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relatedpublications [2017/09/02 11:07]
J. Ignacio Hidalgo gpem
relatedpublications [2017/09/02 11:32] (current)
J. Ignacio Hidalgo empirical economics added
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 This paper presents an implementation of Grammatical Evolution on a GPU architecture. Our proposal, Embedded Grammars, implements the grammar directly in the code. Although more rigid, it allows to compute the decodification in parallel with the evaluation of the individuals. We tested three different grammars with a set of eight symbolic regression problems. The symbolic regression problems consists on obtaining a mathematical expression in the form  y=f(x), in our case, from a set of 288 pairs x, y. The analysis of the results shows that Embedded Grammars are better not only in terms of execution time, but also in quality when compared with an implementation on a CPU. Speed-up results are also better than those presented in the literature. This paper presents an implementation of Grammatical Evolution on a GPU architecture. Our proposal, Embedded Grammars, implements the grammar directly in the code. Although more rigid, it allows to compute the decodification in parallel with the evaluation of the individuals. We tested three different grammars with a set of eight symbolic regression problems. The symbolic regression problems consists on obtaining a mathematical expression in the form  y=f(x), in our case, from a set of 288 pairs x, y. The analysis of the results shows that Embedded Grammars are better not only in terms of execution time, but also in quality when compared with an implementation on a CPU. Speed-up results are also better than those presented in the literature.
  
 +{{: empirical.jpg?50|}}[[http://dx.doi.org/10.1007/s00181-017-1303-9| Exploring the influence of industries and randomness in stock prices ]].Empirical Economics 201, pp 1-21
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 Empirical economics-Exploring the influence of industries and randomness in stock prices-This study explores the behavior of time series of historical prices and makes two additional contributions to the literature. In summarized form, we present an overview of each of the financial theories that discuss the movements of stock prices and their connection with industry trends. Within this theoretical framework, we first propose that prices be distinguished by following stock prices and a random-walk approach, and second, that the analysis of historical prices be broken down by industries. Similarities among price series are extracted through a clustering methodology based on an approach to non-computable Kolmogorov complexity. We model price series by following geometric Brownian motion and compare them to historical series of stock prices. Our first contribution confirms the existence of hidden common patterns in time series of historical prices that are clearly distinguishable from simulated series. The second contribution claims strong connections among firms carrying out similar industrial activities. The results confirm that stock prices belonging to the same industry behave similarly, whereas they behave differently from those of firms in other industries. Our research sheds new light on the stylized feature of the non-randomness of stock prices by pointing at fundamental aspects related to the industry as partial explanatory factors behind price movements. Empirical economics-Exploring the influence of industries and randomness in stock prices-This study explores the behavior of time series of historical prices and makes two additional contributions to the literature. In summarized form, we present an overview of each of the financial theories that discuss the movements of stock prices and their connection with industry trends. Within this theoretical framework, we first propose that prices be distinguished by following stock prices and a random-walk approach, and second, that the analysis of historical prices be broken down by industries. Similarities among price series are extracted through a clustering methodology based on an approach to non-computable Kolmogorov complexity. We model price series by following geometric Brownian motion and compare them to historical series of stock prices. Our first contribution confirms the existence of hidden common patterns in time series of historical prices that are clearly distinguishable from simulated series. The second contribution claims strong connections among firms carrying out similar industrial activities. The results confirm that stock prices belonging to the same industry behave similarly, whereas they behave differently from those of firms in other industries. Our research sheds new light on the stylized feature of the non-randomness of stock prices by pointing at fundamental aspects related to the industry as partial explanatory factors behind price movements.
  
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relatedpublications.txt · Last modified: 2017/09/02 11:32 by J. Ignacio Hidalgo