81 research outputs found

    Effects of Contrarians in the Minority Game

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    We study the effects of the presence of contrarians in an agent-based model of competing populations. Contrarians are common in societies. These contrarians are agents who deliberately prefer to hold an opinion that is contrary to the prevailing idea of the commons or normal agents. Contrarians are introduced within the context of the Minority Game (MG), which is a binary model for an evolving and adaptive population of agents competing for a limited resource. Results of numerical simulations reveal that the average success rate among the agents depends non-monotonically on the fraction aca_{c} of contrarians. For small aca_{c}, the contrarians systematically outperform the normal agents by avoiding the crowd effect and enhance the overall success rate. For high aca_{c}, the anti-persistent nature of the MG is disturbed and the few normal agents outperform the contrarians. Qualitative discussion and analytic results for the small aca_{c} and high aca_{c} regimes are also presented, and the crossover behavior between the two regimes is discussed.Comment: revtex, 11 pages, 4 figure

    Market inefficiency identified by both single and multiple currency trends

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    Many studies have shown that there are good reasons to claim very low predictability of currency nevertheless, the deviations from true randomness exist which have potential predictive and prognostic power [J.James, Quantitative finance 3 (2003) C75-C77]. We analyze the local trends which are of the main focus of the technical analysis. In this article we introduced various statistical quantities examining role of single temporal discretized trend or multitude of trends corresponding to different time delays. Our specific analysis based on Euro-dollar currency pair data at the one minute frequency suggests the importance of cumulative nonrandom effect of trends on the forecasting performance

    The role of conviction and narrative in decision-making under radical uncertainty

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    We propose conviction narrative theory (CNT) to broaden decision-making theory for it better to understand and analyse how subjectively means-end rational actors cope in contexts in which the traditional assumptions in decision-making models fail to hold. Conviction narratives enable actors to draw on their beliefs, causal models and rules of thumb to identify opportunities worth acting on, to simulate the future outcome of their actions and to feel sufficiently convinced to act. The framework focuses on how narrative and emotion combine to allow actors to deliberate and to select actions that they think will produce the outcomes they desire. It specifies connections between particular emotions and deliberative thought, hypothesizing that approach and avoidance emotions evoked during narrative simulation play a crucial role. Two mental states, Divided and Integrated, in which narratives can be formed or updated, are introduced and used to explain some familiar problems that traditional models cannot

    Women out, children out : the effect of female labor on portuguese preschool enrollment rates

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    This article tests whether Portuguese female activity rates have increased preschool enrollment rates. Particularly during the last 20 years, Portuguese women have assumed new roles in the marketplace and have become active workers outside of the home environment. This change has encouraged more sensible decisions with respect to preschool enrollment. Using cointegration techniques, we concluded that female activity rates and real income per capita caused a long-term increase in preschool enrollment rates. Although the percentage of agricultural gross value added to the gross domestic product and the number of preschool institutes were also found to be significant in the estimated vector error correction model, their causal relationship with preschool enrollment was only short term.COMPETE; QREN; FEDER; Fundação para a Ciência e a Tecnologia (FCT

    Analysis of Stock Price Return Using Textual Data and Numerical Data Through Text Mining

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