Thomas E. Kida
Autore di Don't Believe Everything You Think: The 6 Basic Mistakes We Make in Thinking
Opere di Thomas E. Kida
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Informazioni generali
- Sesso
- male
- Breve biografia
- Thomas Kida is a professor in the Isenberg School of Management at the University of Massachusetts at Amherst and the author of many articles on decision-making. For the last 25 years he has been researching and teaching how we form our beliefs and make decisions. His new book, Don’t Believe Everything You Think: The 6 Basic Mistakes We Make in Thinking, presents this research. The book is about the ways our beliefs and decision making skills can go wrong. Do we all fall prey to problems in thinking? Why do we make these mistakes? Why do we believe the unbelievable?
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Don't Believe Everything You Think: The 6 Basic… di Thomas E. Kida
This is a very interesting, well written book that debunks common myths about how we think and remember. It provides a great corrective to those who think that human anecdotes are better evidence than hard data.
1 
Devil_llama | 2 altre recensioni | Apr 17, 2011 |
Devil_llama | 2 altre recensioni | Apr 17, 2011 | There is some very good stuff in this book on tools for critical thinking. But there is also a rather worrying example of exactly the kind of sloppy thinking Kida is supposed to be warning us against. He spends considerable space on the gambler’s fallacy, and then launches into a discussion of the unpredictability of the Stock Market, and how research has shown that there is no evidence that highly paid fund managers add any value to an investment fund, and that over the long haul, no funds significantly beat the index.
All this may well be right, but Kida’s error lies in what he does with these data:
"Oftentimes investors move their money into a fund that has experienced good recent performance. However, statistcs tell us that we have regression to the mean. That is, if a fund is currently outperforming the market, its performance is likely to drop in the future to bring it back to average. And so, if we buy into a fund right after it has posted recent gains, we’re likely to be in for a fall. In effect, going after strong past performance often means we take money out of funds that are likely to rebound, and put it into funds that are ready to drop. "
Kida has misunderstood regression towards the mean, and has committed an error known as the gambler’s fallacy (which he had already discussed in an earlier chapter).
Let us suppose that fund manager’s are indeed irrelevent, and that a fund has a 50/50 chance of underperforming or overperforming the market each year. If this assumption is indeed correct - and this is indeed Kida’s argument, then whether the fund will outperform or underperform the market this year is entirely unconnected with whether the fund outperformed or underperfomed the market last year.
If Kida is correct, then it makes no difference in the long run whether we leave the money where it is or move it (except for dealing charges incurred of course), because all funds will eventually do equally well.
If we buy into a fund right after it has posted gains then it is wrong to expect that we are in for a big fall. We are just as likely to do well (or badly) as if we buy into a fund that recently posted very poor gains.
But what is regression towards the mean then?
If we take the whole “population” of funds, and we measure all their respective gains each year, we come up with a mean (average) gain for all funds. Now, suppose we choose the 100 best performing funds and measure their gains - because these are the best perfroming funds, their mean gain will, of course, be higher than the mean for all funds.
Let us suppose that their mean gain was twice that of all funds.
Now next year we measure these means again. The mean gain for all funds and the mean for what were last year’s 100 best funds. What we find is that the mean for the 100 best funds of last year is now much closer to the mean of all funds. If fund performance is entirely random then that mean may be less than the mean for all funds, or more - but it will almost certainly be less than twice that of all funds.
Why does this happen? Because there was nothing special about the 100 best funds, and there is no guarantee that the funds that did well last year will do well this year. Thus their average should approach the population average.
But any individual fund could still be in the top 100 - and we would expect that to be the case. Regression towards the mean is only concerned with averages.
Still not convinced?
By Kida’s principle - moving money into an outperforming fund sets you up for a fall. Thus it would follow that moving money into an underperforming fund will set you up for a gain. Therefore, one should put money into the underperforming funds as the best strategy for success.
But it doesn’t work. Because Kida is wrong.
If the performance of a fund is random, the best strategy for success is to buy the fund with the lowest charges and leave your money where it is (or better still - just buy the shares that all the funds hold, and hold the shares).… (altro)
All this may well be right, but Kida’s error lies in what he does with these data:
"Oftentimes investors move their money into a fund that has experienced good recent performance. However, statistcs tell us that we have regression to the mean. That is, if a fund is currently outperforming the market, its performance is likely to drop in the future to bring it back to average. And so, if we buy into a fund right after it has posted recent gains, we’re likely to be in for a fall. In effect, going after strong past performance often means we take money out of funds that are likely to rebound, and put it into funds that are ready to drop. "
Kida has misunderstood regression towards the mean, and has committed an error known as the gambler’s fallacy (which he had already discussed in an earlier chapter).
Let us suppose that fund manager’s are indeed irrelevent, and that a fund has a 50/50 chance of underperforming or overperforming the market each year. If this assumption is indeed correct - and this is indeed Kida’s argument, then whether the fund will outperform or underperform the market this year is entirely unconnected with whether the fund outperformed or underperfomed the market last year.
If Kida is correct, then it makes no difference in the long run whether we leave the money where it is or move it (except for dealing charges incurred of course), because all funds will eventually do equally well.
If we buy into a fund right after it has posted gains then it is wrong to expect that we are in for a big fall. We are just as likely to do well (or badly) as if we buy into a fund that recently posted very poor gains.
But what is regression towards the mean then?
If we take the whole “population” of funds, and we measure all their respective gains each year, we come up with a mean (average) gain for all funds. Now, suppose we choose the 100 best performing funds and measure their gains - because these are the best perfroming funds, their mean gain will, of course, be higher than the mean for all funds.
Let us suppose that their mean gain was twice that of all funds.
Now next year we measure these means again. The mean gain for all funds and the mean for what were last year’s 100 best funds. What we find is that the mean for the 100 best funds of last year is now much closer to the mean of all funds. If fund performance is entirely random then that mean may be less than the mean for all funds, or more - but it will almost certainly be less than twice that of all funds.
Why does this happen? Because there was nothing special about the 100 best funds, and there is no guarantee that the funds that did well last year will do well this year. Thus their average should approach the population average.
But any individual fund could still be in the top 100 - and we would expect that to be the case. Regression towards the mean is only concerned with averages.
Still not convinced?
By Kida’s principle - moving money into an outperforming fund sets you up for a fall. Thus it would follow that moving money into an underperforming fund will set you up for a gain. Therefore, one should put money into the underperforming funds as the best strategy for success.
But it doesn’t work. Because Kida is wrong.
If the performance of a fund is random, the best strategy for success is to buy the fund with the lowest charges and leave your money where it is (or better still - just buy the shares that all the funds hold, and hold the shares).… (altro)
1 sirfurboy | 2 altre recensioni | May 21, 2009 |
sirfurboy | 2 altre recensioni | May 21, 2009 | Although this book has a good framework, the points are a bit belabored.
1. We perfer stories to statistics
2. We seek to confirm, not to question, our ideas
3. We rarely appreciate the role of chance and coincidence in shaping events
4. We sometimes mispervieve the world around us
5. We tend to oversimplify our thinking
6. We have faulty memories.
I only tolerated the condecention through #2. It wasn't faulty logic, but proof-by-repeated-assertion becomed pedantic.
The summary (for as far as I got) was that critical thinking (the scientific method) is a good tool, but isn't often used. A mistake he makes, IMHO, is that it is the only tool. There's are a number of versions of the "Faith without Science is XXX; Science without Faith is YYY" that encapsulate some knowledge this author does not acknowledge.… (altro)
1. We perfer stories to statistics
2. We seek to confirm, not to question, our ideas
3. We rarely appreciate the role of chance and coincidence in shaping events
4. We sometimes mispervieve the world around us
5. We tend to oversimplify our thinking
6. We have faulty memories.
I only tolerated the condecention through #2. It wasn't faulty logic, but proof-by-repeated-assertion becomed pedantic.
The summary (for as far as I got) was that critical thinking (the scientific method) is a good tool, but isn't often used. A mistake he makes, IMHO, is that it is the only tool. There's are a number of versions of the "Faith without Science is XXX; Science without Faith is YYY" that encapsulate some knowledge this author does not acknowledge.… (altro)
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