nep-cmp New Economics Papers
on Computational Economics
Issue of 2017‒07‒30
five papers chosen by
Stan Miles
Thompson Rivers University

  1. Agent Inspired Trading Using Recurrent Reinforcement Learning and LSTM Neural Networks By David W. Lu
  2. Stock Prediction: a method based on extraction of news features and recurrent neural networks By Zeya Zhang; Weizheng Chen; Hongfei Yan
  3. The Relationship between Energy Use, GDP, Carbon Dioxide Emissions, Population, Financial Development, and Industrialization: The Case of Turkey By Esra Ball?; Salih Çam; Müge Manga; Çiler Sigeze
  4. The Influence of Renewable Energy Sources on the Czech Electricity Transmission System By Karel Janda; Jan Malek; Lukas Recka
  5. Influence of Renewable Energy Sources on Electricity Transmission Networks in Central Europe By Karel Janda; Jan Malek; Lukas Recka

  1. By: David W. Lu
    Abstract: With the breakthrough of computational power and deep neural networks, many areas that we haven't explore with various techniques that was researched rigorously in past is feasible. In this paper, we will walk through possible concepts to achieve robo-like trading or advising. In order to accomplish similar level of performance and generality, like a human trader, our agents learn for themselves to create successful strategies that lead to the human-level long-term rewards. The learning model is implemented in Long Short Term Memory (LSTM) recurrent structures with Reinforcement Learning or Evolution Strategies acting as agents The robustness and feasibility of the system is verified on GBPUSD trading.
    Date: 2017–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1707.07338&r=cmp
  2. By: Zeya Zhang; Weizheng Chen; Hongfei Yan
    Abstract: This paper proposed a method for stock prediction. In terms of feature extraction, we extract the features of stock-related news besides stock prices. We first select some seed words based on experience which are the symbols of good news and bad news. Then we propose an optimization method and calculate the positive polar of all words. After that, we construct the features of news based on the positive polar of their words. In consideration of sequential stock prices and continuous news effects, we propose a recurrent neural network model to help predict stock prices. Compared to SVM classifier with price features, we find our proposed method has an over 5% improvement on stock prediction accuracy in experiments.
    Date: 2017–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1707.07585&r=cmp
  3. By: Esra Ball? (Çukurova University); Salih Çam (Çukurova University); Müge Manga (Çukurova University); Çiler Sigeze (Çukurova University)
    Abstract: This study investigates the relationship between energy use, GDP, carbon dioxide emissions, population, financial development, and industrialization utilizing ARDL and artificial neural network for Turkey. The data covers the period from 1968 to 2013. The study performed a two stage analysis. At the first stage, we examined the long run relationship and causality between variables. The variables are found to be cointegrated. The Granger causality test results shows that there is a unidirectional causality running from energy use to both carbon dioxide emissions and industrialization. According to the artificial neural network results, the most important effect on energy use comes from GDP. The predicted energy use from 1968 to 2013 has maximum absolute error of % 11. 31 and minimum absolute error of %0.07. Neural network evidence shows that the R-square coefficient is 98% for the sample period.
    Keywords: Energy use, ARDL, Neural network, Turkey
    JEL: C10 Q43 C22
    Date: 2017–07
    URL: http://d.repec.org/n?u=RePEc:sek:iacpro:4607826&r=cmp
  4. By: Karel Janda (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nabrezi 6, 111 01 Prague 1, Czech Republic; Department of Banking and Insurance, Faculty of Finance and Accounting, University of Economics, Namesti Winstona Churchilla 4, 13067 Prague, Czech Republic); Jan Malek (Universiteit van Amsterdam, Amsterdam); Lukas Recka (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nabrezi 6, 111 01 Prague 1, Czech Republic)
    Abstract: This paper provides the first academic economic simulation analysis of the impact of increase in predominantly German wind and solar energy production on the Czech electricity transmission network. To assess the exact impact on the transmission grid, updated state-of-the-art techno-economic model ELMOD is employed. Two scenarios for the year 2025 are evaluated on the basis of two representative weeks. The first scenario is considered as baseline and models currently used production mix. The second scenario focuses on the effect of German Energiewende policy on the transmission networks as expected in 2025. The results confirm that higher feed-in of solar and wind power increases the total transport of electricity between transmission system operator areas as well as the average load of lines and volatility of flows. Also, an increase in number of critical high-load hours is observable. Taking into account only the Czech transmission system, considerable rise both in transported volume and volatility are observed only on border transmission lines, not inside the country. Moreover, our qualitative analysis shows that all these mentioned effects are strenghtened by the presence of German-Austrian bidding zone.
    Keywords: Energiewende, wind, solar, transmission networks, ELMOD
    JEL: L94 Q21 Q48 C61
    Date: 2017–03
    URL: http://d.repec.org/n?u=RePEc:fau:wpaper:wp2017_06&r=cmp
  5. By: Karel Janda (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nabrezi 6, 111 01 Prague 1, Czech Republic; Department of Banking and Insurance, Faculty of Finance and Accounting, University of Economics, Namesti Winstona Churchilla 4, 13067 Prague, Czech Republic); Jan Malek (Universiteit van Amsterdam, Amsterdam); Lukas Recka (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nabrezi 6, 111 01 Prague 1, Czech Republic)
    Abstract: This paper focuses on the influence of increased wind and solar power production on the transmission networks in Central Europe. The model ELMOD is employed. Two development scenarios for the year 2025 are evaluated on the basis of four representative weeks. The first scenario focuses on the effect of Energiewende on the transmission networks, the second one drops out nuclear phase-out and thus assesses isolated effect of increased feed-in. The results indicate that higher feed-in of solar and wind power increases the exchange balance and total transport of electricity between transmission system operator areas as well as the average load of lines and volatility of flows. Solar power is identified as a key contributor to the volatility increase, wind power is identified as a key loop-flow contributor. Eventually, it is concluded that German nuclear phase-out does not significantly exacerbate mentioned problems.
    Keywords: Energiewende, RES, transmission networks, congestion, loop flows, ELMOD, Central Europe
    JEL: L94 Q21 Q48 C61
    Date: 2017–02
    URL: http://d.repec.org/n?u=RePEc:fau:wpaper:wp2017_05&r=cmp

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