基于信息粒化和支持向量机的股票价格预测

信息粒化是进行海量数据挖掘和模糊信息处理的有效工具。本文提出了一种基于信息粒化和支持向量机的股票价格预测方法。利用长安汽车的股票数据,建立股票开盘价回归预测模型,该模型克服了传统时间序列模型仅局限于线性系统的情况。应用实例表明:该方法能有效地预测股票价格的变化范围。     

The price impact of foreign institutional herding on large-size stocks in the Taiwan stock market

This study constructs a panel threshold regression model to explore the price impact of foreign institutional herding of firms listed in the Taiwan Stock Exchange during January 2000 to June 2008. Our panel threshold model is constructed to explore the price impact of foreign institutional investors?? herding in the Taiwan stock market after controlling the firm size. By examining the presence of threshold effect, this study analyzes whether firm size would obviously and asymmetrically affect the explanation for the effect of changes in foreign investors?? share ownership on abnormal returns. The empirical results of this study find the significant evidence of threshold effect which divides the stocks into large-size and small-size firms. It is found that foreign institutional investors in the Taiwan stock market tend to hold large-size stocks listed in the Taiwan Stock Exchange. There is an apparent increase in the subsequent abnormal returns on large-size stocks bought in bulk by foreign investors. The signals of changes in share ownership initiated by foreign institutional investors would reveal further information for improving the performance of asset reallocation decisions in Taiwan. The panel threshold model constructed in this paper well describes the price impact of institutional herding yet eschews the possibly subjective data snooping issue resulting from the two-pass sorting method as proposed by previous related researches.     

基于面板分位数回归的住宅价格影响因素分析

采用面板分位数回归方法,以全国35个大中城市为样本,利用2006—2015年的数据,对影响住宅价格的因素进行研究。结果表明：土地价格、人均储蓄余额、在岗职工平均工资、人口密度、空气质量对住宅价格有正向影响,每亿人医院或卫生院数量对住宅价格有负向影响;并且不同分位数水平下各影响因素的作用大小具有明显差异。研究结论对不同城市依据自身特征采取相应的调控政策具有一定的参考价值。     

A predictive system integrating intrinsic mode functions,artificial neural networks,and genetic algorithms for forecasting S&P500 intra‐day data

There is an abundant literature on the design of intelligent systems to forecast stock market indices. In general, the existing stock market price forecasting approaches can achieve good results. The goal of our study is to develop an effective intelligent predictive system to improve the forecasting accuracy. Therefore, our proposed predictive system integrates adaptive filtering, artificial neural networks (ANNs), and evolutionary optimization. Specifically, it is based on the empirical mode decomposition (EMD), which is a useful adaptive signal‐processing technique, and ANNs, which are powerful adaptive intelligent systems suitable for noisy data learning and prediction, such as stock market intra‐day data. Our system hybridizes intrinsic mode functions (IMFs) obtained from EMD and ANNs optimized by genetic algorithms (GAs) for the analysis and forecasting of S&P500 intra‐day price data. For comparison purposes, the performance of the EMD‐GA‐ANN presented is compared with that of a GA‐ANN trained with a wavelet transform's (WT's) resulting approximation and details coefficients, and a GA‐general regression neural network (GRNN) trained with price historical data. The mean absolute deviation, mean absolute error, and root‐mean‐squared errors show evidence of the superiority of EMD‐GA‐ANN over WT‐GA‐ANN and GA‐GRNN. In addition, it outperformed existing predictive systems tested on the same data set. Furthermore, our hybrid predictive system is relatively easy to implement and not highly time‐consuming to run. Furthermore, it was found that the Daubechies wavelet showed quite a higher prediction accuracy than the Haar wavelet. Moreover, prediction errors decrease with the level of decomposition.     

原油价格与经济政策不确定性对大宗商品市场非对称冲击效应研究

利用面板分位回归模型,考量不同市场环境下原油价格与经济政策不确定性对大宗商品市场非对称性冲击效应。结果表明：油价冲击对中国大宗商品收益的影响具有非对称性,正负油价冲击对其均有促进作用,但随着市场环境好转,正油价冲击的作用逐渐增强,负油价冲击则逐渐减弱;政策不确定性对大宗商品收益有促进作用,但在牛市环境下有抑制作用;且危机前后,油价冲击对大宗商品收益的影响存在非对称性效应。     

Speculation or currency? Multi-scale analysis of cryptocurrencies—The case of Bitcoin

This paper proposes a novel two-stage VMD-based multi-scale regression to analyze various cryptocurrency attributes that are still unclear in the existing literature. In the first stage, Variational Mode Decomposition (VMD) is used to decompose the cryptocurrency prices into low, medium and high frequency modes with different attributes. In the second stage, the VMD-based multi-scale regression is proposed for these modes with selected explanatory variables. Using the proposed framework, we focus on analyzing the multiple attributes of daily Bitcoin price data as a case study. Empirical results indicate that the low-frequency mode has specific currency or long-term investment characteristics, unlike the short/medium-term investment attributes for the medium-frequency mode, while the high-frequency mode represents some speculation. Some events merely affect a single frequency mode, but others impact all frequency modes. The results of events analysis based on VMD could enhance the identification of the multiple attributes of Bitcoin. Our findings are insightful for future regulation and management of virtual currencies.     

Cryptocurrency price prediction using traditional statistical and machine-learning techniques: A survey

Cryptocurrencies are decentralized electronic counterparts of government-issued money. The first and best-known cryptocurrency example is bitcoin. Cryptocurrencies are used to make transactions anonymously and securely over the internet. The decentralization behavior of a cryptocurrency has radically reduced central control over them, thereby influencing international trade and relations. Wide fluctuations in cryptocurrency prices motivate the urgent requirement for an accurate model to predict its price. Cryptocurrency price prediction is one of the trending areas among researchers. Research work in this field uses traditional statistical and machine-learning techniques, such as Bayesian regression, logistic regression, linear regression, support vector machine, artificial neural network, deep learning, and reinforcement learning. No seasonal effects exist in cryptocurrency, making it hard to predict using a statistical approach. Traditional statistical methods, although simple to implement and interpret, require a lot of statistical assumptions that could be unrealistic, leaving machine learning as the best technology in this field, being capable of predicting price based on experience. This article provides a comprehensive summary of the previous studies in the field of cryptocurrency price prediction from 2010 to 2020. The discussion presented in this article will help researchers to fill the gap in existing studies and gain more future insight.     

机构持股对股价宏观波动影响的非对称性

本文利用Topview机构投资者日持股数据,构建机构投资者日净买率等指标,使用GMM回归、递归回归,从波动、收益两个角度动态分析机构投资者对股价宏观波动的影响。实证结果表明,机构投资者对股价宏观波动的影响因不同的市场状态而具有非对称性,并可以用信息假说进行解释。管理层应依据不同的市场状态而采取合适的措施以实现机构投资者的稳定作用。     

中国房地产市场价格泡沫与空间传染效应

本文使用对数周期性幂律(Log Period Power Law, LPPL)模型对房地产市场价格泡沫进行测度,运用空间计量模型对我国房地产市场价格泡沫和空间传染效应进行研究。LPPL模型认为由价格泡沫产生并最终破裂的金融市场与地震系统具有很多相似之处,即金融资产的价格呈周期性变化规律,价格持续上涨到临界状态直至反转。本文采用2010年6月至2017年11月间我国100个城市的房地产市场数据对各城市房地产价格泡沫进行测度和物理/经济空间传染效应研究。研究发现,LPPL模型能够对我国100个城市房地产价格泡沫进行甄别且主要存在两种泡沫状态:正向泡沫(房价持续上升)和反转泡沫(房价整体下降却存在反转点)。各个城市(地区)房地产价格具有较强的空间传染性;存在正向泡沫区域的空间传染性相较反转泡沫区域更为明显,在考虑经济空间测度而不是物理空间测度的情况下,各城市间的空间传染性更强。与现有文献不同,我们发现反转泡沫区域的新房价格指数特别是二手房价格指数的上升对周边城市的房地产价格指数存在强烈的正向推高影响。最后,本文发现城市的房地产调控政策在一定程度上抑制了房价传统影响(比如信贷、新房、二手房价等)因素的推高影响,但各城市房地产价格之间的联动变化特征应该引起监管部门的注意。     

Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms

Security indices are the main tools for evaluation of the status of financial markets. Moreover, a main part of the economy of any country is constituted of investment in stock markets. Therefore, investors could maximize the return of investment if it becomes possible to predict the future trend of stock market with appropriate methods. The nonlinearity and nonstationarity of financial series make their prediction complicated. This study seeks to evaluate the prediction power of machine‐learning models in a stock market. The data used in this study include the daily close price data of iShares MSCI United Kingdom exchange‐traded fund from January 2015 to June 2018. The prediction process is done through four models of machine‐learning algorithms. The results indicate that the deep learning method is better in prediction than the other methods, and the support vector regression method is in the next rank with respect to neural network and random forest methods with less error.     

基于ARIMA—SVM组合模型的我国农产品价格预测研究

为准确把握国内农产品价格波动规律,提高农产品价格预测精度,构建农产品价格自回归移动平均与支持向量机（ARIMA—SVM）组合预测模型,以ARIMA模型揭示农产品价格线性变动规律,以SVM模型揭示非线性变动规律,并结合1999—2011年我国农产品价格指数月度数据,使用组合模型和ARIMA、SVM单个模型对农产品价格进行预测。预测结果显示：组合模型比单个ARIMA、SVM模型预测精度高,能够提高农产品价格预测的准确性,是一种有效的农产品价格预测模型。     

The impact of transaction duration,volume and direction on price dynamics and volatility

We explore the role of trade volume, trade direction, and the duration between trades in explaining price dynamics and volatility using an Asymmetric Autoregressive Conditional Duration model applied to intraday transactions data. Our results suggest that volume, direction and duration are important determinants of price dynamics, while duration is also an important determinant of volatility. However, the impact of volume and direction on volatility is marginal after controlling for duration, and the impact of volume on volatility appears to be confined to periods of infrequent trading.     

How do macroeconomic news surprises affect round-the-clock price discovery of gold?

We examine round-the-clock international price discovery of gold among the major gold markets—New York, London and Shanghai during news-intensive and no-news time zones using one-minute data. Using GMM based parallel price discovery measure, we find global leadership of the US as New York gold futures lead across five time zones with 56% information share. New York/London (Nylon) timezone (51%) is the most informative trading session in sequential price discovery for all markets in 24-h. Our aggregate and disaggregate news analysis reveals that the US news surprises have a substantial and positive impact on its price discovery leadership while Eurozone news surprises have a negative impact and Chinese news have negligible impact. Using least absolute shrinkage and selection operator (LASSO) regression, we find scheduled news with a large surprise index has a significant yet asymmetric impact as negative news triggers a strong reaction. The impact of news surprise is state-dependent and display sign-reversals during extreme uncertainty, adverse macroeconomic conditions and abnormal investor behaviour.     

基于灰色系统和神经网络的创业板股票价格预测研究

股票价格预测是投资领域的一个重点关注课题。由于股票价格受到诸多非线性因 素的影响,得到精确的预测结果较为困难。为了消除股票指标的多重共线性,采用Adaptive- Lasso算法对指标变量进行筛选,实现了数据降维。之后,利用灰色预测对股票价格影响指标 进行预测,并在此基础上利用神经网络模型对股票收盘价进行预测。结果表明,利用灰色系统 和BP神经网络结合的模型所得预测结果平均相对误差为0.095,且运行效率较高,对股票预测 具有一定的积极意义。     

Repeat Sales Index for Thin Markets

The repeat sales model is commonly used to construct reliable house price indices in absence of individual characteristics of the real estate. Several adaptations of the original model by Bailey et al. (J Am Stat Assoc 58:933–942, 1963) are proposed in literature. They all have in common using a dummy variable approach for measuring price indices. In order to reduce the impact of transaction price noise on the estimates of price indices, Goetzmann (J Real Estate Finance Econ 5:5–53, 1992) used a random walk with drift process for the log price levels instead of the dummy variable approach. The model that is proposed in this article can be interpreted as a generalization of the Goetzmann methodology. We replace the random walk with drift model by a structural time series model, in particular by a local linear trend model in which both the level and the drift parameter can vary over time. An additional variable—the reciprocal of the time between sales—is included in the repeat sales model to deal with the effect of the time between sales on the estimated returns. This approach is robust can be applied in thin markets where relatively few selling prices are available. Contrary to the dummy variable approach, the structural time series model enables prediction of the price level based on preceding and subsequent information, implying that even for particular time periods where no observations are available an estimate of the price level can be provided. Conditional on the variance parameters, an estimate of the price level can be obtained by applying regression in the general linear model with a prior for the price level, generated by the local linear trend model. The variance parameters can be estimated by maximum likelihood. The model is applied to several subsets of selling prices in the Netherlands. Results are compared to standard repeat sales models, including the Goetzmann model.     

Hedonic price models and indices based on boosting applied to the Dutch housing market

We create a hedonic price model for house prices for six geographical submarkets in the Netherlands. Our model is based on a recent data-mining technique called boosting. Boosting is an ensemble technique that combines multiple models, in our case decision trees, into a combined prediction. Boosting enables capturing of complex nonlinear relationships and interaction effects between input variables. We report mean relative errors and mean absolute error for all regions and compare our models with a standard linear regression approach. Our model improves prediction performance by up to 39% compared with linear regression and by up to 20% compared with a log-linear regression model. Next, we interpret the boosted models: we determine the most influential characteristics and graphically depict the relationship between the most important input variables and the house price. We find the size of the house to be the most important input for all but one region, and find some interesting nonlinear relationships between inputs and price. Finally, we construct hedonic price indices and compare these with the mean and median index and find that these indices differ notably in the urban regions of Amsterdam and Rotterdam. Copyright © 2008 John Wiley & Sons, Ltd.     

A NOTE ON THE EFFECT OF INSTITUTIONAL TRADING ACTIVITIES ON THE REAL VALUE OF THE FINANCIAL TIMES ALL-SHARE INDEX

The impact of financial institutions' equity trading activities on share prices is examined using inflation-adjusted data. The regression results show that institutional purchasing pressure tends to raise prices and institutional selling activity tends to lower prices, but that no single financial sector may be identified as price leader within the UK equity market. These results are in complete agreement with those obtained in a previous study where the data were not corrected for inflation.     

Histogram-based prediction of directional price relatives

A model of directional prediction of price relatives is proposed following the histogram-based scheme developed in Györfi et al. (2006). This methodology allows us to exploit potential information contained in multivariate series of price relatives. The impact of the model is studied from the perspective of an economic agent through the use of double linear loss functions. A numerical example with real data is presented to illustrate the model.     

财务重述、管理层权力与股价崩盘风险：来自中国证券市场的经验证据

股市震荡引发投资者和监管层对股价崩盘风险的关注。从财务重述背后所反映的财务信息质量低下和公司治理失效出发,探讨其对股价崩盘风险的影响,结合管理层权力这一影响组织行为和产出能力的代理人特征,探讨其对财务重述与股价崩盘风险之间关系的影响。研究结果表明：相比未发生财务重述的公司,发生了财务重述的公司的股价崩盘风险明显更高;进一步纳入代理人特征———管理层权力后,发现代理人的这一特征对上述关系有明显的促进作用。     

The determinants of the price impact of block trades: further evidence

This article extends previous literature which examines the determinants of the price impact of block trades on the Australian Stock Exchange. As previous literature suggests that liquidity exhibits intraday patterns, we introduce time of day dummy variables to explore time dependencies in price impact. Following theoretical developments in previous literature, the explanatory power of the bid–ask spread, a lagged cumulative stock return variable and a refined measure of market returns are also examined. The model estimated explains approximately 29 per cent of the variation in price impact. Block trades executed in the first hour of trading experience the greatest price impact, while market conditions, lagged stock returns and bid–ask spreads are positively related to price impact. The bid–ask spread provides most of the explanatory power. This suggests that liquidity is the main driver of price impact.