Do bookmakers possess superior skills to bettors in predicting outcomes?

In this paper we test the hypothesis that bookmakers display superior skills to bettors in predicting the outcome of sporting events by using matched data from traditional bookmaking and person-to-person exchanges. Employing a conditional logistic regression model on horse racing data from the UK we find that, in high liquidity betting markets, betting exchange odds have more predictive value than the corresponding bookmaker odds. To control for potential spillovers between the two markets, we repeat the analysis for cases where prices diverge significantly. Once again, exchange odds yield more valuable information concerning race outcomes than the bookmaker equivalents.     

并行工程视角下产品绿色设计评估模式构建

环保性的相关决策及绿色产业是未来企业实现可持续发展所必须重视的议题。因此,运用并行工程的概念构建了一套新产品进行绿色设计时评选开发方案的评估模式,并结合模糊理论及灰色系统的理念与方法以处理决策过程中可能存在的不确定性、模糊性及评估过程中信息不完整性的情况,最后以模拟的方式完成此评估模式。     

全域旅游视角下县域旅游产业发展思路探讨——以博兴县为例

在全域旅游发展大背景下,通过分析博兴县三次产业+旅游资源的现状,运用灰色关联度方法分析博兴县旅游产业与三次产业之间的关联性,提出博兴县全域旅游发展的路径模式,为探索县域全域旅游发展提供一定的参考依据。     

信息共享还是利益冲突?——基于买方单独调研与买卖双方联合调研的实证检验

基于买方(Buy Side)与卖方(Sell Side)存在的利益关系(交易佣金),本文考察了买方(基金)调研对于卖方证券分析行业信息精度的影响。结果发现,基金调研行为对卖方机构预测准确度的影响机制并不单一。一方面通过“信息共享”效应提高了券商的平均预测准确性;另一方面,两者之间存在明显的“利益冲突”效应,该作用降低了卖方分析师的预测公允性。此外,本文更具体地研究了两种效应的内在传递机制。从“信息共享”的角度发现基金调研吸引更多券商分析师对上市公司的关注,从而为证券分析行业带来“信息增量”;从“利益冲突”的角度,实证结果表明在中国的二级市场中佣金关系(交易佣金席位)更能代表中国市场买卖双方之间的利益关系,这种利益关系带来的“利益冲突”降低信息有效性。进一步,本文通过TF-IDF方法挖掘调研文本信息,分析了牛熊市、投资者情绪和调研报告文本情感色彩对调研信息质量的影响。     

基于旅客需求的高速铁路客运服务质量评价

高速铁路客运服务应以旅客需求为导向,借助客运服务质量评价体系,完善高速铁路客运服务,满足旅客多样化出行需求。以马斯洛需求理论为基础,针对高速铁路旅客需求和感知建立三级评价指标体系,并提出运用灰色关联度-模糊综合评价法对高速铁路客运服务质量进行评价。对成都东站、重庆西站,以及G2885和G2890次列车高速铁路旅客进行现场调研,基于调研数据,运用灰色关联度-模糊综合评价法评价高速铁路客运服务质量。结果表明,高速铁路列车服务质量总体高于车站服务质量;相比于高速铁路客运服务的基本需求和物质需求,提升服务等级和注重精神尊重是提升高速铁路客运服务质量的努力方向之一。     

网上技术交易趋势预测模型及应用研究

网上技术交易平台开展交易趋势预测和交易潜力识别，可提高平台技术信息管理水平和网上技术市场运作效率。从网上技术市场和专利情报分析理论出发，提出网上技术交易趋势预测分析指标，设计构建预测模型和相关流程方法，并以广州知识产权交易中心的网上挂牌专利数据为例，进行定量测度和评价分析，从而提出发展网上交易服务能力的建议。结果表明，提出的技术交易趋势预测模型与流程具有适用性和可操作性，可针对网上平台技术资源确定差异化预测类型及其关键影响指标，建立基于技术领域分类的平台分析体系。研究结论可为网上平台设计基于自身特点的技术信息管理策略，提高信息服务专业化能力提供参考。     

Inefficiency and predictability in the Brexit Pound market: a natural experiment

Exploiting the near-experimental conditions provided by the GBPUSD exchange rate during the Brexit vote of 2016, we quantify a significant delay of the market price in reflecting the increasing probability of a Brexit outcome over the vote counting period. We claim that the Brexit outcome could realistically have been predicted hours before the market adjusted to the outcome. This inefficiency is identified by comparing the market-implied probability of a Brexit outcome with a separate probability, estimated by a standard Monte-Carlo algorithm based on a simple linear regression model, representative of what should have been easily possible in real time. The core of the method is the real-time re-calibration of ex-ante ‘pollster’ predictions for the voting district outcomes by regressing the observed voting results onto them. For comparative purposes, a study of the MXNUSD exchange rate in the 2016 US Presidential Election was done, finding that the market-implied and model-estimated probabilities moved more consistently toward the Trump outcome. Put together, this identifies a somewhat anomalous breakdown in market efficiency in the case of the Brexit vote, which we attribute to its novelty as well as a kind of political bubble and subsequent crash, generated by confirmation bias and social herding.     

Accuracy of German federal election forecasts, 2013 & 2017

The present study reviews the accuracy of four methods (polls, prediction markets, expert judgment, and quantitative models) for forecasting the two German federal elections in 2013 and 2017. On average across both elections, polls and prediction markets were most accurate, while experts and quantitative models were least accurate. However, the accuracy of individual forecasts did not correlate across elections. That is, the methods that were most accurate in 2013 did not perform particularly well in 2017. A combined forecast, calculated by averaging forecasts within and across methods, was more accurate than three of the four component forecasts. The results conform to prior research on US presidential elections in showing that combining is effective in generating accurate forecasts and avoiding large errors.