Taleb and Business Ethics

Nassim Nicholas Taleb first rose to fame with his 2007 book Black Swan, the title of which refers to rare Black swan 1events. Northern hemisphere inhabitants took the whiteness of swans as a key defining feature of them, only to discover that New Zealand and Australia are home to black swans – a thoroughly unexpected and unpredicted eventuality. Black Swan outlines many of the ways our modes of thinking simply break down and fail when it comes to rare events – in attempting to predict them, in efforts to explain them after the fact, and finally, in trying to plan for them in the future in order to mitigate their worst affects.

Taleb has made a lot of his money by noticing when markets are fragile and betting against them. He became financially independent – meaning he made enough money to live on for the rest of his life – due to the market crash in 1987. He also made money from the drop in the Nasdaq in 2000, and he was one of the few people warning of the likelihood of the real estate crash in 2008, a black swan (there had never been such a crash before), and made money by shorting the market (betting against it). All this has added to his general credibility and gave credence to his criticisms of the financial world’s failings on the topic of risk. One of Taleb’s principles is to have “skin in the game;” meaning, putting his money where his mouth is. If he says the market will go down, he withdraws his money, or shorts the market and, if he claims the market will rise, he invests his money, and goes long on the market. On the big negative events, he has been right. He would like all people, as a matter of ethics, giving advice or making market predictions, to follow suit and to play with their own money. Otherwise, it is just empty talk. Those who go bust, can safely be ignored in the future.

Taleb has an MBA, is a former options trader and risk analyst, and is a mathematical statistician. He is friends with Nobel Prize winners and eminent scientists like Benoit Mandelbrot, famous for his work on fractals. He has a big ego and does not sugarcoat his criticisms of those he thinks jeopardize the economy and ruin people’s lives by making the economy more fragile, and by those who make incorrect financial predictions. These erroneous predictions cost people money when they subsequently rely upon these forecasts. Worst of all, is that the false predictors pay no vocational or financial penalty for being wrong. This topic and its ramifications are discussed at length in the book.

Business schools teach many things their professors know to be false. They do this in order to retain “accreditation.” Accreditation is necessary for those schools, and students attending those schools, to receive various forms of state and federal support. This is motivated by the understandable desire to stop unqualified persons and nonsense institutions from offering “business” degrees and scamming the government and thus defrauding the taxpayers. Unfortunately, the consequence is that business schools end up lying to their students and misleading them in order to stay open. This course will expose some of these lies so that business students have a better idea about what is true and what is false. This is particularly important in the area of financial advice, which is something that those trained in accounting might become involved with at some point in their career. It is immoral, for instance, to, in particular, give elderly people financial advice based on lies. If they lose all their money, they are unable to earn it back, as they are no longer being employed.

Part of the problem is that business school professors are not trained in science and mathematics. As such, they are prone to being fooled by graphs and statistics. It is just not their area. But, as just mentioned, they often teach subjects like “risk assessment,” and they teach the employment of various mathematical “models” used to try to predict the stock market, even when they know they do not work, simply because those models are in fact used by the financial industry and “risk assessor” is an actual job for which business schools are supposed to train students.

Taleb 2Concerning these models, after Taleb has categorically proven that they do not work, he has had people come up to him and claim, “Yes, they don’t work. But they are better than nothing!” Taleb’s reaction is incredulity. Following a model you know does not work, is more foolish and worse than not following such a model. If someone claimed that having a parachute that does not work is better than not having a parachute, that person is wrong. Having a parachute that does not work will give someone flying a false sense of security (at least I have a parachute!) causing him to act in even riskier ways. He may feel calmer and safer, which might be nice, but someone should only feel as calm and safe as the situation warrants. Feeling calm and safe when faced with a lion, for instance, is pathological. Someone who is wearing a parachute he thinks functions is more likely to board a plane he would not otherwise risk flying in.

There is a recently retired professor of engineering at Queens University, Canada whose field was “turbulence,” which exists at the border of chaos and order, and involves very complicated mathematics and computer modeling. He has a strong hatred for Taleb’s writings. When asked why, he explained that he found Taleb arrogant. Taleb is arrogant. However, this ad hominem attack on the person, is absolutely irrelevant to Taleb’s arguments. Arguments, which involve premises used to support conclusions, do not depend in any way on the character of the arguer. Whether someone is morally admirable or not is irrelevant to evidence. Evidence stands or falls on its own. Most importantly, he found Taleb’s arrogance unwarranted. Most tellingly, and what should be of extreme interest to business students of all stripes, is that he found Taleb’s criticisms so painfully obvious that the professor felt Taleb should receive absolutely no credit for making them. The professor stated that the mathematical models used by those in the business world are being completely misemployed and that fact is obvious to any idiot. The trouble is, the professor’s definition of “idiot” in this context is someone with a thorough grounding in advanced mathematics, statistics, probability theory, and physics – the bread and butter of engineers – and it would have to be someone who was also familiar with the world of finance.

Taleb’s innovation is to combine the expertise of the typical engineer with experience in and intimate knowledge of financial markets. Most engineers will know nothing about the stock market nor what goes on in business schools, and most business students know nothing about engineering. Innovation and creativity are often the product of someone coming in from the outside with a new perspective. This is not quite the case with Taleb, since he started out as an options trader, but he studied mathematical statistics for the pure love of it and earned advanced degrees in it, and that is what made the misuse of statistics in the financial markets so clear to him. He also discovered while working as an options trader that traders do not use the heuristics taught by business schools; at least only newbies who do not know what they are doing do that. And such people risk blowing up as a result.  Traders have their own non-theoretical ad hoc rules of thumb, that work better than these business school “systems.” Business schools have come in after the fact and tried to systematize what the traders are actually doing – not particularly successfully. The engineering professor feels that he could have pointed out the same misuse of mathematics and statistics, since Taleb’s points are, to him, painfully obviously true – except the professor has no interest in nor expertise in financial matters; which means he could not have contributed in this way. It also seems strange to hate someone with whom you vociferously agree. It seems mostly a matter of resentment, jealousy, and matter different ideas about style.

As Taleb points out, there is no such thing as a generic “expert.” People, if they are expert in anything, are expert in some fairly narrow domain. And we are only truly competent to judge things in those domains. As a philosopher, I frequently come across biologists trying to discuss ethics, or neuroscientists attempting to make definitive statements about consciousness, and I frequently feel like rolling my eyes. I cannot assess their biological claims, nor their specific neuroscience assertions, but once they step into philosophical areas, specifically, ethics and consciousness, they trip over their own feet and contradict themselves. Neuroscientists will even confuse cause and effect. One of them claimed, for instance, that we like helping people because our pleasure centers light up when we help people (at least some of the time.) But, of course, our pleasure centers get activated because we enjoy helping people. The pleasure center being activated is the effect of doing an enjoyable activity. The activity is the cause.  If I find what someone is saying in conversation to me interesting, I might derive some pleasure from this. But, I do not find it interesting because my pleasure center is activated. It is the other way around. My pleasure center lights up, if at all, because I find it interesting. The neuroscientist is predisposed to thinking in terms of bottom-up causation (the physical causing the mental) and sometimes cannot wrap his mind around top-down causation (the mind affecting the brain). Since his colleagues are likely to suffer from the same problem, he does not get corrected.

Thinking 3An example of an expert in one field coming in and bringing insights to another field occurred with one of Taleb’s friends, Daniel Kahneman, winner of the Nobel Prize in economics, and author of Thinking Fast and Slow, his most famous book. Kahneman’s real area of knowledge is social psychology, specifically organizational behavior. His collaborator Tarsky would have been joint winner, but Tarsky had died already. After he won the Nobel Prize, Kahneman was invited to address a firm specializing in investing in the stock market; an anecdote he relates in Thinking Fast and Slow. When invited, Kahneman replied that he had no real knowledge of economics, not being an economist, despite his Nobel Prize. The people who invited him said they did not care, they wanted him to come and give a speech anyway.

So, Kahneman decided if he was going to address stock brokers, he had better acquaint himself with their business, so he asked the company whether they would give him a record of every single transaction the firm had undertaken in the last eight years. Kahneman imagined that they would regard this information as secret and say no, instead they provided him with the information. As an expert statistician, he then subjected the data to a thorough analysis. He discovered beyond doubt, proving it mathematically, that the results of the stock brokers’ transactions were no better than chance and were effectively random with regard to outcome. As a consequence, every year some broker or other would be the most “successful” and get some kind of bonus, but, it was someone different each year. One sign that something is the result of chance and is a fluke, rather than the product of skill, is someone’s inability to reliably repeat his success.

Warren Buffett did not make his money by predicting which stocks would go up, and which would go down.Coin 4 He did it by investing in companies that he knew a great deal about personally, and admired the way they were managed, and who was managing them – not by looking into a crystal ball and buying and selling stocks in an effort to get rid of poorly performing stock and betting that some other stock would perform better in the future. Buffett, in the last few years, made a bet that over a ten-year period, an index fund would do better than a highly managed hedge fund. An index fund is one that distributes investments widely in an effort to try to track the market, rising and falling with the market, rather than trying to outperform the market. One advantage of index funds is that they are cheap to manage and so the fees are lower. From any profit made by hedge funds, the relatively high fees of the hedge fund managers must be deducted. Protégé Partners LLC took his bet – with the winner getting a million dollars. The hedge funds were up 22% after nine years, while the index funds were up 85.4%. The final results after ten years remained the same. So, Warren Buffett, the most famous investor who ever lived, is decidedly not a believer in predicting stock behavior.

Craps 5You would think that the stock brokers would be dismayed; perhaps quit their jobs, or contemplate killing themselves. After all, Kahneman had proved to them conclusively that they were deluding themselves. Instead, they continued to smile and nod as he spoke. Stock brokers had imagined that they were highly skilled. They spent hours each day, and potentially decades of their lives, poring over facts and figures and deciding which stocks to buy and which to sell. It turns out that they might as well have been flipping a coin each time; literally. One thing that should have tipped them off, perhaps, is that each time they decide to buy or sell, some other stockbroker was reaching the opposite conclusion. Genuine “experts” would not disagree over every single decision like that. Buying stocks that are about to go up in price, and selling stocks that are destined to go down in price, is not something that anyone can become expert in. For some things, there just is no relevant data to reach an informed decision. It does not matter how much experience a person has, that experience will not help them predict the future in this way. It is exactly comparable to playing craps (throwing two dice and guessing what number will come up.) Since the result is effectively random, there is no way to predict it. It is a matter of luck and nothing else. It is possible to be right, but not as the result of superior knowledge; just bonne chance. As Kahneman was being driven home after the talk by one of the attendees, the broker said, “Well, I have devoted my life to this firm, and you can’t take that away from me.” And Kahneman thought to himself, “I just did.”

Chaos Theory is an area of science dealing with chaotic phenomena. Included in that category are the weather, the stock market, political events, and inventions. The butterfly effect governs these phenomena. This is where tiny little variations in, for instance, temperature, or wind velocity – a butterfly flapping its wings in Brazil, will, over time amount to enormous differences in the future, such as a hurricane in the Caribbean. The further in the future one tries to foresee, the more these effects build up. That is why the weather remains a mystery beyond just a few days. Even near-term forecasts are frequently wrong. The stock market is influenced by purely subjective things like fear and hope. People merely having an illusory Chaos 6“confidence” increases demand and pushes the price of stocks up. If people run scared, the demand slumps and prices plummet, sometimes based on nothing at all. It can be merely rumor. The weather can affect the stock market; one unpredictable thing affecting another. New inventions like Twitter and Facebook can affect the stock market with unforeseen results. Most major inventions were the results of accidents, which of course cannot be predicted. And, if it were possible to predict future inventions, these predictions would be pretty close to being the inventions themselves. A key part of inventing anything is coming up with the idea. The scientist who invented lasers had no idea about their future applications. His colleagues made fun of him for his fascination with pretty lights.

When students are asked “Who has a better chance of predicting whether the stock market will finish up or down at the end of any given day? You or a professional stock broker?” Students almost invariably say the stock broker. They are absolutely wrong. Any random student is equal in ability to the stock broker because predicting the market is not something someone can be an expert in and practice does not improve your performance. Again, the stock market is a random phenomenon. Kahneman proved that stock brokers could not beat chance. In exactly the same way, the professional craps player is no better at predicting the roll of the dice than anyone else. This can be contrasted with poker or blackjack – games that combine luck and skill.

One factor that might make students wish to defer to professional stock brokers is that with activities that are easy, like driving a car, we tend overestimate our abilities. Most people think they are an above average driver – which is mathematically impossible. But when asked to compare ourselves with other people about something difficult like calculus, then our overconfidence disappears and we tend to underestimate our abilities vis-à-vis other people.

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Most people are awful at calculus. Maybe the questionnaire should say: “You are terrible at calculus. How terrible do you think you are compared with all the other people who are also terrible at calculus?” You know you are no good at predicting stock prices, so you imagine that an “expert” will be better, but there are no “expert” stock brokers just as there are no expert craps players. Predicting the stock market is not just difficult, it is impossible.

One of the annoying things about stock brokers is that their job is one where they cannot lose their own money; they can only get richer. Stock brokers subtract a fee for their services of buying and selling stocks on your behalf regardless of whether the stocks rise or fall in value. Brokers buy and sell stocks for the common man. A trader buys and sells on behalf of his firm. Traders can get in serious trouble because their firm suffer the consequences of their selling and buying decisions, whereas brokers are protected.  A French trader invested $7.2 billion dollars without the knowledge of his superiors and things went horribly wrong. There had to be an investigation into how he had been allowed to invest so much.

From the NYT:

A French bank announced Thursday that it had lost $7.2 billion, not because of complex subprime loans, but the old-fashioned way because a 31-year-old rogue trader made bad bets on stocks and then, in trying to cover up those losses, dug himself deeper into a hole.

Société Générale, one of France’s largest and most respected banks, said an unassuming midlevel employee who made about 100,000 euros ($147,000) a year identified by others as Jérôme Kerviel managed to evade multiple layers of computer controls and audits for as long as a year, stacking up 4.9 billion euros in losses for the bank.

If the risky investment pays off, such a person might be richly rewarded. Periodically, traders will “blow up,” lose everything, and get ejected from the system, never to be employed again.

If any stock broker could consistently predict whether the market would end high or low on any given day, he would quickly become the richest person alive. But, such an ability would also have paradoxical effects on that very market, negating this ability. Once other people realized what was going on, they would watch this broker with eagle-eyed interest. If he sold, we would all sell. If he bought, we would all buy. However, this would mean no one would be buying when we sold, and no one would sell when we wanted to buy. No one could make any money, including the omniscient broker. Even if just the majority followed the mysterious individual, then as soon as he signaled his interest in selling, the price of the stock would drop instantly, and when he wanted to buy, the price would go up, making it impossible for him to profit.

So, the paradox would be that having the ability to consistently make money off the stock market, would make it impossible to have that ability. If you could do it, you could not do it. Being right concerning conditions of luck means always being in the realm of flukes. Hitting a free throw once means nothing. Doing it thirty times out of thirty is skill. With chaotic phenomena, it is all flukes and no skill.  Again, it can be asked who has a better chance of predicting end of year employment figures? You or an economist? Neither. If you do not believe this, check the record of economists in making precisely this prediction. Economists do not know what variables may occur that will affect this outcome. You know that you are in no position to guess employment figures, but you might not realize that neither is anyone else. You are just as good as anyone else on this topic – namely absolutely terrible. You have no ability at all to know what the figures will be. The number of times that reporters have said “Contrary to economists’ predictions, employment figures were higher (or lower) than expected” is ridiculous. In fact, it is the norm for this to be the case, which means that paying anyChicken 8 attention to economists’ predictions about employment figures is stupid. Do not fall into the trap of saying “But, it’s better than nothing.” It is not better than nothing because it induces a false sense of confidence, thinking you know something when you do not, and any decisions based on this unreliable activity will also be unwarranted. It is epistemologically identical to watching the scratchings of a chicken and saying “If he pecks on the left first, we do plan A, and if he pecks on the right, plan B.” The key thing, as Taleb points out repeatedly, is to check the predictor’s record of success. Economists have no record of success on this topic.

A similar question could be asked about the future of tax law.  An accountant who studies tax law has no more data to help him make predictions about the future of tax law than anyone else. It is possible to be an expert about tax law. It is not possible to be an expert on what tax law will be in the future. Politicians make such decisions and they are influenced by public opinion, what they think might benefit their parties and themselves, and which party gains control of the presidency, Senate, or House of Representatives, and then any deals or compromises made between all these entities. A student is likely to think that he is worse than an expert accountant at predicting tax law changes partly because he is very aware that he has no such ability, but neither does anyone else. There are no experts on the future. Studying taxes does not make you clairvoyant about the future. If you disagree, show me the evidence of people who are knowledgeable about taxes consistently making correct predictions.

Taleb’s response to this kind of uncertainty is to employ positive optionality – make bets with big upsides and little downside – a lot to gain, a little to lose, knowing you are making a bet.

Homo 9Another strange falsehood in economics is homo economicus as a model for the consumer: the perfectly selfish and rational individual making economic decisions based on purely rational estimates of what is in his narrow self-interest.[1]

Nearly all thinking, and certainly theorizing, requires simplification. Thoughts that leave nothing out; that are as complicated as, say, the physical world, would simply reproduce the world one to one. The model of the world would simply duplicate the world. It would be another world of the same size and complexity. However, if the model is too inadequate, like homo economicus, it can make estimates about how people are likely to behave worse than completely untutored intuition based on ordinary experience. The person comes out dumber than if he had learned nothing.

Rationality is restricted to the few. It is estimated only ten percent of Americans would qualify. So, homo economicus would be a rare bird indeed. Secondly, when it comes to buying things, a lot depends on fashion and mimesis (copying other people) which is not a rational reason for buying something – that ties in to the fallacy of popularity. The most popular music, food, clothes, TV programs, cellphones, is not thereby the best.

Thirdly, human beings are not exclusively selfish. Parents love children, children love parents, friends love each other, etc. Complete selfishness would mean the end of humanity. Sociopaths love no one and they are tormented by boredom. Nothing has much point without other people. Even learning things can become pointless, depending on what it is, if it is not possible to share what is learned with others.

Philosophers can take a lot of blame for having influenced the ideas involved in homo economicus. British philosophers in particular tended to love the idea that hedonism, the selfish pursuit of pleasure, is the only driving force in human behavior, since they usually liked “science” and dislike the complexity and frequent mystery of human motivations. They regarded people as a kind of soulless windup doll and wanted as simple an attribution of human interiority as possible. In truth, they would have liked to dispense with it entirely, but they needed some reason for why we ever get up off the couch. The obvious counterexample to the claim of selfish hedonism is all the things we do for other people. The hedonist claims that since all behavior is driven by the pursuit of pleasure, helping other people is due to the fact that people enjoy doing so. Pleasure is the driver. However, the word “selfish” means exclusive concern for one’s own welfare. If someone enjoys helping other people, he is, by definition, not selfish. You have to actually like helping other people to derive any pleasure from it. Claims are only assertions about facts when they could hypothetically be wrong. Only tautologies can never be wrong because they are true by definition – the most famous example being “all bachelors are unmarried men.” There can be no exceptions. The claim that all behavior is driven by pleasure and therefore, in the end, selfish, admits of no possible counterexample. Hence, it is not a claim about the world at all but is supposed to be true by definition. It is not.

There is a regular cottage industry debunking homo economicus. It has made whole careers. Richard Thaler even won a Nobel Prize for challenging it, which seems ridiculous. There is a famous and easy experiment that is well-replicated called “The Ultimatum Game” where someone is offered one hundred dollars. The condition is that he only gets the money if he offers some of the money to another person. If the offer is rejected, neither gets anything. The perfectly rational and selfish point of view about this is supposed to be that the other person should accept any amount, no matter how low. If someone offers you one dollar for nothing, you should accept it. You will be one dollar richer, having done no work. Refusing the money, no matter how little, is irrational. Accept it! However, most people do not behave in this manner. The other person knows that you too are getting the money for free. If they decide you are being too selfish, greedy, and ungenerous, they will typically refuse the offer in order to teach the offerer a moral lesson. The lop-sided offer is regarded as unfair. Typically, anything below about thirty dollars is rejected. This behavior is universally regarded as “irrational” by the people who write about it in the context of the experiment, as though there is something irrational about worrying about fairness. But, this definition of “rational” equates rationality with amorality, which is a very odd thing to do. Since we are social creatures who live in communities and moral considerations govern our interactions with people we depend on, not taking morality into account would be Chimps 10the irrational thing to do. Try it! See how long you last.  The fact is that people think in moral categories, and a high percentage of people are willing to forego free money in this context. Chimpanzees who also understand and employ the notion of fairness/reciprocity behave in the same way.[2] People include moral categories in their decision making. Who knew? Everyone other than economists.

Quantifying risk

Risk assessors who offer quantitative assessments of future risks are charlatans. It is possible to extrapolate from past data when it comes to types of surgeries or types of diseases. It is reasonable to state that there is a 5% fatality rate for a certain kind of surgery, or a 25% survival rate for a particular kind of cancer treatment based on past cases. Of course, these are still broad generalizations. What the patient would really like to know, what are my chances of dying or surviving, is not known. A church-going health-fanatic patient with a very positive attitude, lots of social support, and high compliance to medical instructions, is likely to have a different outcome to someone with the opposite characteristics. But some kind of meaningful numbers can be provided to the patient about patients in general in those circumstances. When it comes to making predictions about the future involving chaotic events, and not things like laws of physics, then there is no data to extrapolate from. There is particularly no meaningful data concerning black swans; one off, or extremely rare JFK 11events. This is related to the informal fallacy in logic called “hasty generalization.”  The world avoided nuclear conflagration after the Cuban Missile Crisis – when there was a nuclear standoff between the USA and the USSR in the early 1960s – therefore the next time there is a standoff between the Russia and the USA, it will end the same way with the same favorable result, is unwarranted. If something has happened hundreds or thousands of times, a meaningful prediction might be possible if the events are not chaotic or random. If something has never happened before then there is nothing on which to base a generalization. Michael Burry, depicted in the movie and described in the book The Big Short about the housing crisis of 2008, consistently shorted (bet against) the housing market. His superiors put more and more pressure on him to stop, because it costs money to short the market. Their argument? The housing market has never failed before therefore it will not fail this time. That is not a reasonable argument. They have no evidence one way or another. Many rare events are one off affairs. By the same logic, 9/11 should not have happened. It had also never happened before. When looking to extrapolate from “data” with black swans, there is no data.

As Taleb argues, when it comes to black swans, “data” of the kind that can be graphed, for instance, is meaningless. What Michael Burry had done was to read thousands of pages of documents concerning how mortgages were being bundled – and they were intentionally bundled in an obfuscatory fashion; mixing reasonable and legitimate mortgages with ones where the mortgagee had almost no chance of paying the loan off. These documents were as interesting to read as the “Terms and Conditions” contracts we all sign to use software and web services – i.e.,

Dilber 12

not at all, so no one else read them. Burry discovered that the housing market at the time was built on a house of cards. That it was fragile – to use Taleb’s term – meaning prone to catastrophic collapse, that is, over-exposed to risk. People who are overly in debt are fragile. If you are in debt, and barely paying off your minimum credit card payments, rent, food, etc. then you are fragile. If you have six months of income saved in case of unemployment, etc., then you are robust. Meaningful risk assessment is about noticing fragility and taking steps to minimize it. You are fragile (over-exposed to risk) if all your stocks are invested in just a few companies. You are less fragile if your risk is spread out relatively evenly across multiple companies and investment types.

A certain podcaster with a background in economics likes to assign percentages to various eventualities. He will say “I estimate that X, this one off event, has a 5% chance of happening,” such as a new American Civil War. This is both meaningless and misleading. Karl Popper, the philosopher of science, stated that a key feature of scientific theories and assertions was “falsifiability.” If an assertion is made that cannot be proven wrong, no matter what, then the assertion is not scientifically valid. When the podcaster makes his “5%” prediction, what data could possibly either confirm or disconfirm his statement? If the event happens, he will say “Well, the event fell within that 5% chance.” If it does not happen, he can say “Well, I said that there was a 95% chance of it not happening.” His prediction is not based on past experience, because the event has never happened before, and it cannot be falsified. Thus, his prediction is meaningless. And where does “5%” figure come from with one-off events in the first place? Could it be 5.5%, 16%, 32.3%? The “risk assessor” is just making the numbers up. The predicted event happening or not happening does not provide information to intelligently and rationally alter the numerical figure.  Thus, making quantitative guesses about black swans is not something the risk assessor can get better at. He receives no feedback whether his quantitative analysis was correct or not. It could be compared to shooting free throws in basketball and never finding out whether the ball is going in or not. There is no way to improve. So, there is no data to base the 5% on in the first place, and no data to verify or reject the figure even after the fact. Both problems are terminal.

When trying to predict who will win the US presidency, pundits will say “So-and-so has a 95% chance of winning.” Under what conditions could this prediction be proven wrong? Some such pundits got very upset when people accused them of being wrong when the other candidate won. They said, “You don’t understand how statistics work. When something happens that we said was highly unlikely happens, that does not mean we were wrong. It just means the rare event, the possibility of which we accounted for, happened.” The problem? Their prediction is unfalsifiable because they hedged their bets. They are pretty close to simply contradicting themselves. They say “X will win (and then name an arbitrary percentage), or Y will win” (and then name an arbitrary percentage). With contradictions, you can never be wrong. X wins. See I was right! Y wins. See I was right! This fails Poppers test of falsifiability. The predictor claims to be right either way and there is no way to test the prediction, or even to track the record of the predictor. Effectively, they are not claiming anything meaningful at all. That business about putting a number to likelihood is baloney. Again, it only makes sense when there has been a long history of exactly those kind of events to extrapolate from. When something has never happened before, like a housing crisis, or a particular individual winning the US presidency, then no predictions using quantitative degrees of certainty can be made. But, it is possible to identify and note fragility. Fragility does not tell you when something will happen – which is why Michael Burry had to wait so long – but it does note over-exposure to risk this makes it possible to minimize that exposure. It is helpful, perhaps, not to call this a “prediction.” Burry is not looking into the future. He is looking at over-exposure to risk right now and pointing it out. It is not different in principle from a car mechanic telling you you need to service your brakes. It is about risk, not foretelling the future, though dire consequences of ignoring the advice are likely.

13A falsifiable election prediction is “I think X will win.” Or, “I think Y will win.” At least you are not saying X or Y will win. We can then track the record of such predictions. There will, however, still be a high degree of luck involved and it would be unwise to put much at stake on guesses.

We know that making predictions makes people take more risks even when the person knows that the forecast was fictional. Quantifying risk is a prediction.  Quantifying something means using mathematical models.  Mathematical models work for casino-style gambling because all the variables are known in advance. Casinos only need to “win” 52% of the time to make money and they can determine this with absolute precision.  They do not need to predict the outcome of any particular gamble. They just need to know the odds in the long run. Short term losses mean nothing to them. If someone games the system and changes the odds by counting cards, etc., then this becomes a threat and the casino will ban them. Crucially, all casinos who stay in business also put an absolute limit on the size of bets to restrict their losses.

The idea that a good analogy can be made between stock markets and casino gambling Taleb calls the Ludic (game-playing) Fallacy. Casinos determine upper and lower limit to how much money can be won or lost. This makes it amenable to the application of Gaussian bell curves.  Human height is nicely distributed in this way; with a mean, and with deviation from the mean being exponentially rare. There are a similar tiny number of five foot men and seven foot men – in fact, seven footers will be even rarer, since 5’9” is the average US male height. But, if this upper limit is thrown out, and someone could be a mile tall, or a 50 billion light years tall, 14then the bell curve would be inapplicable. Trying to use it would radically underestimate the likelihood of huge deviations from the mean. And the misapplication of bell curves actually happens with black swans. Since black swans live in the “tails” at either extreme of the bell curve, they cannot be distinguished from “noise;” randomness. There are too few data points. What having upper limits does with casino gambling, together with having all the games known in advance with their precise rules of play, and thus knowing all the possible “moves,” is it makes this kind of gambling absolutely calculable and amenable to statistical analysis. The exact odds can be ascertained. In the world of finance, people can come up with brand new things like credit default swaps (in 1994), or whatever.

With stock markets, the variables are not known. The Harvard professors who invented the use of mathematical models for predicting the stock market started a company called LTCM – Long Term Capital Management in the 1990s. The company went bust in 6 months because of unpredicted events in Russia. The professors claimed it was not their fault because the models did not account for those events. It was not because there was something inherently wrong with their models, they said.  But, there is something wrong with both. Their models are vulnerable to rare and unpredictable events which cannot be quantified, with potentially catastrophic results. Therefore, no mathematical models can predict the stock market, which is a provably chaotic phenomenon. Saying “But we didn’t know that would happen” does not save you from the conclusion that it is unwise to rely on models that cannot take account of things no one knew were going to happen. People need to know what they do not know. They need to know that they are vulnerable to catastrophic black swans that are impossible to model, and to act accordingly. Thus, it makes sense to take account of fragility, but not to make predictions about such things.

The mathematical models that LTCM used are still being taught in business schools. If they failed the professors who invented them, they will fail you and your clients too. The idea that you will employ them better and more successfully than them is ridiculous.

15

It is not possible to rely on “worst case scenario” predictions. Every “worst case” that has ever happened had never happened before. Therefore, no one predicted it. You cannot assess how high the Nile will flood from the high-water mark from last time.  What percentage of risk would risk assessors have given to 9/11? Upon what evidence and data would they have made that risk assessment? Zero.

The Turkey Problem

The turkey problem is named after Bertrand Russell’s observation that from the point of view of a turkey, the turkey farmer loves him. Every day the farmer feeds him and every day the turkey’s confidence in the farmer’s love grows. The number of data points increases, each instance confirming the turkey’s claim to be loved, at least in the mind of turkey, until the day when the turkey’s expectations proved to be unfounded and he has his head chopped off. Do not be a turkey!

Quantitative analyses are almost necessarily going to suffer from the Turkey Problem. I say the stock market is likely to crash because an area of the economy is fragile. You start checking the data to see if this is true. What you see is no crash 1, no crash 2, no crash 3…etc. I will be wrong, wrong, wrong, wrong. Every time your sample sees no evidence of a crash, you will take this as confirmation that I am wrong. Taleb was wrong, wrong, wrong, about the 1987 stock market crash, until it crashed. He was right just once. Then he was wrong, wrong, wrong, about the early 2000s dip, and wrong, wrong, wrong, about the 2008 housing crisis, and again right just once. The Turkey Problem is very real and very problematic. Taleb was right just four times and wrong countless times, depending on how often outcomes were sampled. The people who bet against Taleb are now poor, or out of the business despite being “right” oh so many times.

  1. Bridge example
  2. Running into the road without looking
  3. Searching for sunken treasure

16An engineer notes that a particular bridge is fragile because of age or structural design problems. But, using your quantitative risk assessment models, every car and truck that passes unscathed over that bridge will be taken as evidence that the bridge is safe. This might go on for years. Every time the bridge does not collapse under the weight of a truck or car, this will be taken as evidence that it is perfectly safe. No amount of sampling will reveal the fragility.  It is possible to identify fragility, but not to predict when exactly the bridge will collapse; what will be the final straw that breaks the camel’s back.  A mother could warn a child to look before entering a busy road. If the child took note of every time he entered the traffic without looking, and tallied the score, he could claim that every instance of entering traffic without looking counted as evidence that it is safe to do so. He would be seemingly “right” countless times while his mother would seem wrong repeatedly.  We know that in fact he is exposing himself to risk each time he runs onto the road without looking, and also that a black swan event, one that has never happened before (to this boy), is inevitable. The “evidence” that he is wrong will come too late to save him. The evidence will be his own demise.

When searching for sunken treasure, the treasure hunters will employ a grid over the area they believe the treasure might be. Each time the hunters examine a square of the grid they will come up empty-handed. They will be “wrong.” But actually, because the search is done systematically, each failure is useful information. Assuming the treasure is there at all, the searchers are narrowing down the area that needs to be explored. Instead of a one in twenty chance, as the number of squares reduce, it becomes a one in ten, then, nine, then eight, etc. chance. They will be wrong perhaps nineteen times out of twenty. They need to be right once.

It is worth memorizing the gnomic sounding phrase of Taleb’s, “absence of evidence is not evidence of absence.” The fact that there is no evidence that the child is in danger in this case does not mean the child is not in danger. Again, the evidence of catastrophe comes too late.  Alan Greenspan, the former head of the Federal Reserve, was quoted just before the housing market collapse as saying that the chance of a collapse happening was effectively zero. He had falsely been regarded by many as some kind of seer and guru up to that point.

The turkey problem involves misunderstanding exposure to rare bad events.  Because of the nature of the economic system, rare events can wipe out all profits made in just one moment. If the economic system was like losing or gaining weight, the increments of change would be small and relatively predictable. Your money would be relatively safe and you would have time to change direction before you lost it all. You could also rely on bell curves. But the economic system exists in Extremistan where a single rare event can destroy all gains.

The result of quantitative risk assessment is to radically underestimate risk. How can you quantify events that you don’t know are going to happen? If you are walking in total darkness near the edge of a cliff, which is better – to know that you do not know where the edge is or to think you know, but to be wrong? What if I do something that makes you more likely to take risks with regard to that cliff? Have I helped you or harmed you?  Nassim Nicholas Taleb is sick of business professors and others saying “But these models are all we have. They are better than nothing.” You don’t “have” anything other than a fiction and thinking you have a reliable map when you do not is much worse than knowing you do not know.  Trial and error is a method of taking small risks with the expectation of being wrong until the time that you are hopefully right.  Trial and error = positive optionality. Small risks mean low downside.  Positive optionality is a way of using uncertainty and unpredictability to your advantage.  It is immoral, however, to set up a system where you repeat all the possible benefits of instability and uncertainty while leaving the risk with someone else. That is the situation with “too big to fail.”

[1] Enlightened self-interest is quite different, and means that being all-consumed with “self” is, paradoxically, not in your own best interest. Generous people who care about other people; family, friends, community, nation, do better, generally speaking, than an egocentric oaf.

[2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3568338/

26 thoughts on “Taleb and Business Ethics

  1. Hi Richard,

    I am an accountant with a casual interest in the stock market and currently in an MBA program, and have mused on the subject of markets myself over at my space relatively recently. Given these qualifications (meager they may be) I can confirm that business professors are not really teaching anything that can prepare students for any specific business career. Or at least, neither the professors at my institution have impressed me nor have my classmates. I get the feeling that a lot of the content I have covered so far could (or should) be undergraduate level, but the race to appeal to the lowest common denominator has caused a majority of the business curriculum to review “basics” so that it can be approachable by literally anyone. My classes, which I take online, are populated by a fairly even mix of:
    – People who just finished their undergraduate studies and believe graduate studies are the natural next step
    – People who have 5+ years of work experience after their undergraduate studies and have hit an arbitrary credential ceiling, limiting their advancement in business (this is me, and I have plenty of company)
    – People who have 20+ years of work experience and want to reinvent themselves

    The professors, for their part, are typically:
    – Failed venture capitalists who are good at talking
    – career educators with limited business experience
    – Successful business people who teach as a side-hustle
    – Retirees who want to make an income in their twilight years

    This is something Smith, Charlton, Bonald, have all recently observed as a phenomenon plaguing education. IF we supposed that these institutions are supposed to produce learned professionals, they are failing. They may have done that at one point, but don’t anymore. If we supposed that these institutions are supposed to produce pieces of paper that cost sixty-thousand dollars or more, they are a huge success!

    Regarding the stock market, to my mind there are three ways of looking at it. Some people look at it as magic–and i don’t mean this in a perjorative sense. In most fictions, Wizards and Mages study ancient magical tomes to hone their craft and become masters. The more powerful wizards have spent the most time studying and practicing their craft. People who view the stock market this way believe it is a science that can be learned, and the fact that it is difficult means that they just haven’t learned it yet. People can be very successful viewing the stock market this way, otherwise we wouldn’t have huge brokerage houses, but Taleb’s observation that success is not repeatable feeds my doubts that it can be considered science.

    The second way you can view it is gambling. As you note, as long as you win a preponderance of your wagers, you’ll be successful. People who view it this way don’t care so much about WHY the market behaves the way it does, but rather care about the outcomes of bets. Professional gamblers, as I understand it, develop frameworks for deciding what to do. If such and such event, then make such and such bet. For stock market speculators, their frameworks tend to be news or regulatory filing driven.

    The third way is to view the stock market as a supermarket. When I walk into the store, I want to buy eggs. I will buy eggs if they are within a reasonably expected price range, but not if the price has swung wildly ($5 for a dozen, sure. $50 for a dozen, maybe not). Sure, I can probably time the market and buy eggs more cheaply. I could probably even buy eggs at the store and then go down the street and sell them to someone for a profit. But the important thing is not the price of the eggs, but the eggs themselves. Likewise, the stock market is the price, but the thing you’re buying is dividend income. Dividend income is a share of the actual productivity of the firm whose stock you have bought, so is slightly more tangible than the stock price itself.

    Again–there’s nothing inherently wrong or right about any of these three ways of viewing the market because people make (and lose) tons of money from all three perspectives.

    Risk assessment–particularly when looking at the stock market–tends to fall under the purview of the Wizard-brokers. There is a whole field of Finance which describes hedging, and how you can structure investments such that no matter the outcome, you win. Gamblers do risk assessment too but it’s structured into their frameworks, they don’t necessarily use financial math to evaluate it. Financial Models are like all models in that they only reflect our current understanding of things. Financial models won’t predict surprising events because surprising events can’t be incorporated into the model until afterwards. So Risk assessment, to a certain extent, means protecting against known risks. It takes creative thinking (and cross-disciplinary knowledge) to anticipate unknown risks. (Donald Rumsfeld somewhere is smiling).

    The bottom line, from what I have gathered from your article and from my own reading and my own observations of business, is that businesses care most about confidence and feeling warm and fuzzy, and they will pursue things that give them that, whether it makes philosophical sense or not. Most financial forecasts are tailored to the tastes and attitudes of the executives for whom they are made, and so are divergent from reality only insofar as they can be considered “optimistic”. A company would fire a pessimistic forecaster, and a “realistic” forecaster doesn’t provide as much of a warm-n-fuzzy as the optimistic forecaster. Taleb can make money being a pessimist because he knows that the business world promotes optimists, and they won’t see black swans coming because they aren’t looking for them.

    • … business professors are not really teaching anything that can prepare students for any specific business career.

      Years ago, we hired a young finance graduate of Haas Business School at Berkeley. It’s a world class institution. He got a really excellent education in finance. Some of his professors had won the Nobel. Six months after we hired him, he came into my office unbidden and said, shaking his head, “I know I learned a ton of good stuff about finance at Cal, but now that I’ve been working in the real world for a while, I can tell that I learned *nothing that I will need to know in order to actually do any job in this field.*”

      We laughed and laughed. My response: “Do you think it is different in any other field?” He replied by laughing even harder.

      There is just no substitute for apprenticeship. One way or another, you have to get through it.

      The Royal Navy of the ancient age had it right: a midshipman started out *on board,* with command authority. At 12. His professor of navigation and seamanship was his captain. The men who taught him how to lead were the experienced seamen in the gun crews under his command. His sea daddy – an elderly sailor, old enough to be his great grandfather – taught him knotting and splicing, and comforted him in his bouts of homesickness.

      • That is very true. I like to joke that my accounting education taught me nothing about the trade, but instead taught me how to talk about accounting so i can ask good questions once in the field.

        I would love to see more apprenticeship in all fields. Internship programs function kind of like that, they are very valuable and very rare.

      • RC: This was very interesting. Thank you.

        KL: A silly note — I noticed a little uneasiness in myself after having read your Royal Navy account. As I read it, I imagined the scenes — with the ship rocking back and forth. Even for such a short period (a few lines), it was enough to induce a different physical state. I’m not given to seasickness, but I noticed the feeling. The mind is an interesting force.

        I also hear voices when reading . . . and I’m startled when I (rarely) discover that the writer was the opposite sex of what I had presumed. How dare Jane Smith impersonate a man in an article, giving herself away only at the end!

      • Man A can teach Man B nothing besides how to be Man A. And that only works if Man B really wants to be Man A. And even then it doesn’t always work. Academics can only reproduce themselves, and can only do this with young men and women who aspire to be academics, and cannot always do this. Aspiration presupposes admiration, and this grows out of charisma. I think this is why coaches are often so much more popular than teachers. Young men want to be a man like Coach, and very definitely not a man like Mr. Snuffly in sixth period algebra. I think this is what Plato means by the eros of education. It only works when the student looks at his teacher and feels, “I will be incomplete until I possess what he has and I lack.” So I think the original academic from the original Academy agrees with you. Find the man who has what you lack and ask him to teach you.

      • JMSmith: New Zealand had no “coaches” to admire. My boarding school had many estimable men who were teachers, one of whom became my pedagogic model. Another, I didn’t want to be like exactly, except to emulate his oddballness/individuality – his honesty, concern, skill, and humor. When I mentioned to an American friend years ago that I admired my high school teachers, he couldn’t imagine it – and I wondered in turn about his incomprehension. Some, at least, were serious, educated, individuals.

  2. Richard, it looks as though you’ve read much of the stuff my own investment firm has published over the years. We abjure predictions of all sorts, and argue that beating the market is effectually impossible over the long run. The way I put it to clients in order to make the notion as concrete as possible: “OK, you want to beat the market; that means you want to beat *millions* of smart professional investors and traders who want the same thing; they have decades of experience, access to information you know nothing about, and can trade instantly: are you gong to beat all of them more than 50% of the time?”

    The other thing I tell them: “Say that event x is going to move the market up; who learns about it first, and places the trades that discount for that event and also earn him a profit: you?” The idea is that no matter what the event might be, the overwhelming likelihood is that some other trader has learnt of it first, so that by the time any one of us takes action on the news of event x, the market impact of that news has already been exhausted of its potential economic profits.

      • They don’t. They either buy the argument – in which case they generally say something like, “O, gosh, of course that’s right; never thought of it that way before” – or else, they don’t become clients.

    • Max, the answer is that business expenses need to be covered: rent, salaries, accounting and legal, compliance, on and on. And taxes, which soak up at least 50% of net profits. You’d be shocked at how it adds up.

      Say that you won 52% of total bets of $10 million. So, your gross revenue would be 2% of $10 million, or $200,000 (you would put up half the $10 million, and your counterparties would too). Now, ask yourself this: how much money would you need to spend every year in order to keep up an establishment where people would be willing to bet $5 million? Would $200K be enough? No, of course not.

      Here’s the real kicker. To get the operation going, you had to bet $5 million. On that, you earned a gross profit of $200K, or 4%. That’s before all expenses, hassle, time, business risk, taxes, etc. Do you think 4% is enough to pay for all that? No. It is not. You could earn more than 4% by investing in an S&P 500 Index fund, easy.

      No enterprise of any sort can keep going on gross margins of 2%.

  3. Pingback: Taleb and Business Ethics | Reaction Times

  4. Nathan Rothschild had a legendary intelligence network, so, as Richard almost said, his every move was watched hawkishly by other share traders. On the day of the Battle of Waterloo, Rothschild was pacing about at the Exchange, waiting for news. A messenger came, and Rothschild looked crestfallen. Other traders rushed to sell, and Rothschild, pulling himself together, went in to buy.

    How accurate the story is, Kristor can probably tell us.

    • Thanks, pbw. That sounds like a trick that could be pulled off just once – but perhaps enough to make one’s fortune.

    • I had not heard that particular story about Rothschild, but such tales are all over Wall Street. Head fakes are part of the game.

      The interesting thing about that story is that the news of the news became itself the news that moved the market. Consider then what fake news does to the market – i.e., to our society’s economic adjustment to reality. Consider how much damage it does to our economic prosperity to deliberately insert noise into the system.

      It ought to be a crime to publish falsehoods intentionally. The potential liability of being in the business of propagating information – of propaganda – ought to be massive. There ought to be enormous bounties available to any citizen who was able to catch a journalist in an inaccuracy. Such bounties would complete a feedback circuit that is now unable to function as such, and so correct a market failure as big as a barn.

      Lest journalists take offence at this notion, let them consider two things: the crushing malpractice insurance premia paid by doctors, lawyers, accountants, and financial advisors; and the huge bump up in their income that would accompany their assumption of that same sort of business risk.

      • “Fake news bounties” is a great idea but requires someone to pay them–news companies won’t pay it, but there are “citizen journalists” all around who are already doing this job for free. Good opening for a non-profit or endowment. Token for the find, additional bonus if they publish a retraction? What an interesting idea!

      • The way to do this would be for the sovereign to make journalistic malpractice legally tortious – after all, it does significant damage in the real world. Market perfection involves internalizing such real factors of economic value that are not accounted for by the economy – externalities, as they are called – so that the pricing system can reckon them properly. Completing the feedback circuit must involve making them pay, who cause such damages.

        That would open journalists to the risk of class action civil suits for publishing falsehoods. The plaintiff’s bar – those insatiable vultures, God bless them – would take care of the rest. They’d pay bounties to the autists – who now fact check journalists only for fun – to scour the web looking for inaccuracies, on the condition that such inaccuracies as they found were legally actionable. There could be a kicker for the freelancers if the plaintiffs won the case.

        Some such jury verdicts could be massive, running into the millions.

        Publicly traded companies are often sued by shareholders when their stock prices go down, so there is a highly evolved methodology for calculating damages in such cases. It could be extended. For example, a news organization that reported a wildfire was heading north west when really it was heading west could conceivably be sued by affected land owners – and their insurers – for damages.

        Just riffing here.

        Not sure how a politician or political party would calculate damages for inaccurate stories about them. Believe me: the plaintiff’s bar, together with their high priced economists and political scientists, would figure it out, pronto.

        That could be a great business opportunity for political scientists. The constant selection pressure of the rigorous standards of proof imposed by litigation might also do wonders for the scientific vigor and power of political science.

        The same sort of thing could be done to pollsters. Misleading polls are tortious in the real world; so should they be tortious in the law. It ought to be child’s play for a forensic sociologist to demolish a noisy poll.

        In practice, it should be pretty easy for journalists and pollsters to avoid litigation: just don’t make shit up, but rather publish only what you are pretty doggone sure is the truth. This constraint should cut way, way back on the yellow journalism out there, and thus on the sheer volume of “news” that is inflicted upon us all every moment.

        One interesting knock on effect: credible news organizations would be able to charge premium subscription fees for their services in rooting out the truth of the situation. This could save outfits like the NY Times and the WSJ. It would force outfits like CNN into a radically different business model. All good.

      • The idea is an enticing one, I like how you’ve laid it out. From a cynical perspective, I think it breaks down at the beginning where it relies on some sensible change in law; the rationale is there but the current political environment won’t support it. It would be an entire cottage industry that I am certain would crop up overnight if it were able to happen, in both evaluating damages and searching out errors.

        Your knock on effect is interesting because that’s been the great problem of journalism lately, is how to have a profitable business in the age of cheap information. Whenever i spitball business ideas with my friends, the question I always ask is “what is our value add?” because that’s what people are ultimately paying for. Current journalism has trouble generating revenue because there is no value add. Why would I pay money to listen to someone elses opinion when I know my opinions perfectly well? I can even publish them for free on the internet (and I do!). Opinions are cheap. But an entity that actually publishes verifiable facts would definitely be adding value. I don’t know what is happening on the ground in Syria, I know what people want me to think is happening on the ground in syria. But it might be valuable to me to know factually what is happening. This used to be enforced by concern for reputation, but no one cares about reputation anymore as long as you’ve got the platform.

        A thought tangent to this, that I had: In Heinlein’s “Stranger in a Strange Land”, he described an organization known as the “Fair Witness” which has always fascinated me. They were a licensed and professional organization whose duty was to verifiable truth. If you asked a fair witness to tell you the color of the house on yonder hill, they would respond “One side of it is white” because they cannot see the other sides. I’ve always thought “Fair Witness” would be a great name for a newspaper based on that ironclad commitment to objectivity.

      • The Fair Witness Journal could be a big money maker. It would have to charge a pretty steep fee to subscribers, because the job of winnowing fact from noise in this internet age would be enormous. So it would have to pay a lot of expenses. But the margin on that large base of expense would itself be relatively large.

  5. I am not really sure what this article is about. Business ethics – the lack of them – is something like ENRON case and that is something I would be more or less capable to comment intelligently on, various kinds of accounting shenanigans etc. but apparently this is about something else. So I will just add what I can.

    Professors. I had a lot of painful lessons in how business processes cannot be organized, document, calculated or accounted for the way professors think it should be. And it was only halfway their fault, their job is to figure it out perfectly, to have complete and exhaustive knowledge. And when I want to improve one bit of a process in real life, I can borrow that bit from their books. But not all of it. But the reason it was halfway their fault is that they taught it via clearly saying we should be doing all that. One cannot 1:1 apply academia to the real world and in some professions this is perfectly well known, mostly because there is a clear filtering process in academia, doctors need on part of what biologists know, biologists need only part of what chemists know, chemists need only part of what physicists know, and physicists need only part of what what mathemathicians know. And doctors not only need part of what biologists know, they also need other things biologists don’t know, because it emerged in practice. And we do not seem to have this in business, as if business “science” could be applied 1:1 to business. It cannot be…

    Risk, ethics and skin in the game. Skin in the game helps, but is not a magic cure. Suppose you own 80% of a business but do not run it, you are absent, and the CEO owns 20% of it. This means you pay 80% of every cost and he pays 20% of it. Thus, every cost that somehow benefits him personally more than paying 20% of it costs himm, he is inclined to take. The personal benefit might not always be easy to express in monetary terms. Imagine something like investing a lot into researching a “green” product that will be a loss, but brings him personally political standing. So you only avoid moral hazard if the manager, the agent, decision maker, is a 100% owner. So it does not fix all problems if the manager of an investment fund also invests some his own money there.

    Numbers that can be fit to a graph. Well, it is like, when a company wants to buy another one, the balance sheet says very little about how much that company worths. There is a big inventory, but how do you know it is sellable products or utter duds? Only people who know those kinds of products can decide. OK there is an asset like a building, real estate, but maybe 30 years ago that was in a good neighborhood and now the neighborhood is not good and the real estte prices dropped there. Only the estimate of a realtor helps. So in short, no method replaces judgement. Judgement cannot be automatized.

    I do a lot of work like writing SQL statements to deliver numbers to managers. Like how profitable their products are. And then they tell me look this cannot be right that this product has 28% margin, I am sure it must be between 18% and 24%. And I check it and there was indeed either a data entry mistake or a bug and it is really 21%. Which means a lot of things. First, good managers to some extent don’t even need these numbers, they just know. Judgement. Second, if the mistake is only making it 23%, not 28%, no one ever will notice it. So I really wonder what these numbers I generate even useful for, other than for spotting mistakes that could influence other numbers, those being ones managers do not already know. Aggregate ones like total profit.

    As a very general note on the ethics of professors, investment advisors and others, it is generally unethical to sound more sure than you actually are. Science is in many ways an aesthetics. Read Seeing Like A State. That book calls it “high modernism”. Using scientific aesthetics, like having a model and graph, sounds like we are fairly sure. The only way to be ethical would be for the professor or analyst to explicitly go out of his way and tell people no, we are not at so sure about it. Lacking that, they will assume something that looks “sciency” is accurate.

    And that’s hard. Bad people lie. Not telling every aspect of the truth unless explicitly asked is not literally a lie, and that is how a lot of people make a compromise with their conscience: I am not bad, because I did not lie. “I would have told it is not very reliable if anyone would have asked.” It is like when I was young and had only one job experience, I have put it as “2002-2004” in my job seeking resume, making the impression it was three years of experience. I have told myself if they would have asked about the date or month, I would have told them it was just Nov 2002 – Feb 2004, less than one and half years. But they did not ask because I created an impression and they just bought that impression. So technically I did not lie, but… Later on I also told myself maybe it did not matter, if they would have cared, they would have asked. I was just, like, making the resume quicker, easier to read. So many such excuses can be found to ease a troubled conscience.

    I don’t know if this trick has a name, but really common. And when “everybody does that?” When a whole profession is all about making impressive, “sciencey” looking models about things they really are not sure about? Who will be professionally-suicidal hero that one who comes forth and tells their customers to not trust it?

    Really good investment advice is like this: you must almost violate the laws against insider trading. You must understand their product, you must have a good estimate how capable their management is etc. Not literal nonpublic business secrets, but not just the obviously public information either.

    • Thanks, Dividualist. Skin in the game is not claimed to be a cure-all, but it would quickly eliminate dunderheaded prognosticators so we would never have to hear from them again, which would be nice. The example you give, however, is precisely a problem of the manager having insufficient skin in the game.

      To repeat what is stated in the article, what all this has to do with business ethics, is me teaching my mostly accounting students which parts of their education, to the extent that I know what that is, have been actual lies or misinformation promulgated by their professors for accreditation purposes so the students can avoid passing on this garbage to their clients; assuming they give a damn about the financial welfare of their clients or the companies they work for. You correctly identify a real problem that I can’t fix – will it be professionally suicidal for them to ignore these lies?

      Lying by omission has a name and is still lying. I heard someone recently say “Most news is not fake. It’s accurate, but lies by omission.” Since both regular lies, and lying by omission, are lies, and intentional lies are told with the intent to deceive, then the phrase “fake news” should include news that lies by omission. If you lie by omission to your stockholders you are still lying. You are perhaps slightly less likely to be prosecuted for your lie though, depending on specifics. Ken Lay of Enron told stockholders “I personally have bought 5 million dollars of Enron stock just recently.” What he didn’t say was that he had sold 100 millions dollars worth (or something like that). He only bought the stock so he could lie by omission.

  6. ““Most news is not fake. It’s accurate, but lies by omission.””

    A Good example of that which it describes. Many facts are accurate, but a few are lies. The lies structure the interpretation of the accurate facts. Thus, *in effect*, All news is fake.

  7. I just thought I’d point out that philosophers are by no means immune to inducing eye-rolling. I’ve seldom seen anything written by philosophers (even ones I otherwise really like and have learned much from) on the subject of quantum mechanics that didn’t make me roll my eyes.

    • @Garth Rose. Absolutely, we are all prone to sounding like idiots when straying into topics that other people know a lot about but we don’t. I have criticized my own father, who has degrees in philosophy and theology, for plumping for a “spiritual” interpretation of quantum mechanics. He points to the fact that a Nobel Prize physicist, Brian Josephson, and David Bohm, agree with him, which is to ignore all the ones who do not. He, as a non-physicist, has no business adjudicating disputes among physicists who still have no idea what quantum mechanics actually implies about physical reality.

      I listen with interest to scientists throwing scorn on string theory as a career-making but otherwise pointless exercise with way too much influence in the academy but I’m not about to write an article about it.

      I have tried listening to Sean Carroll “solo” podcasts where he just tries to explain quantum gravity, or the nature of time, and after twenty minutes or so it becomes clear to me that it is pointless to keep listening. I would love to know what percentage of his audience can actually follow these explanations.

      Most academic philosophers I do not regard as philosophers at all, but careerist shills. In fact, the academy does an excellent job of weeding out actual philosophers.

  8. Stock appreciation over time is nothing more than inflation, that is, new government fiat money creation handed out/smoke and mirrors borrowed at interest, from … and to … itself. It’s a movement of new cash flow from the left pocket to the right – same pair of pants. The illusion of separation of power/responsibilities in financial institutions like the private U.S. Federal Reserve and Treasury and Banks is just that.

    The government creates debt it owes to itself. If cash was a fixed pie, that pie would be consumed to zero – no pie. New cash pies (to generate impressive pie graph projections to be distrusted must be baked to stimulate the body politic and more importantly – new venture entrepreneurial risk taking). Corporatist capitalistic countries generally create opportunity for entrepreneurs to desire to take on risk of reward/failure in order to discover innovation. Warren Buffet said he would not have had the prescient skill to pick the winners from hundreds of early automotive companies that would rise and fall.

    Governments are doing something similar by allowing different crypto currencies to emerge and be traded in real government backed inflated currency. Their goal in competition – to discover the winner, participate in the infrastructure development, and step in when readiness is achieved to anoint the eventual new electronic format government endorsed currency. They might allow some real anonymous crypto to remain in parallel so they can spy on and engage with Dark Web participants.

    If the currency were Weimar Republic Reichsbank Paper Marks, then the money would be worthless, much less the paper stocks the worthless cash had flowed into.

    The U.S. 2008-2009 housing market mortgage backed securities collateralized debt obligation derivative financial instrument insurance against failure of same run-on-sentence crisis was created by the same government. The never met I crisis I didn’t like because I created it same government printed/electronically created x $ trillions and handed it to institutions deemed too big to fail. They also forced other healthy institutions to take the new fiat $ vaccine. These quantitative leaders (“quants”) created dramatic sounding acronyms stressing importance like Quantitative Easing and Toxic Asset Relief.

    The gold bugs waited for the world to end, but it never did.

    There is value in all this imperfect struggle because it keeps most busy doing some type of mental abstraction work that’s not physical. The industrial technology revolution creates excess commodities – physical abundance – for equal/inequal distribution, depending on the eye beholder, glass half empty full – grateful revengeful.

    As for the financial advisor class, they lost credibility during 2008-2009 by not beating index funds over time, and not mitigating their clients risks against disaster. And they were compensated either way regardless of their clients success or failure. Unless they washed their hands prior as fiduciaries selling only advice and not products.

    As for predicting stock prices using magic math models, how would any expert do that with accuracy (they have not historically because they don’t outperform the passive index), unless there was true collusion and inside information sharing within the anointed financial class? They couldn’t. The major shareholders drive the price of stocks up and down mostly – not mom and pop retail consumers (the recent Robin Hood drama around the insignificant game retailer notwithstanding).

    If that is true for individual publicly held companies, how much more is it true when total market indexes take a 50% or more haircut? Of course it is collusion. Outsiders cannot however accuse and prove it with authority. And insiders don’t want to lose their job enrichment goose that lays the golden fiat eggs.

    How does one say and print “buy when there is blood in the streets” on a professional financial advisor brochure? It lacks a certain quantifiable presentation style.

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