simulated Annealing; Artificial Neural

Networks- Biological and Artificial Neural Networks, Basic Concepts, Generic Algorithm,

Constraint Programming- Problem Formulation in Constraint Programming, Basic Search and

Constraint Propagation, Constraint Programming vs Mathematical Programming; Applications of

the above mentioned heuristic methods to solve different types of optimization problems.

Expected Outcome

After Completion of course,

1. The students will have the knowledge of various metaheuristic solution algorithms and their applications.

2. The students will have the skill to model real life problems and will be able to apply proper heuristic techniques to solve them.

References

1. GüntherZäpfel , Roland Braune, Michael Bögl, “Metaheuristic Search Concepts-A Tutorial with Applications to Production and Logistics”, Springer.

2. Michalewicz Z, “Genetic Algorithms + Data Structures = Evolution Programms”, Springer-Verlag,Berlin.

3. J.Dreo,A.Petrowski, EricTaillard , “Metaheuristics for Hard Optimization:Methods and case studies”, Springer.

4. Colin R. Reeves, “Modern Heuristic Techniques for Combinatorial Problems”, John Wiley and Sons.



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Introduction to Non-traditional optimization, Computational Complexity; Heuristics – classification: Construction Heuristics, Local Search, Multi-Start Procedures; Assessing the Quality of Heuristics.

4

10 Metaheuristics- Definition, Classification.Introduction to evolutionary computation 2

II

Genetic Algorithm - Genetic Algorithms: Basic concepts, Encoding, Selection, Crossover, Mutation-Binary GA, Continuous GA, Hybrid GA, Parallel GA-Application of GA in solving Constrained and Combinatorial Optimization problems, Reliability problem, Sequencing problem, Scheduling problem, Transportation problem etc.

4

20

Scatter Search-Components, Algorithm, Applications. 1

Multi objective evolutionary optimization: Pareto optimality, Multi-objective evolutionary algorithms. 3

FIRST INTERNAL EXAM

III

Greedy Randomized Adaptive Search Procedure 1

20

Ant Colony Algorithms: Overview, Basic algorithm, Variants, Formalization and properties of ant colony optimization, Applications in Scheduling, VRP etc

4

Particle Swarm Optimization – Basic Concepts: Social Concepts, Swarm Intelligence Principles, Computational Characteristics; PSO in Real Number Space: Velocity Updating, Topology of the Particle Swarm, Parameter Selection; Discrete PSO; PSO Variants; PSO Applications in TSP, Knapsack Problems, Quadratic Assignment Problem etc.

3

IV

Lagrangean Relaxation: Basic Methodology, Lagrangean heuristic and problem reduction, Lagrangean multipliers, Dual Ascent algorithm, Tree search. Applications of Lagrangean Relaxation in solving facility location problems, Logistics, Inventory Problems etc.

6 10


V

Local Search Algorithms, Tabu Search –Tabu Search Principles, Neighborhood, Candidate list, Short term and Long term memory, Threshold Accepting, Application of TS in Planning and Scheduling, Telecommunications, Portfolio management, Facility layout, Transportation, Routing and Network Design.

5 20



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Simulated Annealing -Main Components of Simulated Annealing, Homogenous vs. Inhomogenous Simulated Annealing, Annealing Schedules, Applications in sequencing and scheduling, Travelling salesman problem etc. Variants of Simulated Annealing.

3

VI

Artificial Neural Networks- Biological and Artificial Neural Networks, Basic Concepts, Generic Algorithm, Application Areas, Application of ANN to solve TSP, Knapsack Problems etc.

3

20 Constraint Programming- Problem Formulation in Constraint Programming, Basic Search and Constraint Propagation, Constraint Programming vs Mathematical Programming, Application of Constraint Programming in Bin Packing, Scheduling, Sequencing, Facility Location problems etc.

3

END SEMESTER EXAM



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01ME7617 PREDICTIVE MODELING 3-0-0 3 2015

Course Objectives

1. To understand the terms and terminologies of predictive modeling.

2. To study the various predictive models, their merits, demerits and application.

3. To get exposure to various analytical tools available for predictive modeling.

Syllabus

Data Mining, Partitioning, Cleaning, Splitting; Artificial Neural Networks; Multivariate Analysis;

Association Rules; Clustering Models; Time Series Models; Predictive Modeling Markup Language;

Tools and Technologies in Predictive Modeling; Modeling Business Problems and solution

programs using R language.

Expected Outcome


1. Design and analyze appropriate predictive models.

2. Define the predictive models using PMML.

3. Apply statistical tools for analysis.

References

1. Kattamuri S. Sarma, “Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications”, 2nd Edition, SAS Publishing, 2007.

2. Alex Guazzelli, Wen-Ching Lin, Tridivesh Jena, James Taylor, “PMML in Action Unleashing the Power of Open Standards for Data Mining and Predictive Analytics”, 2nd Edition, Create Space Independent Publishing Platform,2012.

3. Ian H. Witten, Eibe Frank, “Data Mining: Practical Machine Learning Tools and Techniques”, Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann, 3rd Edition, 2011.

4. Eric Siegel, “Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die”, 1st Edition, Wiley, 2013.

5. Conrad Carlberg, “Predictive Analytics: Microsoft Excel”, 1st Edition, Que Publishing, 2012.

6. Jeremy Howard, Margit Zwemer, Mike Loukides, “Designing Great Data Products- Inside the Drivetrain Approach, a Four-Step Process for Building Data Products – Ebook”, 1st Edition, O'Reilly Media, March 2012.

7. Thomas W Miller, “Modeling Techniques in Predictive Analytics: Business Problems and Solutions with R”, Pearson Education, Inc., 2014.



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Core ideas in data mining - Supervised and unsupervised learning -

Classification vs Prediction -Steps in data mining- SEMMA Approach –

Sampling -Pre-processing - Data cleaning - Data Partitioning - Building

a model - Statistical models - Statistical models for predictive analytics.

7 15

II

Data splitting – Balancing- Overfitting –Oversampling –Multiple

Regression - Artificial neural networks (MLP) - Variable importance-

Profit/loss/prior probabilities - Model specification - Model selection -

Multivariate Analysis.

7 15

FIRST INTERNAL EXAM

III

Association Rules-Clustering Models –Decision Trees- Ruleset Models-

K-Nearest Neighbors – Naive Bayes - Neural Network Model –

Regression Models – Regression Trees – Classification & Regression

Trees (CART) – Logistic Regression – Mulitple Linear Regression

Scorecards –Support Vector Machines – Time Series Models -

Comparison between models - Lift chart - Assessment of a single model.

10 20

IV

Introduction to Predictive Modeling Markup Language (PMML) –

PMML Converter - PMML Structure – Data Manipulation in PMML –

PMML Modeling Techniques - Multiple Model Support – Model

Verification.

4 15


V

Tools and Technologies in Predictive Modeling: Weka – RapidMiner –

IBM SPSS Statistics- IBM SPSS Modeler – SAS Enterprise Miner –

Apache Mahout – R Programming Language. 6 15

VI

Modeling Business Problems and solution programs using R language:

Advertising and Promotion, Preference and Choice, Market Basket

Analysis, Economic Data Analysis, Operations Management, Text

Analytics, Sentiment Analysis, Brand and Price, Sports Analytics.

8 20

END SEMESTER EXAM



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01ME7419 MANAGERIAL ECONOMICS 3-0-0 3 2015

Course Objectives The main objectives of this course are:-

1. To develop an understanding of the basic concepts, tools and techniques of economics 2. Application of these techniques to various areas of decision making. 3. To help the students to appraise business around him and to develop skills and to relate

corporate decision on the future prospects of business.

Syllabus

Introduction, economic profit, firms, demand theory and forecasting, production theory and

analysis, cost theory and analysis, market structure and equilibrium, CVP analysis pricing

decisions and introduction to taxes and duties.

Expected Outcome

1. The students will acquire the knowledge of economic theory to ascertain the demand to help decision making on managerial perspective.

2. The students will become able to analyze a business situation based on the knowledge on pricing, costing and production functions of firms.

3. The students will become conversant on tax and tax laws to enable the nation's and the firm's growth.

References 1. H. Craig Petersen and W. Cris Lewis, “Managerial Economics”, Pearson Education 2. D. N. Dwivedi, “Microeconomics: Theory and Applications”, Pearson Education 3. H. Scott Bierman and Luis Fernandez, “Game Theory with Economic Applications”,

Pearson Education. 4. Karl.E.Case and R.C.Fair, “Principles of Economics”, Pearson Education 5. A. Ramachandra Aryasry and V.V. Ramana Murthy,“Engineering Economics and

Financial Accounting”:, Tata Mc graw Hill Publishing Company Ltd., New Delhi, 2004.

6. V. L. Mote, Samuel and G. S. Gupta, “Managerial Economics – Concepts and cases”, Tata McGraw Hill Publishing Coimpany Ltd, New Delhi, 1981.

7. A.Nag, “Macro Economics for Management Students”, MacMillan India Ltd., New Delhi, 1999.

8. Jawaharlal, “Cost Accounting”, Tata McGraw Hill. 9. Norman N Barish, “Economic Analysis for Engineering and Managerial Decision

Making”, McGraw Hill Book Company, 1983.



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I Introduction to Managerial Economics, The Nature of the Firm, Economic Profit, Profit in a marketsystem, Economics and Decision making, Total, Average and Marginal concepts, Economic models.

6 15

II

Demand Theory and Analysis:- Individual demand, Market demand, Total and Marginal Revenue, PriceElasticity, Income Elasticity and Cross Elasticity, Use of Regression analysis for Demand estimation.

3 15

Economic Forecasting: - Sources of data, Time Series Analysis – Trend projection, ExponentialSmoothing; Barometric Forecasting, Input / Output analysis.

3

FIRST INTERNAL EXAM

III

Production Theory and Analysis:- The Production Function, Isoquants – Expansion path, Cobb Douglas function – Cost concepts – Cost output relationship – Economies and diseconomies of scale – Cost functions- Determination of cost- Estimation of cost. Economies of Scale and Scope, Estimating the Production Function.

7 15

IV

Cost Theory and Analysis:- Economic concept of cost, Production and Cost, Short-Run and Long-RunCost functions, Profit Contribution Analysis, Operating Leverage, Estimating Cost Functions.

7 15


V

Market Structure – Various forms – Equilibrium of a firm – Perfect competition – Monopolistic competition – Oligopolistic competition – Pricing of products under different market structures – Methods of pricing – Factors affecting pricing decision – Differential pricing – Government Intervention and pricing.

4 20

Monopolypower and its measurement - regulation in practice - pricing under Oligopoly – NashEquilibrium - Cournot Model - Collusion and Cartel

4

VI

Pricing Decisions:- Pricing of Goods and Services, Pricing of Multiple Products, Price Discrimination,Product bundling, Peak-Load pricing, Markup Pricing, Input pricing and Employment, Economic Rent,Wage and Income Differentials

4 20

The concept of profit: Profit planning, control and measurement of profits. Profit maximization – Cost volume profit analysis – Investment Analysis. Introduction to Excise duty,Taxes on Profit, Taxes on Inputs, Property taxes and Tax preferences.

4

END SEMESTER EXAM



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01ME7621 FINANCIAL MODELING 3-0-0 3 2015

Course Objectives

1. To enable the student to model, design and implement a wide range of financial models for derivatives pricing and asset allocation

Syllabus

Financial Markets; Diffusion Models; Models with Jumps; Multi-Dimensional Models; Option

Pricing by Transform Techniques and Direct Integration; Pricing Non-Standard Vanilla Options;

Bermudan and American Options; The Cosine Method and Barrier Options; Monte Carlo

Simulation and Applications; Calibration and Optimization; Model Risk.

Expected Outcome

1. The students will be able to describe and review market models

2. The students will be able to use numerical methods for pricing and risk management

References

1. Joerg Kienitz, Daniel Wetterau, “Financial Modelling: Theory, Implementation and Practice with MATLAB Source”, John Wiley & Sons

2. Alastair Day ; “Mastering Financial Modelling”, Penguin Books Ltd, 2/e, 2008 3. Swan Jonathan, “Practical Financial Modelling: A Guide to Current Practice”,

Elsevier, 2/e, 2008.

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Financial Markets- Financial Time-Series, Statistical Properties of Market Data and Invariants, Implied Volatility Surfaces and Volatility Dynamics, Applications- Asset Allocation, Pricing, Hedging and Risk Management.

5 15

II

Diffusion Models: Local Volatility Models- The Bachelier and the Black–Scholes Model, The Hull–White Model, The Constant Elasticity of Variance Model, The Displaced Diffusion Model, CEV and DD Models; Stochastic Volatility Models; Stochastic Volatility and Stochastic Rates

5 15



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Models.

Models with Jumps: Poisson Processes and Jump Diffusions; Exponential Lévy Models; Exponential Lévy Models with Stochastic Volatility, Stochastic Clocks, Martingale Correction.

4

FIRST INTERNAL EXAM

III

Multi-Dimensional Models: Multi-Dimensional Diffusions; Multi-Dimensional Heston and SABR Models; Parameter Averaging; Markovian Projection; Copulae. 5 15

IV

Option Pricing by Transform Techniques and Direct Integration: Fourier Transform; he Carr–Madan Method; The Lewis Method; The Attari Method; The Cosine Method; Comparison, Stability and Performance; Extending the Methods to Forward Start Options; Density Recovery.

5

15

Pricing Non-Standard Vanilla Options; Bermudan and American Options; The Cosine Method and Barrier Options. 4


V

Monte Carlo Simulation and Applications: Sampling Diffusion Processes; Special Purpose Scheme; Adding Jumps; Bridge Sampling; Libor Market Model; Multi-Dimensional Lévy Models.

5 20

VI

Calibration and Optimization: The Nelder–Mead Method; he Levenberg–Marquardt Method; The L-BFGS Method; The SQP Method; Differential Evolution; Simulated Annealing.

5

20

Model Risk – Calibration, Pricing and Hedging: Calibration; Pricing Exotic Options; Hedging 4

END SEMESTER EXAM



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01ME7691 SEMINAR II 0-0-2 2 2015


1. Identify the current topics in the specific stream. 2. Collect the recent publications related to the identified topics. 3. Do a detailed study of a selected topic based on current journals, published papers

and books. 4. Present a seminar on the selected topic on which a detailed study has been done. 5. Improve the writing and presentation skills.

Approach

Students shall make a presentation for 20-25 minutes based on the detailed study of the topic and submit a report based on the study.

Expected Outcome

Upon successful completion of the seminar, the student should be able to 1. Get good exposure in the current topics in the specific stream. 2. Improve the writing and presentation skills. 3. . Explore domains of interest so as to pursue the course project.



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01ME7693 PROJECT (PHASE 1) 0-0-12 6 2015


1. Do an original and independent study on the area of specialization. 2. Explore in depth a subject of his/her own choice. 3. Start the preliminary background studies towards the project by conducting

literature survey in the relevant field. 4. Broadly identify the area of the project work, familiarize with the tools required for

the design and analysis of the project. 5. Plan the experimental platform, if any, required for project work.

Approach

The student has to present two seminars and submit an interim Project report. The first seminar would highlight the topic, objectives, methodology and expected results. The first seminar shall be conducted in the first half of this semester. The second seminar is the presentation of the interim project report of the work completed and scope of the work which has to be accomplished in the fourth semester.

Expected Outcome

Upon successful completion of the project phase 1, the student should be able to 1. Identify the topic, objectives and methodology to carry out the project. 2. Finalize the project plan for their course project.



67

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01ME7694 PROJECT (PHASE 2) 0-0-23 12 2015

Course Objectives

To continue and complete the project work identified in project phase 1.

Approach

There shall be two seminars (a mid term evaluation on the progress of the work and pre submission seminar to assess the quality and quantum of the work). At least one technical paper has to be prepared for possible publication in journals / conferences based on their project work.

Expected Outcome

Upon successful completion of the project phase II, the student should be able to 1. Get a good exposure to a domain of interest. 2. Get a good domain and experience to pursue future research activities.

Documents

financial engineering syllabus