for Journals by Title or ISSN for Articles by Keywords help
 Subjects -> BUSINESS AND ECONOMICS (Total: 3133 journals)     - ACCOUNTING (92 journals)    - BANKING AND FINANCE (267 journals)    - BUSINESS AND ECONOMICS (1158 journals)    - CONSUMER EDUCATION AND PROTECTION (24 journals)    - COOPERATIVES (4 journals)    - ECONOMIC SCIENCES: GENERAL (169 journals)    - ECONOMIC SYSTEMS, THEORIES AND HISTORY (181 journals)    - FASHION AND CONSUMER TRENDS (13 journals)    - HUMAN RESOURCES (94 journals)    - INSURANCE (22 journals)    - INTERNATIONAL COMMERCE (126 journals)    - INTERNATIONAL DEVELOPMENT AND AID (85 journals)    - INVESTMENTS (27 journals)    - LABOR AND INDUSTRIAL RELATIONS (43 journals)    - MACROECONOMICS (15 journals)    - MANAGEMENT (526 journals)    - MARKETING AND PURCHASING (89 journals)    - MICROECONOMICS (24 journals)    - PRODUCTION OF GOODS AND SERVICES (137 journals)    - PUBLIC FINANCE, TAXATION (35 journals)    - TRADE AND INDUSTRIAL DIRECTORIES (2 journals) BUSINESS AND ECONOMICS (1158 journals)                  1 2 3 4 5 6 | Last
 Computational Economics   [SJR: 0.24]   [H-I: 30]   [9 followers]  Follow         Hybrid journal (It can contain Open Access articles)    ISSN (Print) 1572-9974 - ISSN (Online) 0927-7099    Published by Springer-Verlag  [2352 journals]
• An Agent-Based Simulation of the Stolper–Samuelson Effect
• Authors: Luzius Meisser; C. Friedrich Kreuser
Pages: 533 - 547
Abstract: We demonstrate that agent-based simulations can exhibit results in line with classic macroeconomic theory. In particular, we present an agent-based simulation of an Arrow–Debreu economy that accurately exhibits the Stolper–Samuelson effect as an emergent property. Absent of a Walrasian auctioneer or any other central coordination, we let firm and consumer agents of different types interact in an open, money-driven market. Exogenous preference shocks result in price and wage shifts that are in accordance with the general equilibrium solution, not only qualitatively but also quantitatively with high accuracy. Key to this achievement are three independent measures. First, we overcome the poor input synchronization of conventional price finding heuristics of firms in agent-based models by introducing sensor prices, a novel approach to price finding that decouples information exploitation from information exploration. Second, we improve accuracy and convergence by employing exponential search as exploration algorithm. Third, we normalize prices indirectly by fixing dividends, thereby stabilizing the system’s dynamics.
PubDate: 2017-12-01
DOI: 10.1007/s10614-016-9616-x
Issue No: Vol. 50, No. 4 (2017)

• Influence of Inefficiency in Government Expenditure on the Multiplier of
Public Investment
• Authors: Shigeaki Ogibayashi; Kosei Takashima
Pages: 549 - 577
Abstract: The multiplier of public investment has been expected to far exceed 1, owing to the indirect influence of public spending. However, it has been reported that actual multipliers for a real economy are sometimes <1; the reason for this has not been adequately explained in the literature. This study analyzes the influence of inefficient public expenditure on gross domestic product, using both an agent-based model and a theoretical derivation of the equation for the multiplier of public investment, the latter of which is based on our revised version of Morishima’s economic linkage table. The use of both of these instruments indicates that gross domestic product decreases with an increase in the inefficiency of public expenditure, which is defined as the ratio of firm subsidies to the government’s total expenditure. The multiplier of public investment becomes <1 when the degree of inefficiency is sufficiently large, and the ratio of the firm’s investment spending to the total amount of subsidy funding is sufficiently small. A multiplier lower than 1 is thought to appear when the degree of inefficiency in public expenditure is sufficiently large and firms are reluctant to invest; much of the surplus amount of subsidized funds can be deposited into a bank account, thus reducing the money stock in the market.
PubDate: 2017-12-01
DOI: 10.1007/s10614-017-9671-y
Issue No: Vol. 50, No. 4 (2017)

• Computational Experiments Successfully Predict the Emergence of
Autocorrelations in Ultra-High-Frequency Stock Returns
• Authors: Jian Zhou; Gao-Feng Gu; Zhi-Qiang Jiang; Xiong Xiong; Wei Chen; Wei Zhang; Wei-Xing Zhou
Pages: 579 - 594
Abstract: Social and economic systems are complex adaptive systems, in which heterogenous agents interact and evolve in a self-organized manner, and macroscopic laws emerge from microscopic properties. To understand the behaviors of complex systems, computational experiments based on physical and mathematical models provide a useful tools. Here, we perform computational experiments using a phenomenological order-driven model called the modified Mike–Farmer (MMF) to predict the impacts of order flows on the autocorrelations in ultra-high-frequency returns, quantified by Hurst index $$H_r$$ . Three possible determinants embedded in the MMF model are investigated, including the Hurst index $$H_s$$ of order directions, the Hurst index $$H_x$$ and the power-law tail index $$\alpha _x$$ of the relative prices of placed orders. The computational experiments predict that $$H_r$$ is negatively correlated with $$\alpha _x$$ and $$H_x$$ and positively correlated with $$H_s$$ . In addition, the values of $$\alpha _x$$ and $$H_x$$ have negligible impacts on $$H_r$$ , whereas $$H_s$$ exhibits a dominating impact on $$H_r$$ . The predictions of the MMF model on the dependence of $$H_r$$ upon $$H_s$$ and $$H_x$$ are verified by the empirical results obtained from the order flow data of 43 Chinese stocks.
PubDate: 2017-12-01
DOI: 10.1007/s10614-016-9612-1
Issue No: Vol. 50, No. 4 (2017)

• Is the Extension of Trading Hours Always Beneficial' An Artificial
Agent-Based Analysis
• Authors: Kotaro Miwa; Kazuhiro Ueda
Pages: 595 - 627
PubDate: 2017-12-01
DOI: 10.1007/s10614-016-9613-0
Issue No: Vol. 50, No. 4 (2017)

• Endogenous Fundamental and Stock Cycles
• Authors: Weihong Huang; Yu Zhang
Pages: 629 - 653
Abstract: A heterogeneous agent model of a financial market with endogenous fundamental value is built to study the recurrence of stock cycles. In a hypothetical economy, a firm produces consumption goods and issues a risk-free corporate bond and a risky stock in the financial market. Heterogeneous agents provide either capital or labor to the production, and they trade in the financial market by using fundamental or technical strategies. The fundamental value of the firm’s stock is endogenously determined by the firm’s production output. Agents’ investment in the risk-free bond is reinvested into future production. Steady-state analysis shows possible economic equilibrium under a proper parameter setting. In numerical simulations, stock cycles recur, and each stock cycle consists of the following four phases: accumulation, boom, crash, and recovery. A close investigation of stock cycles shows that a prosperous stock market may accelerate the formation of bubbles by drawing resources from future production. Although chartists are less wealthy than fundamentalists, they are capable of having a significant effect on the stock market.
PubDate: 2017-12-01
DOI: 10.1007/s10614-016-9631-y
Issue No: Vol. 50, No. 4 (2017)

• The Psychological Force Model for Lowest Unique Bid Auction
• Authors: Rui Hu; Jinzhong Guo; Qinghua Chen; Tao Zheng
Pages: 655 - 667
Abstract: We study a type of complex system arising from economics, the lowest unique bid auction (LUBA) system which is a new generation of online markets. Different from the traditional auction in which the winner is who bids the highest price, in LUBA, the winner is whoever places the lowest of all unique bids. In this paper, we propose a multi-agent model to factually describes the human psychologies of the decision-making process in LUBA. The model produces bid-price distributions that are in excellent agreement with those from the real data, including the whole inverted-J shape which is a general feature of the real bid price distribution, and the exponential decreasing shape in the higher price range. This implies that it is possible for us to capture the essential features of human psychologies in the competitive environment as exemplified by LUBA and that we may provide significant quantitative insights into complex socio-economic phenomena.
PubDate: 2017-12-01
DOI: 10.1007/s10614-016-9614-z
Issue No: Vol. 50, No. 4 (2017)

• Can Sentiment Analysis and Options Volume Anticipate Future Returns'
• Authors: Patrick Houlihan; Germán G. Creamer
Pages: 669 - 685
Abstract: This paper evaluates the question of whether sentiment extracted from social media and options volume anticipates future asset return. The research utilized both textual based data and a particular market data derived call-put ratio, collected between July 2009 and September 2012. It shows that: (1) features derived from market data and a call-put ratio can improve model performance, (2) sentiment derived from StockTwits, a social media platform for the financial community, further enhances model performance, (3) aggregating all features together also facilitates performance, and (4) sentiment from social media and market data can be used as risk factors in an asset pricing framework.
PubDate: 2017-12-01
DOI: 10.1007/s10614-017-9694-4
Issue No: Vol. 50, No. 4 (2017)

• Emergent Heterogeneity in Keyword Valuation in Sponsored Search Markets: A
Closer-to-Practice Perspective
• Authors: Agam Gupta; Biswatosh Saha; Uttam K. Sarkar
Pages: 687 - 710
PubDate: 2017-12-01
DOI: 10.1007/s10614-016-9637-5
Issue No: Vol. 50, No. 4 (2017)

• Experimental Analysis of Corporate Wage Negotiations Based on the
Ultimatum Game: A New Approach Using a Combination of Laboratory and fMRI
Experiments
• Authors: Hidetoshi Yamaji; Masatoshi Gotoh; Yoshinori Yamakawa
Abstract: Workers who have limited wealth are also at a disadvantage in terms of income distribution. In accounting this brings to mind the way in which managers may limit wages by manipulating accounting information when negotiating with workers. While researchers have investigated whether or not managers manipulate information to keep workers’ wages low, they have rarely been able to produce sufficient empirical evidence to support their arguments. So we seek to bridge this gap between the present conditions and academic research. Focusing on the recent tendency for labor-management negotiations to take the form of individual bargaining between managers and workers, we conduct experiments of psychology and perform neuro-experiments using fMRI. It was found that a trend existed where managers who had a high level of empathy and would normally be expected to recognize workers’ difficult circumstances, conversely tended to compel workers to accept unfavorable outcomes.
PubDate: 2017-11-10
DOI: 10.1007/s10614-017-9769-2

• Improving Financial Distress Prediction Using Financial Network-Based
Information and GA-Based Gradient Boosting Method
• Authors: Jiaming Liu; Chong Wu; Yongli Li
Abstract: Previous studies on financial distress prediction have chiefly used financial indicators which derived from financial statements as explanatory variables, so some potentially useful information that contained in the financial network was not considered. The listed companies can be represented as a complex financial network which the firms are regarded as nodes and the links account for stock returns correlation. The purpose of this study is to investigate whether network-based variables can improve the predictive power of financial distress prediction. Therefore, this study proposed a genetic algorithm (GA) approach to parameter selection in gradient boosting decision tree and integrated network-based variables for financial distress prediction. In order to verify the prediction capability of network-based variables and GA-based gradient boosting method in financial distress prediction, empirical study based on Chinese listed firms’ real data is employed, and comparative analysis is conducted. The experiment results indicate that the introduction of network-based variables and GA-based gradient boosting method for financial distress prediction can enhance predictive performance in terms of accuracy, recall, precision, F-score, type I error, and type II error.
PubDate: 2017-11-09
DOI: 10.1007/s10614-017-9768-3

• An Artificial Neural Network-Based Approach to the Monetary Model of
Exchange Rate
• Authors: Huseyin Ince; Ali Fehim Cebeci; Salih Zeki Imamoglu
Abstract: This paper aims to investigate the predictive accuracy of the flexible price monetary model of the exchange rate, estimated by an approach based on combining the vector autoregressive model and multilayer feedforward neural networks. The forecasting performance of this nonlinear, nonparametric model is analyzed comparatively with a monetary model estimated in a linear static framework; the monetary model estimated in a linear dynamic vector autoregressive framework; the monetary model estimated in a parametric nonlinear dynamic threshold vector autoregressive framework; and the naïve random walk model applied to six different exchange rates over three forecasting periods. The models are compared in terms of both the magnitude of their forecast errors and the economic value of their forecasts. The proposed model yielded promising outcomes by performing better than the random walk model in 16 out of 18 instances in terms of the root mean square error and 15 out of 18 instances in terms of mean return and Sharpe ratio. The model also performed better than linear models in 17 out of 18 instances for root mean square error and 14 out of 18 instances for mean returns and Sharpe ratio. The distinguishing feature of the proposed model versus the present models in the literature is its robustness to outperform the random walk model, regardless of whether the magnitude of forecast errors or the economic value of the forecasts is chosen as a performance measure.
PubDate: 2017-11-07
DOI: 10.1007/s10614-017-9765-6

• Evolutionary Computation for Macroeconomic Forecasting
• Authors: Oscar Claveria; Enric Monte; Salvador Torra
Abstract: The main objective of this study is twofold. First, we propose an empirical modelling approach based on genetic programming to forecast economic growth by means of survey data on expectations. We use evolutionary algorithms to estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick, deriving mathematical functional forms that approximate the target variable. The set of empirically-generated proxies of economic growth are used as building blocks to forecast the evolution of GDP. Second, we use these estimates of GDP to assess the impact of the 2008 financial crisis on the accuracy of agents’ expectations about the evolution of the economic activity in four Scandinavian economies. While we find an improvement in the capacity of agents’ to anticipate economic growth after the crisis, predictive accuracy worsens in relation to the period prior to the crisis. The most accurate GDP forecasts are obtained for Sweden.
PubDate: 2017-11-07
DOI: 10.1007/s10614-017-9767-4

• Getting the Best of Both Worlds' Developing Complementary
Equation-Based and Agent-Based Models
• Authors: Claudius Gräbner; Catherine S. E. Bale; Bernardo Alves Furtado; Brais Alvarez-Pereira; James E. Gentile; Heath Henderson; Francesca Lipari
Abstract: We argue that building agent-based and equation-based versions of the same theoretical model is a fruitful way of gaining insights into real-world phenomena. We use the epistemological concept of “models as isolations and surrogate systems” as the philosophical underpinning of this argument. In particular, we show that agent-based and equation-based approaches align well when used simultaneously and, contrary to some common misconceptions, should be considered complements rather than substitutes. We illustrate the usefulness of the approach by examining a model of the long-run relationship between economic development and inequality (i.e., the Kuznets hypothesis).
PubDate: 2017-11-01
DOI: 10.1007/s10614-017-9763-8

• Tail-Related Risk Measurement and Forecasting in Equity Markets
• Authors: Stelios Bekiros; Nikolaos Loukeris; Iordanis Eleftheriadis; Christos Avdoulas
Abstract: Parametric, simulation-based and hybrid methods are utilized to estimate various risk measures such as Value-at-Risk (VaR), Conditional VaR and coherent Expected Shortfall. An exhaustive backtesting analysis is performed for London’s FTSE 100 index and a comparative evaluation of the predictability of the investigated models is performed with the use of various statistical tests. We show that optimal tail risk forecasting necessitates that many factors be considered such as asset structure and capitalization and specific market conditions i.e., normal or crisis periods. Specifically, for large capitalization stocks and long investment horizons parametric modeling accounted for relatively better risk estimation in normal quantiles, whilst for short-term trading strategies, the non-parametric methods are more suitable for measuring extreme tail risk of small-cap stocks.
PubDate: 2017-11-01
DOI: 10.1007/s10614-017-9766-5

• Forecasting Crude Oil Prices: A Comparison Between Artificial Neural
Networks and Vector Autoregressive Models
• Authors: Sepehr Ramyar; Farhad Kianfar
Abstract: Given the importance of crude oil prices for businesses, governments and policy makers, this paper investigates predictability of oil prices using artificial neural networks taking into account the exhaustible nature of crude oil and impact of monetary policy along with other major drivers of crude oil prices. A multilayer perceptron neural network is developed and trained with historical data from 1980 to 2014 and using mean square error for testing data, optimal number of hidden layer neurons is determined and the designed MLP neural network is used for estimation of the forecasting model. Meanwhile, an economic model for crude oil prices is developed and estimated using a vector autoregressive model. Results from the proposed ANN are then compared to those of the vector autoregressive model and based on the corresponding R-squared for each model, it is concluded that the MLP neural network can more accurately predict crude oil prices than a VAR model. It is shown, via empirical analysis, that with a combination of appropriate neural network design, feature engineering, and incorporation of crude oil market realities in the model, an accurate prediction of crude oil prices can be attained.
PubDate: 2017-10-30
DOI: 10.1007/s10614-017-9764-7

• Trade Costs and Endogenous Nontradability in a Model with Sectoral and
Firm-Level Heterogeneity
• Authors: Manoj Atolia
Abstract: The paper takes a first step in the direction of simultaneously incorporating sectoral and firm-level heterogeneity in the models of international trade and macroeconomics in a tractable manner: without increasing the complexity of numerical computations compared to the existing models with heterogeneity in one dimension. In a model with sectoral heterogeneity in trade costs and firm-level heterogeneity in productivity, introducing one source of heterogeneity at a time and piecing together the results implies that, on reduction in trade costs, more goods and more varieties of every tradable good become traded. In contrast, in the correctly specified model with simultaneous heterogeneity in both dimensions, while more goods do indeed become tradable, but for more than 50% of the previously traded goods, the number of traded varieties falls. The model also reconciles apparently contrasting predictions for the differences in the deviation of domestic price from the world price for the traded and nontraded goods when heterogeneity is introduced, one dimension at a time.
PubDate: 2017-10-23
DOI: 10.1007/s10614-017-9761-x

• Exploring Dynamic Impact of Foreign Direct Investment on China’s CO

• Authors: Xiongfeng Pan; Jing Zhang; Changyu Li; Rong Quan; Bin Li
Abstract: The impact of foreign direct investment (FDI) on China’s CO $$_{2}$$ emissions is an important index to evaluate the effect of foreign investment policy. This paper uses the monthly data of CO $$_{2}$$ emissions and FDI from January 1997 to December 2013 to analyze the regime states, switching probability and regime correlation between FDI and CO $$_{2}$$ emissions with the help of nonlinear Markov-switching vector error correction model (MS-VECM), The results indicate that the influence of FDI on CO $$_{2}$$ emissions shows the two-regime dynamic characteristics, FDI has played a stimulating role in promoting China’s CO $$_{2}$$ emissions during the period from January 1997 to October 2003, while played an inhibiting role during the period from November 2003 to December 2013. The duration of the inhibiting effect of FDI on CO $$_{2}$$ emissions is longer, and the frequency is higher than that of the stimulating effect. Therefore, the overall influence of FDI on CO $$_{2}$$ emissions during the period from January 1997 to December 2013 is inhibitive, which means FDI has contributed to CO $$_{2}$$ emissions reduction. The innovation points of this study are mainly reflected in the following two aspects: first, nonlinear MS-VECM is introduced to dynamically study the relationship between FDI and CO $$_{2}$$ emissions in contrast to prior studies that simply use static analysis method; second, the effect of China’s foreign investment policies on CO $$_{2}$$ emissions is evaluated in each period according to the empirical results of MS-VECM.
PubDate: 2017-10-19
DOI: 10.1007/s10614-017-9745-x

• The Income Gap Between Urban and Rural Residents in China: Since 1978
• Authors: Xiao Ma; Feiran Wang; Jiandong Chen; Yang Zhang
Abstract: Previous studies on the income gap between rural and urban areas have concentrated mainly on the flow factor in the income measure. This study investigates income inequality between rural and urban residents during 1978–2014 in China based on both urban–rural flow and the accumulated income Gini coefficient. In addition, the study compares the general changes in trends in these Gini coefficients in terms of urbanization and the ratio of urban-to-rural average income by decomposing the Gini ratios. The results show that the Gini coefficient of flow income has an inverted U-shaped pattern, while the Gini coefficient of accumulated income decreases significantly in most years, showing a decreasing trend since China’s economic reform and opening, with a time lag and fluctuation. Thus, either the accumulated or flow income Gini coefficients decline continuously as urbanization progresses, which could help governments craft fair policies to promote urbanization, narrowing the income gap between rural and urban areas.
PubDate: 2017-10-19
DOI: 10.1007/s10614-017-9759-4

• A Numerical Algorithm for the Coupled PDEs Control Problem
• Authors: Gonglin Yuan; Xiangrong Li
Abstract: For the coupled PDE control problem, at time $$t_i$$ with the ith point, the standard algorithm will first obtain the two space variables $$(z_i,v_i)$$ and then obtain the control variables $$(\varsigma _i^{opt},\mu _i^{opt})$$ from the given initial points $$(\varsigma _i^0,\mu _i^0)$$ . How many points i are determined by the facts of the case' We usually believe that the largest i defined by n is big because the small step size $$\tau =\frac{T-t_0}{n}$$ will generate a good approximation, where T denotes the terminal time. Thus, the solution process is very tedious, and much CPU time is required. In this paper, we present a new method to overcome this drawback. This presented method, which fully utilizes the first-order conditions, simultaneously considers the two space variables $$(z_i,v_i)$$ and the control variables $$(\varsigma _i^{opt},\mu _i^{opt})$$ with $$t_i$$ at i. The computational complexity of the new algorithm is $$O(N^3)$$ , whereas that of the normal algorithm is $$O(N^3+N^3K)$$ . The performance of the proposed algorithm is tested using an example.
PubDate: 2017-10-10
DOI: 10.1007/s10614-017-9757-6

• Identification in Models with Discrete Variables
• Authors: Lukáš Lafférs
Abstract: This paper provides a novel, simple, and computationally tractable method for determining an identified set that can account for a broad set of economic models when the economic variables are discrete. Using this method, we show using a simple example how imperfect instruments affect the size of the identified set when the assumption of strict exogeneity is relaxed. This knowledge can be of great value, as it is interesting to know the extent to which the exogeneity assumption drives results, given it is often a matter of some controversy. Moreover, the flexibility obtained from our newly proposed method suggests that the determination of the identified set need no longer be application specific, with the analysis presenting a unifying framework that algorithmically approaches the question of identification.
PubDate: 2017-10-05
DOI: 10.1007/s10614-017-9758-5

JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327

Home (Search)
Subjects A-Z
Publishers A-Z
Customise
APIs