Python For Corporate Finance And Investment Analysis

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Python For Corporate Finance And Investment Analysis

Published 1/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 10.50 GB | Duration: 7h 21m


Introduction to Financial Automation: Empowering Financial Decision-Making Through Python Programming

What you'll learn
Learn to manipulate and analyze financial data using Python
Understand the Basics of Python Programming
Gain a foundational understanding of Python programming, including data types, control structures, functions, and libraries essential for financial analysis.
Acquire the ability to construct financial models and forecasts using Python, including cash flow analysis, budgeting, and financial statement analysis.
Acquire the ability to construct financial models and forecasts using Python, including cash flow analysis, budgeting, and financial statement analysis.
Applying the Black-Scholes model, bond yield calculation for options pricing.

Requirements
There are no prerequisites for taking this course as it will go over understanding financial concepts and Python coding concepts.
No Prior Programming Experience Required: While prior experience with programming can be beneficial, it is not a prerequisite.
Willingness to Learn and Experiment: An open mindset and willingness to engage with both the programming and financial aspects of the course, including a readiness to solve problems and work on projects.
Our slogan is, if you're reasonably good at math, have a basic understanding of programming, you love it, and you have time to devote to it, then this course is completely fine for you." "It's fun," she says. "It's just like any other course. You know, we watch the lecture, and then do the quiz, and then we do the problem set."

Description
From Data to Decisions: Python in Corporate FinanceReal-World Python Applications in Corporate FinanceHere are some of the topics we will cover in this course:Basic Understanding of Finance and Accounting Principles:Familiarity with fundamental concepts of corporate finance, such as cash flows, financial statements (income statement, balance sheet, cash flow statement), and basic financial metrics (ROI, ROE, etc.).Basic knowledge of investment principles, including stocks, bonds, and other financial instruments.Foundational Mathematical Skills:Gain comfort with basic mathematics, including algebra and elementary statistics. Understanding of financial mathematics concepts like compounding, discounting, and basic statistical measures (mean, median, standard deviation)Introductory-Level Knowledge of Economics:Basic understanding of macroeconomic and microeconomic principles, as they underpin many financial theories and models.Basic Computer Literacyroficiency in using computers, especially for tasks like installing software, managing files, and navigating the internet.No Prior Programming Experience Required:While prior experience with programming can be beneficial, it is not a prerequisite. The course is designed to start with the basics of Python programming.This course builds a solid foundation upon which to build your understanding of using Python in corporate finance and investment analysis. The course focuses on bridging the gap between finance and Python programming.Harnessing Python for Effective Investment StrategiesLeveraging Python for Strategic Investment InsightsNavigating Financial Markets with Python SkillsTransformative Skills for the Modern Financial ProfessionalPython for the Future of Finance: Analytics and BeyondThis course includes many coding exercises in Python. These exercises will help turbo charge your career.Integrating Python coding exercises into finance education offers several significant benefits for students. These benefits stem from the increasing role of technology and data analysis in the finance sector. Here are some key reasons why Python coding exercises are beneficial for finance students:1. Enhanced Data Analysis Skills:o Python is widely used for data analysis and data science. Finance students can leverage Python to analyze complex financial datasets, perform statistical analysis, and visualize data, skills that are highly valuable in today's data-driven finance industry.2. Automation of Financial Tasks:o Python can automate many routine tasks in finance, such as calculating financial ratios, risk assessments, and portfolio management. By learning Python, students can understand how to streamline these processes, improving efficiency and accuracy.3. Integration with Advanced Financial Models:o Python is versatile and can be used to develop sophisticated financial models for risk management, pricing derivatives, asset management, and more. Understanding these models is crucial for modern finance professionals.4. Machine Learning and Predictive Analytics:o Python is a leading language in machine learning and AI. Finance students can learn to apply machine learning techniques for predictive analytics in stock market trends, credit scoring, fraud detection, and customer behavior analysis.5. Access to a Wide Range of Libraries:o Python offers a vast array of libraries and tools specifically designed for finance and economics, such as NumPy, pandas, matplotlib, scikit-learn, and QuantLib. Familiarity with these libraries expands a student's toolkit for financial analysis.6. Preparation for Industry Demands:o The finance industry increasingly values tech-savvy professionals. Familiarity with Python and coding in general prepares students for the current demands of the finance sector and enhances their employability.7. Understanding of Algorithmic Trading:o Python is extensively used in algorithmic trading. Finance students can learn to code trading algorithms, understand backtesting, and gain insights into the technological aspects of trading strategies.8. Improved Problem-Solving Skills:o Coding in Python fosters logical thinking and problem-solving skills. These skills are transferable and beneficial in various areas of finance, from analyzing financial markets to strategic planning.9. Broad Applicability:o Python is not just limited to one area of finance but is applicable across various domains, including investment banking, corporate finance, risk management, and personal finance.10. Collaboration and Innovation:o By learning Python, finance students can more effectively collaborate with IT departments and data scientists, bridging the gap between financial theory and applied technology, leading to innovative solutions in finance.Incorporating Python into finance education equips students with a practical skill set that complements their theoretical knowledge, making them well-rounded professionals ready to tackle modern financial challenges.Python: Your Gateway to Advanced Finance AnalyticsThis course, "Python for Corporate Finance and Investment Analysis," is tailored for a diverse range of participants who share an interest in integrating Python programming skills with financial analysis and investment strategies. The target audience includes:Finance Professionals:Individuals working in corporate finance, investment banking, portfolio management, risk management, and financial planning who want to enhance their analytical skills and embrace automation and data-driven decision-making in their workflows.Business Analysts and Consultantsrofessionals in business analysis and consulting roles who seek to deepen their analytical capabilities and provide more sophisticated insights into financial performance, market trends, and investment opportunities.Students and Academics in Finance and Economics:University students and academic researchers in finance, economics, business administration, and related fields who aim to supplement their theoretical knowledge with practical, hands-on experience in Python for data analysis and financial modeling.Investment Enthusiasts and Individual Traders:Individuals managing their investments or interested in stock market trading, who want to learn how to use Python for investment analysis, portfolio optimization, and developing algorithmic trading strategies.Career Changers and Lifelong Learnersrofessionals from non-finance backgrounds aspiring to transition into finance or investment roles, or those who are interested in personal development and acquiring new, marketable skills at the intersection of finance and technology.Technology Professionals Seeking Finance Domain Knowledge:IT and tech professionals, including software developers, who are looking to diversify their skillset by gaining knowledge in financial analysis and investment strategies.This course is designed to be accessible to those new to programming while still being challenging enough for those with some experience in Python. It offers a unique blend of financial theory and practical application, making it suitable for anyone looking to enhance their skill set at the nexus of finance and technology.

Overview
Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Python Primer

Lecture 3 Introduction to Quizzes

Lecture 4 Corporate Book download

Lecture 5 Investing Book download

Section 2: Financial Statements Review

Lecture 6 Financial Statements in 60 Minutes

Lecture 7 Intro to Understanding Financial Statements

Lecture 8 Financial Statements Overview Lecture

Lecture 9 The Income Statement: Revenue

Lecture 10 The Income Statement: Expenses

Lecture 11 The Income Statement: Net Income

Lecture 12 The Balance Sheet

Lecture 13 The Cash Flow Statement

Lecture 14 Financial Statements Interconnection and Flow

Section 3: Financial Statement Analysis

Lecture 15 Introduction to Financial Statement Analysis Lecture

Lecture 16 Intro to Financial Statement Ratio Analysis

Lecture 17 Financial Ratio Analysis

Lecture 18 Liquidity and Solvency Ratios

Lecture 19 Financial Ratio Analysis Conclusion

Section 4: Python Coding Exercises for Financial Statement Analysis

Lecture 20 Python Coding Primer

Lecture 21 Python Coding for Financial Statement Information

Lecture 22 Python coding exercise for calculating Financial Ratios

Lecture 23 Add Python code to display results.

Section 5: Intermission

Lecture 24 What we have covered so far, and what's next.

Lecture 25 Finance is Empowering

Section 6: The Time Value of Money

Lecture 26 Introduction to the Time Value of Money

Lecture 27 The Time Value of Money TVM

Lecture 28 Discounting Cash Flows DCF: Present Value and Future Value

Section 7: Python coding exercise for calculating DCF

Section 8: The Weighted Average Cost of Capital WACC

Lecture 29 The Weighted Average Cost of Capital

Lecture 30 The Debt Subsidy

Lecture 31 Modigliani Miller Theorem

Lecture 32 WACC Quiz

Lecture 33 WACC Quiz Excel Solution

Lecture 34 Intro to Python coding exercise to calculate WACC

Section 9: Free Cash Flow FCF

Lecture 35 Free Cash Flow

Lecture 36 Free Cash Flow Case Studies

Section 10: Net Present Value NPV

Lecture 37 Introduction to Net Present Value

Lecture 38 NPV

Lecture 39 Net Present Value Calculation

Lecture 40 Capex vs. Opex

Lecture 41 NPV Review

Lecture 42 NPV Excel Spreadsheet Quiz Answers

Section 11: Internal Rate of Return IRR

Lecture 43 IRR calculations and analysis

Lecture 44 The Limitations of IRR

Section 12: Risk

Lecture 45 How to Define and Measure Risk

Lecture 46 Exploring Financial Risk with Case Study

Lecture 47 Managing Risk

Lecture 48 Summary: "Against the Gods: The Remarkable Story of Risk" by Peter L. Bernstein

Section 13: Beta and the Capital Asset Pricing Model CAPM

Lecture 49 Risk, Return, and Diversification

Lecture 50 Understanding Market Volatility: A Deep Dive into Beta and Investment Risk

Lecture 51 Decoding Market Risk: The Mathematics of Beta Slope Calculation

Lecture 52 CAPM: the Dynamics of Risk and Return in Financial Markets and Asset Pricing

Lecture 53 Beta and CAPM

Lecture 54 Maximizing Returns: Mastering the Sharpe Ratio for Optimal Portfolio Performance

Section 14: Price Earnings Ratio and PEG Ratio

Lecture 55 Price to Earnings Ratio P/E

Lecture 56 Create a Python Script for P/E Ratio

Lecture 57 Calculate P/E with data

Lecture 58 Calculate EPS and P/E

Lecture 59 PEG Ratio

Lecture 60 P/E and PEG Comparison

Lecture 61 PEG Quiz

Lecture 62 PEG Quiz Answers

Lecture 63 An example comparing two stock market indexes using P/E and PEG.

Section 15: Stock Markets: Stock Price and Valuation

Lecture 64 Price of Stocks: how stock prices are determined

Lecture 65 Stock Valuation: present value of future cash flows

Section 16: Derivatives: Stock Options

Lecture 66 Introduction to Stock Options

Lecture 67 Stock Options: Puts and Calls

Lecture 68 Options Trading Practices

Lecture 69 Black-Scholes Option Pricing Model

Lecture 70 Implied Volatility

Lecture 71 Option Pricing with Quantum Computing

Section 17: Bonds and Debt Financing

Lecture 72 The Bond Market: Unlocking the Secrets of Debt Financing

Lecture 73 Bond Math

This course, "Python for Corporate Finance and Investment Analysis," is tailored for a diverse range of participants who share an interest in integrating Python programming skills with financial analysis and investment strategies. The target audience includes:,Finance Professionals: Individuals working in corporate finance, investment banking, portfolio management, risk management, and financial planning who want to enhance their analytical skills and embrace automation and data-driven decision-making in their workflows.,Business Analysts and Consultants: Professionals in business analysis and consulting roles who seek to deepen their analytical capabilities and provide more sophisticated insights into financial performance, market trends, and investment opportunities.,Students and Academics in Finance and Economics: University students and academic researchers in finance, economics, business administration, and related fields who aim to supplement their theoretical knowledge with practical, hands-on experience in Python for data analysis and financial modeling.,Investment Enthusiasts and Individual Traders: Individuals managing their investments or interested in stock market trading, who want to learn how to use Python for investment analysis, portfolio optimization, and developing algorithmic trading strategies.,Career Changers and Lifelong Learners: Professionals from non-finance backgrounds aspiring to transition into finance or investment roles, or those who are interested in personal development and acquiring new, marketable skills at the intersection of finance and technology.,Technology Professionals Seeking Finance Domain Knowledge: IT and tech professionals, including software developers, who are looking to diversify their skillset by gaining knowledge in financial analysis and investment strategies.,This course is designed to be accessible to those new to programming while still being challenging enough for those with some experience in Python. It offers a unique blend of financial theory and practical application, making it suitable for anyone looking to enhance their skill set at the nexus of finance and technology.

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