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on Computational Economics |
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Issue of 2017‒07‒09
nine papers chosen by |
| By: | Rengs, Bernhard; Scholz-Waeckerle, Manuel |
| Abstract: | This article contributes to the field of evolutionary macroeconomics by highlighting the dynamic interlinkages between micro-meso-macro with a Veblenian meso foundation in an agent-based macroeconomic model. Consumption is dependent on endogenously changing social class and signaling, such as bandwagon, Veblen and snob effects. In particular we test the macroeconomic effects of this meso foundation in a generic agent-based model of a closed artificial economy. The model is stock-flow consistent and builds upon local decision heuristics of heterogeneous agents characterized by bounded rationality and satisficing behavior. These agents include a multitude of households (workers and capitalists), firms, banks as well as a capital goods firm, a government and a central bank. Simulation experiments indicate co-evolutionary dynamics between signaling-by-consuming and firm specialization that eventually effect employment, consumer prices as well as other macroeconomic aggregates substantially. |
| Keywords: | Evolutionary macroeconomics; agent-based modelling; micro-meso-macro; conspicuous consumption; social class; firm specialization |
| JEL: | B52 C63 E21 E23 L11 |
| Date: | 2017–03–03 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:80021&r=cmp |
| By: | Zhengyao Jiang; Dixing Xu; Jinjun Liang |
| Abstract: | Financial portfolio management is the process of constant redistribution of a fund into different financial products. This paper presents a financial-model-free Reinforcement Learning framework to provide a deep machine learning solution to the portfolio management problem. The framework consists of the Ensemble of Identical Independent Evaluators (EIIE) topology, a Portfolio-Vector Memory (PVM), an Online Stochastic Batch Learning (OSBL) scheme, and a fully exploiting and explicit reward function. This framework is realized in three instants in this work with a Convolutional Neural Network (CNN), a basic Recurrent Neural Network (RNN), and a Long Short-Term Memory (LSTM). They are, along with a number of recently reviewed or published portfolio-selection strategies, examined in three back-test experiments with a trading period of 30 minutes in a cryptocurrency market. Cryptocurrencies are electronic and decentralized alternatives to government-issued money, with Bitcoin as the best-known example of a cryptocurrency. All three instances of the framework monopolize the top three positions in all experiments, outdistancing other compared trading algorithms. Although with a high commission rate of 0.25% in the backtests, the framework is able to achieve at least 4-fold returns in 30 days. |
| Date: | 2017–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1706.10059&r=cmp |
| By: | Ahn, SeHyoun; Kaplan, Greg; Moll, Benjamin; Winberry, Thomas; Wolf, Christian |
| Abstract: | We develop an efficient and easy-to-use computational method for solving a wide class of general equilibrium heterogeneous agent models with aggregate shocks, together with an open source suite of codes that implement our algorithms in an easy-to-use toolbox. Our method extends standard linearization techniques and is designed to work in cases when inequality matters for the dynamics of macroeconomic aggregates. We present two applications that analyze a two-asset incomplete markets model parameterized to match the distribution of income, wealth, and marginal propensities to consume. First, we show that our model is consistent with two key features of aggregate consumption dynamics that are difficult to match with representative agent models: (i) the sensitivity of aggregate consumption to predictable changes in aggregate income and (ii) the relative smoothness of aggregate consumption. Second, we extend the model to feature capital-skill complementarity and show how factor-specific productivity shocks shape dynamics of income and consumption inequality. |
| Date: | 2017–06 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:12123&r=cmp |
| By: | Masafumi Nakano (Graduate School of Economics, University of Tokyo); Akihiko Takahashi (Graduate School of Economics, University of Tokyo); Soichiro Takahashi (Graduate School of Economics, University of Tokyo) |
| Abstract: | This paper proposes a framework of robust technical trading with fuzzy knowledge-based systems (KBSs). Particularly, our framework consists of two modules, i.e., (i) a module for preparing candidate investment proposals and (ii) a module for their evaluation to construct a well-performed portfolio. Moreover, our framework effectively utilizes fuzzy KBSs for representation of human expert knowledge: Precisely, in the 1st module, three sets of fuzzy IF-THEN rules implement linguistic technical trading rules, which are designed specifically for getting well performance in different market phases. On the other hand, the 2nd module exploits fuzzy logic to evaluate the prepared investment candidates in terms of multilateral performance measures frequently used in practice. In an out-of-sample numerical experiment, our framework successfully generates a series of portfolios, which show long-term satisfactory records in the prolonged slumping Japanese stock market. |
| Date: | 2017–07 |
| URL: | http://d.repec.org/n?u=RePEc:cfi:fseres:cf413&r=cmp |
| By: | Tim Felling; Christoph Weber (Chair for Management Sciences and Energy Economics, University of Duisburg-Essen (Campus Essen)) |
| Abstract: | New and alternative delimitations of price zones for Central Western Europe (CWE) might constitute a mid-term solution to cope with the increasing congestion in the electricity transmission grids. The significantly growing infeed from renewable energy sources puts more and more pressure on the grid and emphasizes the need for improved congestion management. Thus, a new delimitation of price zones is frequently considered in current discussions and research. The present paper applies a novel hierarchical cluster algorithm that clusters locational marginal prices and weights nodes depending on their demand- and supply situation to identify possible new price zone configurations. The algorithm is applied in a scenario analysis of six scenarios reflecting main drivers that influence the future development of European Electricity markets in line with the trilemma of energy policy targets. Robustness of the new configuration is an important criterion for price zone configurations according to the European Guideline on Capacity Allocation and Congestion Management (CACM). Therefore, a robust price zone configuration is computed taking into account all the six individual scenarios. Results show that shape, size and price variations of price zones on the one hand strongly depend on the individual scenario. On the other hand, the identified robust configuration is shown to outperform other configurations, particularly also the current price zone configuration in CWE. |
| Keywords: | Cluster Analysis, Electricity Market Design, Nodal Pricing, Congestion Management, Energy Markets and Regulation; Bidding zones, Price Zone Configuration, Bidding Zone Configuration |
| JEL: | C38 C61 D47 L51 Q41 Q48 |
| Date: | 2017–06 |
| URL: | http://d.repec.org/n?u=RePEc:dui:wpaper:1706&r=cmp |
| By: | Panpan Ren; Jiang-Lun Wu |
| Abstract: | In this paper, we introduce a matrix-valued time series model for foreign exchange market. We then formulate trading matrices, foreign exchange options and return options (matrices), as well as on-line portfolio strategies. Moreover, we attempt to predict returns of portfolios by developing a cross rate method. This leads us to construct an on-line portfolio selection algorithm for this model. At the end, we prove the profitability and the universality of our algorithm. |
| Date: | 2017–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1707.00203&r=cmp |
| By: | Graham Gudgin; Ken Coutts; Neil Gibson |
| Abstract: | This working paper provides a detailed exposition of the assumptions, structure and statistical evidence that support a new macroeconomic forecasting and simulation model of the UK economy. The model is based on an annual dataset that produces conditional forecasts or simulations over a five to ten year horizon. The model enables us to discuss issues of policy in quantitative terms so that the orders of magnitude of the economic consequences can be assessed. Readers of our forecast reports will find in this paper the information that justifies the modelling methodology and the empirical evidence supporting the key behavioural relationships of the model. |
| Keywords: | Macroeconomic policy; fiscal and monetary policy; macroeconomic forecasts; macroeconomic models |
| JEL: | E12 E17 E27 E44 E47 |
| Date: | 2015–09 |
| URL: | http://d.repec.org/n?u=RePEc:cbr:cbrwps:wp472&r=cmp |
| By: | Rosenmüller, Joachim (Center for Mathematical Economics, Bielefeld University) |
| Date: | 2017–04–04 |
| URL: | http://d.repec.org/n?u=RePEc:bie:wpaper:148&r=cmp |
| By: | Larisa Adamyan; Wolfgang Kirill Efimov; Wolfgang Karl Härdle; Cathy Yi-Hsuan Chen |
| Abstract: | The JEL classification system is a standard way of assigning key topics to economic articles in order to make them more easily retrievable in the bulk of nowadays massive literature. Usually the JEL (Journal of Economic Literature) is picked by the author(s) bearing the risk of suboptimal assignment. Using the database of a Collaborative Research Center from Humboldt-Universit¨at zu Berlin and Xiamen University, China we employ a new adaptive clustering technique to identify interpretable JEL (sub)clusters. The proposed Adaptive Weights Clustering (AWC) is available on www.quantlet.de and is based on the idea of locally weighting each point (document, abstract) in terms of cluster membership. Comparison with k-means or CLUTO reveals excellent performance of AWC. |
| Keywords: | Clustering, JEL system, Adaptive algorithm, Economic articles, Nonparametric |
| JEL: | C00 |
| Date: | 2017–07 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2017-013&r=cmp |