Presentation on theme: "Rischio ed Incertezza nelle decisioni economiche: un approccio “behavioral” Massimo Egidi, Luiss University"— Presentation transcript:
Rischio ed Incertezza nelle decisioni economiche: un approccio “behavioral” Massimo Egidi, Luiss University firstname.lastname@example.org
Comment :The Minsky Moment by John Cassidy February 4, 2008 (The New Yorker)
Comment :The Minsky Moment by John Cassidy February 4, 2008 Twenty-five years ago, when most economists were extolling the virtues of financial deregulation and innovation, a maverick named Hyman P. Minsky maintained a more negative view of Wall Street; in fact, he noted that bankers, traders, and other financiers periodically played the role of arsonists, setting the entire economy ablaze. Wall Street encouraged businesses and individuals to take on too much risk, he believed, generating ruinous boom-and-bust cycles. The only way to break this pattern was for the government to step in and regulate the moneymen. Many of Minsky’s colleagues regarded his “financial-instability hypothesis,” which he first developed in the nineteen- sixties, as radical, if not crackpot.
Comment :The Minsky Moment by John Cassidy February 4, 2008 Today, with the subprime crisis seemingly on the verge of metamorphosing into a recession, references to it have become commonplace on financial Web sites and in the reports of Wall Street analysts. Minsky’s hypothesis is well worth revisiting. In trying to revive the economy, President Bush and the House have already agreed on the outlines of a “stimulus package,” but the first stage in curing any malady is making a correct diagnosis. Minsky, who died in 1996, at the age of seventy-seven, earned a Ph.D. from Harvard and taught at Brown, Berkeley, and Washington University. He didn’t have anything against financial institutions—for many years, he served as a director of the Mark Twain Bank, in St. Louis— but he knew more about how they worked than most deskbound economists.
Comment :The Minsky Moment by John Cassidy February 4, 2008 There are basically five stages in Minsky’s model of the credit cycle: displacement, boom, euphoria, profit taking, and panic. A displacement occurs when investors get excited about something— an invention, such as the Internet, or a war, or an abrupt change of economic policy. The current cycle began in 2003, with the Fed chief Alan Greenspan’s decision to reduce short-term interest rates to one per cent, and an unexpected influx of foreign money, particularly Chinese money, into U.S. Treasury bonds. With the cost of borrowing—mortgage rates, in particular—at historic lows, a speculative real-estate boom quickly developed that was much bigger, in terms of over-all valuation, than the previous bubble in technology stocks. As a boom leads to euphoria, Minsky said, banks and other commercial lenders extend credit to ever more dubious borrowers, often creating new financial instruments to do the job. During the nineteen-eighties, junk bonds played that role.
Comment :The Minsky Moment by John Cassidy February 4, 2008 More recently, it was the securitization of mortgages, which enabled banks to provide home loans without worrying if they would ever be repaid. (Investors who bought the newfangled securities would be left to deal with any defaults.) Then, at the top of the market (in this case, mid-2006), some smart traders start to cash in their profits. The onset of panic is usually heralded by a dramatic effect: in July, two Bear Stearns hedge funds that had invested heavily in mortgage securities collapsed. Six months and four interest-rate cuts later, Ben Bernanke and his colleagues at the Fed are struggling to contain the bust. Despite last week’s rebound, the outlook remains grim. According to Dean Baker, the co-director of the Center for Economic and Policy Research, average house prices are falling nationwide at an annual rate of more than ten per cent, something not seen since before the Second World War. This means that American households are getting poorer at a rate of more than two trillion dollars a year.
The psychology of Risk and Uncertainty since Frank Knight
Thaler :Behavioral Finance In the traditional framework where agents are rational and there are no frictions, a security’s price equals its “fundamental value”. This is the discounted sum of expected future cash flows, where in forming expectations, investors correctly process all available information, and where the discount rate is consistent with a normatively acceptable preference specification. The hypothesis that actual prices reflect fundamental values is the Efficient Markets Hypothesis (EMH). Put simply, under this hypothesis, “prices are right”, in that they are set by agents who understand Bayes’ law and have sensible preferences.
Thaler :Behavioral Finance Behavioral finance argues that some features of asset prices are most plausibly interpreted as deviations from fundamental value, and that these deviations are brought about by the presence of traders who are not fully rational. A long-standing objection to this view that goes back to Friedman (1953) is that rational traders will quickly undo any dislocations caused by irrational traders. To illustrate the argument, suppose that the fundamental value of a share of Ford is $20. Imagine that a group of irrational traders becomes excessively pessimistic about Ford’s future prospects and through its selling, pushes the price to $15. Defenders of the EMH argue that rational traders, sensing an attractive opportunity, will buy the security at its bargain price and at the same time, hedge their bet by shorting a “substitute” security, such as General Motors, that has similar cash flows to Ford in future states of the world. The buying pressure on Ford shares will then bring their price back to fundamental value.
Thaler :Behavioral Finance Friedman’s line of argument is initially compelling, but it has not survived careful theoretical scrutiny. In essence, it is based on two assertions. First, as soon as there is a deviation from fundamental value – in short, a mispricing – an attractive investment opportunity is created. Second, rational traders will immediately snap up the opportunity, thereby correcting the mispricing. Behavioral finance does not take issue with the second step in this argument: when attractive investment opportunities come to light, it is hard to believe that they are not quickly exploited. Rather, it disputes the first step. The argument is that even when an asset is wildly mispriced, strategies designed to correct the mispricing can be both risky and costly, rendering them unattractive. As a result, the mispricing can remain unchallenged.
In 1907, Royal Dutch and Shell Transport, at the time completely independent companies, agreed to merge their interests on a 60:40 basis while remaining separate entities. Shares of Royal Dutch, which are primarily traded in the USA and in the Netherlands, are a claim to 60% of the total cash flow of the two companies, while Shell, which trades primarily in the UK, is a claim to the remaining 40%. If prices equal fundamental value, the market value of Royal Dutch equity should always be 1.5 times the market value of Shell equity. Remarkably, it isn’t.
Figure 1, taken from Froot and Dabora’s (1999) analysis of this case, shows the ratio of Royal Dutch equity value to Shell equity value relative to the efficient markets benchmark of 1.5. The picture provides strong evidence of a persistent inefficiency. Moreover, the deviations are not small. Royal Dutch is sometimes 35% underpriced relative to parity, and sometimes 15% overpriced.
Expectations Overconfidence. Optimism and wishful thinking. Representativeness Anchoring. Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and socialjustice, and also participated in anti-nuclear demonstrations. When asked which of “Linda is a bank teller” (statement A) and “Linda is a bank teller and is active in the feminist movement” (statement B) is more likely, subjects typically assign greater probability to B. This is, of course, impossible.
« Lorsqu'a la loterie de France un numéro n'est pas sorti depuis longtemps, la foule s'empresse de le couvrir de mises. Elle juge que le numéro resté longtemps sans sortir doit, au premier tirage, sortir de préférence aux autres. Une erreur aussi commune me parait tenir à une illusion par laquelle on se reporte involontairement à l'origine des événements. Il est, par exemple, très peu vraisemblable qu'au jeu de croix ou pile on amènera croix dix fois de suite. Cette invraisemblance qui nous frappe encore, lorsqu'il est arrivé neuf fois, nous porte à croire qu'au dixième coup pile arrivera. Cependant le passé, en indiquant dans la pièce une plus grande pente que pour pile, rend le premier dé ces événements plus probable que l'autre; il augmente, comme on 1'a vu, la probabilité d’amener croix au coup suivant. » (Laplace 1814, introduction, CXIII)
Reihnardt Selten “Modern mainstream economic theory is largely based on an unrealistic picture of human decision making. Economic agents are portrayed as fully rational Bayesian maximizers of subjective utility. This view of economics is not based on empirical evidence, but rather on the simultaneous axiomatization of utility and subjective probability. In the fundamental book of Savage the axioms are consistency requirements on actions with actions defined as mappings from states of the world to consequences (Savage 1954). One can only admire the imposing structure built by Savage. It has a strong intellectual appeal as a concept of ideal rationality. However, it is wrong to assume that human beings conform to this ideal.” (Reihnardt Selten, 1999)
Maurice Allais Maurice Allais pointed to conclusions the reverse of those obtained by Savage’s approach. He carried out experiments on individual preferences - using an ingenious falsificationist method - that showed systematic failures in the theory’s predictions. In 1952, at a symposium held in Paris, Allais presented two studies in which he criticized the descriptive and predictive power of the ‘American School’s’ choice theory, and especially Friedman’s version of it (Allais, 1953). He submitted experiments in which subjects faced with alternative choices in conditions of risk systematically violate the assumptions of the Expected Utility theory.
Many proposals were put forward, especially from the mid 1970’s onwards, and all of them based on the attempt of relaxing or slightly modifying the original axioms of expected utility Theory. Among others, we have: - Weighted Utility Theory (Chew and MacCrimmon); - Regret Theory ( Loomes and Sugden,1982); - Disappointment Theory, (Gul,1991). None of them had a statistical confirmation over the full domain of applicability (Tversky and Kahnemann, 1987, p.88). Therefore this response to Allais’ criticism did not prove successful. Only gradually economists came to recognize the systematic discrepancy between the predictions of expected utility theory and economic behavior; this opened a dramatic and still unsolved question: how to model in a more realistic way human behavior in economics.
The “Classical” Experiment - Framing Effect Problem 1 Assume to be 300 $ richer than you are today. Choose between: - A the certainty of earning 100$ - B 50% probability of winning 200$ and 50% of not winning anything Problem 2 Assume you are 500 $ richer than today. Choose between: - C A sure loss of 100$ - D 50% chance of not losing anything and 50% chance of losing 200$
From Camerer,Loewenstein,Prelec “Neuroeconomics….”
On the basis of this function, an immediate loss is given a more negative evaluation than the positive evaluation of an immediate gain to the same amount. Moreover, given the non-linearity of the subjective value function, losses or gains with the same expected value are assessed differently.
…. behavior emerges from the interplay between controlled and automatic systems on the one hand, and between cognitive and affective systems on the other. Moreover, many behaviors that are clearly established to be caused by automatic or affective systems are interpreted by human subjects, spuriously, as the product of cognitive deliberation (Wolford, Miller and Gazzaniga 2000). The deliberative system, which is the system that is responsible for making sense of behavior, does not have perfect access to the output of the other systems, and exaggerates the importance of processes it understands when it attempts to make sense of the body’s behavior. ( Camerer Loewenstein Prelec )
3 Automatic and deliberate thinking : interferences While not denying that deliberation is always an option for human decision making, neuroscience research points to two generic inadequacies of this approach. First, much of the brain is constructed to support ‘automatic’ processes (Bargh, Chaiken, Raymond and Hymes 1996; Bargh and Chartrand 1999; Schneider and Shiffrin 1977; Shiffrin and Schneider 1977), which are faster than conscious deliberations and which occur with little or no awareness or feeling of effort. Because the person has little or no introspective access to, or volitional control over them, the behavior these processes generate need not conform to normative axioms of inference and choice (and hence cannot be adequately represented by the usual maximization models). (Camerer Loewenstein Prelec)
4 Brain’s regions involved in automatic / in deliberate / thinking Automatic and controlled processes can be very roughly distinguished by where they occur in the brain (Lieberman, Gaunt, Gilbert and Trope 2002). Regions that support cognitive automatic activity are concentrated in the back (occipital), top (parietal) and side (temporal) parts of the brain (see Figure 1). The amygdala, buried below the cortex, is responsible for many important automatic affective responses, especially fear. Controlled processes occur mainly in the front (orbital and prefrontal) parts of the brain. The prefrontal cortex (pFC) is sometimes called the "executive" region, because it draws inputs from almost all other regions, integrates them to form near and long-term goals, and plans actions that take these goals into account (Shallice and Burgess, 1998). The prefrontal area is the region that has grown the most in the course of human evolution and which, therefore, most sharply differentiates us from our closest primate relatives (Manuck, Flory, Muldoon and Ferrell 2003). (Camerer Loewenstein Prelec)
5 Modelling the interferences between automatic and deliberate thinking : Modularity and specialization Specialization: In a process that is not well understood, the brain figures out how to do the tasks it is assigned, efficiently, using the modules it has at its disposal. When the brain is confronted with a new problem it initially draws heavily on diverse modules, including, often, the prefrontal cortex. But over time, activity becomes more streamlined, concentrating in modules that specialized in processing relevant to the task. In one study, subjects' brains were imaged as they played the computer game Tetris, which requires rapid hand-eye coordination and spatial reasoning. When subjects began playing, they were highly aroused and many parts of the brain were active. As they got better at the game, overall blood-flow to the brain decreased markedly, and activity became localized in only a few brain regions. (Camerer Loewenstein Prelec)
Interactions between automatic and deliberate thinking In one now famous study, Gobet and Simon (1996) tested memory for configurations of chess pieces positioned on a chess board. They found that expert chess players were able to store the positions of players almost instantly – but only if they were in positions corresponding to a plausible game. For randomly arranged chess pieces, the experts were not much better than novices. More recent research in decision making suggests that this is a far more general phenomenon; much decision making takes the form of pattern matching rather than of an explicit weighing of costs and benefits (e.g., Leboeuf 2002; Medin and Bazerman 1999) ( Camerer Loewenstein Prelec )
5 With experience at a task or problem, the brain seems to gradually shift toward modules that can solve problems automatically and efficiently with low-effort. It naturally follows, that, for a wide range of problems and tasks, people will rely on cognitive capabilities that are relatively well developed, such as visual perception and object recognition rather than operations that we are not very good at, like decomposing and then summing up costs and benefits. .. Decision making may be explained as the product of a complex interaction between two processes: automatic pattern matching and an explicit weighing of costs and benefits. ( Camerer Loewenstein Prelec )