A network experiment in continuous time: The influence of link costs

In recent work on non-cooperative network formation star-shaped networks play an important role. In a particular theoretical model of Bala and Goyal (2000) center-sponsored stars are the only strict Nash networks. In testing this theoretical model, Falk and Kosfeld (2003) do not find experimental evidence that players select the center-sponsored star. Based on a slight modification of Bala and Goyal’s model, we design a network formation experiment in which, depending on link costs, periphery-sponsored stars and the empty network are the only strict Nash networks. We observe that almost all groups not only reach a strict Nash network once but also switch the center player in periphery-sponsored stars several times. The main innovation in our experiment is to use a continuous time framework which we believe to be a more realistic setting to study behavior in network formation situations and which makes coordination on stars much easier than simultaneous strategy adaptation in discrete time. Electronic Supplementary Material Supplementary material is available in the online version of this article at http://dx.doi.org/10.1007/s10683-006-9125-1. JEL Classification C72 · C92 · D81     

A simple model of homophily in social networks

Biases in meeting opportunities have been recently shown to play a key role for the emergence of homophily in social networks (see Currarini et al., 2009). The aim of this paper is to provide a simple microfoundation of these biases in a model where the size and type-composition of the meeting pools are shaped by agents׳ socialization decisions. In particular, agents either inbreed (direct search only to similar types) or outbreed (direct search to population at large). When outbreeding is costly, this is shown to induce stark equilibrium behavior of a threshold type: agents “inbreed” (i.e. mostly meet their own type) if, and only if, their group is above certain size. We show that this threshold equilibrium generates patterns of in-group and cross-group ties that are consistent with empirical evidence of homophily in two paradigmatic instances: high school friendships and interethnic marriages.     

Exploring the predictability of range‐based volatility estimators using recurrent neural networks

We investigate the predictability of several range‐based stock volatility estimates and compare them with the standard close‐to‐close estimate, which is most commonly acknowledged as the volatility. The patterns of volatility changes are analysed using long short‐term memory recurrent neural networks, which are a state‐of‐the‐art method of sequence learning. We implement the analysis on all current constituents of the Dow Jones Industrial Average index and report averaged evaluation results. We find that the direction of changes in the values of range‐based estimates are more predictable than that of the estimate from daily closing values only.     

Fostering entrepreneurship in distressed urban neighbourhoods

During the last decade entrepreneurship research has focused increasingly on spatial aspects of entrepreneurship. Many systematic studies have been conducted on the national or regional scale, although other geographical scales—cities and their single districts and neighbourhoods—have drawn less attention. In this context, the paper aims, firstly, at identifying spatial success factors of entrepreneurial activities and their promotion at the local scale. Secondly, the paper aims at contributing to the development of a conceptual frame at the interface of entrepreneurship research, urban and economic geography, and regional economics. Empirically, the paper draws on results from explorative case studies in two distressed urban areas.     

Forecasting and Arbitrage of the Nikkei Stock Index Futures: An Application of Backpropagation Networks

A neural network model was used in forecasting the basis in SIMEX Nikkei Stock Index futures. Results for out of sample show that the neural network forecast performance was better than that of the ARIMA model. Also, a two-way ANOVA confirms that the employed neural network was able to provide the trader with more arbitrage profits than the traditional cost-of-carry model even though it observed relative less profitable arbitrage timing. The results can be attributed to the network';s higher ability to capture nonlinear market patterns.     

基于小波神经网络的高阶CAPM实证研究

本文在传统CAPM的基础上,引入了一个高阶的CAPM。借助小波神经网络在非线性函数逼近方面的优势,使用上海证券交易所股票数据分别对二阶至四阶CAPM进行了实证分析。最终的研究结果表明:就上海股市而言,12只大盘股组合已经能够有效分散非系统风险,而12只小盘股不能充分化解非系统风险,存在所谓的规模效应;训练后的网络预测显示,高阶CAPM无论是在预测精度还是预测稳定性上都要明显优于传统的CAPM,在一个非系统风险得到充分分散的证券组合中,加入三阶矩的CAPM已经能够比较准确地把握风险资产的市场定价。     

Free‐riding in multi‐party alliances: The role of perceived alliance effectiveness and peers' collaboration in a research consortium

Research summary : Multi‐party alliances rely on partners' willingness to commit and pool their efforts in joint endeavors. However, partners face the dilemma of how much to commit to the alliance. We shed light on this issue by analyzing the relationship between partners' free‐riding—defined as their effort‐withholding—and their perceptions of alliance effectiveness and peers' collaboration. Specifically, we posit a U‐shaped relationship between partners' subjective evaluations of alliance effectiveness and their free‐riding. We also hypothesize a negative relation between partners' perceptions of the collaboration of peer organizations and their free‐riding. Results from a mixed‐method study—combining regression analysis of primary data on a major inter‐organizational research consortium and evidence from two experimental designs—support our hypotheses, bearing implications for the multi‐party alliances literature. Managerial summary : Free‐riding is a major concern in multi‐party alliances such as large research consortia, since the performance of these governance forms hinges on the joint contribution of multiple partners that often operate according to different logics (e.g., universities, firms, and government agencies). We show that, in such alliances, partners' perceptions have relevant implications for their willingness to contribute to the consortium's shared goals. Specifically, we find that partners free‐ride more—that is, contribute less—when they perceive the effectiveness of the overall alliance to be either very low or very high. Partners also gauge their commitment to the alliance on the perception of the effort of their peers—that is, other organizations similar to them. These findings provide managers of multi‐party alliances with additional levers to motivate partners to contribute fairly to such joint endeavor. Copyright © 2015 John Wiley & Sons, Ltd.     

模糊层次分析法优化BP神经网络的交通冲突预测模型

本文通过全面剖析影响交通冲突的原因,以交通流量、道路几何设计和道路环境三方面的因素建立指标层次结构体系。提出基于模糊层次分析(FAHP)法优化BP神经网络(BPNN)的预测模型,应用于交通冲突预测。     

基于BP神经网络方法的城市土地生态安全评价——以广州市为例

选取18个指标构建城市土地生态安全评价指标体系,运用BP神经网络方法评价城市土地生态安全。研究结果:2006年及2007年广州市土地生态安全等级为Ⅲ,2008年安全等级为Ⅳ。研究结论:BP神经网络通过对历史数据的学习,能够实现城市土地生态安全评价,对探索城市土地生态安全评价方法具有重要意义。     

基于DEA和BP神经网络的企业科技成果转化综合评价及实证分析

构建了企业科技成果转化综合评价指标体系,将数据包络分析（DEA）与BP神经网络两种评价方法有机结合,建立了DEA-BP神经网络综合评价模型,利用该模型对1998年至2009年间上海市规模以上企业的相关数据进行了实证分析,验证了本文所构建的评价模型具有较高的精确度和预测能力,并根据评价结果,提出了上海市促进科技成果转化的一系列建议。