Upload
others
View
5
Download
0
Embed Size (px)
Citation preview
You're not made to crunch numbers.You're made to crush the numbers game.
FINANCE & FINANCIAL TECHNOLOGIES
FORBUSINESS
herovired.com/vired-for-business
MODULE ABOUT THE MODULE LEARNING OUTCOMES
Foundations of Modern Finance I
It provides a rigorous and comprehensive introduction to the fundamentals of modern finance and their applications to business challenges in valuation, investments, and corporate financial decisions under a unified framework.
Valuation of fixed income securities and common stocksRisk analysis, the Arbitrage Pricing Theory (APT), and the Efficient Market Hypothesis Introduction to corporate finance and capital budgeting Valuation of derivative securities Portfolio theory and the Capital Asset Pricing Model (CAPM)Corporate financial decisions Real options, capital structure, payout policy, corporate bonds; and Interaction between investment and financing decisions
Foundations of Modern Finance II
We build on the core set of basic principles taught in the first part, and continue to develop a powerful and general framework for making financial decisions in business and in personal financial planning. We introduce financial derivative securities, and their valuation models, discuss the capital structure decision of firms, and explore the interaction between investing and financing
Forwards and Futures Options
Portfolio Theory
Capital Asset Pricing Model (CAPM)
Capital Budgeting and Real Options
Financing/Capital Structure
Interaction between Investing and Financing
Payout and Risk Management
Financial Accounting
It provides a rigorous introduction to the principles of financial accounting. We focus on the preparation and analysis of financial statements, and on why financial statements take the form they do. We cover the basic structure of financial reports and the process of recording transactions. We will also learn how investors, creditors, and other users analyze financial statements to assess corporate performance. The course focuses on using the financial statements to gather inputs to valuation models and for corporate finance decisions.
Introduction, Accrual accounting, Balance Sheet/Income Statement, Allowance Accounting, and Revenue RecognitionInventory/COGS, Depreciation/Property, Plant, and Equipment, Statement of Cash Flows, Introduction to financial statement analysis Marketable Securities, Accounting for Banks, Intangible Assets, and Acquisitions Income Taxes, Long-Term Debt, Leases, Shareholders’ Equity, Ethics, and Conclusion
Financial Modelling (in Excel)
This module focuses on creating a variety of valuation and investment banking models using MS Excel
Time Value of Money (NPV and IRR)
How to build an Investment Banking equities valuation model from scratch
Writing equities research reports
Top-down valuation models for Venture Capitalists
Industry research and modeling
Ratios based valuation models
Valuation models and ratings models for bonds and fixed income securities
Modeling currencies and commodities
MODULE ABOUT THE MODULE LEARNING OUTCOMES
Behavioural Finance and Technical Analysis
This module is about the psychological foundations of market behaviour and finance, and how trends and patterns tend to repeat themselves
Introduction to Behavioural Finance and Heuristics and BiasesProspect Theory, Loss Aversion and Hyperbolic DiscountingBubbles and Crashes
Fundamentals of Charting and Technical Analysis and how it connects to Market Psychology
Self-fulfilling prophecies
Mathematical Methods for Quantitative Finance
Modern finance is the science of decision making in an uncertain world, and its language is mathematics. This course develops the tools needed to describe financial markets, make predictions in the face of uncertainty, and find optimal solutions to business and investment decisions.
This course will help anyone seeking to confidently model risky or uncertain outcomes. Its topics are essential knowledge for applying the theory of modern finance to real-world settings.
You will learn the following in this course:• Probability• Statistics• Time-series models• Continuous time stochastic processes• Linear algebra• Optimization• Numerical methods
Review of laws probability; common distributions of financial mathematics; CLT, LLN, characteristic functions, asymptotics.statistical inference and hypothesis tests; time series tests and econometric analysis; regression methods random walks and Bernoulli trials; recursive calculations for Markov processes; basic properties of linear time series models (AR(p), MA(q), GARCH(1,1)); first-passage properties; applications to forecasting and trading strategies.
continuous time limits of discrete processes; properties of Brownian motion; introduction to Itô calculus; solving differential equations of finance; applications to derivative pricing and risk management.
Review of axioms and operations on linear spaces; covariance and correlation matrices; applications to asset pricing.Lagrange multipliers and multivariate optimization; inequality constraints and quadratic programming; Markov decision processes and dynamic programming; variational methods; applications to portfolio construction, algorithmic trading, and best execution
Monte Carlo techniques; quadratic programming
CURRICULUM COVERAGE
MODULE ABOUT THE MODULE LEARNING OUTCOMES
Derivatives Markets: Advanced Modeling and Strategies
Financial derivatives are ubiquitous in global capital markets, and those products and the institutions around them continue to evolve at a rapid pace. This course is designed for students seeking to develop a sophisticated and durable understanding of valuation and hedging methods, and a basic familiarity with major markets and instruments. Tools for quantifying, hedging, and speculating on risk are emphasized.
Topics include forwards, futures and options in the stock, fixed income and commodity markets, exotic options, real options, interest rate and currency swaps, mortgages, credit risk, securitization, the yield curve, duration and convexity.
Advanced derivatives pricing approaches adaptable to valuing new products
The many ways to shape risk exposure with derivativesImportant facts about the world’s largest financial markets
Overview of Machine Learning, Artificial Intelligence and Python Programming for Financial Applications
This module will focus on understanding key analytics concepts, solutions, and modus operandi, through real-world finance use-cases.
The module will additionally also introduce you to the core ideas of Machine Learning, Artificial Intelligence and programming on Python.
Understand why and how businesses use analytics through use-cases; the qualities of a good analyst; analytics methodologies and problem definitions; and the CRISP-DM architecture.
Understand the goal of machine learning; elements of supervised learning, and the difference between the training set and the test set; the difference of classification and regression - two representative kinds of supervised learning. Introduction to algorithms.
Introduced to python environments and ML packages, concept of Object-Oriented Programming, programming for a live environment, debugging, IDEs, and python basics such as class, objects, functions, conditions, loops/iterators, array, dictionary, lambda, mathematical and statistical operations, numpy for matrix algebra, exception handling, and file handling.
Financial Regulations and Analytics
This module will introduce global financial and data regulation and how it relates to analytical models of risk modeling
Global Financial Regulations – DFAST, CCAR, IFRS, GARPProbability of Default Models, Loss Given Default and Exposure at Default
Risk weighted assets and Credit worthiness of banks and necessary collaterals
MODULE ABOUT THE MODULE LEARNING OUTCOMES
Financial Technology
This module will focus on understanding key analytics concepts, solutions, and modus operandi, through real-world finance use-cases.
The module will additionally also introduce you to the core ideas of Machine Learning, Artificial Intelligence and programming on Python.
Fundamentals of Blockchain and Hyperledger, Cryptocurrencies and their valuation, technologies, Non-Fungible Tokens.
Use of AI-ML in FinTech. Application development in PropTech and InsurTech.
Use of new data sources including unstructured data for understanding financial risk
Finance, Fintech, Regulations and Data from an Indian Perspective
This module will introduce and dive into the details of previous modules from an Indian context
Financial regulations for banking and non-banking financial corporations in India
Data availability in India
The Indian fintech, proptech and insurtech landscape
KEY FACULTY PROFILES
ShivakumarBavamala• Post Graduate Diploma in Management - IIM Bangalore• BTech - Aerospace Engineering - IIT Madras
Experience
Founder/Investortamu tamu
DirectorTrendline Risk Ltd.
Lalit M. FularaCFAI - Charlottesville (VA), USA
Experience
Lead TrainerIMS Proschool
Visiting FacultyEduPristine
Senior AnalystErnst & Young
Dipyaman Sanyal• Ph.D Economics• CFA Institute, US: Charter Holder• Master of Science (MS), Applied Economics, The University of Texas at Dallas
Experience
Co-Founder & CEOdōnō consulting
Adjunct FacultyNorthwestern University
Program DirectorJigsaw Academy
Satyam AroraCFA Institute, US: CFA
Experience
Head of Personal Health, Strategy M&A and PartnershipsPhilips
Vice President Equity ResearchRafferty Capital Markets
Financial AnalystRochdale Securities LLC
Dr. AnirbanChakraborti• Doctorate in PhD, Physics - Jadavpur University• Habilitation à diriger des recherches, Physics, Université Pierre et Marie Curie (Paris VI)
Experience
Dean of Research, Dean of School of Engineering and TechnologyBML Munjal University
ProfessorJawaharlal Nehru University
KEY FACULTY PROFILES
Want more information on the program?Reach us at 1800 309 3939 | Visit us at www.herovired.com
FORBUSINESS
herovired.com/vired-for-business