nep-cmp New Economics Papers
on Computational Economics
Issue of 2014‒06‒07
six papers chosen by
Stan Miles
Thompson Rivers University

  1. Non-Price Competition in a Modular Economy By Bin-Tzong Chie; Shu-Heng Chen
  2. The Future of Spanish Pensions By Javier Diaz Gimenez; Julian Diaz Saavedra
  3. Trading Volume and Market Efficiency: An Agent Based Model with Heterogenous Knowledge about Fundamentals By Vivien Lespagnol; Juliette Rouchier
  4. Integration of a Predictive, Continuous Time Neural Network into Securities Market Trading Operations By Christopher S Kirk
  5. Supervised classification-based stock prediction and portfolio optimization By Sercan Arik; Sukru Burc Eryilmaz; Adam Goldberg
  6. Buyer to Seller Recommendation under Constraints By Cheng Chen; Lan Zheng; Venkatesh Srinivasan; Alex Thomo; Kui Wu; Anthony Sukow

  1. By: Bin-Tzong Chie; Shu-Heng Chen
    Abstract: While it has been well acknowledged by economists for a long time that competition is not just about price, the conventional quantity-based economic models have difficulties integrating price competition and quality competition into a coherent framework. In this paper, motivated by Herbert Simon’s view of near decomposability or modularity, we propose a quality-based economic model called the modular economy. In this modular economy, quality is manifested by the evolutionary design of more sophisticated and customized products that can satisfy consumers’ satisfaction to a higher degree. Two essential features of the modular economy are founded through the agent-based simulation of a duopolistic competition. First, market competition tends to be self-annihilating; the competition will eventually end up with a dominant or a monopoly firm (conglomerate). Second, the high-markup firm has a better chance to be the only survivor than its low-markup competitor. We analyze these features through the complex cyclical dynamics of prices, profits, dividends, investment, working capital, and quality.
    Keywords: Modularity, Near Decomposability, Modular Economy, Nonprice Competition, Co-Evolving, Agent-Based Modeling
    Date: 2014
    URL: http://d.repec.org/n?u=RePEc:trn:utwpas:1401&r=cmp
  2. By: Javier Diaz Gimenez (IESE Business School); Julian Diaz Saavedra (Department of Economic Theory and Economic History, University of Granada.)
    Abstract: We use an overlapping generations model economy with endogenous retirement to study the 2011 and 2013 reforms of the Spanish public pension system. We nd that this latest reforms, which extend the number of years os contributions used to compute the pensions, delay the retirement ages, introduce two sustainability factors, and eectively transform the Spanish pay-as-yougo system into a dened-contribution system, succeed in making Spanish pensions sustainable until 2037, but they fail to do so afterwards. The success until 2037 is achieved reducing the real value of the average pension and leaving the many loopholes of the contributivity and the transparency of the system unchanged. This reduction in pensions is progressive and, by 2037, the average pension will be approximately 20 percent smaller in real terms than what it would have been under the pension rules prevailing in 2010. The 2013 pension reform fails after 2037 because, from that year onwards, approximately 50 percent of the Spanish retirees will be paid the minimum pension, which is exempt from the sustainability factors. We conjecture that further reforms lurk in the future of Spanish pensions.
    Keywords: Computable general equilibrium, social security reform, retirement.
    JEL: C68 H55 J26
    Date: 2014–05–27
    URL: http://d.repec.org/n?u=RePEc:gra:wpaper:14/03&r=cmp
  3. By: Vivien Lespagnol (AMSE - Aix-Marseille School of Economics - Centre national de la recherche scientifique (CNRS) - École des Hautes Études en Sciences Sociales (EHESS) - Ecole Centrale Marseille (ECM)); Juliette Rouchier (AMSE - Aix-Marseille School of Economics - Centre national de la recherche scientifique (CNRS) - École des Hautes Études en Sciences Sociales (EHESS) - Ecole Centrale Marseille (ECM))
    Abstract: This paper studies the effect of investor's bounded rationality on market dynamics. In an order driven market, we consider a few-types model where two risky assets are exchanged. Agents differ by their behavior, knowledge, risk aversion and investment horizon. The investor's demand is defined by a utility maximization under constant absolute risk aversion. Relaxing the assumption of perfect knowledge of the fundamentals enables to identify two components in a bubble. The first one comes from the unperceived fundamental changes due to trader's belief perseverance. The second one is generated by chartist behavior. In all simulations, speculators make the market less efficient and more volatile. They also increase the maximum amount of assets exchanged in the most liquid time step. However, our model is not showing raising average volatility on long term. Concerning the fundamentalists, the unknown fundamental has a stabilization impact on the trading price. The closer the anchor is to the true fundamental value, the more efficient the market is, because the prices change smoothly.
    Keywords: agent-based modeling; market microstructure; fundamental value; trading volume; efficient market
    Date: 2014–05
    URL: http://d.repec.org/n?u=RePEc:hal:wpaper:halshs-00997573&r=cmp
  4. By: Christopher S Kirk
    Abstract: This paper describes recent development and test implementation of a continuous time recurrent neural network that has been configured to predict rates of change in securities. It presents outcomes in the context of popular technical analysis indicators and highlights the potential impact of continuous predictive capability on securities market trading operations.
    Date: 2014–06
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1406.0968&r=cmp
  5. By: Sercan Arik; Sukru Burc Eryilmaz; Adam Goldberg
    Abstract: As the number of publicly traded companies as well as the amount of their financial data grows rapidly, it is highly desired to have tracking, analysis, and eventually stock selections automated. There have been few works focusing on estimating the stock prices of individual companies. However, many of those have worked with very small number of financial parameters. In this work, we apply machine learning techniques to address automated stock picking, while using a larger number of financial parameters for individual companies than the previous studies. Our approaches are based on the supervision of prediction parameters using company fundamentals, time-series properties, and correlation information between different stocks. We examine a variety of supervised learning techniques and found that using stock fundamentals is a useful approach for the classification problem, when combined with the high dimensional data handling capabilities of support vector machine. The portfolio our system suggests by predicting the behavior of stocks results in a 3% larger growth on average than the overall market within a 3-month time period, as the out-of-sample test suggests.
    Date: 2014–06
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1406.0824&r=cmp
  6. By: Cheng Chen; Lan Zheng; Venkatesh Srinivasan; Alex Thomo; Kui Wu; Anthony Sukow
    Abstract: The majority of recommender systems are designed to recommend items (such as movies and products) to users. We focus on the problem of recommending buyers to sellers which comes with new challenges: (1) constraints on the number of recommendations buyers are part of before they become overwhelmed, (2) constraints on the number of recommendations sellers receive within their budget, and (3) constraints on the set of buyers that sellers want to receive (e.g., no more than two people from the same household). We propose the following critical problems of recommending buyers to sellers: Constrained Recommendation (C-REC) capturing the first two challenges, and Conflict-Aware Constrained Recommendation (CAC-REC) capturing all three challenges at the same time. We show that C-REC can be modeled using linear programming and can be efficiently solved using modern solvers. On the other hand, we show that CAC-REC is NP-hard. We propose two approximate algorithms to solve CAC-REC and show that they achieve close to optimal solutions via comprehensive experiments using real-world datasets.
    Date: 2014–06
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1406.0455&r=cmp

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