A Genetic Programming Approach to Model International Short-Term Capital Flow

Yu, Tina; Chen, Shu‐Heng; Kuo, Tzu-Wen

doi:10.1016/s0731-9053(04)19002-6

Cited by 12 publications

(4 citation statements)

References 18 publications

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“…In recent years there has been a growing number of applications of evolutionary computing, especially in finance (Acosta-González and Fernández, 2014;Chen and Kuo, 2002;Fogel, 2006;Lawrenz and Westerhoff, 2003;Vasilakis et al, 2013;Wei, 2013;Yu et al, 2004), and for stock price forecasting in particular (Chen et al, 2008;Kaboudan, 2000;Larkin and Ryan, 2008;Sheta et al, 2015;Wilson and Banzhaf, 2009). See Drake and Marks ( 2002) for a review of the applications of GAs in financial forecasting.…”

Section: Applications Of Gp In Economic Researchmentioning

confidence: 99%

Unemployment expectations: A socio-demographic analysis of the effect of news

et al. 2019

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In this study, we evaluate the effect of news on consumer unemployment expectations for sixteen socio-demographic groups. To this end, we construct an unemployment sentiment indicator and extract news about several economic variables. By means of genetic programming we estimate symbolic regressions that link unemployment rates in the Euro Area to qualitative expectations about a wide range of economic variables. We then use the evolved expressions to compute unemployment expectations for each consumer group. We first assess the out-of-sample forecast accuracy of the evolved indicators, obtaining better forecasts for the leading unemployment sentiment indicator than for the coincident one. Results are similar across the different socio-demographic groups. The best forecast results are obtained for respondents between 30 and 49 years. The group where we observe the bigger differences among categories is the occupation, where the lowest forecast errors are obtained for the unemployed respondents. Next, we link news about inflation, industrial production, and stock markets to unemployment expectations. With this aim we match positive and negative news with consumers' unemployment sentiment using a distributed lag regression model for each news item. We find asymmetries in the responses of consumers' unemployment expectations to economic news: they tend to be stronger in the case of negative news, especially in the case of inflation.

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Section: Applications Of Gp In Economic Researchmentioning

confidence: 99%

Unemployment expectations: A socio-demographic analysis of the effect of news

et al. 2019

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“…In [34], the authors applied genetic algorithms to optimize the signals generated by technical trading tools. However, most of the works focus on the prediction of exchange rates or stock price trends (e.g., [35][36][37][38][39][40]). One can refer to [41] for a recent review of the applications of genetic algorithms to forecasting prices of commodities.…”

Section: Methodsmentioning

confidence: 99%

A Genetic Programming Approach for Economic Forecasting with Survey Expectations

2022

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We apply a soft computing method to generate country-specific economic sentiment indicators that provide estimates of year-on-year GDP growth rates for 19 European economies. First, genetic programming is used to evolve business and consumer economic expectations to derive sentiment indicators for each country. To assess the performance of the proposed indicators, we first design a nowcasting experiment in which we recursively generate estimates of GDP at the end of each quarter, using the latest business and consumer survey data available. Second, we design a forecasting exercise in which we iteratively re-compute the sentiment indicators in each out-of-sample period. When evaluating the accuracy of the predictions obtained for different forecast horizons, we find that the evolved sentiment indicators outperform the time-series models used as a benchmark. These results show the potential of the proposed approach for prediction purposes.

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“…Regarding GP, Vasilakis et al (2013) proposed a GP-based technique to predict returns in the trading of the euro/dollar exchange rate. GP has also been applied to to model short-term capital flows (Yu et al, 2004), to forecast exchange rates (Álvarez-Díaz and Álvarez, 2005), and for stock price forecasting (Chen et al, 2008;Kaboudan, 2000;Larkin and Ryan, 2008;Wilson and Banzhaf, 2009). Wilson and Banzhaf (2009) compared a developmental co-evolutionary GP approach to standard linear GP for interday stock prices prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Empirical modelling of survey-based expectations for the design of economic indicators in five European regions

2018

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This is a post-peer-review, pre-copyedit version of an article published in Empirica. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10663-017-9395-1”.In this study we use agents’ expectations about the state of the economy to generate indicators of economic activity in twenty-six European countries grouped in five regions (Western, Eastern, and Southern Europe, and Baltic and Scandinavian countries). We apply a data-driven procedure based on evolutionary computation to transform survey variables in economic growth rates. In a first step,\ud we design five independent experiments to derive a formula using survey variables that best replicates the evolution of economic growth in each region by means of genetic programming, limiting the integration schemes to the main mathematical operations. We then rank survey variables according to their performance in tracking economic activity, finding that agents’ ‘‘perception about the overall\ud economy compared to last year’’ is the survey variable with the highest predictive power. In a second step, we assess the out-of-sample forecast accuracy of the evolved indicators. Although we obtain different results across regions, Austria, Slovakia, Portugal, Lithuania and Sweden are the economies of each region that show the best forecast results. We also find evidence that the forecasting performance of the survey-based indicators improves during periods of higher growth.This is a post-peer-review, pre-copyedit version of an article published in Empirica. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10663-017-9395-1”.Peer ReviewedPostprint (author's final draft

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A Genetic Programming Approach to Model International Short-Term Capital Flow

Cited by 12 publications

References 18 publications

Unemployment expectations: A socio-demographic analysis of the effect of news

Unemployment expectations: A socio-demographic analysis of the effect of news

A Genetic Programming Approach for Economic Forecasting with Survey Expectations

Empirical modelling of survey-based expectations for the design of economic indicators in five European regions

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