Python For Corporate Finance And Investment Analysis
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 Literacy:Proficiency 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 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.
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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