
Discover what you'll learn in this robust course on Investment Analysis & Portfolio Management with Python.
The course assumes coding knowledge. See precisely what we assume, and what we don't.
Learn about incredibly powerful relationships between Price, Risk, and Return. These relationships will guide our entire Investment Analysis and Portfolio Management process.
Often considered "complicated" and counterintuitive, understanding shorting is pivotal to any sort of financial analysis. In this video, you'll see exactly what it is and how it works; and most importantly, how dangerous it can be.
Explore how to calculate stock returns from scratch (manually).
Build on your knowledge of calculating stock returns by working with real world financial data. You'll learn how to calculate returns on Python using built in as well as 'custom' methods.
We're not huge fans of "cheat sheets", but get that notations can get overwhelming. Here's a list of all the core variables used in the course, with simple, one line explanations.
Investing decisions are based on future expectations. Learn how to estimate 'Expected Returns', starting with the the easiest method.
Explore what automating computations with Python looks like. Create your own 'function' which calculates the Daily or Annualised Expected Return of any security.
Learn how to estimate Expected Returns when the economy could be in a recession, boom, or other 'state'.
Overcome the limitations of the mean method and state contingent based methods by learning how to estimate expected returns using state of the art, industry relevant Asset Pricing Models.
Apply your theoretical knowledge by estimating the expected return of a stock using real world data and the Capital Asset Pricing Model ("CAPM").
Explore more robust Asset Pricing Models including the Fama-French 3 factor model, Carhart 4 factor model, and Fama-French 5 factor model, in addition to looking at other factors that influence returns.
Investments are risky. But how do we measure risk? We'll start from scratch, so you understand the logic and rationale behind the measure.
Apply your knowledge of measuring total risk by calculating the risk of a stock on Python using real world data.
Learn how to measure the market risk of a stock - the risk that no one can eliminate, no matter what. As a by-product of this, you'll also learn how to quantify the relationships between securities - something that will become imperative when we learn to manage portfolios.
Work with real world data and measure the market risk of a stock from scratch, as well as using methods built into Python's NumPy package. Explore why knowing what formulas are doing behind the scenes is imperative for great investment analysis and portfolio management.
Learn how to calculate portfolio returns from scratch, starting with a simple 2 asset case and then moving on to the multi-asset case. And of course, you'll see precisely why the equation works the way it does.
Analysing our de-facto equation for risk - the standard deviation - will result in seeing something incredibly interesting indeed. You'll see exactly how (and why) the risk of a portfolio is impacted by 3 factors.
As with the 2 asset case, the risk of a multi-asset portfolio is impacted by 3 factors. Solving for the equation algebraically however, is inefficient. See how we optimise and use matrix math to make our lives easier.
Explore real world applications of the estimation of portfolio risk, as we walk you through calculating the risk of a 10 asset portfolio using data from Google Sheets, working on Python.
You've come a long way so far! It's time for a bit of a breather, and time to reflect. Keep an eye out for a hint we may or may not give you during this video!
Explore how you can optimise your portfolio weights to achieve a target expected return.
Build on your knowledge and use the 'optimize' framework within Python's SciPy framework to optimise weights of a multi-asset portfolio to achieve a target expected return.
Discover how you can not only control - but actually minimise your portfolio risk, from scratch.
Work with real world data while you learn how to minimise the risk of a multi-asset portfolio on Python!
We ended up with some pretty remarkable results while minimising the risk of our portfolio. But those results created 3 (seemingly) counterintuitive puzzles. Explore what these 3 puzzles are.
Rethink the way you measure security relationships by exploring the Correlation, and how this one simple number allows us to explicitly explore the strength of relationships between securities.
Learn how to estimate the Correlation on Python - manually, as well as by using a function built into Pandas.
Discover precisely why diversification works - explore why correlation reduces portfolio risk and prove it mathematically.
Build on the formal proof by exploring how correlation, risk, and returns interact, visually.
When puzzles aren't really puzzles anymore. Decrypt the puzzles and explore just how intuitive those seemingly counterintuitive results actually are!
Say hello to Financial Analysis done right. Become a PRO at Investment Analysis & Portfolio Management with Python. Apply robust techniques that are rigorously grounded in academic and practitioner literature using Python for Finance.
Explore Python's robust modules including Pandas, NumPy, Matplotlib, Seaborn, and a whole lot more, working extensively with real world Finance data.
Discover the simplicity and power of Python for Finance. Take command by creating your own functions, cleaning and wrangling real world data.
Remove the guesswork by conquering the mathematics behind your own Investment Analysis & Portfolio Management process.
Explore and master powerful relationships between stock prices, returns, and risk. Quantify and measure your investment risk, from scratch.
Discover what your financial advisor should be doing to manage your portfolio - to manage your investments.
While you do need to know how to code, there’s no prior Finance knowledge required. We’ll start you from the very basics, and build you to a financial analysis PRO, leveraging Python for Finance, thanks to:
6 SECTIONS TO MASTERY (plus, all future updates included).
Introduction: Understanding Investment Security Relationships & Estimating Returns
Explore powerful relationships between risk, return, and price.
Gain a solid command of the baseline fundamental law of Financial Analysis - The Law of One Price.
Calculate stock returns for dividend and non-dividend paying stocks, manually.
Download and work with real world data, and estimate stock returns on Python from scratch.
Estimating Expected Returns
Estimate expected returns using the average (mean) method.
Create your own function on Python to automate the estimation of Expected Returns using the mean method.
Estimate expected returns using 'state contingent weighted probabilities'.
Take the analysis further by learning how to estimate expected returns using Asset Pricing Models including the Capital Asset Pricing Model (CAPM).
You'll learn each approach theoretically AND practically, ensuring you fully understand why the formulas work the way they do.
Understanding and Measuring Risk and Relationships
Estimate the total risk of a stock manually and on Python.
Estimate the market risk of a stock; again, manually and on Python!
As a by-product of learning to measure the market risk, you'll also learn how to quantity the relationships between securities - something that will be a focal theme of portfolio management and investment / financial analysis.
As with the expected returns, you'll learn to measure risk manually as on Python. Thanks to a solid understanding of why the equations work the way they do, you'll see how some defaults in Python's NumPy module can lead to inaccurate estimates.
Measuring Portfolio Risk and Return
Estimate the return of a 2 asset and multi-asset portfolio.
Measure the risk of a 2 asset and multi-asset portfolio.
Discover the 3 factors that influence / impact portfolio risk - 1 of which is more important than the other two combined!
Explore how to calculate portfolio risk and returns on Python, from scratch.
Exploring Diversification & Optimisation
Risk reduction by diversification.
Explore Optimal Diversification - identify the 'optimal' number of securities to hold.
Optimise your portfolio weights to achieve a target expected return.
Minimise your portfolio risk (mathematically) using robust financial analysis techniques, leveraging Python for Finance.
Explore the power of Python's SciPy library to quickly and efficiently optimise your portfolios.
Decomposing Diversification
Investigate and explore why, fundamentally, diversification works for financial analysis / investment analysis.
Rethink the way you measure the relationships between securities for financial analysis by extending the current measure.
Explore precisely how and why the most important factor of risk influences / impacts portfolio risk.
DESIGNED FOR DISTINCTION
We've used the same tried and tested, proven to work teaching techniques that've helped our clients ace their exams and become chartered certified accountants, get hired by the most renowned investment banks in the world, and indeed, manage their own portfolios. Here's how we'll help you master financial analysis, take command of one of the most important concepts in Finance, and turn you into an Investment Analysis & Portfolio Management PRO:
A Solid Foundation
You’ll gain a solid foundation of the core fundamentals that drive the entire investment analysis and portfolio management process. These fundamentals are the essence of financial analysis done right. And form an integral part of Finance as a whole.
Example Walkthroughs
Every major concept is taught with example question walkthroughs, so you can literally see how we analyse investments and conduct rigorous financial analysis, one step at a time.
Loads of Practice Questions
Apply what you learn immediately with 150+ practice questions, all with impeccably detailed solutions.
Cheat Sheets & Resources
Mathematical proofs, one page cheat sheets, workable .ipynb and .py Python code – all included.
Say goodbye to information overload.
Engage with carefully thought out, clutter-free, and engaging study materials that focus on the 20% finance fundamentals that drive 80% of the results.
Easily follow through complex financial analysis concepts with great visuals that don’t overdo it.
Explore byte-sized lectures that don’t cut corners – so you receive the right amount of information which will hold you in good stead wherever you go, whatever you move on to do.
Finally understand why the math works.
Learn why we divide some variables by something, and multiply other variables by something else. Get past the painful approach of memorising countless equations. Not only will we rip apart each equation one variable at a time, we’ll also give you mathematical proofs that show the equation’s logic one step at a time. Save yourself time and effort by understanding why the equation works the way it does. Then go out and create your own equations, and redefine the way you conduct your own financial analysis.
Watch your confidence grow.
Apply what you learn immediately in example question walkthroughs and progressively challenging quizzes with impeccably detailed solutions.
Engage with over 150 questions ranging from simple true and false ones to more complex problems that take you outside your comfort zone.
Questions are relevant for Ivy League / Russell Group University students studying any core Finance / Financial Analysis course, as well as professionals studying for the ICAEW CFAB, ACA, ACCA, and CFA qualifications.
All questions designed in-house, by Russell Group Distinction Tutors.