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