The drawdown is nothing more than the distance between the blue line and the green line at any given point in time. But then when you fall, when your wealth falls, because the market is fallen, then your peak stays the same. So at any point in time, you want to keep track of what is the highest value that I have experienced in this strategy at that point of time since inception? So that is marked with a green line here, and you can see that when the wealth rises, that is your peak. The next step is to compute the peaks, the previous peaks. The first thing that I did is converted into what's called a wealth index. How do we measure it? So I took the returns. You see that you've gone up at some point, then you go down. So let's look at large-cap, similar thing. A drawdown is from the peak to the trough at any given point in time. But you see here that there are periods where it was as high as $175,000 and then just a short while later, it was down to a much lower number. But $1,000 by the time you've done with this 40-year period is approximately $300,000 if you had invested in small cap. So you can see that sometimes it goes up, sometimes it goes down. Just kept it in that asset all the way through that period. A wealth index is just a fancy way of saying, what would have happened if I had taken let's say a dollar or a $1,000, and invested it over time. So the first step is you take the return series and convert it to what's called a wealth index. We've seen this return series already before, the small cap and the large cap, US large cap stocks. So now, let's figure out how we're going to take a return series and convert it to a drawdown. It is not the loss you would have experienced if you'd been in the strategy necessarily, it just is the worst case if you had timed it absolutely horribly. It's a very interesting worst-case measure. It is the worst return of the peak to trough that you could have experienced over the time that the return series are being analyzed, that makes sense? So one way of thinking about it is, this is the worst possible return you would have seen if you had been unlucky enough to buy it at its peak, and sell it at its trough, at the worst point. At its very peak, and you sold it at the very bottom. So what is The maximum drawdown it is the maximum loss that you could have experienced, if you had been unlucky enough to buy the asset or the strategy or whatever you're looking at. So what we're going to look at is a very popular measure of risk that is called The maximum drawdown. So if I have money and I lost it, that's bad, that's risk. What this line of thought says is that, what is risk is really the possibility of losing money. If you deviate from the mean on the upside, well, that's actually not a bad thing. Remember, it's just deviation from the mean. The argument here is that some people say that volatility really is not necessarily a bad thing because it's what is it. We're going to now look at another measure of risk, which is not volatility. In other words, the excess return over the risk-free rate per unit of volatility. We made the point that it's the ratio of the excess return that you would get over what you could get with no risk. So we ended last session with a discussion of a risk-adjusted measure that we called the sharp ratio. By the time you are done, not only will you have a foundational understanding of modern computational methods in investment management, you'll have practical mastery in the implementation of those methods. We'll cover some of the most popular practical techniques in modern, state of the art investment management and portfolio construction.Īs we cover the theory and math in lecture videos, we'll also implement the concepts in Python, and you'll be able to code along with us so that you have a deep and practical understanding of how those methods work. We'll start with the very basics of risk and return and quickly progress to cover a range of topics including several Nobel Prize winning concepts. In this course, we cover the basics of Investment Science, and we'll build practical implementations of each of the concepts along the way. This course is the first in a four course specialization in Data Science and Machine Learning in Asset Management but can be taken independently. However, instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language. This course provides an introduction to the underlying science, with the aim of giving you a thorough understanding of that scientific basis. The practice of investment management has been transformed in recent years by computational methods.
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