593 research outputs found
Billionaires
Existing studies of entrepreneurship focus on entrepreneurs whose individual contribution to wealth creation is typically trivial: self-employed persons. This paper investigates entrepreneurs whose individual contribution to wealth creation is enormous: billionaires. We explore the relationship between economic development, institutions, and these contrasting kinds of entrepreneurs. We find that the institutions consistent with self-employed entrepreneurs di¤er markedly from the ones consistent with billionaires. Further, only the latter are consistent with the institutions that underlie economic prosperity. Where well-protected private property rights and supporting, market-enhancing institutions flourish, so do billionaires. But self-employed entrepreneurs don't. Where private property rights are weakly protected and interventionist institutions flourish, so do self-employed entrepreneurs. But billionaires don't.Billionaires; Entrepreneurship; Self-employment; Institutions
Non-Parametric Causality Detection: An Application to Social Media and Financial Data
According to behavioral finance, stock market returns are influenced by
emotional, social and psychological factors. Several recent works support this
theory by providing evidence of correlation between stock market prices and
collective sentiment indexes measured using social media data. However, a pure
correlation analysis is not sufficient to prove that stock market returns are
influenced by such emotional factors since both stock market prices and
collective sentiment may be driven by a third unmeasured factor. Controlling
for factors that could influence the study by applying multivariate regression
models is challenging given the complexity of stock market data. False
assumptions about the linearity or non-linearity of the model and inaccuracies
on model specification may result in misleading conclusions.
In this work, we propose a novel framework for causal inference that does not
require any assumption about the statistical relationships among the variables
of the study and can effectively control a large number of factors. We apply
our method in order to estimate the causal impact that information posted in
social media may have on stock market returns of four big companies. Our
results indicate that social media data not only correlate with stock market
returns but also influence them.Comment: Physica A: Statistical Mechanics and its Applications 201
Probabilistic Matching: Causal Inference under Measurement Errors
The abundance of data produced daily from large variety of sources has
boosted the need of novel approaches on causal inference analysis from
observational data. Observational data often contain noisy or missing entries.
Moreover, causal inference studies may require unobserved high-level
information which needs to be inferred from other observed attributes. In such
cases, inaccuracies of the applied inference methods will result in noisy
outputs. In this study, we propose a novel approach for causal inference when
one or more key variables are noisy. Our method utilizes the knowledge about
the uncertainty of the real values of key variables in order to reduce the bias
induced by noisy measurements. We evaluate our approach in comparison with
existing methods both on simulated and real scenarios and we demonstrate that
our method reduces the bias and avoids false causal inference conclusions in
most cases.Comment: In Proceedings of International Joint Conference Of Neural Networks
(IJCNN) 201
Dynamical systems as temporal feature spaces
Parameterized state space models in the form of recurrent networks are often
used in machine learning to learn from data streams exhibiting temporal
dependencies. To break the black box nature of such models it is important to
understand the dynamical features of the input driving time series that are
formed in the state space. We propose a framework for rigorous analysis of such
state representations in vanishing memory state space models such as echo state
networks (ESN). In particular, we consider the state space a temporal feature
space and the readout mapping from the state space a kernel machine operating
in that feature space. We show that: (1) The usual ESN strategy of randomly
generating input-to-state, as well as state coupling leads to shallow memory
time series representations, corresponding to cross-correlation operator with
fast exponentially decaying coefficients; (2) Imposing symmetry on dynamic
coupling yields a constrained dynamic kernel matching the input time series
with straightforward exponentially decaying motifs or exponentially decaying
motifs of the highest frequency; (3) Simple cycle high-dimensional reservoir
topology specified only through two free parameters can implement deep memory
dynamic kernels with a rich variety of matching motifs. We quantify richness of
feature representations imposed by dynamic kernels and demonstrate that for
dynamic kernel associated with cycle reservoir topology, the kernel richness
undergoes a phase transition close to the edge of stability.Comment: 45 pages, 17 figures, accepte
- …