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Story - antoq - 10/15/2010 - 06:53 - 0 comments - 0 attachments
Judging from the literature, in particular the wide range of popular finance books, the possibility of predicting future movements in financial markets ranges from significant (see, for example, the many books on chartism) to impossible (see, for example, [14]). Another scenario of course does exist that financial markets may neither be predictable or unpredictable all the time, but may instead have periods where they are predictable (i.e. non-random) and periods where they are not (i.e. random). Evidence for such ‘pockets of predictability’ were found several years ago, by Johnson et al. [10]. A similar study was reported subsequently by Sornette et al. [2]. However, a formal report of a theoretical framework for identifying such periods of predictability has not appeared in the literature to date.
The rationale behind our initial proposal to predict financial markets using artificial market models, is as follows. Financial markets produce time-series, as does any dynamical system which is evolving in time, such as the ambient air-temperature, or the electrical signals generated by heart rhythms. In principle, one could use any time-series analysis technique to build up a picture of these statistical fluctuations and variations - and then use this technique to attempt some form of prediction, either on the long or short time-scale. One example would be to use a multivariate analysis of the prices themselves in order to build up an estimate of the parameters in the multivariate expansion, and then run this model forwards. However, such a multivariate model may not bear a relation to any physical representation of the market itself. Instead, imagine that we are able to identify an artificial market model which seems to produce the aggregate statistical behavior (i.e. the stylized facts) observed in the financial market itself. It now has the additional advantage that it also mimics the microscopic structure of the market, i.e. it contains populations of artificial traders who use strategies in order to make decisions based on available information, and will adapt their behavior according to past successes or failures. All other things being equal, we believe that such a model may be intrinsically ‘better’ than a purely numerical multivariate one - and may even be preferable to many more sophisticated models such as certain neural network approaches, which also may not be correctly capturing a realistic representation of the microscopic details of a physical market. The question then arises as to whether such an artificial market model could be ‘trained’ on the real market in order to produce reliable forecasts.
How do price and entry regulations affect market structure, industry evolution, management quality, and through these, dynamic efficiency? Relatively little is known about this question. The literature on the effects of price and entry regulation on efficiency has shown that such interventions can reduce static efficiency by preventing firms from allocating their assets optimally. For example, trucking regulations prevented carriers from hauling regulated commodities on return trips, making empty backhauls a serious problem.
USDA and other public health agencies have a long history of disseminating information about why and how to make food choices that promote health and prevent disease. Since 1980, recommendations on attaining adequate nutrition also include information about the benefits of maintaining a healthy body weight and limiting consumption of nutrients linked to chronic diseases. On the other side of the table, food manufacturers and marketers have discovered that certain psychological cues, such as packaging and presentation, are efficient ways to increase consumption of their products.These approaches have not been widely used in public health efforts aimed at improving diet quality and reducing body weight.
There are several behavioral and cognitive biases affecting food consumption decisions. The food psychology literature has found that external cues can drastically alter not only consumption volume, but also individual perceptions of how much they should and actually do eat. Wansink (1996) finds that larger packages lead to greater consumption and Diliberti et al.(2004) find that by increasing restaurant portion sizes from 248 grams to 377 grams of pasta, individuals increase caloric intake by an average of 43 percent. Also, more standard elements that are thought to be the main drivers of food choices, such as price, will sometimes prove to have little influence over consumption volume. For example, individuals appear to consume much larger quantities of food when it is stockpiled regardless of the initial cost (Wansink and Deshpande, 1994).
