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.     

企业执行力问题诸说比较分析及研究趋势预测

近年来,全球学术界开始关注企业执行力问题,有必要对国内外执行力研究的理论内容进行系统梳理,并应以企业外部环境的演变为背景对已有的企业执行力相关理论和案例研究结论进行客观的分析和评价,以逻辑推理的形式对执行力理论未来的基本发展态势进行预测。     

应用回归分析加强预测管理

文章利用历史资料，运用数理统计中回归分析的方法，预测分析了居民非商品支出与居民文化生活服务支出的关系，建立了回归方程和进行相关性检验。     

Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants

In recent years, Bitcoin exchange rate prediction has attracted the interest of researchers and investors. Some studies have used traditional statistical and econometric methods to understand the economic and technology determinants of Bitcoin, few have considered the development of predictive models using these determinants. In this study, we developed a two-stage approach for exploring whether the information hidden in economic and technology determinants can accurately predict the Bitcoin exchange rate. In the first stage, two nonlinear feature selection methods comprising an artificial neural network and random forest are used to reduce the subset of potential predictors by measuring the importance of economic and technology factors. In the second stage, the potential predictors are integrated into long short-term memory (LSTM) to predict the Bitcoin exchange rate regardless of the previous exchange rate. Our results showed that by using the economic and technology determinants, LSTM could achieve better predictive performance than the autoregressive integrated moving average, support vector regression, adaptive network fuzzy inference system, and LSTM methods, which all use the previous exchange rate. Thus, information obtained from economic and technology determinants is more important for predicting the Bitcoin exchange rate than the previous exchange rate.     

Conformal prediction interval estimation and applications to day-ahead and intraday power markets

In this study, we investigated the application of the conformal prediction (CP) concept in the context of short-term electricity price forecasting. In particular, we determined the most important aspects related to the utility of CP, as well as explaining why this simple but highly effective idea has proved useful in other application areas and why its characteristics make it promising for short-term power applications. We compared the performance of CP with various state-of-the-art electricity price forecasting models, such as quantile regression averaging, in an empirical out-of-sample study of three short-term electricity time series. We combined CP with various underlying point forecast models to demonstrate its versatility and behavior under changing conditions. Our findings suggest that CP yields sharp and reliable prediction intervals in short-term power markets. We also inspected the effects of each of the model components to provide path-based guideline regarding how to find the best CP model for each market.     

地下水动态预报的一种新方法

预测作为决策的前提,在现化社会越来越为人们所重视,预测方法也越来越多。然而,有些预测方法由于对数据要求高、计算复杂,在目前计算机编程操作技术尚未被科技工作者所普遍掌据的情况下,显然是难以为实际工作者所接受的。鉴于此,本文介绍了一种新的预测方法:采用对时变参数建模和预测的方法来处理含有时变参数的预测模型,借以改善预测的精度。文中给出了三个较典型的应用实例,借以说明该方法的应用及不同情况下对时变参数K的处理方法。“新方法”对数据要求不高,计算简便、易为现场科技工作者所接受,预测精度也较高。应用实例表明:该方法用于地下水动态预测预报,可以获得非常满意的效果。     

工程造价动态快速预测的模糊数学方法

在已有的应用模糊数学原理进行工程造价快速预测方法的基础上,本文提出了将待估工程和典型工程以统一时间基准年来考虑的动态快速,并以工程实例说明了该法同样可以估算建造要素的单方消耗量。     

会计信息质量对企业并购估值的影响研究

本文利用1995年到2015年的公共企业收购公告的数据样本研究会计信息质量对企业并购估值的影响,提出在企业并购中高的会计信息质量更有利于企业收购方这一假设.通过数据分析验证假设的正确性.具体来说,当目标企业的会计信息质量较高时,收购方的回报会更高,目标公司的回报则表现稍低.这与收购人通过更少的收购付出而获得更大收益是相一致的.研究结果表明,高质量的会计信息促进更好的收购投标决策,并对于会计信息质量的不确定性和控制替代措施有很强的鲁棒性.     

基于改进多尺度阈值去噪的人民币汇率实证分析

考虑到金融时间序列中噪声的干扰,有必要在金融建模研究中做适当的去噪处理。不同于传统的小波阈值去噪,本文应用改进的多尺度阈值技术对人民币/美元汇率序列进行去噪处理,并基于不同误差分布情况下综合分析,确定ARMA（1,1）-GARCH（1,1）-T为最佳拟合模型,最终给出预测效果,证实了应用新的多尺度阈值方法去噪后的汇率模型预测精度较高。     

期货市场量价分析法的短期预测力检验——基于久期理论的分析

久期作为重要的市场微观结构信号,对于改善资产价格波动预测准确性和流动性风险评价具有重要的作用。本文首先提出了反映价格、成交量和持仓量共同变化的共同久期概念,并分析了不同变量高频数据所具有的日内效应特征。然后,在久期理论框架下构建了扩展的二元选择模型,对上海燃料油期货市场量价分析法的短期预测力进行了实证检验。结果表明,期货市场量价分析法中一半以上的经验法则证明是有效的,说明量价分析法在短期价格预测中具有较高的预测力,是值得信赖的重要技术分析方法之一。