177,283 research outputs found
Deep Neural Network Model for Hurst Exponent: Learning from R/S Analysis
No full textThis paper proposes a deep neural network (DNN) model to estimate the Hurst exponent, a crucial parameter in modelling stock market price movements driven by fractional geometric Brownian motion. We randomly selected 446 indices from the S&P 500 and extracted their price movements over the last 2010 trading days. Using the rescaled range (R/S) analysis and the detrended fluctuation analysis (DFA), we computed the Hurst exponent and related parameters, which serve as the target parameters in the DNN architecture. The DNN model demonstrated remarkable learning capabilities, making accurate predictions even with small sample sizes. This addresses a limitation of R/S analysis, known for biased estimates in such instances. The significance of this model lies in its ability, once trained, to rapidly estimate the Hurst exponent, providing results in a small fraction of a second
Stochastic port--Hamiltonian systems
No full textIn the present work we formally extend the theory of port-Hamiltonian systems
to include random perturbations. In particular, suitably choosing the space of
flow and effort variables we will show how several elements coming from
possibly different physical domains can be interconnected in order to describe
a dynamic system perturbed by general continuous semimartingale. Relevant
enough, the noise does not enter into the system solely as an external random
perturbation, since each port is itself intrinsically stochastic. Coherently to
the classical deterministic setting, we will show how such an approach extends
existing literature of stochastic Hamiltonian systems on pseudo-Poisson and
pre-symplectic manifolds. Moreover, we will prove that a power-preserving
interconnection of stochastic port-Hamiltonian systems is a stochastic
port-Hamiltonian system as well
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Mild solutions to the dynamic programming equation for stochastic optimal control problems
No full textWe show via the nonlinear semigroup theory in L1(R) that the 1-D dynamic programming equation associated with a stochastic optimal control problem with multiplicative noise has a unique mild solution in a sense to be made precis
"Closing the R&D Gap, Evaluating the Sources of R&D Spending"
No full textBoth spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.
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