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Python for Corporate Finance and Investment Analysis
Role Play
Rating: 4.3 out of 5(17 ratings)
150 students

Python for Corporate Finance and Investment Analysis

Introduction to Financial Automation: Empowering Financial Decision-Making Through Python Programming
Created byJohn Cousins
Last updated 7/2025
English

What you'll learn

  • Learn to manipulate and analyze financial data using Python
  • Develop theories about asset prices that are informed by real-world financial and economic relationships, and then rigorously test them.
  • Understand the Basics of Python Programming
  • Python availability in Excel introduces a fresh realm of possibilities for data analysis that was once primarily accessible to data scientists and developers.
  • 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.
  • Programming with Python Write effective Python code for solving complex business problems.

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

See a demo
Image of coding exercise example

Course content

45 sections223 lectures11h 28m total length
  • Introduction1:56

    This course includes many coding exercises in Python.  If you don't know computer coding, don't worry, you will get lots of valuable skill set training from this course.


    For those of you who have a basic knowledge of Python and coding 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:

      • 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:

      • 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:

      • 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:

      • 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:

      • 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:

      • 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:

      • 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:

      • 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:

      • 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:

      • 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.

  • I have put some Python materials at the beginning of the course. Check them out:0:32

    I have put some Python materials at the beginning of the course. Check them out:

    • a quick primer,

    • an entire PDF book on mastering Python,

    • and an article about how I would learn Python from scratch in 2024.


    Also, there are lots of free courses:

    • Udacity has a great one,

    • CodeAcademy,

    • And tons of YouTube instruction.

    • There are also some great courses here on Udemy.


    You can get up to speed in a week or two, and then the coding exercises in this course will help you train up fast by providing bite-sized real-world problems.


    If you have any coding training, then Python is easy; it's just formatting, conditional statements, and loops, like any other programming language.


    Let me know if this helps and how your journey progresses!

  • Mastering Python: a beginner's roadmap2:37

    Mastering Python in 2024: A Beginner's Roadmap


    As we navigate through 2024, Python has cemented its position as a cornerstone language in data science. With most of the leading-edge machine learning tools written in Python, it's increasingly becoming a staple requirement for data science positions.


    Yet, Python's utility extends far beyond data science, permeating various domains of computer science such as:

    • Web development

    • Video game creation

    • Backend system engineering


    Thus, for aspiring programmers, data scientists, or those aiming to become proficient developers, Python is a highly adaptable and valuable skill.


    My four-year journey with Python has equipped me with insights that I'm eager to share, particularly on how beginners can effectively learn Python from scratch.


    Step 1: Course Selection


    Initially, I'd seek out a foundational Python course that resonates with me, one endorsed by peers with robust Python expertise. Remember, there isn't a singular "correct" course. While some courses are more esteemed than others, any top-tier course will impart the same foundational knowledge. The key is to commence your learning journey without overthinking the choices.


    For instance, I began with the W3Schools Python tutorial, appreciating its simplicity and the practical exercises accompanying each module. While completion doesn't equate to mastering Python, it does provide a comprehensive overview and a firmer grasp of the basics.


    What's essential here is commitment. Choose a course and see it through. You'll want to ensure you grasp the following concepts:


    • Variables and Data Types

    • Boolean and Comparison Operators

    • Control Structures and Conditionals

    • Iteration using For and While Loops

    • Functions

    • Native Data Structures (Lists, Dictionaries, Tuples, etc.)

    • Object-oriented Programming with Classes

    • Utilizing Packages


    This framework is incomplete, as each of these topics encompasses additional sub-concepts.


    Step 2: Persistent Practice

    There is a saying that I like from Naval Ravikant, a famous entrepreneur and investor, that goes:


    It's not 10,000 hours, it's 10,000 iterations.


    Echoing Naval Ravikant's sentiment, mastery arises not from mere hours but from iterative practice. This philosophy, contrasting Malcolm Gladwell's "10,000-hour rule," emphasizes the quality and frequency of practice over quantity.


    To hone your Python skills, establish a routine that embeds Python coding into your daily or weekly schedule, striving for consistency. Regular engagement can yield significant progress, even if it's just a couple of hours a week.


    This course will give your lots of programming exercises to gain proficiency with Python.


    Platforms like HackerRank also offer an excellent starting point, presenting a gamut of coding challenges that facilitate rapid learning through problem-solving.


    Alternatives like LeetCode and Codeacademy also provide a plethora of problems to solve. There's no set number of problems to tackle; aim for comfort and familiarity with Python's syntax and problem-solving approaches.


    Step 3: Embark on Projects


    Project-based learning is paramount. It's the synthesis of knowledge where you apply what you've learned to create and troubleshoot real-world applications.


    Your project choice should align with your career aspirations. If data science beckons, engage in machine learning or data analysis projects. For web development aspirations, delve into building websites using frameworks like Django.


    RealPython offers excellent tutorials and project ideas for budding backend developers and other Python enthusiasts.


    Ultimately, the secret is to dive in. Select a project that intrigues you and invest yourself fully into it, focusing on the educational journey rather than the project's complexity.


    In Conclusion


    Python's ubiquity across various technological realms makes it an invaluable asset for any career in programming. The pathway I've outlined—beginning with a fundamental course, followed by consistent practice, and culminating in hands-on projects—is designed to solidify your Python skills efficiently.


    While these steps alone may not instantaneously land you your dream job, they will accelerate your Python learning curve and enhance your ability to apply your knowledge practically. Embrace the challenges and learning process; the effort will undoubtedly pay off.

  • Python Primer0:03

    Attached is a downloadable document to get you started and familiar with Python. 

  • The Python Handbook1:14

    The Python Handbook


    Welcome to the Python Handbook, your comprehensive and all-encompassing guide to mastering one of the world's most versatile and powerful programming languages. Whether you are a seasoned developer looking to expand your skill set or a complete beginner eager to dive into the world of coding, this handbook is designed to equip you with the knowledge and tools you need to succeed.


    Python, the language of choice for many industries, is a versatile powerhouse. From web development and data science to artificial intelligence and automation, Python's simplicity and readability make it an ideal starting point for newcomers. Its robust libraries and frameworks offer seasoned programmers the flexibility to easily tackle complex projects, opening up a world of possibilities.


    In this handbook, you will embark on a journey that begins with Python programming fundamentals, laying a solid foundation with core concepts such as variables, data types, and control structures. As you progress, you will explore more advanced topics, including object-oriented programming, web development with Django and Flask, data analysis with Pandas, and machine learning with TensorFlow and Scikit-Learn.


    Each chapter is crafted to provide clear explanations, practical examples, and hands-on exercises that reinforce your understanding and help you apply what you've learned. You'll find tips and best practices from industry experts, ensuring you learn how to code and write clean, efficient, and maintainable Python code.


    The Python Handbook is more than just a tutorial; it's a resource you'll return to repeatedly as you grow your skills and take on new challenges. With this handbook by your side, you'll have the confidence to navigate the Python ecosystem and unlock its full potential.


    Prepare to embark on an exciting journey of discovery and innovation. Let's dive into the world of Python and start building the future today!

  • Master Python Programming Books0:09

    Attached are downloadable PDFs of fantastic books on Python Programming.  This course assumes you are relatively familiar with Python, but if not or you need a refresher or reference guide, these books will do it.  : )

  • Additional Reference Books on AI0:04

    Here are some more books that you will find interesting on artificial intelligence, coding, and algorithms.

  • AI Algorithms0:40

    AI Algorithms Explained



    1. Logistic Regression: Predicts yes/no outcomes.


    2. Recurrent Neural Networks (RNN): Understands sequences like stories.


    3. K-Means Clustering: Groups similar items together.


    4. Principal Component Analysis (PCA): Packs important data into a small space.


    5. Autoencoders: Compresses and reconstructs images.


    6. Neural Networks: Learns from examples like our brain cells.


    7. Reinforcement Learning: Learns with rewards, like training a dog.


    8. Q-Learning: Finds the best path in a maze.


    9. Naive Bayes: Predicts outcomes based on prior knowledge.


    10. k-Nearest Neighbors (k-NN): Finds similar items by asking friends.


    11. Bayesian Networks: Predicts by considering different factors.


    12. Support Vector Machine (SVM): Separates items with the straightest line.


    13. Genetic Algorithms: Mixes traits to create the best solution.


    14. Linear Regression: Predicts outcomes based on past data.


    15. Random Forests: Combines multiple answers for accuracy.


    16. Convolutional Neural Networks (CNN): Recognizes patterns like faces.


    17. Decision Trees: Makes decisions with yes/no questions.


    18. Gradient Boosting: Improves with each small mistake.

  • AI Tools0:04

    30+ Best AI tools to 10x Productivity!

    AI is the future. All should take AI seriously.

  • Introduction to Quizzes1:00

    I have added a bunch of quizzes to test your comprehension after video lectures. Here is why:


    Practice with struggle > practice without struggle.


    An example is a study of two groups of students. Group A studied a paper for 4 days. Group B studied it for 1 day and was tested on it for 3 days.


    At the final test, Group B scored 50% more than Group A.


    Why?


    With every test, group B struggled. And that targeted struggle made them acquire more knowledge in the same amount of time.


    This is about self-motivation and the measure of self-motivation in a person is the best predictor of upward mobility. Congratulations you have it.


    Let me know what you think of the quizzes and this approach.


    Cognitively the act of taking a quiz, calling up knowledge from memory, makes that memory stronger and easier to access. So students who are frequently quizzed retain more knowledge of the subject they are studying.


    Here are some of the benefits of using quizzes in online courses:

    · Retrieval practice occurring during quizzes can greatly enhance retention of the retrieved information. An even higher level of retention than from restudying or rereading the course material.

    · Quizzes permit students to discover gaps in their knowledge and focus study efforts on difficult material.

    · An indirect effect of quizzes was found that if quizzed frequently, students tended to study more and with more regularity.

    · Quizzing has been found to enable better metacognitive monitoring for both students and teachers because it provides feedback as to how well learning is progressing. Quizzes can be a beneficial self-learning check for students.

    · Every time a student calls up knowledge from memory like when taking a quiz, that memory solidifies becoming more stable and more accessible.


    Quizzes help us identify we know and what we don't know.


    Repeated testing with quizzes and exams improves the cognitive process that can amplify long-term memory retention and retrieval. It doesn't just measure knowledge, but challenges it. If you test yourself more regularly, you are going to learn in greater detail than before.


    Practice with struggle > practice without struggle.


  • Corporate Finance Book download0:05

    Attached is my award winning Corporate Finance book.  It will help you get familiar with the financial concepts in this course.

  • Investing Book download0:06

    Attached is a PDF copy of my stock investing book.  It will help in covering the capital markets concepts that we explore in this course.

  • The Art of Quality Investing0:09

    The Art of Quality Investing



    This book summary will teach you what you need to know



    • An introduction to quality investing


    • Checklist to find quality stocks


    • Qualitative criteria


    • Quantitative criteria


    • How to build a portfolio with quality stocks

Requirements

  • This course doesn't go through basic programming and Python. It does go through basic Finance and then we crush some basic code.
  • There are no financial prerequisites for taking this course as it will go over understanding financial concepts and Python coding concepts.
  • Prior Programming Experience Required: While prior experience with programming is beneficial, it is not a prerequisite, but you should be familiar with programming concepts and Python syntax,
  • 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

"All Analysts and Associates will learn Python moving forward."

JP Morgan


Supercharge Your FP&A Career with Python!

Are you looking for your next big opportunity in FP&A? The game is changing fast—top companies like Amazon, Microsoft, The Home Depot, Toyota, and BMW now require or prefer Python skills for their finance professionals.

We analyzed over 2,500 job postings and the trend is crystal clear: mastering Python isn’t optional anymore—it’s the key to unlocking high-paying roles and career growth in finance.

Why are the world’s smartest companies making Python a must-have skill?

  • It won’t replace Excel, but it will amplify your abilities

  • Automate tedious tasks and gain deeper insights

  • Focus on high-impact strategic work instead of manual data wrangling

Future-proof your career with MBA ASAP’s Python for Finance training!

  • Tailored for finance professionals

  • Hands-on, practical learning

  • 100% money-back guarantee—zero risk, all reward


Invest in your finance career today. Enroll now and stay ahead of the competition!


Enroll Now!


Python and Machine Learning can double the predictive accuracy of your financial models. Move beyond the limits of excel with this world class self-study-course used to train professionals under the mandate of the top banks in the world.


The adage of the entire internet once went, “I just replaced your entire industry with 100 lines of Python code”.


The smartest people invest heavily in their education and skill development, recognizing that their human capital is their most marketable resource.

The future belongs to those who learn more skills and combine them in creative ways.

Skills are the most valuable thing you can acquire in this lifetime because they keep compounding until the day you die.

“Whatever abilities you have can't be taken away from you,” says Warren Buffett, “The best investment by far is anything that develops yourself, and it's not taxed at all.”

While this isn’t a traditional investment tip, Buffett firmly believes that by regularly investing in knowledge and self-improvement, you yourself become an asset and can more easily access opportunities for growing your wealth.


From Data to Decisions: Python in Corporate Finance

Real-World Python Applications in Corporate Finance


Develop theories about asset prices that are informed by real-world financial and economic relationships, and then rigorously test them.


Programming with Python

Write effective Python code for solving complex business problems


Python and Excel

Python availability in Excel introduces a fresh realm of possibilities for data analysis that was once primarily accessible to data scientists and developers. Now, within the comfort of your well-known spreadsheet environment, you can tap into the capabilities of Python.


When it comes to programming languages, Python shines brightest when dealing with tasks related to data processing, machine learning, and web development. Python has all the necessary tools to help you succeed.


With a foundation in finance laid down, you will acquire the skills needed to develop various financial applications using Python.


Here are some of the topics we will cover in this course:


  1. 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.

  2. 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)

  3. Introductory-Level Knowledge of Economics:

    • Basic understanding of macroeconomic and microeconomic principles, as they underpin many financial theories and models.

  4. Basic Computer Literacy:

    • Proficiency in using computers, especially for tasks like installing software, managing files, and navigating the internet.

  5. 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 Strategies

  • Leveraging Python for Strategic Investment Insights

  • Navigating Financial Markets with Python Skills

  • Transformative Skills for the Modern Financial Professional

  • Python for the Future of Finance: Analytics and Beyond


This 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 Analytics


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:

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.


Why Python?


Python is a good starting point for first-time coders. It uses simple, natural language syntax, almost like spoken English. It is powerful and it is versatile, favored by such diverse industry giants as Netflix, PayPal, NASA, Disney, and Dropbox. Python is used by 87% of data scientists.


User-Friendly Syntax: As an interpreted language, Python has simpler, more concise syntax than Java. Python's simple, concise syntax makes it easy to write algorithms with just a few lines of code

Open-Source Libraries: Pre-written code is readily available, with algorithms at your disposal, so you do not have to start every project from scratch. You can benefit from highly specific libraries – physics, web development, gaming, machine learning – by simply importing algorithms and applying them to your own data. It is plug and play at its best, with new functionalities being added all the time

Community Exchanges: Python’s popularity means it has great community support, with almost 8 million Python developers across the world to help you debug or resolve a programming challenge

Compatibility: Python is a cross-platform language and can be integrated easily with Windows and other platforms

Adaptability: Almost every field is adopting Python and needs both generalists and specialists who know how to use it. Fields as varied as gaming, web development, healthcare, and fintech prefer Python over other programming languages, making it the must-learn language for STEM professionals and data scientists

Who this course is for:

  • 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.