20,851 research outputs found
Physicists, stamp collectors, human mobility forecasters
One of the two reviewers studied in high school to be a physicist. In the end, he became something else, but he never lost his awe of physics. The other reviewer never intended to become a physicist, but he sometimes asks himself why he didn’t become one. Today, they are both sociologists who practice their science on an action theory basis and believe that regularities exist in the
world of social actions which can be perceived, understood, explained – and even used for making predictions
Synchronizing to the Environment: Information Theoretic Constraints on Agent Learning
We show that the way in which the Shannon entropy of sequences produced by an
information source converges to the source's entropy rate can be used to
monitor how an intelligent agent builds and effectively uses a predictive model
of its environment. We introduce natural measures of the environment's apparent
memory and the amounts of information that must be (i) extracted from
observations for an agent to synchronize to the environment and (ii) stored by
an agent for optimal prediction. If structural properties are ignored, the
missed regularities are converted to apparent randomness. Conversely, using
representations that assume too much memory results in false predictability.Comment: 6 pages, 5 figures, Santa Fe Institute Working Paper 01-03-020,
http://www.santafe.edu/projects/CompMech/papers/stte.htm
Estimating the Algorithmic Complexity of Stock Markets
Randomness and regularities in Finance are usually treated in probabilistic
terms. In this paper, we develop a completely different approach in using a
non-probabilistic framework based on the algorithmic information theory
initially developed by Kolmogorov (1965). We present some elements of this
theory and show why it is particularly relevant to Finance, and potentially to
other sub-fields of Economics as well. We develop a generic method to estimate
the Kolmogorov complexity of numeric series. This approach is based on an
iterative "regularity erasing procedure" implemented to use lossless
compression algorithms on financial data. Examples are provided with both
simulated and real-world financial time series. The contributions of this
article are twofold. The first one is methodological : we show that some
structural regularities, invisible with classical statistical tests, can be
detected by this algorithmic method. The second one consists in illustrations
on the daily Dow-Jones Index suggesting that beyond several well-known
regularities, hidden structure may in this index remain to be identified
An empirical behavioral model of liquidity and volatility
We develop a behavioral model for liquidity and volatility based on empirical
regularities in trading order flow in the London Stock Exchange. This can be
viewed as a very simple agent based model in which all components of the model
are validated against real data. Our empirical studies of order flow uncover
several interesting regularities in the way trading orders are placed and
cancelled. The resulting simple model of order flow is used to simulate price
formation under a continuous double auction, and the statistical properties of
the resulting simulated sequence of prices are compared to those of real data.
The model is constructed using one stock (AZN) and tested on 24 other stocks.
For low volatility, small tick size stocks (called Group I) the predictions are
very good, but for stocks outside Group I they are not good. For Group I, the
model predicts the correct magnitude and functional form of the distribution of
the volatility and the bid-ask spread, without adjusting any parameters based
on prices. This suggests that at least for Group I stocks, the volatility and
heavy tails of prices are related to market microstructure effects, and
supports the hypothesis that, at least on short time scales, the large
fluctuations of absolute returns are well described by a power law with an
exponent that varies from stock to stock
Empirical Regularities in Distributions of Individual Consumption Expenditure
We empirically investigate distributions of individual consumption
expenditure f or four commodity categories conditional on fixed income levels.
The data stems from the Family Expenditure Survey carried out annually in the
United Kingdom. W e use graphical techniques to test for normality and
lognormality of these distributions. While mainstream economic theory does not
predict any structure for these distributions, we find that in at least three
commodity categories individual consumption expenditure conditional on a fixed
income level is lognormally distributed.Comment: 9 pages including figures; for Int. J. Mod. Phys. C 13, No.
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