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.     

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.     

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。     

Optimization of loading sequence and rehandling strategy for multi-quay crane operations in container terminals

In this paper, we consider the optimization of loading sequence and rehandling strategy in the terminal operation. We present an optimization strategy to minimize the number of rehandles, and establish a mathematical model to integrate the loading sequence and the rehandling strategy under the parallel operation of multi-quay cranes. Furthermore, we give an improved genetic algorithm to solve the model. We show the efficiency of the optimization strategy and algorithm by comparing them with previous strategies and heuristics.     

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。     

Bootstrapping Financial Time Series

It is well known that time series of returns are characterized by volatility clustering and excess kurtosis. Therefore, when modelling the dynamic behavior of returns, inference and prediction methods, based on independent and/or Gaussian observations may be inadequate. As bootstrap methods are not, in general, based on any particular assumption on the distribution of the data, they are well suited for the analysis of returns. This paper reviews the application of bootstrap procedures for inference and prediction of financial time series. In relation to inference, bootstrap techniques have been applied to obtain the sample distribution of statistics for testing, for example, autoregressive dynamics in the conditional mean and variance, unit roots in the mean, fractional integration in volatility and the predictive ability of technical trading rules. On the other hand, bootstrap procedures have been used to estimate the distribution of returns which is of interest, for example, for Value at Risk (VaR) models or for prediction purposes. Although the application of bootstrap techniques to the empirical analysis of financial time series is very broad, there are few analytical results on the statistical properties of these techniques when applied to heteroscedastic time series. Furthermore, there are quite a few papers where the bootstrap procedures used are not adequate.     

中国未来10年通胀周期预测及其验证

采用增广Pisarenko方法对当代中国通货膨胀周期进行分析验证，可得出通货膨胀周期函数。运用这一周期函数对中国通货膨胀周期进行10年预测，可得出2009年左右、2013年左右中国可能出现较高的通货膨胀预测结果。因此，必须对可能出现的较高的通货膨胀保持足够的警惕，做好必要准备。