1,014 research outputs found
Executive compensation : a modern primer
This article studies traditional and modern theories of executive compensation, bringing them together under a unifying framework. We analyze assignment models of the level of pay, and static and dynamic moral hazard models of incentives, and compare their predictions to empirical findings. We make two broad points. First, traditional optimal contracting theories find it difficult to explain the data, suggesting that compensation results from "rent extraction" by CEOs. In contrast, more modern theories that arguably better capture the CEO setting do deliver predictions consistent with observed practices, suggesting that these practices need not be inefficient. Second, seemingly innocuous features of the modeling setup, often made for tractability or convenience, can lead to significant differences in the model's implications and conclusions on the efficiency of observed practices. We close by highlighting apparent inefficiencies in executive compensation and additional directions for future research
Stock mechanics: predicting recession in S&P500, DJIA, and NASDAQ
An original method, assuming potential and kinetic energy for prices and
conservation of their sum is developed for forecasting exchanges. Connections
with power law are shown. Semiempirical applications on S&P500, DJIA, and
NASDAQ predict a coming recession in them. An emerging market, Istanbul Stock
Exchange index ISE-100 is found involving a potential to continue to rise.Comment: 14 pages, 4 figure
The Power (Law) of Indian Markets: Analysing NSE and BSE trading statistics
The nature of fluctuations in the Indian financial market is analyzed in this
paper. We have looked at the price returns of individual stocks, with
tick-by-tick data from the National Stock Exchange (NSE) and daily closing
price data from both NSE and the Bombay Stock Exchange (BSE), the two largest
exchanges in India. We find that the price returns in Indian markets follow a
fat-tailed cumulative distribution, consistent with a power law having exponent
, similar to that observed in developed markets. However, the
distributions of trading volume and the number of trades have a different
nature than that seen in the New York Stock Exchange (NYSE). Further, the price
movement of different stocks are highly correlated in Indian markets.Comment: 10 pages, 7 figures, to appear in Proceedings of International
Workshop on "Econophysics of Stock Markets and Minority Games"
(Econophys-Kolkata II), Feb 14-17, 200
Power-Law Distributions in a Two-sided Market and Net Neutrality
"Net neutrality" often refers to the policy dictating that an Internet
service provider (ISP) cannot charge content providers (CPs) for delivering
their content to consumers. Many past quantitative models designed to determine
whether net neutrality is a good idea have been rather equivocal in their
conclusions. Here we propose a very simple two-sided market model, in which the
types of the consumers and the CPs are {\em power-law distributed} --- a kind
of distribution known to often arise precisely in connection with
Internet-related phenomena. We derive mostly analytical, closed-form results
for several regimes: (a) Net neutrality, (b) social optimum, (c) maximum
revenue by the ISP, or (d) maximum ISP revenue under quality differentiation.
One unexpected conclusion is that (a) and (b) will differ significantly, unless
average CP productivity is very high
The class of nonlinear stochastic models as a background for the bursty behavior in financial markets
We investigate large changes, bursts, of the continuous stochastic signals,
when the exponent of multiplicativity is higher than one. Earlier we have
proposed a general nonlinear stochastic model which can be transformed into
Bessel process with known first hitting (first passage) time statistics. Using
these results we derive PDF of burst duration for the proposed model. We
confirm analytical expressions by numerical evaluation and discuss bursty
behavior of return in financial markets in the framework of modeling by
nonlinear SDE.Comment: 9 pages, 5 figure
Pareto versus lognormal: a maximum entropy test
It is commonly found that distributions that seem to be lognormal over a broad range change to a power-law (Pareto) distribution for the last few percentiles. The distributions of many physical, natural, and social events (earthquake size, species abundance, income and wealth, as well as file, city, and firm sizes) display this structure. We present a test for the occurrence of power-law tails in statistical distributions based on maximum entropy. This methodology allows one to identify the true data-generating processes even in the case when it is neither lognormal nor Pareto. The maximum entropy approach is then compared with other widely used methods and applied to different levels of aggregation of complex systems. Our results provide support for the theory that distributions with lognormal body and Pareto tail can be generated as mixtures of lognormally distributed units
On a kinetic model for a simple market economy
In this paper, we consider a simple kinetic model of economy involving both
exchanges between agents and speculative trading. We show that the kinetic
model admits non trivial quasi-stationary states with power law tails of Pareto
type. In order to do this we consider a suitable asymptotic limit of the model
yielding a Fokker-Planck equation for the distribution of wealth among
individuals. For this equation the stationary state can be easily derived and
shows a Pareto power law tail. Numerical results confirm the previous analysis
Neuropsychological constraints to human data production on a global scale
Which are the factors underlying human information production on a global
level? In order to gain an insight into this question we study a corpus of
252-633 Million publicly available data files on the Internet corresponding to
an overall storage volume of 284-675 Terabytes. Analyzing the file size
distribution for several distinct data types we find indications that the
neuropsychological capacity of the human brain to process and record
information may constitute the dominant limiting factor for the overall growth
of globally stored information, with real-world economic constraints having
only a negligible influence. This supposition draws support from the
observation that the files size distributions follow a power law for data
without a time component, like images, and a log-normal distribution for
multimedia files, for which time is a defining qualia.Comment: to be published in: European Physical Journal
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