Poker And Risk Management

Gambling – the willingness to take actions whose outcomes cannot be known for certain – is a basic human instinct. The riskier you perceive a particular action to be, the higher its potential payoff should be to justify your taking the action.

As it happens, the risk inherent in many actions can be roughly quantified. You can rank actions by their estimated riskiness, compare them to each other and to their potential payoffs, and make intelligent judgments about which (if any) actions to take.

This is known as managing risk. And the widespread failure to manage risk sensibly was a major reason why the financial industry melted down so catastrophically in the fall of 2008. To their peril, Wall Street firms relied on oversimplified models for managing their risk.

Many people insist that financial markets are simply a large collection of gambling casinos that offer investors a variety of “games” to bet on. This is almost right – but the almost is significant.

When you walk into a casino, you face an immediate choice. Are you going to play slot machines and table games like roulette and craps, or seek out the poker rooms?

If you choose slots or table games, you are likely to lose because you are playing against the house. The payoffs of these games are structured (with the blessings of state gaming commissions) to give the house an edge that assures you will lose in the long run. This is unlike the situation in the financial industry.

But if you choose the poker rooms, you have a chance of winning, because you are playing against other gamblers like yourself. The house simply hosts the games (i.e. provides the space, tables, and chairs, decks of cards, professional dealers and so on) and takes a modest cut of the pot for doing so. This is a lot more like the situation in the financial industry.

You can sit down at a table and become a “player” (which is like being a “professional investor” in the financial industry). But you have another option.

You can engage in side betting. People who visit poker rooms simply to watch the games can place side bets among themselves about the winner of the next hand. But since they’re unable to influence the hand’s outcome, their betting decisions simply reflect their estimates of the raw probabilities. These bettors are spectators with no influence over who wins the next hand.

But there are ways to refine your initial assumption about the win/lose probabilities.

One way is to simply watch a half-a-dozen or so hands and see which player or two seem to be dominating, then make a subjective judgment about the player’s probability of winning. Another is to look at the chip stacks in front of each player. If one player’s stack is twice as large as anybody else’s, it may be evidence of that player’s superior poker skills.

But suppose you recognize at the outset that one of the players is Jennifer Harman or some other highly regarded poker maven who tends to win a significant percentage of the hands they play. You reflect this by assigning him or her a higher win probability. You place most of your bets on the maven winning, possibly adjusting the size of each bet based on how well the maven is doing as the game progresses and what kind of payoff odds you’re getting from the other spectators.

An important point stands out about this poker example: You have a relatively large number of variables to keep track of, and their interrelationships and relative impacts are constantly changing.

This is especially true in financial markets. During the years leading up to the beginning of 2008 many firms bowed to the temptation to oversimplify their models. Many of them turned out to be less than worthless when the proverbial expletive hit the fan and blew up the world or at least lit the fuse.

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