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ECON-252-11: FINANCIAL MARKETS (2011)

Lecture 3 - Technology and Invention in Finance [January 19, 2011]

Chapter 1. Introduction [00:00:00]

Professor Robert Shiller: OK, good morning. Today I decided not to use PowerPoint, I'm using index cards. This is traditional lecture style. I want to talk today about — this is our third lecture for financial markets. I wanted to talk today about invention in finance. I think of finance, I don't know whether this will encourage you to be interested or not, but I think of it as a form of engineering. Finance is all about inventions. Devices that solve problems and that help people do things and get on with their purposes in life. And the inventions have many small details, just like any invention.

Like an airplane. You look at an airplane, how many parts are in there? How many different people worked on the different parts? And it's so complicated that you might have disbelief that this whole thing is going to work, but somehow it does work. And another part that I want to emphasize in today's lecture is that engineering requires a human element. Engineers know that their devices will be run by people, and people are imperfect. And so, they have a course in engineering schools called Human Factors Engineering. And that's about designing machines, so that human beings won't mess up when they try to use them. That gets us into psychology. To me, when we talk about behavioral finance, which is going to be a theme of this course, human psychology and finance, it's fundamental to the inventive side of finance.

So that's what I want to talk about. I'm going to give you some examples of invention and talk about how they solve the risk problem. The fundamental problem of maintaining incentives in the face of risks. But before I start this lecture, I wanted to just briefly review the last lecture, which was a very important lecture for this course, because it talked about the underlying probability theory and applications of probability theory to finance.

Chapter 2. Review of Probability Theory and the Central Limit Theorem [00:02:38]

So, let me just mention some of the concepts that I talked about last time. The first one was return. We talked about the return on an investment which has two components, the capital gain, which is the increase in the price of the investment, and the other is the dividend, which is something that comes separately in the form of a check maybe, or electronic entry. But we then moved quickly to probability theory. We talked about random variables. A random variable is a quantity that's created by some kind of experiment or event that is uncertain in advance and becomes known later. And then, we talked about measures of probability distributions.

We talked about central tendency, we talked about the average or mean, and the geometric average, and we talked about measures of risk, notably variance. But then, there are also measures of co-movement between two random variables. We talked about covariance and correlation, and we talked about regression. And then finally we talked about distributions of random variables, and the normal distribution, which is famous. It's the famous bell-shaped curve, which is thought by many people to represent a typical distribution.

And then finally, we talked about failures of the — the idea of independent random variables that are normally distributed is a powerful idea. That we have some idea that there's a bell-shaped curve.

Lecture 3 - Technology and Invention in Finance [January 19, 2011]

Chapter 1. Introduction [00:00:00]

Professor Robert Shiller: OK, good morning. Today I decided not to use PowerPoint, I'm using index cards. This is traditional lecture style. I want to talk today about — this is our third lecture for financial markets. I wanted to talk today about invention in finance. I think of finance, I don't know whether this will encourage you to be interested or not, but I think of it as a form of engineering. Finance is all about inventions. Devices that solve problems and that help people do things and get on with their purposes in life. And the inventions have many small details, just like any invention.

Like an airplane. You look at an airplane, how many parts are in there? How many different people worked on the different parts? And it's so complicated that you might have disbelief that this whole thing is going to work, but somehow it does work. And another part that I want to emphasize in today's lecture is that engineering requires a human element. Engineers know that their devices will be run by people, and people are imperfect. And so, they have a course in engineering schools called Human Factors Engineering. And that's about designing machines, so that human beings won't mess up when they try to use them. That gets us into psychology. To me, when we talk about behavioral finance, which is going to be a theme of this course, human psychology and finance, it's fundamental to the inventive side of finance.

So that's what I want to talk about. I'm going to give you some examples of invention and talk about how they solve the risk problem. The fundamental problem of maintaining incentives in the face of risks. But before I start this lecture, I wanted to just briefly review the last lecture, which was a very important lecture for this course, because it talked about the underlying probability theory and applications of probability theory to finance.

Chapter 2. Review of Probability Theory and the Central Limit Theorem [00:02:38]

So, let me just mention some of the concepts that I talked about last time. The first one was return. We talked about the return on an investment which has two components, the capital gain, which is the increase in the price of the investment, and the other is the dividend, which is something that comes separately in the form of a check maybe, or electronic entry. But we then moved quickly to probability theory. We talked about random variables. A random variable is a quantity that's created by some kind of experiment or event that is uncertain in advance and becomes known later. And then, we talked about measures of probability distributions.

We talked about central tendency, we talked about the average or mean, and the geometric average, and we talked about measures of risk, notably variance. But then, there are also measures of co-movement between two random variables. We talked about covariance and correlation, and we talked about regression. And then finally we talked about distributions of random variables, and the normal distribution, which is famous. It's the famous bell-shaped curve, which is thought by many people to represent a typical distribution.

And then finally, we talked about failures of the — the idea of independent random variables that are normally distributed is a powerful idea. That we have some idea that there's a bell-shaped curve.

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