Lecture 7 - Behavioral Finance: The Role of Psychology
Overview:
Behavioral Finance is a relatively recent revolution in finance that applies insights from all of the social sciences to finance. New decision-making models incorporate psychology and sociology, among other disciplines, to explain economic and financial phenomenon, such as erratic stock price variations. Psychological patterns such as overconfidence and perceived kinks in the value function seem to impact financial decision-making, but are not included in classical theories such as the Expected Utility Theory. Kahneman and Tversky's Prospect Theory addresses such issues and sheds light on irrational deviations from traditional decision-making models.
Reading assignment:
Robert Shiller, Irrational Exuberance, chapters 3, 4, 8, and 9
Jeremy Siegel, Stocks for the Long Run, chapter 19
Fisher, Irving. "The Stock Market Panic in 1929." Journal of the American Statistical Association, Proceedings, 25 (169), pp. 93-6, 1930.
Jenter, Dirk, and Fadi Kanaan. "CEO Turnover and Relative Performance Evaluation." NBER Working Paper No. 12068, February 2006.
Financial Markets: Lecture 7 Transcript
February 4, 2008
Professor Robert Shiller: Today's lecture is about behavioral finance and this is a term that emerged into public consciousness around the mid-1990s; before that it was unknown. The term "efficient markets" is much older; I mentioned the idea goes back to the nineteenth century and the term goes back to the 1960s. But behavioral finance is a newer revolution in finance and it's something that I have been very involved with. I have been organizing workshops in behavioral finance ever since 1991, working with Professor Richard Thaler at University of Chicago. We've been doing that for eighteen years; amazing, that's a long time for you, right? When we started we were total outcasts, we thought; nobody appreciated us. I had tenure so I could do it but the problem is, you don't want to do things that are too out of fashion. Fortunately, we have a system that allows it to happen and I'm very happy to have that.
What behavioral finance is a reaction against extreme--some extremes--that we see in efficient markets theory or also in mathematical finance. Mathematical finance is a beautiful structure and I admire what the people have done and I've worked in it myself, but it has its limits. Eventually--you know the way a paradigm develops--it goes through a certain phase. When mathematical finance was new, say in the 1960s, it was the exciting thing and nobody wanted to work on anything else; you wanted to be doing the exciting thing. As the '70s and '80s wore on, it got to be a little bit overdone; people run with it too far, they think that's all we want to do, and we
don't want to think about anything else. Then they start to get sometimes a little crazy. Than we had to reflect that, well, things aren't perfect. The world isn't perfect and we have real people in the world, so that led to the behavioral finance.
Behavioral finance really means--what does it mean? It's not like behavioral psychology. It doesn't mean behavioral psychology applied to finance. It really means something much more broad than that. It means all of the other social sciences applied to finance. The economics department is just one of many departments in the university that teaches us something about how people behave, so if we want to understand how people behave we can't rely only on the economics department. I think that it's coming around to a unifying of our understanding. Since then--since the beginnings in the '90s, our behavioral finance workshops have grown and grown and, of course, so many people are involved in it now; it's now very well-established.
Before I get into that, I want to give some additional reflections on the last lecture. I have this chart, which you saw last time--actually it's an Excel spreadsheet that--I also put it up already on the classes V2 website so you can play with it. I just want to reflect again--I know I'm repeating myself a little bit, but it's very important. What we have in this chart is the blue line, which is the Standard & Poor Composite Stock Price Index going back to 1871--from 1871 to 2008, right now--so that's like 130 years of data. That's the blue line. You can see the--do you know what that is there? That's 1929 and that is the Crash of 1929. Well, actually it extended to 1932 and you can see other historic movements. There's the bull market of the 1990s--a very big upswing--and then there's the crash from 2000 to 2003. I don't know if you remember these things, they were big news, not as big as the 1929 crash, but the upswing was just as big as the 1920s upswing, wasn't it? Here's the 1920s upswing and here's the 1990s upswing--huge upswing in stock prices. This is in logs, by the way, so that means that everything--the same vertical distance refers to the same percentage change in the price.
Then I had, as I said last period, I have a random walk shown--that's the pink line. The random walk is generated by the random number generator. I fixed the random number generator, so I made it truly normal this time. It slows it down a little bit, but if you press F9 we get another random walk, but it's always the same stock price. This is a random walk with a trend that matches the uptrend of the stock price. I can press--it kind of looks similar, doesn't it? It kind of shows that in some basic sense the stock market and the random walk are the same. Here we have the crash of--here we have the market peak of 1929 except it turned out in this simulation to have occurred in 1910 or thereabout. Then we have the--that's The Depression of the '30s except it's not the '30s.
I can just push a button and we get something else. I find this amusing. I don't know.
Unfortunately, we live through only one of these in our lifetime. There's a TV show about parallel universes, right? What's the name of that show? I can't remember it. Don't you know this show? Where they go in some kind of time machine and they emerge in another parallel universe where history took another course. Well anyway, these are parallel universes that we see. In some of these universes, Jeremy Siegel would write his book, Stocks for the Long Run, and in some of them he would not because–well, this one he might not because in this case the stock market was just declining for the better part of a century.
The thing I don't see in these charts and I think we haven't captured it perfectly with just the standard random walk is I don't see any crash as big as the 1929 crash. It's hard to get them. I keep pushing F9--this just seems to dominate, right? There's nothing as big here--press F9 again--you can keep pushing and pushing, maybe you'll get one but you have--you get the idea that there's something anomalous about that crash from the standpoint of this random walk theory. I'm not getting one, right? That's something that we'll talk about. I would--I'm not--I can push for a long time and I don't see--well there's a pretty big one. Isn't that just about as--not quite as sharp as the 1929 crash, but it's hard to get them.
I think that one thing–there are a couple of things that we'll come back to. One is--I think I've already mentioned it--fat tales. Stock price movements have a tendency to show some extreme outliers that are not represented by the normal distribution. But also, there are variations in the variance. So, in this period here--in the '20s and '30s--the stock market was extremely variable on a day-to-day basis; it was way beyond anything we've observed since. So, that's why it seems to be more volatile in that period because the accumulation of bigger random shocks.
Anyway, we can play this game for a while but now I want to go and talk about--remember that the random walk that we see in stock prices is not the behavior of a drunk, even though you can describe a random walk as drunken behavior. The idea in the theory is that these movements only appear random because they're news and news is always unpredictable. If the market is doing the best job--this is efficient markets--in predicting the future, that means then that any time the stock market moves it's because something surprising happened. Like there might be a new breakthrough in science or there could be war or something outside--this is the story--outside of the economic system that disrupts things.
The next question then–now, I've added something--it's on this little tab here--I've added something, which is a plot of present values. This is something that I published in 1981. That's a long time ago, isn't it? It was my first big success. Not everyone liked this article, but what I had--I got into a lot of trouble for it. I learned some people react with hostility when you offend their cherished beliefs, so I was on the outs for a while with this article. I said, it's kind of interesting to think that all these apparently random movements are really resulting in news about something that is fundamental--that's the efficient markets. Every time the stock market moves it's because there was some news about what? Well, it's about present value.
The efficient markets theory, in its simplest incarnation, says that the price is the expected present value of future dividends. What I did, in a paper that I published in 1981, is I said, well let's just plot the present value of dividends through time. That's how I constructed this long time series back to 1871; nobody else was looking at it. Typically, researchers want the best data, the high quality data, and so they would look at recent data, which was the best data, and they would think going back to 1871 is crazy because that's so long ago. We have daily or minute-by-minute data by now, but we can't get it for that remote period. On the one hand, as I argued, the stock market is pricing things that occur over long periods of time. The present value formula is pricing dividends into the future, decades into the future--well, actually to the infinite future, but most of the weight
is on the next few decades. So, we can't evaluate the theory by just looking at ten years of data we've got to get a lot of data.
What I did then in that paper was I computed the actual present value of subsequent dividends for each year--that's on this tab--and compared it with the stock price; so that's what I did. This is an update of a plot that I showed in my 1981 American Economic Review paper. The blue line--because when I published it I was right here. It's amazing how time goes by; it was 1979, I was right here. We had just come off from the big stock market drop of the--it was the '73-'75 drop and it was a couple of years later, so we were kind of bumbling around down here. We didn't have any idea whether this was coming at the time.
What I did was I just, for each year, computed the present value of the dividend. I have a dividend series for every year. In fact, it's right over here. I have to--this is the data, so I have the--this is the S&P Price Index monthly, back to 1871, and here are the dividends they paid per share every year since 1871. I just, for each year, I took all subsequent dividends and I priced them out at the present value formula and I used the constant discount rate of 6% a year. So, you see how we get what the present value was. Of course, there's a problem because we don't know dividends after 2007 because we don't have data on dividends past then. But, I just made some assumptions, so the value at the end is maybe a little bit arbitrary. It could be dragged up or down if I made a different assumption about dividends at the end. More or less, this is going to be what actually the present value of dividends was over this whole period.
Here's the dilemma--this is what I said in my 1981 article--this is the thing that is supposed to be forecasted. That's the present value and the blue line is the forecast of that thing. Then you ask, does this look like a good forecast? Were people doing a good job of forecasting the red line with the blue line? Now, that may be a loaded question; but, I think that you get the impression that there's something possibly wrong here with efficient markets because the red line is just a smooth growth path like nothing happens to it and yet the stock market is going up and down all over the place. It's a little bit like if you had a weather forecaster and this morning he says, I predict today that the low today will be -100º and then two days later he says, I predict that the low today will be +150º. You would eventually start concluding that this weather forecaster can't be trusted because we never get to those temperatures. That's sort of what the stock market is doing; it's fluctuating much more than the thing that's forecasted. You've got to be careful; I ended up with so many critics.
There are lots of issues here that--some people said, well people don't know where the red line was last period. And other people said, well you just are showing one reality for the realization--you're showing--they kind of get back to this parallel universe story. There must be another universe where there's another Earth and where everything looks the same except that the red line did something very different; that could be. So, people are saying, you never know, there could have been a communist revolution in America in the 1930s and they could have nationalized the whole stock market and then the red line would be down at zero--they would have taken the whole thing. Or there could have been some good news, some great breakthrough that we haven't discovered yet but in another reality they could have. So, all this noise in the stock market could
have somehow been new information about things that didn't happen. I think we're getting kind of philosophical when we go to that. The point is that we've never seen any movement in the present value of dividends that would justify the movement. If we knew the future with certainty, according to this model, then the stock market would behave like the red line, not like the blue line.
Well, anyway. For example, let's look at the Great Depression of the 1930s, at the very least I think this chart will reveal some misconceptions that some people have. The Great Depression of the 1930s was awful, right? I mean you hear these stories; I assume you hear these stories. We had 25% unemployment at the peak--it sounds really bad. We had people selling apples on the street; you must know these images, right? It sounds awful, but look what happened to p* in the Great Depression. I can hardly see anything. Well, what actually happened was businesses continued paying their dividends right through the whole Depression and some of them cut their dividends, but it was only for a few years. The present value of--the value of stock depends on what it pays out over decades not just next year. The stock market--if people knew the--even if they knew the depression was coming, they shouldn't have marked down the stock market so much, according to this simple efficient markets story--according to the present value story.
At the very least, I think that this diagram helps you to see what is wrong or what simple theories are wrong. So, it must be that if the stock market is reacting to new information over all this century of history, it must have been new information about things that just didn't happen. It could be that an asteroid almost struck the Earth and then it just missed and so the stock market crashed. Then when it missed it came back up again, so we don't see any interruption in dividends. But it has to be something like that. The problem is, I can't think of anything like that. I don't think that any asteroid came close to the Earth--not close enough to be worried about--and I can't think the communist revolution had much chance of taking place in the United States--but you can imagine--so we don't know.
Behavioral finance kind of tends to reach the opposite conclusion: that this volatility in the stock market is the sign of something else; it's some social force, some speculative bubbles, some activity that is not related to anything fundamental. The reason I got so much hostility when I wrote these papers is I was striking a nerve, I guess, because many people have developed these beautiful mathematical theories that said that the stock market was the optimal predictor of everything and I was saying the emperor has no clothes; so there were others like that. What is happening? What I'm coming around to think--maybe it's my cynical view, I've always been a cynic. I don't know if you are cynics or not, but I think people convinced themselves of things. They--people think they understand things better than they do. You spend your whole life looking at this one picture of the stock market and you think you have an explanation for all of it--all rational good--but it's just over-confidence that's doing that; it's an illusion.
I want to talk about over-confidence and I thought I'd try–there's also no eraser. Can you find another eraser? There's probably one in this closet. I wanted to try an experiment of asking you a series of a few short questions. It's a game we'll play, which I'll need your cooperation with. So, these are questions about over-confidence. Actually, I just want you to try to give me 90% confidence intervals for the answers to these questions. Do you know what a 90% confidence
interval is? It's, for example, if I were to ask you what--how many people are there in New Haven?
I want you to not just give me a number, I want you to give me a range such that you're 90% sure that you're right. I could say, well, it's between 90,000 and 100,000 people and I'm 90% sure I'm right. If you give me a true 90% confidence interval, then you should be right 90% of the time, right? What I'm going to do is give you a few questions and ask you for a 90--ask you to write down--you have a piece of paper there--a 90% confidence interval. I have five questions; this is just an experiment.
The first one is about the Statue of Liberty. What does it weigh in pounds--in tons? Good, thank you. So, weight. Incidentally, just to remind you, a U.S. ton is 2,000 pounds--not a British pound, which is 2,240 pounds--and a ton is 907 kilograms. Can you write down on your paper your 90% confidence interval? For example, I won't use realistic numbers. If you thought it was--you might write down, it's between one pound and three pounds and that you're 90% sure it falls in that interval. I didn't say--its tons, tons. I'm asking--it's more than a pound--I'll give you a hint, it's in tons. Let me also say, we're not weighing the base. The Statue of Liberty stands on a tall edifice; we're not counting that but we're counting also the steel reinforcing that they put in a few years ago. Remember, the Statue of Liberty was getting weak and they were worried that something might topple down, so they reinforced it and we're counting that. So, it's a copper structure with steel reinforcement.
Can you write down on your notes a range within which you're 90% sure that the statue weighs, in tons? If you could do that--I'm going to come back--what I'm going to do is come back and see how often you are right so we'll go back through these. Have you all written down a weight for the Statute of Liberty? Population of the country, Turkey. Since I don't have the current population, I want it in the year 2000. I didn't get the latest estimate; so, how many people were there in Turkey in 2000? And again, put down a range, a low and a high, that's 90% sure. Third, the Sahara Desert. How many square miles in the Sahara Desert? Remember that a square mile is 2.6 square kilometers; just in case you think in terms of kilometers, you can devise your answer in kilometers and then multiply by 2.6. Again, write down a range. Enrollment at Yale.
By the way, I should have asked, you have to be honest with it. You could game me by writing really wide intervals for nine of ten questions and then an extremely narrow interval for the tenth. I'm expecting some sincere cooperation here--then you would guarantee that you were right exactly 90% of the time, right? I mean, you could say the Statute of Liberty weighs between zero and a hundred quintillion tons and you know you're right. Then you could deliberately say the population of Turkey is between one and two people and then you know you're wrong. You could do--you're not supposed to do that. I want the enrollment in Yale; I don't have the latest number--2005. That's the total number of students at Yale University in 2005, including Yale College and all the graduate schools. The fifth question is about the Pulitzer Prize. Do you know this prize? It's a prize that journalists win for writing great articles or books. I want to know, what is--how much do you get in cash if you win the Pulitzer Prize? I have it for last year, 2007; it might be different in 2008, so I'm asking for the 2007, in dollars. I hope you were honest in putting confidence intervals. Have you gotten them?
Now, what I'm going to do, if you've answered all five questions, I'm going to tell you the answers--the correct answer--and then ask for a show of hands of how many--please be honest and don't be embarrassed. Raise your hand if you were right, meaning that if my answer falls within your 90% confidence interval, okay? Let's go to the Statue of Liberty. The Statue of Liberty weighs 252 tons. So, can I have a show of hands--how many people here have 252 in the interval? You're doing fairly well; what fraction–keep your hands up--looks like it's about, what would you say, 20-25%? Thank you for being honest and not gaming me. It should have been 90% who were right.
What is the 2000 population of Turkey? I'll give you the exact number that I got from their statistic: 65,666,677. That's a little over sixty-five million. How many people have that in their interval? That's better; that's like 40%--40% or 50%. You're doing better but it's still not 90%. How many square miles in the Sahara Desert? 3.5 million. Can I get a show of hands--how many were right on that? Well, this one really got you, that was like 5%. Was anyone right on all of them so far? Nobody. Enrollment in Yale, Fall 2005? 11,483 students. How many were right? Okay, that's about 40%--right close to 40%, I'd say. Finally, how much do you win if--how much do you receive if you win the Pulitzer Prize? $10,000. Can I have a show of hands? That was really low, that's like 5%. I knew that was a trick because you've heard about the Nobel Prize. Those are both prestigious, right? Nobel Prize gives you something on the order of $1,000,000 and the Pulitzer Prize gives you something like--it only gives you $10,000. So how can that be? I sort of picked something that I thought you might be wrong on.
That reveals something about human behavior. It's a choice in life. You go into different walks of life. This is something that's fundamental to economics: there are just different expectations about how much money you're going to make. If you go into the news media--and I think that's a wonderful career--you're not going to make much money probably. The whole thing is just scaled down and I think there's something revealing about this that we just have social norms for how much someone is to be paid. If you were to give Stephen Schwartzman a $10,000 prize, it would be more like an insult than anything. But if you are working for The New Haven Register and you get this prize, it's a life-changing event, not because of the $10,000 maybe--even they get more money than that.
Anyway, the point was that people tend to be overconfident. Incidentally, it's not just males; females are well-known to be overconfident too. There is a thing about macho males--"know it all"--but experiments prove that women have the same problem. That's why I think that when we look at charts of the stock market, we see things that we think we understand, especially young people. They get deluded into thinking they understand more than they really do. I wanted to talk about some authors that I admire who have written about this. These are books that I don't have on the reading list, but they're fun to read. There's a professor at The Harvard Business School, Rakesh Khurana; he has a book on the search for charismatic CEOs. It's not just overconfidence in yourself; we tend also to put overconfidence in leaders. We have a sense that some people are just natural geniuses and know everything, so we think that they can transform our lives or our companies. So, boards of directors are constantly looking for a CEO who is a genius and they keep getting fooled and disappointed. They bring someone in and this person often messes things
up more than helps because this person realizes that he or she has to live up to this genius role, so they better do something. So, they do something in a flailing way, not understanding what they're doing, and they mess up the whole company.
Really, a lot of what happens--good things that happen in human society--are the result of lots of people doing their own special things and all working together. There's no great genius but there's this idea in our mind that we are going to be such a thing. Related to that--and I wanted to mention, it's on the reading list--an article by one of my students in this class, who's now at MIT. He took this class about ten years ago, Fadi Kanaan and co-authored with another MIT professor, Dirk Jenter, again looking at overconfidence in our judgments. Again, they looked at CEOs, chief executive officers of companies, and they found that companies in industries that fail tend to fire their CEOs. This is unjust; this is an overreaction. You bring in this CEO who's supposed to be brilliant and then the business fails, so you fire the guy right after that.
We're kind of manic-depressive about these guys. When the business fails, we think we were such a mistake. This guy had such promise and he just didn't live up so we get rid of him; but in fact, they found that the CEO gets fired even if the whole industry went down. So, you can't blame the CEO for the fact--if you're one company in an industry and the whole industry goes down--or the remaining industry even not including that firm--it's not the CEO's fault. We tend to be kind of wild and extreme in our judgments. You've seen that a lot--a lot of CEOs lost their jobs recently in the subprime crisis. Was it their fault? Probably not, but they get fired anyway. We go through this manic-depressive--we try to hire charismatic CEOs, then we get disappointed and we keep going through musical chairs one after another.
Nassim Taleb, who lives here in Connecticut and I know him well, has a book called Fooled by Randomness, which was a best-seller and it's very fun to read. It's a story--he's a Wall Street--he had an investment management firm and he observed a lot of people. It's a book about how people over interpret--they tend to blame themselves for failures and congratulate themselves for successes too much and they don't realize that it's just random. Some guy who's in a business--business is succeeding--why is it succeeding? Because the guy came in, dumb luck at the right time and everything is supporting that, concludes that he's a genius. Then Taleb observes them later, after things don't go so well, and suddenly they're depressed. –
I talked to stockbrokers before and after the '87 stock market crash and one of them told me--or maybe more than one of them told me--I can tell that the crash occurred from the tone of voice of the people when they call up the phone. When the market was soaring just before the '87 peak, he said they would call up and they were brash and rude to me and they would say, let's trade this, get this done--kind of just disparaging subtlety, the stockbroker. Then after the crash, when these people were sort of--many of them--wiped out, they'd answer the phone in a sheepish way. You could just tell in the tone of their voice that they were crushed. So, that's what happens.
I also have down on this part of the reading list Irving Fisher, who was a professor at Yale, who was a very prominent economist in the first half of the century. He's another Yale graduate, Yale Class of 1895, I think. I'm sure he lectured on this stage because this building was--his office was in this building, I believe. He died around the mid-1940s but he's famous for overconfidence. In
1929, he was interviewed just before--two weeks before--the 1929 peak and--do you know what I'm referring to? He said he thought the stock market was on a permanently high plateau and he wrote a book in 1929--actually it came out in 1930--with this extremely optimistic outlook for the market. He had a beautiful mansion; he was a wealthy man for a while, but he lost everything in the stock market crash. In fact he had to--he had borrowed against his home and he lost his house, so Yale University bought his house for him and rented it out to him; otherwise, he would be on the street.
I have an article written by him in 1930--I think it's 1930 or at the end of 1929--discussing the stock market crash. He still is unrepentant. This was our most brilliant professor here at Yale, but he just totally misjudged the market, He's just totally unrepentant--he just went back over his book. There are so many good reasons--the 20s were a spectacular era--so many good reasons the stock market will keep going up and he just wouldn't back down. In fact, what he actually did was he started borrowing from his relatives--he had wealthy relatives--and he lost all of it. He just couldn't have imagined that the stock market would go down--there was just no reason that he could think of--and that's what he says in the article.
Anyway, I want to talk more precisely about how people behave; this is all general about overconfidence, but there's some other factors that I want to start with. The most important theory in behavioral finance is the Kahneman and Tversky Prospect Theory. Danny Kahneman, who is now a professor of psychology at Princeton, and Amos Tversky, who died a few years ago--they wrote, I think, the most famous article on behavioral economics; it goes beyond just finance. The title of the article was Prospect Theory and that was 1979. This is, I think, the–actually, I think there was a ranking of economics articles--scholarly articles--by numbers of quotations and this was number two out of all articles written in the last fifty years. Number one, it was quoted for some other reason--I'm not sure--it was some statistical method that everyone quoted.
In terms of an intellectual contribution, this is the most important economics article in the last fifty years, at least judged by how many times it's cited. Kahneman and Tversky are not really talking about overconfidence but something, well, perhaps related to it--something more general. It's how people make choices and there are two elements to this theory. It replaces expected utility and it has--what it does, it replaces the utility function with a value function--with value function--and replaces the probabilities with, what they call, weights. I'm going to explain what that is and we'll move on.
Let me give a little story that leads up to it and it's a story that Paul Samuelson, who's a professor at MIT, told. Paul Samuelson was a highly esteemed--he is--I think he's ninety-two or about ninety-two years old now and still writing and still working. He was a--he is a mathematical economist, retired now, but he told a story that illustrates some of the beginnings of Prospect--in fact, he kind of anticipated Prospect Theory. This goes back to an article that he wrote in 1963. In 1963, he was having lunch with one of his colleagues, another economist; he doesn't name this other person because it would be embarrassing, but everyone knows it was E. Cary Brown, a professor at MIT. Samuelson, in a playful mood--he was always sort of a playful person--he said at lunch--he said, hey let's toss a coin. Let's make a bet just for the fun of it and if it comes up
heads, I'll give you $200, but if it comes up tails, you give me $100. He said, let's do it I'm ready. This kind of took E. Cary Brown by surprise. That sounds like a lot of money, especially in 1963; prices were much lower--that's like $1,000 or $2000. It was big money. But of course, these professors could afford it; it's not that much money. So, let's just say it's $100 and $200.
Do you feel like--if I were to offer that to you right now--let's do it because you don't have cash on you now, but you'd have to promise to pay me if you came out wrong. Do you feel like doing that? No, someone is answering me honestly. Introspect and think about it while this is suddenly thrust on you. E. Cary Brown said, come on I don't want to do this--Samuelson was being annoying by doing this. Then Samuelson thought--had another idea-he said, what if I offered--he didn't actually offer this--what if I offered to--let's do this 100 times. We'll toss a coin 100 times and each time it comes up heads I give you $200 and each time it comes up tails you give me $100. Well, E. Cary Brown, knowing mathematics of statistics and the law of probabilities, he said, well, if we do it 100 times, by the binomial theorem, I'm sure to win. I couldn't possibly--this is elementary--100 times is a lot of times. In fact, I'll make thousands of dollars. So, E. Cary Brown said, I'll do it. I would do it, but he didn't actually do it.
Samuelson then said--he went back to his office and he wrote a paper--that's this 1963 paper--proving that E. Cary Brown was irrational. You cannot possibly say, I will take 100 of them but I won't take one of them. That's not rational. That was one of the motivating things in Kahneman and Tversky. What Kahneman and Tversky said is that people behave--if you can introspect and imagine why some of you didn't feel like taking this bet--people behave as if they have a kink in their utility. This may sound an abstract way of putting it, but expected utility theory--the traditional theory says that everybody has a utility function that they consistently refer to when making calculations. I'm going to put Kahneman and Tversky over here and I'm going to put Expected Utility Theory over here.
Expected Utility Theory says that I want wealth--and I'll call w wealth--and I get utility from wealth--that's U. My utility curve--it has maybe any number of shapes, but its concave downward and smooth, so you have what's called diminishing marginal utility; that's Expected Utility Theory. What Expected Utility Theory means--the slope is always decreasing. Every extra dollar of wealth gives me less happiness but it always give me a little bit more, so I always want more. Expected Utility Theory would say that that's a two-for-one bet that Samuelson is offering and it's small compared to my lifetime wealth. My utility is essentially linear over the relevant range, plus $200 or minus $100, so I don't really concern myself about risk. I should just take every bet like that all the time. You should always be looking--if you are behaving this way--you should always be looking. Anyone who wants to make a bet with me anytime, I'll always take it if it's in my advantage--even a little bit in my advantage.
People seem to like to gamble but they don't like to do it consistently. They like to go to--they end up going to gambling casinos where it's stacked against them, not for them, but it's somehow arranged as an entertainment. Well, Kahneman and Tversky said that people don't behave this way and it's as if they have a value function as a function of their money. Let's put in the middle of the value function, the reference point. Reference point means where you are today and your value--that's V, which is like utility, but now we're talking in psychological terms, so we give it a
different name. The value function has a kink; it's something like that at the reference point. I'm trying to draw it--it's not necessarily--it looks here like two straight lines and that's not quite the way to do it. Let me try and do this again--it's curved downward a little bit, but it becomes much less--I don't--I'm having trouble drawing this on the board well. I don't want to ever--kind of going down. There's a kink here, where the slope--I think I've got it sort of there.
It's concave down everywhere, just like the utility function is, but there's a discontinuity of slope right here. Where is that? That's where I am now. What it means is that I value losses much more than I value gains from wherever I am. There's a big difference between losing and winning, so when I reflect on this bet I'm thinking of--I could lose $100 and that scares me. It feels bad--the idea that--I would just feel bad. So gaining $200 is positive for me but it doesn't offset the loss that I might make. So, if I have equal probabilities, what you want to do is weight the gains and losses and the losses tend to dominate, so you don't want to take the bet. The weighting function incorporates Samuelson's lunch colleague's problem: that people don't want to take bets that are to their advantage. It goes back to a kink in the utility function.
Now, incidentally, this is fundamentally different from--in economic theory, economists would say, well you can put a kink in the utility function. There could be some wealth level that's special to you. But a theory economist--that kink has to stay at a certain wealth level. With Kahneman and Tversky, this kink moves around with you, so whatever--it's whatever--you're always at the kink because it's not rational; this is not rational Expected Utility Theory. This is--I'm always looking at where I am now and exaggerating in my mind the importance of deviations from that. People are very concerned with small losses; that's what th kink in the value function is.
Now, I want to talk about another Kahneman and Tversky thing, called the weighting function. The weighting function refers to the fact that people distort probabilities in their mind. It's not that they don't know probabilities but they distort them in their thinking. I'll give an example that illustrates the Kahneman-Tversky weighting function and it goes back years before Kahneman and Tversky. It's a famous example from a French economist, Maurice Allais, and it's called the Allais Paradox. It illustrates thinking that violates Expected Utility Theory. I'm going to give you a choice between two "prospects," as Kahneman and Tversky called them. Suppose I offered you a 25% chance to win $3,000 or, alternatively, a 20% chance to win $4,000. Maybe I can get a show of hands. This is like Samuelson's lunch colleague again, but a little different.
Suppose I'm offering--I'm not offering this, but suppose I offered this--you have a choice between Prospect One or Prospect Two. Prospect One--I'm going to toss a four-sided coin and if it comes up with a probability of one-fourth, in a certain way, you will win $3,000. In Prospect Two, I'm going to give you a chance of 20% to win $4,000. Can you tell me which of these you'd pick if you had to pick only one of these? Do you understand the question? How many would pick number One? It seems like it's about 20%. How many would pick number Two? So, most of you would pick number Two.
Now, let's do a variation on this question here--a very simple variation. Which would you prefer? This is the one that you picked--most people picked. Another prospect--100% chance of winning
$3,000--or Two, that would be an 80% chance of winning $4,000. Do you see the--if you pick Prospect One, you're going to just get $3,000 for sure. If you pick Prospect Two, you'll probably get $4,000 but an 80% chance of it. How many would pick One? That looks like the--how many would pick Two? V ery few of you would pick Two. Have to reflect--so we picked One this time.
Now, you might want to reflect on that. Why was it such a different--why did you pick One in this case and pick Two in this case? The thing I want to point out is that the number--the cash amounts are the same in the two examples but the probabilities are just multiplied by four. So the expected utility of the two is just four times as great, no matter what. They're the same--the utilities are the same, with the same numbers. All I've done is multiply your expected utility by four in this case, so you can't make a different choice. If you picked Two over here when comparing these two prospects you should also have picked Two when you compared these two prospects. Why didn't you? Most of you switched. Can you tell me why? Yes.
Student: I would prefer not to gamble, so if I had the chance a--in the first situation, I would take the chance to make $4,000.
Professor Robert Shiller: You would choose not to gamble. Does this mean it's like a moral judgment or--
Student: No, I prefer certainty.
Professor Robert Shiller: Okay, you got it exactly. That's yeah--you got--you prefer certainty. There's some anxiety about maybe--you got it exactly right. I think people like certainty and ambiguity is difficult for them to adjust to.
Kahneman and Tversky put it in this following way: it's a little bit like we're cavemen. It turns out, we were all taught to count and to do arithmetic but primitive people actually have difficulty counting. There's an old story that cavemen had only three numbers: one, two, and many. I used to disbelieve this story but I'm not--actually it was a psychologist at Princeton told me that, as a matter of fact, it's proven that there are some people whose languages have only those numbers: one, two, and many. For example, they're called--in Laos in Thailand, there's a very primitive group of people with primitive technology. I don't mean that they're primitive people but they only have one, two, and many; and there are others that have been discovered. Emotionally we're like that. I used to wonder how could they have only those numbers: one, two, and many. You ask a mother, how many children do you have? She couldn't answer; she didn't have the word three, but as a matter of fact they didn't. So I guess, if you asked the mother, how many children you have, she would probably just name them. She couldn't say, I have three children.
But anyway, we're all kind of like that when we think about probability; that's Kahneman and Tversky. Kahneman and Tversky say what we do is that in our minds we weight the probabilities in a distorted way and this is the weighting function. So, we have the weight--that's weight not wealth here--against the probability and I'm going to exaggerate a little bit. This is zero and this is one because probabilities range from zero to one. The weighting function looks like this--I'm
exaggerating a little bit so you can see but--and then it jumps up or it jumps down here; this is the idea. What Kahneman and Tversky said in their original 1979 article is, we act as--there's a wide range of probabilities here that are all kind of blurred and put together. We minimize and–emotionally, the difference between probabilities--they're all kind of in the middle. So, when I said twenty or twenty-five, in your mind you said, here's twenty and here's twenty-five but I don't think they're much different to me emotionally. The money sounds different but the probability sounds the same. It's like I have only three probabilities: can't happen, might happen, and it's certain to happen. You tend to be totally in to the certainty story, so you give it much more weight. The way--what people do then, summing up--in expected utility theory, you maximize the probability-weighted sum of utilities. You maximize the summation of the probability of the ith outcome times utility in the ith outcome. But in Prospect Theory, you maximize the sum of the weights times the value function--the values. This is the Kahneman and Tversky variation on Expected Utility Theory.
There's something related to it that psychologists talk about, it's called Regret Theory. It's a little bit different but it's essentially the same as–well, it's consistent with Prospect Theory. That is, people experience pain of regret and they do a lot of things to try to avoid the pain of regret. For example, when the stock market goes up they try to sell it and lock in the gain because they are worried that if it goes down again they will regret not having sold it; that's not a rational calculation. If you come to something, you just have it and then it escapes you, you feel pain. I guess that's what happened at the Super Bowl last night when the New England Patriots had a winning streak and they messed up at the very end--that's exceptionally painful and that's part of Regret Theory. I don't know how pained any of you are but it must have been painful to them anyway.
I just mentioned some other things that are related to Prospect Theory. There's something called "mental compartments" that people--Expected Utility Theory says, your utility depends on your whole lifetime wealth, so you should be always thinking that everything that happens today is just part of a bigger story; I'm always thinking about my lifetime. I had you do an exercise at the beginning where I asked you to estimate the present value of your lifetime income and it probably came out to several million dollars. So, if you were behaving rationally you would always be weighing things against that big sum of several million dollars. That's why plus $100, minus $200--who cares, right? That's the way you should be thinking but you don't think that way because you're human. People put things in mental compartments, all different compartments in your mind, and you have separate values for things depending on which compartment they're in. For example, when you go to the gambling casino, the winnings and losses are completely different. You just put them in a game compartment and you think, I can accept these and it doesn't matter. Investors are that way too, they'll sometimes put part of their portfolio in "I can play with this" mental compartment and others in another mental compartment.
Anyway, I just want to come back--I have maybe a little bit more to say about this, but let me come back and talk just about the problem set we talked about last period. Problem Set #3--you've got your second problem set here--Problem Set #3 is a stock market forecasting exercise and the spreadsheet that I have up here is one spreadsheet that you could use to do that. It's illustrated--I clarified it a little bit in the version I put up. So, you run a regression like that to predict the stock
market. This is actually a hands-on experience that's supposed to help you eliminate your overconfidence by trying to predict the market. This is the example where I tried to use time as a predictor of the stock market and failed pretty decisively to do so. What I want to say is that I have this spreadsheet up here that has some data--it has monthly--this is my 130-year long stock price series, but you could add other data and whatever--if you can find data series somewhere it would be more fun to try to predict using other data. This is just for you to really try to do it. Some people do sports things, so if somebody wins the Super Bowl--I don't know what the story is--this is a famous story actually--the stock market goes up or goes--do you know that. I don't know this exact repeated story--so you could create other variables like a dummy variable for winning the--somebody winning the Super Bowl and put that in.
There's a famous story--it goes back to the 1930s--about skirt lengths and the stock market. Do you know this story? In the 1920s, an unprecedented thing happened in women's fashion, never been seen before in the United States, maybe in--women started wearing short skirts and it was scandalous. They weren't quite mini skirts, but they were scandalous. The women's hemlines rose and peaked in 1929 and then the skirt lengths came down in the 1930s, right with the market; so that was noticed. Some people thought there was some euphoria that was driving women crazy or something about the 1920s--the optimism, the sense; it sort of happened again in the '70s. Remember, the mini skirts came in the 1970s, right? Then the 1970s--'74 crash didn't exactly--I don't know if hemlines came down.
But anyway, I had one student who thought, well maybe there are other fashion things that explain the market and she went back to microfilm newspapers and measured the width of men's ties in fashion advertisements. She thought, wide ties are a sign of--it's like a short skirt I guess--a sign of optimism and excitement, so she collected data on widths of ties. She had a time series--this is a very good answer to a very good problem set--and she collected fifty years of data on the width of men's ties and correlated--to see if it predicted the market. Unfortunately it did not, but it was a wonderful choice. I'm hoping that some of you can think of interesting things to do to try to predict the stock market. Alright, I'll see you again in two days.
[end of transcript]
金融学与经济学的关系 郑振龙陈蓉(《经济学动态》2005年第2期) Zvi Bodie和Robert C.Merton在他们合著的《Finance》一书中开篇明义：“金融学是一门研究人们在不确定环境下如何进行资源跨期配置的学科”。这一看来与经济学含义颇为相似的金融学定义难免让人疑惑：包含了众多子学科的金融学体系能在这一定义下得到涵盖吗?金融学其实就是以金融领域为研究对象的经济学吗?还是有着其独有的学科特征? 回顾历史，人们会对金融学科产生种种疑惑是并不让人感到惊奇的。在这样一个发展时间很短，且涵盖了诸如公司金融学、投资学、金融市场学、金融工程学、商业银行管理学、货币经济学、国际金融等子学科的学科体系中，“学科逻辑混乱”曾经是研究者对其所下的结论。然而，伴随着市场现实的发展和金融研究者的不断努力，金融学的学科脉络逐渐走向清晰完整，学科特征日益显著。因此，我们终于可以对上述几个问题做出基本的回答： 第一，现代金融学分为宏观金融学和微观金融学两个部分，而Bodie和Merton所给出的定义显然是针对微观金融提出的，又被称为新古典意义上的金融学定义。 第二，在金融学体系中，宏观金融学可以在很大程度上看做宏观经济学中的一个重要部分，或者可以理解为现代(开放经济下的)宏观经济学的货币演绎。而微观金融学则不能做类似的理解，因为它在发展过程中已经逐渐显现其独特的学科特征。可以说，今天的微观金融学，已然发展成为一门虽然与(微观)经济学联系密切但确实有所不同的独立学科，其最突出的特征就在于微观金融学所特有的“无套利分析”的研究思想和研究方法，这一思想方法在经济学研究中是没有的。 在本文的后面部分，我们将着重阐述第二个问题，即微观金融学研究方法与经济学研究方法的区别。但在此之前，我们必须首先对第一个问题，即现代金融学的基本学科体系进行一定的回顾和分析。 一、一个历史的视角：宏观金融学和微观金融学 众所周知，在其漫长的历史中，西方主流经济学所经历的是从微观到宏观的发展历程，而金融学则恰恰相反，最早的金融研究是从宏观层面开始的，包括古典经济学家如李嘉图(1821)等人对整体市场价格水平和相应的货币供求问题以 及利率决定等问题的研究；新古典后期的经济学家们，如Wicksell(1898)，则通过利息理论把宏观金融问题与一般经济问题(如经济增长和经济危机)等结合起 来考虑。最后，当凯恩斯革命引致的现代宏观经济学诞生时，宏观金融学也相应形成，其核心内容就是货币经济学，并随着人类社会的发展从封闭的研究环境逐渐拓展到开放的经济环境，国际金融问题(有人将之称为开放经济下的货币经济学)也逐渐成为其中的重要内容。 而在微观金融学方面，由于市场现实的不发达，虽然在今天来看，早在1728年(Gabriel Cramer)和1738年(Bernoulli)就已经出现了对不确定环境下决策准则的初步思考，20世纪早期Marschak(1938)等人也已经开始进行均值—方差的决策分析，然而最初的微观金融仍主要停留在道·琼斯式的简单数据采集、统计分析和经验法则(Rules of Thumb)水平上。直到20世纪50年代前后，以VonNemnann —Morgenstern(1947)的期望效用公理体系的建立和Markowitz (1952)的资产组合理论为标志，微观层面的金融学才开始真正发展起来。
金融学与经济学的关系(DOC 6)
金融学与经济学的关系 金融学和经济学的相关关系探讨 Zvi Bodie和Robert C.Merton在他们合着的《Finance》一书中开篇明义：“金融学是一门研究人们在不确定环境下如何进行资源跨期配置的学科”。这一看来与经济学含义颇为相似的金融学定义难免让人疑惑：包含了众多子学科的金融学体系能在这一定义下得到涵盖吗?金融学其实就是以金融领域为研究对象的经济学吗?还是有着其独有的学科特征? 回顾历史，人们会对金融学科产生种种疑惑是并不让人感到惊奇的。在这样一个发展时间很短，且涵盖了诸如公司金融学、投资学、金融市场学、金融工程学、商业银行管理学、货币经济学、国际金融等子学科的学科体系中，“学科逻辑混乱”曾经是研究者对其所下的结论。然而，伴随着市场现实的发展和金融研究者的不断努力，金融学的学科脉络逐渐走向清晰完整，学科特征日益显着。因此，我们终于可以对上述几个问题做出基本的回答： 第一，现代金融学分为宏观金融学和微观金融学两个部分，而Bodie和Merton所给出的定义显然是针对微观金融提出的，又被称为新古典意义上的金融学定义。 第二，在金融学体系中，宏观金融学可以在很大程度上看做宏观经济学中的一个重要部分，或者可以理解为现代(开放经济下的)宏观经济学的货币演绎。而微观金融学则不能做类似的理解，因为它在发展过程中已经逐渐显现其独特的学科特征。可以说，今天的微观金融学，已然发展成为一门虽然与(微观)经济学联系密切但确实有所不同的独立学科，其最突出的特征就在于微观金融学所特有的“无套利分析”的研究思想和研究方法，这一思想方法在经济学研究中是没有的。 在本文的后面部分，我们将着重阐述第二个问题，即微观金融学研究方法与经济学研究方法的区别。但在此之前，我们必须首先对第一个问题，即现代金融学的基本学科体系进行一定的回顾和分析。 一、一个历史的视角：宏观金融学和微观金融学 众所周知，在其漫长的历史中，西方主流经济学所经历的是从微观到宏观的发展历程，而金融学则恰恰相反，最早的金融研究是从宏观层面开始的，包括古典经济学家如李嘉图(1821)等人对整体市场价格水平和相应的货币供求问题以及利率决定等问题的研究；新古典后期的经济学家们，如Wicksell(1898)，则通过利息理论把宏观金融问题与一般经济问题(如经济增长和经济危机)等结合起来考虑。最后，当凯恩斯革命引致的现代宏观经济学诞生时，宏观金融学也相应形成，其核心内容就是货币经济学，并随着人类社会的发展从封闭的研究环境逐渐拓展到开放的经济环境，国际金融问题(有人将之称为开放经济下的货币经济学)也逐渐成为其中的重要内容。 而在微观金融学方面，由于市场现实的不发达，虽然在今天来看，早在1728年(Gabriel Cramer)和1738年(Bernoulli)就已经出现了对不确定环境下决策准则的初步思考，20世纪早期Marschak(1938)等人也已经开始进行均值—方差的决策分析，然而最初的微观金融仍主要停留在道·琼斯式的简单数据采集、统计分析和经验法则(Rules of Thumb)水平上。直到20世纪50年代前后，以VonNemnann—Morgenstern(1947)的期望效用公理体系的建立和Markowitz (1952)的资产组合理论为标志，微观层面的金融学
需求/供给/价格 P：价格 #price D：需求 #demand S：供给 #Supply E（e）： 弹性 #elasticity Ed：需求的价格弹性 Es：供给的价格弹性 Em：收入弹性 Exy：交叉弹性 Q：数量 #Quantity 效用论 基数效用论 #Cardinal utility 序数效用论 #Ordinal utility TU：总效用 #Total utility MU：边际效用 #Marginal utility I：无差异曲线 #Indifference curve MRS：商品之间的边际替代率 #marginal rate of substitution 生产论 生产要素（Factors of Production） L：劳动 #labour k：资本 #capital N：土地 #land，自然资源 Natural resources E：企业家才能 #Entrepreneurship TP/Q：总产量 #Total Product AP：平均产量 #Average Product MP：边际产量 #Marginal Product MRP：边际收益产品 #marginal revenue product VMP：边际产品价值 #value of marginal product MFC：边际要素成本 #marginal factor cost 成本论 费用 #expense 机会成本 #Opportunity cost
超额利润 #Excess profit Π C：成本 #cost TC/STC：短期总成本 #Short-run total cost TFC：总固定成本 #Total fixed cost TVC：总变动成本 #Total variable cost AC：平均成本 #average cost AVC：平均可变成本 #average variable cost LTC：长期总成本 #long-run total cost R：收益 #Revenue TR：总收益 #total revenue（gross earnings，gross income）AR：平均收益 #average revenue MR：边际收益 #marginal income 完全竞争市场 完全竞争市场 #perfect competiton market LS：长期供给线 #Long-run supply curve CS：消费者剩余 #Consumer‘s surplus PS：生产者剩余 #Producer’s surplus r： 利率 #rate
金融考研和经济学考研的有什么区别? 考研专业的选择是广大考研党比较纠结的问题，考研中专业选择比较火热的金融专业和经济学专业是广大考研同学们报考的热门专业，毕竟未来的就业前景是非常广阔。凯程考研蒙蒙了解到很多同学在金融考研选择专业的时候都有这样的疑惑：究竟金融学和经济学有什么异同，他们的就业方向有差别吗?今天凯程考研蒙蒙就来就为大家介绍一下金融学和经济学考研的区别和差异。 从比较广泛的意义上来说,金融学属于经济学研究领域，金融学是专对金融货币流通市场上的经济活动的研究(如期货股票债券保险银行风险投资等等)。大学里所见的一般性经济学专业主要偏向学术研究，其研究的面很广，课题很大，所以一般不针对具体实用的经济学科领域。但是从经济学基本理论研究衍生出了很多新的经济学分支,比如信息经济学,环境经济学等等. 金融考研和经济学考研的有什么区别? 经济学考研专业 主要课程：政治经济学、《资本论》、西方经济学、会计学、统计学、计量经济学、国际经济学、货币银行学、财政学、经济学说史、发展经济学、企业管理、市场营销、国际金融、国际贸易等 从业方向：能在综合经济管理部门、政策研究部门、金融机构和企业从事经济分析、预测、规划和经济管理工作的专门人才。 金融考研专业 培养目标：本专业培养具备金融学方面的理论知识和业务技能，能在银行、证券、投资、保险及其他经济管理部门和企业从事相关工作的专门人才。 主要课程：本专业学生主要学习货币银行学、国际金融、证券、投资、保险等方面的基本理论和基本知识，受到相关业务的基本训练，具有金融领域实际工作的基本能力。 就业方向：中央银行等金融业监督管理机构、商业银行、政策性银行、证券公司(含基金管理公司)、信托投资公司、金融控股集团等风险性很大的金融公司、四大资产管理公司、金融租赁、担保公司、保险公司、保险经纪公司。社保基金管理中心或社保局等经济金融类的专业目前来说是比较热门的专业，也很受就业市场的欢迎，可以说经济金融类专业的本科生毕业后还是很好就业的，但是要想真正的从事经济金融领域的高端职业，仅仅有一份本科学历是远远不够的。在这一领域普通人才众多，高精尖人才稀少，因此研究
金融经济学 名词解释 自然状态：特定的会影响个体行为的所有外部环境因素。 自然状态的信念：个体会对每一种状态的出现赋予一个主观的判断，即某一特定状态s出现的概率P。 期望效用原则：人们在投资决策时不是用“钱的数学期望”来作为决策准则，而是用“道德期望”来行动的。而道德期望并不与得利多少成正比，而与初始财富有关。即人们关心的是最终财富的效用，而不是财富的价值量，而且，财富增加所带来的边际效用（货币的边际效用）是递减的。 效用函数的定义：不确定性下的选择问题是其效用最大化的决定不仅对自己行动的选择，也取决于自然状态本身的选择或随机变化。 公平博彩：指不改变个体当前期望收益的赌局，如一个博彩的随机收益为ε，期望收益为E(ε)=0，我们就称其为公平博彩。 效用函数的凸凹性的局部性质：经济行为主体效用函数的凸凹性实际上是一种局部性质。即一个经济主体可以在某些情况下是风险厌恶者，在另一种情况下是风险偏好者。效用函数是几个不同的部分组成。在人们财富较少时，部分投资者是风险厌恶的；随着财富的增加，投资者对风险有些漠不关心；而在较高财富水平阶段，投资者则显示出风险偏好。 确定性等价值：是指经济行为主体对于某一博彩行为的支付意愿。即与某一博彩行为的期望效用所对应的数学期望值（财富价值）。 风险溢价：是指风险厌恶者为避免承担风险而愿意放弃的投资收益。或让一个风险厌恶的投资者参与一项博彩所必需获得的风险补偿。 阿罗-普拉特定理：对于递减绝对风险厌恶的经济主体，随着初始财富的增加，其对风险资产的投资逐渐增加，即他视风险资产为正常品；对于递增绝对风险厌恶的经济主体，随着初始财富的增加，他对风险资产的投资减少，即他视风险资产为劣等品；对于常数绝对风险厌恶的经济行为主体，他对风险资产的需求与其初始财富的变化无关。 相对风险厌恶的性质定理：对于递增相对风险厌恶的经济主体，其风险资产的财富需求弹性小于1（即随着财富的增加，投资于风险资产的财富相对于总财富增加的比例下降）；对于递减相对风险厌恶的经济行为主体，风险资产的财富需求弹性大于1；对于常数风险厌恶的经济行为主体，风险资产的需求弹性等于1。 （均值-方差）无差异曲线：对一个特定的投资者而言，任意给定一个证券组合，根据他对期望收益率和风险的偏好态度，按照期望收益率对风险补偿的要求，可以得到一系列满意程度相同的（无差异）证券组合。所有这些组合在均值方差（或标准差）坐标系中形成一条曲线，这条曲线就称为该投资者的均值-方差无差异曲线。 有效集，有效前沿（边界）：同时满足在各种风险水平下，提供最大预期收益和在各种预期收益下能提供最小风险这两个条件就称为有效边界。 分离定理：在存在无风险资产与多个风险资产的情况下，投资者在有关多个风险资产构成的资产组合的决策（投资决策）与无风险资产与风险资产构成的资产组合比例的决策（金融决策）是分离的。 有效组合前沿：期望收益率严格高于最小方差组合期望收益率的前沿边界称为有效组合前沿。 两基金分离定理：在所有风险资产组合的有效组合边界上，任意两个分离的点都代表两个分离的有效投资组合，而有效组合边界上任意其它的点所代表的有效投资组合，都可以由这两个分离的点所代表的有效组合的线性组合生成。
金融经济学与金融工程学的区别 所谓数理金融学这一说法包含着金融工程学和金融经济学这两层意思，其共同点在于上使用了大量的数学符号，目的在于确定资产价格。然而其内在品位和角度却是截然不同的。 金融经济学是微观经济学的扩展。高微的前半部分价格理论研究的是在企业和消费者效用最大化的情况下，商品价格的决定的。而金融经济学则应用了期望效用理论，所研究的是在投资者通过金融市场跨期配置资产获得效用最大化时，金融资产价格的确定。 马克维茨的投资理论假设了投资者的效用函数满足均值方差关系，在这一效用标准下投资者如何选择资产。CAPM理论则回答了，当投资者如此选择资产的情况下，证券的价格（用收益率来表示）应当等于多少。行为金融学则改变了关于传统的效用函数的假设，研究了市场非有效的原因和机制。然而无论如何，金融经济学不得不说仍旧是经济学，效用函数仍旧是基石。 然而金融工程学却完全跳出了这一套路，开始了完全从形式出发的道路。通过观察历史数据，获得有关股票收益率的均值和方差信息，于是把资产定价的问题数学化（dS/S=udt+bdz,把股票价格看成是几何布朗运动，u为均值，b为标准差）。随机过程在衍生品定价上的应用可谓是数学与金融学完美结合的一个范例。此时，资产价格的确定完全取决于人们对证券价格运动模式的假定，而不再考虑任何效用影响。数学家们又为我们提供了风险中性定价方法中众多结论的证明，使得通过找到风险中性概率测度，以及证券在风险中性下的随机过程，可以为资产进行定价。 可以说金融工程学的定价方式在于假设符合直观标准的价格运动过程，从而确定衍生品价格。这并不是经济学，而是工程，重在实现价格计算。因此有比较好的实用意义。经济学也许更倾向于发现解释逻辑，而不是确定具体的数字吧。
行为金融学 前言 随着改革开放以来中国经济飞速发展，中国金融市场也蓬勃向上，股票市场的起起伏伏让我们众多投资者心理更是跌宕起伏。在我国这个风险与机遇并存的股票市场中，我们如何能够消除自身的心理误差，做出正确的投资决策以获得期望的回报，正是我们下文将要着重探讨研究的投资者行为金融学问题。 行为金融学就是将心理学尤其是行为科学的理论融入到金融学之中，是一门新兴边缘学科，它和演化证券学一道，是演化金融学最引人注目的两大重点研究领域。它从微观个体行为以及产生这种行为的心理等动因来解释、研究和预测金融市场的发展。 作为传统金融学的有效补充 传统金融理论认为人们的决策是建立在理性预期、风险回避、效用最大化以及相机抉择等假设基础之上的。 大量的心理学研究表明，人们的实际投资决策并非如此。比如，人们总是过分相信自己的判断，人们往往是根据自己对决策结果的盈亏状况的主观判断进行决策的等等。尤其值得指出的是，研究表明，这种对理性决策的偏离是系统性的，并不能因为统计平均而消除。 传统金融理论同样认为，在市场竞争过程中，理性的投资者总是能抓住每一个由非理性投资者创造的套利机会。因此，能够在市场竞争中幸存下来的只有理性的投资者。 但是在现实世界中，市场并非像理论描述得那么完美，大量“反常现象”的出现使得传统金融理论无法应对。传统理论为我们找到了一条最优化的道路，告诉人们“该怎么做”，让我们知道“应该发生什么”。 可惜，并非每个市场参与者都能完全理性地按照理论中的模型去行动，人的非理性行为在经济系统中发挥着不容忽视的作用。因此，不能再将人的因素仅仅作为假设排斥在外，行为分析应纳入理论分析之中，理论研究应转向“实际发生了什么”，从而指导决策者们进行正确的决策。 传统的经济学通常假定市场行为是由物质动机驱动的，并且人们所做出的经济决策是理性的并且是追求自我利益的必然结果。这里的理性意味着决策者对所有可得的信息进行系统分析，面对众多选择作出最优的决策。决策同时也是前瞻性的，也就是说，决策是建立在对将来的所有可能的后果进行慎密的权衡的基础上的。换言之，传统的西方经济学认为：经济行为是由外在激励决定的。心理学尤其是认知心理学却认为，决策者个体是一个复杂的系统，这个系统可以有意识地，理性地识别并解释一些可得的信息。但同时也存在一些难以被意识觉察的因素系统地影响人类的行为。总体而言，人类的行为是由内在的动机决定的。 行为金融学的主要理论 期望理论 这个理论的表述为：人们对相同情境的反应决取于他是盈利状态还是亏损状态。一般而言，当盈利额与亏损额相同的情况下，人们在亏损状态时会变得更为沮丧，而当盈利时却没有那么快乐。当个体在看到等量损失时的沮丧程度会比同等获利情况下的高兴程度强烈得多。研究还发现：投资者在亏损一美元时的痛苦的强烈程度是在获利一美元时高兴程度的两倍。他们也发现个体对相同情境的不同反应取决于他目前是赢利还是亏损状况。
经济学考研专业分析 经济学考研专业众多，最多可分为将近20个二级学科，那么大家该如何选择研究方向呢，小编为大家一一解释，经济学都有哪些专业，专业对应权威院校，供考生参考。 经济学类的硕士专业分为理论经济学与应用经济学两个一级学科。其中，理论经济学包括政治经济学，经济思想史，经济史，西方经济学，世界经济，人口、资源与环境经济学6个二级学科，有部分学校还新增了网络经济学、企业经济学、可持续发展经济学等新型专业。应用经济学包括国民经济学、区域经济学、财政学、金融学、产业经济学、国际贸易学、劳动经济学、统计学、数量经济学、国防经济10个二级学科。下面对经济学的所有二级学科进行一下介绍。 理论经济学，顾名思义比较侧重于经济理论问题的研究，其中西方经济学和政治经济学值得推荐。 西方经济学 热门分析：很多学科都是在西方经济学基础之上生长、繁衍、裂变而来，学习西方经济学能整体把握经济学知识的框架结构。该专业培养目标是培养能够从事西方经济理论和我国经济理论研究、独立承担高等院校、科研单位教学科研工作和政府经济管理部门实际工作的高级专门人才。研究方向有宏观经济学、微观经济学、国际经济学、经济计量学、发展经济学、比较经济制度、现代西方经济学流派、西方马克思主义经济学等。 就业方向：如果有志在高校或者科研院所工作，这一专业能够提供很好的学术背景。如果想到经济领域的部门从事工作，这个专业没有完全对口的部门，但是该学科的基础性可灵活就业，如政府部门、公共事业单位、经济咨询单位和商业贸易部门。 推荐院校：北京大学光华管理学院、上海交通大学、上海财经大学、武汉大学 政治经济学 热门分析：金融危机的背景下，西方掀起了一股重读马克思、研读《资本论》的热潮。在构建和谐社会和全面发展观的背景下，我国政治经济学颇具中国特色。完善中国特色社会主义经济理论体系，推动“中国模式”的发展都预示这个学科在中国的美好前景。在经济科学中，政治经济学作为一门研究人类社会各个发展阶段上支配物质生活资料的生产和交换、分配、消费规律的科学，为其他各学科提供了理论基础。 人才培养：旨在培养理论经济学专业研究人员、高等院校理论经济学教学人员、政府部门从事宏观经济和区域经济的高级管理人员。可分为中国经济发展理论与政策、劳动理论与收入分配、政府规制与公共选择、企业理论与公司治理、国际经济合作与政策等研究方向。 就业方向：和西方经济学就业方向比较类似，比较常见的是从事科研与教学工作，考公务员也十分合适，进银行或国际企业做实务也是不错的选择。 推荐院校：中国人民大学、北京大学、复旦大学、南开大学 理论经济学中其他几个学科在性质上同上述两个学科比较类似，只是研究的理论内容更细致，抽象性更强。如世界经济，经济思想史，人口、资源与环境经济学等，这些专业中，除世界经济外，大多属于冷门专业，报考人数少，一些院校基本只招收个别研究生，有时还需通过调剂完成招生名额。由于录取名额太少，一些如中国人民大学、西南财经大学这类重点学校会由于报考扎堆而录取线异常高。以上各专业的毕业生主要进入高等院校、科研单位、
岭南学院2013级本科生培养方案 经济学类（含金融学、经济学、国际经济与贸易、财政学、保险 学专业） 一、培养目标： 岭南学院秉承百年岭南的传统，即“作育英才，服务社会”，把博雅教育作为本科教育的基础，学生通过四年的学习，通过不同课程的学习和参与社会活动、体育竞赛，在毕业之时，能够达到以下培养目标： （一）知识层面。通过通识课程和专业教育，学生在知识的广度和深度方面得到提升。通过通识课程的学习，掌握人文、历史、社会、自然科学等不同基础学科和领域的基础知识；通过专业课程的教育，学生在所选择的专业里，系统学习，掌握该领域所需的专业知识。 （二）技能方面。学生能够运用所学的基本技能和解决问题的能力应对未来生活中碰到的问题和挑战。强调对学生沟通和写作能力、数据分析和信息技术运用能力、批判性思维和创造性思维、以及团队合作和领导力的培养。（三）公民意识和社会责任感教育。强调公民意识和公民参与（本土参与和全球化参与）；跨文化知识和能力；道德推理和价值判断的能力；人文关怀和 社会责任感。 （四）整合性学习。包括综合能力和贯通博雅（通识）学习与专业学习的能力。（五）培养学生具有广博的视野、批判思考的能力、道德伦理的认知、良好的沟通能力，并在经济学、金融学、管理科学与工程等某个专业领域学有所长，从而能够适应激烈的时代变革，并在未来的社会竞争中立于不败之地。 二、培养规格和要求： （一）一、二年级实行通识教育，开拓学生视野、提高他们探索世界、认识自我和世界的能力，培养他们一生所需的关键能力。 （二）三、四年级学生专注专业教育，以培养学生解决实际问题的能力为导向，为将来面向实际部门的学生开设工商管理类、应用类、实践类的课程，培养具有竞争力的实战型人才。 （三）开办“数学实验班”，为走学术发展道路的学生提供系统、扎实的数学和现代经济学训练。
经济学专业 一、专业简介及培养目标 为了满足社会对经济学基础理论人才的需要，基于“厚基础、宽口径”的办学理念，经济学专业培养经济理论基础扎实、知识宽厚、综合素质高，具备熟练的外语应用能力、经济数学运用能力、计算机操作能力和经济活动实践能力等方面的理论和实践相结合的复合型人才。经济学专业充分吸收了国内外著名大学经济学系的课程设置思想，通过理论经济学、应用经济学、经济史和经济思想史学、经济分析工具、部门经济等系列课程的教学以及社会调查、毕业论文等实践环节的系统、严格和规范训练，学生将具有在经济学各个分支学科攻读研究生学位、从事高级经济工作和公共管理工作的能力。 二、授予学位 经济学学士学位 三、学分要求与课程设置 总学分：140学分，其中： 1．必修课程：86学分 2．选修课程：51学分 3．毕业论文：3学分 学习好帮手
1）全校公共必修课程：36学分 课程编号课程名称学时学分开课学期 03835061大学英语（一）22全年 03835062大学英语（二）22全年 03835063大学英语（三）22全年 03835067大学英语（四）22全年 02533180政治经济学（上）33秋季 02533190政治经济学（下）33春季 思想道德修养与法律基 04031650 22全年 础 04031660中国近现代史纲要22全年 学习好帮手
马克思主义基本原理概论 04031681 22秋季 （上） 毛泽东思想和中国特色社会 34全年 04031730 主义理论体系概论 00831610文科计算机基础（上）33秋季 00831611文科计算机基础（下）33春季 60730020军事理论42春季 ――――体育系列课程－4全年 注：全校公共必修“马克思主义基本原理概论（下）”课程的内容，分别用本院开设课程“政治经济学（上）”和“政治经济学（下）”涵盖，经教务部批准，准予代替开设。 学习好帮手
行为金融学复习题 一，判断题 1.无套利是均衡条件的推论，如果市场达到均衡，那么一定没有套利机会存在。（） 2.只要有了算法，问题肯定能得到答案。（） 3.现代标准金融学承袭了“经济人”的基本分析假定，提出了有效市场价说，建立了现代 标准金融学完整的理论体系。（） 4.凯恩斯是最早强调心理预期在投资决策中作用的经济学家。（ 5.理性人假设认为，人是理性的且具有理性预期，但对未来的认知可能会存在一定的偏差。 （） 6.对投资的评估频率越高，发现损失的概率越高，就越可能受到损失厌恶的侵袭。（） 7.拇指法则是一种启发式判断法。（） 8.Kenda11与Roberts等人发现股票价格序列类似于随机漫步，他们最终把这些理论形式 化为有效市场价说。（ 9.20世纪20年代至40年代，资本市场分析基本上由两大派所主宰：基本分析派与数量分 析派。（ 10.锚定与调正启发法倾向于低估复杂系统成功的概率和高估其失败的概率。（ 11.损失厌恶反映了人们的风险偏好并不是一致的，当涉及的是收益时，人们表现为风险偏 好；当当涉及的是损失时，人们则表现为风险厌恶。（） 12.人们忽略的遗憾程度通常大于行动的遗憾程度。（） 二，单项选择题 1.有限理性的决策标准是（） A．最优 B.效用最大化 C.满意D,风险最小 2. 卡尼曼通过心理学研究发现人们对所损失东西的价值估计高出所得到价值的（ A.2倍 B.1倍 C.2.5倍 D.4倍 3．投资者通常假定将来的价格模式会与过去相似，这种对股价未来走势的判断属于 （A.算法 B.熟识性思维 C.代表性思维D.投资者情绪 4.现代标准金融学理论产生的标志是（ A．Markowitz资产组合理论B.套利定价理论C.资本资产定价模型D.有效市场假说 5．根据性别，过度自信，频繁交易和所承担的投资风险之间的关系，（）投资者承担的投资风险可能最大。 A.单身女性 B.已婚女性 C.单身男性 D.已婚男性 6. 与问题非相关的信息会导致相关信息的有效性减弱，这种情况被称为信息的（ A.首因效应 B.稀释效应 C.韦伯定律 D.近效应 7. 过度自信通常不会导致人们（） A.高估自己的知识 B.低估风险 C.对预测有效性的变化敏感 D.夸大自己控制事情的能力 8. 对信息反应不足一定程度上可以用来解释） A.价格翻转现象 B.收益序列正相关 C.信息不足 D.信息瀑布 9. 现代标准金融理论中被称为相对定价法的是（ A.一般均衡定价法 B.无套利定价法 C.CPAM定价法 D.APT定价法 10.同质信念下人们的交易动机是（ A.对未来价格走势的看法差异 B.风险偏好 C.流动性偏好 D.从众 11.最早将人的行为研究与经济学研究结合起来的理论是（A.亚当.斯密的“经济人假说”B. Keynes的“选美竞赛”理论C.新古典经济学的“理性人假说”D. Keynes的“空中楼阁”理论
金融经济学思考与练习题（一） 1、在某次实验中，Tversky 和Kahneman 设计了这样两组博彩： 第一组： 博彩A ：（2500，0.33； 2400，0.66；0，0.01） 博彩B ：（2400，1） 第二组： 博彩C ：（2500，0.33； 0，0.67） 博彩D ：（2400，0.34； 0，0.66） 实验结果显示，绝大多数实验参与者在第一组中选择了B ，在第二组中选择了C ，Tversky 和Kahneman 由此认为绝大多数实验参与者并不是按照期望效用理论来决策，他们是如何得到这个结论的？ 解：由于第一组中选择B 说明 1（2400）φ0.33(2500)+0.66(2400)+0.01(0) 相当于 0.66（2400）+0.34（2400）φ0.66(2400)+ 0.34{3433 (2500)+ 34 1 (0)} 根据独立性公理，有 1（2400））φ 3433 (2500)+ 34 1 (0) (*) 第二组选择C 说明 0.33（2500）+0.67(0)φ0.34(2400)+0.66(0) 相当于 0.34{ 3433 (2500)+ 34 1 (0)}+0.66(0)φ0.34(2400)+0.66(0)
根据独立性公理，有 3433 (2500)+ 34 1 (0) φ1(2400) (**) (*)与(**)矛盾，因此独立性公理不成立，绝大多数参与者不是按照期望效应理论决策。 2、如果决策者的效用函数为,1,1)(1≠-=-γγ γ x x u ，问在什么条件下决策者是风险厌恶的，在什么条件下他是风险喜好的？求出决策者的绝对风险厌恶系数和相对风险厌恶系数。 解：1)(",)('----==γγγx x u x x u 绝对风险厌恶系数： 1) (') ("-=- =x x u x u R A γ 相对风险厌恶系数： γγ==- =-x x x u x x u R R 1) (')(" 当γ>0时，决策者是风险厌恶的。当γ<0时，决策者是风险喜好的。 3、决策者的效用函数为指数函数,1)(α αx e x u --= ，问他的绝对风险厌恶系数是 否会随其财富状态的改变而改变？ 投保者与保险公司的效用函数均为指数函数，且投保者的α=0.005，保险公司的α=0.003，问投保者与保险公司谁更加风险厌恶？ 解：αααα=--=- =--x x A e e x u x u R )(')("
经济学(财政金融方向) 一、培养目标 本专业专升本层次培养具备比较扎实的、金融、财政、税收、公共行政管理的基本理论和基本知识，比较熟练地掌握现代经济分析方法，能在综合经济管理部门、政策研究部门、金融部门、工商企业、学校等单位从事经济分析、经济预测、经济规划、经济管理、经济研究与教学等经济理论研究与应用方面的工作。主要致力于培养适应现代财税部门工作、税务代理中介机构业务、政府公务员要求的高素质专门人才。 二、主干学科与相关学科 主干学科：经济学 相关学科：管理科学与工程、金融学 三、专业特点及培养要求 培养方案充分发挥学校的学科和特色。强调掌握经济学、金融、财政、税收、基本理论与基本分析方法，经济理论与经济应用知识并重。要求学生能够熟悉党和国家的经济方针和政策；熟练掌握经济学基本理论与分析方法；了解中外经济学理论动态，了解中外经济发展的历史和现状，了解市场经济的运行机制；培养知识、能力、素质协调发展，尤其是具有较高信息素养和技术的应用型人才。 四、学制及学习形式 本专业层次为专科起点本科，采用学分制，学制年，学习期限～6年。
学习形式为利用网络课件、课件光盘及网上答疑等网络教育方式，在学习中心的协助管理下自主进行学习。 五、主干课程 管理学原理微观经济学会计学金融学统计学宏观经济学财政学国际贸易金融市场学政府预算与预算会计公司金融中央银行学公债学政府采购制度税收筹划与代理等。 六、课程总体设置及学分分配 七、教学要求 1．本专业要系统地学习所有必修课及18学分的论文课。 2．课程考核方式有开卷考试、闭卷考试、考查。
3．实践性教学环节包括课程上机、社会实践、毕业论文(设计)等。八、毕业条件 修完教学计划规定的全部学分（81学分），且统考课程全部合格，方能毕业。 九、选课说明与要求 1、课程设置表中各模块选课要求 (1)公共基础课必修19学分，其中思想政治课程3学分；英语、计算机应用基础13学分，线性代数、远程教育学习学3学分； (2)学科课程必修44学分，其中，专业基础课30学分，专业课14学分； (3)集中实践18学分 2、实践课的说明与要求 (1)上机：对要求上机实践的课程，由学习中心负责安排学生集中上机、辅导。 (2)实验：对有实验要求的课程，学院建立网上在线实验课堂，要求学生在规定时间内进行在线学习，并进行实验报告。 3、集中实践的说明与要求 (1)毕业论文(设计)：从第四学期进入毕业论文(设计)工作，在指导教师的指导下，确定任务书、工作计划、在规定时间内提交各环节稿件等。(2)毕业论文答辩：论文答辩采用“申请学位的学生答辩＋自愿申请参加答辩的学生”相结合的方式，即申请学位的学生必须参加答辩，不申请学位的学生可自愿申请参加答辩；不参加答辩的学生成绩计为及格或不及格”。
金融经济学思考与练习 题答案 TTA standardization office【TTA 5AB- TTAK 08- TTA 2C】
金融经济学思考与练习题（一） 1、在某次实验中，Tversky 和Kahneman 设计了这样两组博彩： 第一组： 博彩A ：（2500，； 2400，；0，） 博彩B ：（2400，1） 第二组： 博彩C ：（2500，； 0，） 博彩D ：（2400，； 0，） 实验结果显示，绝大多数实验参与者在第一组中选择了B ，在第二组中选择了C ，Tversky 和Kahneman 由此认为绝大多数实验参与者并不是按照期望效用理论来决策，他们是如何得到这个结论的？ 解：由于第一组中选择B 说明 1（2400） (2500)+(2400)+(0) 相当于 （2400）+（2400） (2400)+ { 3433 (2500)+ 341 (0)} 根据独立性公理，有 1（2400）） 3433 (2500)+ 341 (0) (*) 第二组选择C 说明 （2500）+(0) (2400)+(0) 相当于
{3433 (2500)+ 34 1 (0)}+(0) (2400)+(0) 根据独立性公理，有 3433 (2500)+ 34 1 (0) 1(2400) (**) (*)与(**)矛盾，因此独立性公理不成立，绝大多数参与者不是按照期望效应理论决策。 2、如果决策者的效用函数为,1,1)(1≠-=-γγ γx x u ，问在什么条件下决策者是风险厌恶的，在什么条件下他是风险喜好的？求出决策者的绝对风险厌恶系数和相对风险厌恶系数。 解：1)(",)('----==γγγx x u x x u 绝对风险厌恶系数： 相对风险厌恶系数： 当γ>0时，决策者是风险厌恶的。当γ<0时，决策者是风险喜好的。 3、决策者的效用函数为指数函数,1)(ααx e x u --= ，问他的绝对风险厌恶系数是否会随 其财富状态的改变而改变？ 投保者与保险公司的效用函数均为指数函数，且投保者的α=，保险公司的α=，问投保者与保险公司谁更加风险厌恶？ 解：αααα=--=-=--x x A e e x u x u R )(')(" 由于投保者的绝对风险厌恶系数为，而保险公司为，因此投保者更加厌恶风险。
经济学，金融学，金融 工程 的区别和联系 “国家上调法定准备金率，存贷款利率上涨，我的股票要赶紧抛售了。” “前一阵财政部上调印花税，我买的股票就亏了呢。” “你帮我算一下这个，是要卖出美元买日元呢，还是要卖出英镑买日元？”…… 听到这些，你会不会以为自己走进了证券大厅呢？其实，这是我们金融专业的学生在金融实验室里参加金融模拟大赛，一本正经地讨论呢。 金融，顾名思义，就是资金融通。金融是每个人在生活中都要接触的，现在人们的日常生活根本离不开金融。简单点说，国际上，币值稳定、汇率问题是金融的研究范围；国内，货币政策的实施、银行汇率的升降属于金融问题；就企业而言，是否要上市发行股票、是否要向银行贷款也是金融问题；就家庭及个人而言，我们学费的缴纳是通过银行转账，就是最日常的水电费缴纳也可以很便捷地通过银行缴纳，而银行，就是金融中介机构。金融，离我们相当近，学习金融最起码可以使我们更理性地进行消费与投资。现在，老百姓的投资理财越来越离不开金融了。而对于一个国家而言，金融更可谓重中之重。 一次，西方经济学老师在谈到通货膨胀时感慨：“你们选择金融专业是相当明智的，金融关系到整个国民经济的稳定。著名经济学家弗里德曼曾经说过…一
切经济问题归根结底就是货币问题。?国家经济发展离不开良好的货币政策，整个国家的经济政策都掌握在你们手中了，你们任重而道远呢。”一席话，说得我们热血澎湃，深感自己肩上承担的分量之重。 在金融专业课上，讨论活动是家常便饭。国家的每一项经济政策出台，都会成为第二天讨论的热点。针对股市，老师有一句很经典的话，“今年以来，股价纷纷上涨，股票市场一片红火，现在的股市已经不是牛市了。”我们一听都愣了，这样的股市还不是牛市么，难道老师有内部情报得知股价要下跌？于是，下边一片骚动。谁知老师紧跟着一句：“是…大牛市?，…巨牛市?！用牛市已经不足以形容了，得用…巨牛市?。”末了，加上一句，“是吧，同学们？”我们边笑边点头。关于人民币升值的问题，大家各抒己见。有的说人民币升值，去国外旅游留学就省钱多了；有的会说不利于中国的出口，而中国是个出口大国；有的会说对于中国进口产品就有利多了……通过辩论，大家会进一步理解国家政策对于日常生活的影响，也会更好地明白课本上的理论，学以致用。 金融学是一个比较热的专业，在整个经济类学科中，差不多是录取分最高的了。公共课上，其他院系的老师经常跟我们开玩笑，说最喜欢给金融专业的学生上课，因为金融专业的学生是最聪明的。 如果说金融学专业的学生聪明，那么，学金融工程专业的学生就是“巨聪明”了。学术界曾有这样一句话“金融工程学使金融学走向象牙塔”。之所以这样说，看看金融工程的来源就明白了。 20世纪90年代初美国的星球大战计划，相信大家都不陌生，尤其是对其衍生出的一系列电影作品是再熟悉不过的了。后来，由于国际形势的变化，
香帅如是说 大清早被刷屏才想起６月5日是“经济学”的狂欢节。历史如此巧合,1７23年６月5日出生的亚当-斯密以石破惊天的姿态,开创了一门研究“国民财富的性质与起源”的学问。160年后的1883年６月５日,罗纳德－约翰-凯恩斯出生，之后将这门学科推到了一个前无古人,后无来者的境界。 《通论》太难读,从希克斯和汉森对凯恩斯的解读开始，后世习惯将凯恩斯贴上“宏观经济学创始人”的标签，更容易将凯氏的“短期波动调节”中的“短期”和“波动”一笔带过，直接贴上一个“政府干预”的肖像。不知爵士地下有知，会否一口老血狂喷？ 窃以为，凯恩斯是对“动态均衡”理解最深的学者。在他的框架中，“动态演化”占有非常重要的地位。每个均衡,都是历史信息的动态表现，而“动态”反映的正是人类时间不曾停歇的流驶。凯恩斯对人性的洞察力更是远远超越了机械经济学的理解范畴，他强调的“动物精神”在其身后的100年的金融市场上不断的以繁荣与危机的方式呈现。从科学的意义上说,理性预期是短期的,波段的，人的认知才是长期市场中最核心的因素,而人的认知，无疑是在螺旋式，波浪式的过程中不断演进变化的 ——这也难怪，忘掉了“演化”的经济学,渐渐踏入了形而上的河流,从此不能回头。 文/徐远 香帅曾经问我:什么是“金融学”？和“经济学”的区别是什么？作为一个经济系的科班生,又曾在金融学上花了十多年的功夫，这应该是个简单的问题。然而,我却一直没有很好地作答。 脱口而出的回答，“金融”是“经济”的一部分。显然,这是个没有意义的回答,既没有说“经济”是什么,也没有说“金融”是什么,更谈不上二者的区别。依据的,不过是一句陈词滥调：金融是经济的一个研究领域。其实，这句话本身也不准确。在传统经济系当中，金融的教授很少，金融的课程也很少，金融学研究的问题,经济系的老师知之甚少,甚至不感兴趣。顶尖大学的金融系,大多在商学院。从学术的组织构架上看，金融学已经独立于经济学，与管理、营销、会计、战略等共同组成广义上的管理学。 真正回答这个问题,要看这二者给我们带来了什么。 先说经济学。现代意义上的经济学,可以追溯到亚当斯密的《国富论》,后来的著作,在方法和分析上有进步（退步）和细化(走偏)，但是在方向上并没有突破。翻开这本书，共分为５篇,第一篇讲劳动产品的提高和分配,包括劳动分工,货币的起源和作用，商品价格的决定，工资、利润、和地租的决定；第二篇讲财富存量的性质、积累和用途，包括财富的分类、资本的积累、利息、和资本的各种用途；第三篇讲不同国家财富发展的不同路径，侧重于罗马帝国崩溃
我们应该如何学习 人类自古以来就有从外部把握事物的能力。孙中山早就观察到这一能力，他列举十大案例，论证“行易知难”（1919年《建国方略》）。 难道我们必须懂得食品的化学结构才可以烹调吗？难道我们必须明白氢原子的物理性质才可以饮水吗？难道人类先有“产科学”才会生孩子吗？……诸如此类的案例充分表明，人类有能力从事物的外部把握事物而不必深究事物内部的细节。行为经济学家当中最早（当然是在孙中山之后）指出人类的这一认知特性的，是诺贝尔经济学家Herbert Simon（他是心理学家、计算机科学家、政治学和公共管理学家、行为科学家）。 跨学科教育的第一步，高屋建瓴，从天空俯瞰人类知识版图，逐渐辨认这张版图里的知识模块，以及这些知识模块之间的关系。这是最自然的认知过程。 从天空俯瞰，我们总是逐渐看出有山川平原，再看出有一块一块的田野，最后才进入某一块田野，认识它的细节。 人类知识版图的初步划分，始于19世纪后半期的西方知识界，专业学者逐渐取代文艺复兴“百科全书”（见林而不见木）式的学者。从那时，到20世纪末叶，是人类“见木而不见林”的时代。1980年代以来，尤其因为“知识技术”的迅速发展，人类开始了一个只能称为“见木亦见林”的知识时代。怀特海的教育理想，只在这一时代才有实现之可能。 教育应使学生有专业技能，但专业技能只不过是生活的地基或立足点，教育更要使学生有文化——怀特海描述的文化是这样的：使生活如美学般高华且如哲学般深切。
你们在跨学科教育实验班里的求学过程，大致如上。 首先要有一张“知识版图”，不论多么粗糙，哪怕只是模糊混沌的一片灰白。自修，辨识出一块一块的知识模块。这是一个循环上升的认识过程，你不可能背诵一张完整的人类知识版图。你只能在一块土地上散步，然后在另一块土地上散步……直到你懂得每一块土地各自的特质，你才明白为何这些土地要如此分块。 你切不可套套逻辑地背诵：因为每一块土地都有特定用途又因为土地的任何划分都是最优的所以土地被如此这般地划分。你关注每一块土地的细节只是为着理解这块土地与另一块土地的本质差异，而不是为着研究这块土地的全部特性。 因此，当一名应试教育的学生将这块土地的全部细节保存在短期记忆里的时候，你应尽力将这块土地与另一块土地的本质差异保存在长期记忆里。 注意，不同于短期记忆，人类的长期记忆是与身体融合为一的。例如，母语、游泳、骑自行车的技能，都保存在长期记忆里。因为与身体融合为一，故而一旦习得就无法遗忘。试问你会游泳之后怎样不会游泳？试问你怎样听不懂母语？这些行为，我们是无法想象的。长期记忆之所以与身体融合为一，主要因为人类有场景记忆能力。我们学习骑自行车的时候常有突然学会了的感觉，因为场景记忆具有整体感——知识与身体终于融合为一的感觉。也因此，至今，研究者们难以确定场景记忆的具体脑区——场景记忆似乎是脑与身的整体功能。 你们是“行为金融学”实验班的学生，你们将要学习的一门核心课程是“金融经济学”。从天空俯瞰，你们可以看到怎样的知识版图？ 参考两位欧洲作者2010年出版的一本教科书（这本书的写作思路与跨学科教育是一致的），你们最初看到的知识模块是“经济学”、“金融学”、“心理学”和“数学”，以及包围着这些知识模块的更广泛的知识模块（社会科学的、