hunch_net hunch_net-2010 hunch_net-2010-397 knowledge-graph by maker-knowledge-mining
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Introduction: After a major financial crisis , there is much discussion about how finance has become a casino gambling with other’s money, keeping the winnings, and walking away when the money is lost. When thinking about financial reform, all the many losers in the above scenario are apt to take the view that this activity should be completely, or nearly completely curtailed. But, a more thoughtful view is that sometimes there is a real sense in which there are right and wrong decisions, and we as a society would really prefer that the people most likely to make right decisions are making them. A crucial question then is: “What is the difference between gambling and rewarding good prediction?” We discussed this before the financial crisis . The cheat-sheet sketch is that the online learning against an adversary problem, algorithm, and theorems, provide a good mathematical model for thinking about this question. What I would like to do here is map this onto various types of financial t
sentIndex sentText sentNum sentScore
1 After a major financial crisis , there is much discussion about how finance has become a casino gambling with other’s money, keeping the winnings, and walking away when the money is lost. [sent-1, score-0.694]
2 ” We discussed this before the financial crisis . [sent-5, score-0.451]
3 The basic mapping is between “wealth” and “weight”, with the essential idea that you can think of wealth as either money or degree of control over decision making. [sent-8, score-0.756]
4 The core algorithms start with a “wealth” spread over many experts, each of which makes predictions and then has it’s wealth updated according to a soft exponential of the value of it’s prediction. [sent-9, score-0.498]
5 This strategy is not inherently sound from a learning theory point of view, because a single purchased item can sometimes drop to zero value. [sent-12, score-0.539]
6 In the zero value case, a good decision maker can be wiped out by one decision, while in the large value case, a lucky decision maker can randomly achieve overwhelming credit. [sent-15, score-0.74]
7 If each item purchased either doubles or halves in value, the fluctuation in the wealth of a decision maker is analogous to the fluctuation in the relative weight of on an expert in the online learning framework. [sent-17, score-1.185]
8 Adding diversification to the “Long” strategy helps it align substantially better with an optimal learning theory strategy. [sent-20, score-0.439]
9 The short strategy is borrowing an item (typically a stock), selling it high, then buying it back low to cover the debt. [sent-23, score-0.702]
10 From the perspective of learning theory, short selling is more dangerous than long, because it’s possible to end up with negative wealth when a stock is sold short, and then it increases in value. [sent-26, score-0.807]
11 If this collateral is at least twice the value when shorting occurs, it’s hard for participants to become wealthy by luck, because wealth at most doubles. [sent-28, score-0.672]
12 In the financial crisis, credit default swaps made the crisis viral, as the “pay up” clauses triggered, particularly wiping out AIG . [sent-32, score-0.838]
13 Insurance has the same general problem as short selling—it can result in negative wealth unless there is sufficient collateral. [sent-33, score-0.658]
14 The first is that for normal people interacting with the financial system a set of financial rules + good sense have developed such that wealth tends to grow and shrink in a manner similar to what learning theory would suggest is near optimal. [sent-41, score-1.27]
15 For example, most people use the going long strategy by default and most diversify. [sent-42, score-0.442]
16 Normal people don’t have access to credit default swaps, and normal insurance has real collateral requirements. [sent-44, score-0.696]
17 The second is that larger actors have become quite skillful at avoiding the rules, with unsecured credit default swaps, unsecured shorts, and no clawback rules. [sent-46, score-0.624]
18 My belief is effective financial reform will impose limits on agents just as learning theory implies. [sent-48, score-0.58]
19 But this needn’t be so, because the math is straightforward, very robust, and designed precisely to pick out the good decision makers giving them wealth as rapidly as responsibly possible to make and control bigger decisions. [sent-54, score-0.573]
20 Some basic questions are: How can we better structure marketplaces to allocate wealth according to the dynamics of an online learning algorithm? [sent-57, score-0.463]
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