Book Title: The Black Swan
Author(s): Nassim Nicholas Taleb (NNT)
Rating: $$$$$
Comment: A book for all, right from members of investment committee to entry-level analysts down the line
Author’s Bio (from the book’s jacket or elsewhere):
Nassim Nicholas Taleb has devoted his life to immersing himself in problems of luck, uncertainty, probability and knowledge. Part literary essayist, part empiricist, part no-nonsense mathematical trader, he is currently taking a break by serving as the Dean’s Professor in the sciences of Uncertainty at the University of Massachusetts at Amherst. His last book, the bestseller Fooled by Randomness, has been published in twenty languages. Taleb lives mostly in New York
Review:
This is the time of the year when every brokerage house, market experts, new media, etc, wipes the dust off their proverbial “crystal ball” and spell out their prediction for the next year. The predictions range from value of various capital market indices to prices of commodities like gold, oil, etc, to various economic statistics, investment strategies and so on. The next in the list of rituals to be performed is the publication of consensus estimates based on the foregoing predictions and finally the consensus are laid on the table of investment committees and all other bodies with similar attributes.
Are these “crystal balls” for real and if yes, then what are they made of, and finally, are they any good at doing their job – to learn more, pick up a copy of “Black Swan” today.
As the title of the book suggests, “Black Swan” is built around “improbable events” that tends to have huge impact and remains largely unpredictable. The central argument of the book is that though we humans fare miserably in predicting events that have a material impact on our lives (one facet or other), we still joyfully (or rather erroneously) continue to predict them and more hazardously, use these predictions in our planning, decision making, etc.
Along with a long list of examples he cites to prove human failure at predicting (and that too by huge margin), NNT also analyses the process of predicting and points out the apparently ordinary looking mistakes, which costs us big. For instance, consider his take on the limitation of induction process in making use of past data for predicting future:
“Consider a turkey that is fed every day. Every single feeding will firm up the bird’s belief that it is the general rule of life to be fed every day by friendly members of the human race “looking out for its best interest,” as a politician would say. On the afternoon of the Wednesday before thanksgiving, something unexpected will happen to the turkey. It will incur a revision of belief – adaptation of Bertrand Russell’s original example using chicken. What can a turkey learn about what is in store for it tomorrow from the events of yesterday? A lot, perhaps, but certainly a little less than it thinks, and it is just that “little less” that may make all the difference – the history of a process over a thousand days tells you nothing about what is happening next. The naïve projection of the future from the past can be applied to anything.”
“Consider that the feeling of safety reached at its maximum when the risk was at the highest! But the problem is even more general than that; it strikes at the nature of empirical knowledge itself. Something has worked in the past, until well, it unexpectedly no longer does, and what we have learned from the past turns out to be at best irrelevant, at worst viciously misleading.”
“Well, the problem in projecting from the past can be even worse than what we have already leaned, because the same past data can confirm a theory and also its exact opposite! If you survive until tomorrow, it could mean that either a) you are more likely to be immortal or b) that you are closer to death. Both conclusions rely on the exact same data. If you are turkey being fed for a long period of time, you can either naively assume that feeding confirms your safety or be shrewd and consider that it considers the danger of being turned into supper.”
NNT also discusses at length the common mistake of “anchoring” we all commit at one time or other. Consider this:
“We cannot work without a point of reference. This is no different from a starting point in a bargaining episode: you open with a high number (“I want a million for this house”); the bidder will answer “only eight-fifty” – the discussion will be determined by that initial level”
Another aspect dealt extensively is the assumption of normal distribution (or Gaussian distribution) in the world of finance, which he, like any other sane human disapproves vehemently. Empirically, assumption of Gaussian Distribution holds no water when it comes to matters like currency, stocks, commodities, or for that matter any article that is traded. Consider this shocking example:
“The Reichsmark (as the German currency was previously called) went from four per dollar to four trillion per dollar in the space of a few years during the 1920s, an outcome that tells you that bell curve is meaningless as a description of the randomness in currency fluctuations”
One can cite n number of events where the assumption of normal distribution has been proved wrong, time and again. Be it crash of 87, LTCM, subprime, etc. and still SD, with its underlying assumption of Gaussian distribution, has not been discarded as a measure of calibrating financial risk.
And his final advice:
“What you should avoid is unnecessary dependence on large-scale harmful predictions – those and only those. Avoid the big subjects that may hurt your future: be fooled in small matters, not in the large. Do not listen to economic forecasters (they are mere entertainers), but do make your own forecast for the picnic. By all means, demand certainty for the next picnic; but avoid government social security forecasts for the year 2040”
The book deals in a lot of other interesting subjects that I have not covered in my review – like our faith in experts, sanctity we attach to financial theory and also, unwarranted rumination into history, philosophy, etc. The initial chapters are a bit drab and possible turn-off but the latter chapters are extremely indulging and will make up for the initial pain.
A book not to be missed!
Author(s): Nassim Nicholas Taleb (NNT)
Rating: $$$$$
Comment: A book for all, right from members of investment committee to entry-level analysts down the line
Author’s Bio (from the book’s jacket or elsewhere):
Nassim Nicholas Taleb has devoted his life to immersing himself in problems of luck, uncertainty, probability and knowledge. Part literary essayist, part empiricist, part no-nonsense mathematical trader, he is currently taking a break by serving as the Dean’s Professor in the sciences of Uncertainty at the University of Massachusetts at Amherst. His last book, the bestseller Fooled by Randomness, has been published in twenty languages. Taleb lives mostly in New York
Review:
This is the time of the year when every brokerage house, market experts, new media, etc, wipes the dust off their proverbial “crystal ball” and spell out their prediction for the next year. The predictions range from value of various capital market indices to prices of commodities like gold, oil, etc, to various economic statistics, investment strategies and so on. The next in the list of rituals to be performed is the publication of consensus estimates based on the foregoing predictions and finally the consensus are laid on the table of investment committees and all other bodies with similar attributes.
Are these “crystal balls” for real and if yes, then what are they made of, and finally, are they any good at doing their job – to learn more, pick up a copy of “Black Swan” today.
As the title of the book suggests, “Black Swan” is built around “improbable events” that tends to have huge impact and remains largely unpredictable. The central argument of the book is that though we humans fare miserably in predicting events that have a material impact on our lives (one facet or other), we still joyfully (or rather erroneously) continue to predict them and more hazardously, use these predictions in our planning, decision making, etc.
Along with a long list of examples he cites to prove human failure at predicting (and that too by huge margin), NNT also analyses the process of predicting and points out the apparently ordinary looking mistakes, which costs us big. For instance, consider his take on the limitation of induction process in making use of past data for predicting future:
“Consider a turkey that is fed every day. Every single feeding will firm up the bird’s belief that it is the general rule of life to be fed every day by friendly members of the human race “looking out for its best interest,” as a politician would say. On the afternoon of the Wednesday before thanksgiving, something unexpected will happen to the turkey. It will incur a revision of belief – adaptation of Bertrand Russell’s original example using chicken. What can a turkey learn about what is in store for it tomorrow from the events of yesterday? A lot, perhaps, but certainly a little less than it thinks, and it is just that “little less” that may make all the difference – the history of a process over a thousand days tells you nothing about what is happening next. The naïve projection of the future from the past can be applied to anything.”
“Consider that the feeling of safety reached at its maximum when the risk was at the highest! But the problem is even more general than that; it strikes at the nature of empirical knowledge itself. Something has worked in the past, until well, it unexpectedly no longer does, and what we have learned from the past turns out to be at best irrelevant, at worst viciously misleading.”
“Well, the problem in projecting from the past can be even worse than what we have already leaned, because the same past data can confirm a theory and also its exact opposite! If you survive until tomorrow, it could mean that either a) you are more likely to be immortal or b) that you are closer to death. Both conclusions rely on the exact same data. If you are turkey being fed for a long period of time, you can either naively assume that feeding confirms your safety or be shrewd and consider that it considers the danger of being turned into supper.”
NNT also discusses at length the common mistake of “anchoring” we all commit at one time or other. Consider this:
“We cannot work without a point of reference. This is no different from a starting point in a bargaining episode: you open with a high number (“I want a million for this house”); the bidder will answer “only eight-fifty” – the discussion will be determined by that initial level”
Another aspect dealt extensively is the assumption of normal distribution (or Gaussian distribution) in the world of finance, which he, like any other sane human disapproves vehemently. Empirically, assumption of Gaussian Distribution holds no water when it comes to matters like currency, stocks, commodities, or for that matter any article that is traded. Consider this shocking example:
“The Reichsmark (as the German currency was previously called) went from four per dollar to four trillion per dollar in the space of a few years during the 1920s, an outcome that tells you that bell curve is meaningless as a description of the randomness in currency fluctuations”
One can cite n number of events where the assumption of normal distribution has been proved wrong, time and again. Be it crash of 87, LTCM, subprime, etc. and still SD, with its underlying assumption of Gaussian distribution, has not been discarded as a measure of calibrating financial risk.
And his final advice:
“What you should avoid is unnecessary dependence on large-scale harmful predictions – those and only those. Avoid the big subjects that may hurt your future: be fooled in small matters, not in the large. Do not listen to economic forecasters (they are mere entertainers), but do make your own forecast for the picnic. By all means, demand certainty for the next picnic; but avoid government social security forecasts for the year 2040”
The book deals in a lot of other interesting subjects that I have not covered in my review – like our faith in experts, sanctity we attach to financial theory and also, unwarranted rumination into history, philosophy, etc. The initial chapters are a bit drab and possible turn-off but the latter chapters are extremely indulging and will make up for the initial pain.
A book not to be missed!
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1 comment:
Just when i was wondering about the black swan book, i get to know your blog. it is very nice to see such a neat blog with so many great book reviews.
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