基于小波的金融时间序列波动性研究

文章针对描述金融时间序列波动性的问题,提出了一种基于小波Mallat算法的时间序列分析方法,即先用Mallat算法对金融时间序列进行分解与重构,继而对各分解层上的单支重构分量进行时间序列分析。在实证分析中,以宝钢股份的股票收益率序列为例,对这种综合分析方法的有效性与准确度进行了验证,并得到了较为满意的结果。     

基于多层网络的银行间市场信用拆借智能风险传染机制

基于多层网络结构对银行间市场进行分析研究，有利于规避或减弱对金融市场的风险冲击。基于信用拆借业务场景模拟的测试数据，结合银行间市场多层网络结构和复杂网络分析方法，从不同角度对银行间市场中重要节点进行判断识别，同时计算层间的Jaccard相似系数数和机构间皮尔逊相似性系数，从宏观和微观角度来衡量银行间市场的风险传染性。实验结果表明，中国银行、国家开发银行等大型国有金融机构系统重要性较高，且机构间的相似度越大，风险传染性就越大。因此，通过计算网络层内的重要性节点衡量指标，全面完整地对整个系统的风险传染情况进行分析，可协助监管部门实现对系统重要性机构的精准监测。同时，从层间分析与层内分析两个角度出发，全面衡量受到金融冲击后的机构间风险传染程度，可为监管机构提供政策上的建议。     

Bayesian dynamic linear modeling for exploring the impact of recent financial crisis on Japan Credit Default Swap market

This paper investigates the effects of recent subprime financial crisis on Japan Credit Default Swap (CDS) market. We first analyze the relationship between the log return series of the reference rates of a CDS contract and the hazard rate. This provides a theoretical foundation for the use of correlation of the log CDS returns as a representation of credit risk correlation. In the dynamic Bayesian linear modeling framework, we consider an algorithm that allow us to obtain dynamic Bayesian updates for the correlation among the reference rates of an underlying CDS contract. Data from the Japan CDS market is analyzed using the proposed methodology. An empirical analyses on the data segmented by different economic environments are carried out. Results indicate that the estimated implied default correlation captures market structure very well and provides useful information for credit risk management.     

Financial forecasting using ANFIS networks with Quantum-behaved Particle Swarm Optimization

To be successful in financial market trading it is necessary to correctly predict future market trends. Most professional traders use technical analysis to forecast future market prices. In this paper, we present a new hybrid intelligent method to forecast financial time series, especially for the Foreign Exchange Market (FX). To emulate the way real traders make predictions, this method uses both historical market data and chart patterns to forecast market trends. First, wavelet full decomposition of time series analysis was used as an Adaptive Network-based Fuzzy Inference System (ANFIS) input data for forecasting future market prices. Also, Quantum-behaved Particle Swarm Optimization (QPSO) for tuning the ANFIS membership functions has been used. The second part of this paper proposes a novel hybrid Dynamic Time Warping (DTW)-Wavelet Transform (WT) method for automatic pattern extraction. The results indicate that the presented hybrid method is a very useful and effective one for financial price forecasting and financial pattern extraction.     

The Price and Trading Volume Dynamics Relationship in the EEX Power Market: A Wavelet Modeling

This paper examines the dynamic relationship between power spot prices and related trading volumes in one of the most emergent energy markets. Traditionally, investigating the bivariate stochastic processes has been dominated by linear econometrical methods that proved helpful especially in finance. However, when dealing with intradaily power data, we cannot rely on models developed for financial or other commodity markets. Therefore, wavelet transforms are applied for power markets data to search for and decode nonlinear regularities and hidden patterns existing between the variables. Given its ability to decompose the time series into their time scale components and thus to reveal structure at different time horizons, wavelets are useful in analyzing situations in which the degree of association between processes is likely to change with the time-horizon. Therefore, a wavelet-based cross-analysis is performed between prices and trading volume time series. On the same basis, causality tests and out-of-sample forecasting tasks are carried out to empirically the strong relationship between the two investigated time series.     

标签传播时间序列聚类的股指期货套期保值策略研究

利用时间序列聚类方法进行股指期货的套期保值,关键要选择合适的聚类方法。本文从新的视角来研究并提高时间序列聚类方法在金融数据分析领域的应用性能,提出一种基于标签传播时间序列聚类的股指期货套期保值模型。该模型以动态时间弯曲为相似性度量方法来构建现货股票网络空间结构,将每只股票看作一个节点,利用标签传播方法将节点划分到不同的簇中,最终实现股票数据聚类。另外,构建最小追踪误差优化模型来确定每支股票在现货组合中的最优权重,从而得到最优组合。实验分别比较新方法和传统聚类方法确定现货组合的追踪误差,结果表明新方法能够提高现货组合的追踪精度,为丰富金融市场投资和管理方式提供新的研究思路。     

Chaotic analysis of predictability versus knowledge discovery techniques: case study of the Polish stock market

Increasing evidence over the past decade indicates that financial markets exhibit nonlinear dynamics in the form of chaotic behavior. Traditionally, the prediction of stock markets has relied on statistical methods including multivariate statistical methods, autoregressive integrated moving average models and autoregressive conditional heteroskedasticity models. In recent years, neural networks and other knowledge techniques have been applied extensively to the task of predicting financial variables.
This paper examines the relationship between chaotic models and learning techniques. In particular, chaotic analysis indicates the upper limits of predictability for a time series. The learning techniques involve neural networks and case–based reasoning. The chaotic models take the form of R/S analysis to measure persistence in a time series, the correlation dimension to encapsulate system complexity, and Lyapunov exponents to indicate predictive horizons. The concepts are illustrated in the context of a major emerging market, namely the Polish stock market.     

Some properties of multivariate INAR(1) processes

INteger-valued AutoRegressive (INAR) processes are common choices for modeling non-negative discrete valued time series. In this framework and motivated by the frequent occurrence of multivariate count time series data in several different disciplines, a generalized specification of the bivariate INAR(1) (BINAR(1)) model is considered. In this new, full BINAR(1) process, dependence between the two series stems from two sources simultaneously. The main focus is on the specific parametric case that arises under the assumption of a bivariate Poisson distribution for the innovations of the process. As it is shown, such an assumption gives rise to a Hermite BINAR(1) process. The method of conditional maximum likelihood is suggested for the estimation of its unknown parameters. A short application on financial count data illustrates the model.     

Financial econometric analysis at ultra-high frequency: Data handling concerns

Data collection at ultra high-frequency on financial markets requires the manipulation of complex databases, and possibly the correction of errors present in the data. The New York Stock Exchange is chosen to provide evidence of problems affecting ultra high-frequency data sets. Standard filters can be applied to remove bad records from the trades and quotes data. A method for outlier detection is proposed to remove data which do not correspond to plausible market activity. Several methods of aggregation of the data are suggested, according to which corresponding time series of interest for econometric analysis can be constructed. As an example of the relevance of the procedure, the autoregressive conditional duration model is estimated on price durations. Failure to purge the data from “wrong” ticks is likely to shorten the financial durations between substantial price movements and to alter the autocorrelation profile of the series. The estimated coefficients and overall model diagnostics are considerably altered in the absence of appropriate steps in data cleaning. Overall the difference in the coefficients is bigger between the dirty series and the clean series than among series filtered with different algorithms.     

Hunting High and Low: Visualising Shifting Correlations in Financial Markets

The analysis of financial assets’ correlations is fundamental to many aspects of finance theory and practice, especially modern portfolio theory and the study of risk. In order to manage investment risk, in‐depth analysis of changing correlations is needed, with both high and low correlations between financial assets (and groups thereof) important to identify. In this paper, we propose a visual analytics framework for the interactive analysis of relations and structures in dynamic, high‐dimensional correlation data. We conduct a series of interviews and review the financial correlation analysis literature to guide our design. Our solution combines concepts from multi‐dimensional scaling, weighted complete graphs and threshold networks to present interactive, animated displays which use proximity as a visual metaphor for correlation and animation stability to encode correlation stability. We devise interaction techniques coupled with context‐sensitive auxiliary views to support the analysis of subsets of correlation networks. As part of our contribution, we also present behaviour profiles to help guide future users of our approach. We evaluate our approach by checking the validity of the layouts produced, presenting a number of analysis stories, and through a user study. We observe that our solutions help unravel complex behaviours and resonate well with study participants in addressing their needs in the context of correlation analysis in finance.     

基于相关性分析的工业时序数据异常检测

多维时间序列上的异常检测,是时态数据分析的重要研究问题之一.近年来,工业互联网中传感器设备采集并积累了大量工业时间序列数据,这些数据具有模式多样、工况多变的特性,给异常检测方法的效率、效果和可靠性均提出更高要求.序列间相互影响、关联,其隐藏的相关性信息可以用于识别、解释异常问题.基于此,提出一种基于序列相关性分析的多维时间序列异常检测方法.首先对多维时间序列进行分段、标准化计算,得到相关性矩阵,提取量化的相关关系;然后建立了时序相关图模型,通过在时序相关图上的相关性强度划分时间序列团,进行时间序列团内、团间以及单维的异常检测.在真实的工业设备传感器数据集上进行了大量实验,实验结果验证了该方法在高维时序数据的异常检测任务上的有效性.通过对比实验,验证了该方法从性能上优于基于统计和基于机器学习模型的基准算法.该研究通过对高维时序数据相关性知识的挖掘,既节约了计算成本,又实现了对复杂模式的异常数据的精准识别.     

股票市场混沌吸引子的特征量——基于G-P算法与小数据量算法

针对金融时间序列的特点,论文分析已有混沌特征量算法的基础上,采用特殊的对数线性趋势消除法(简记为LLD)处理数据、引入Rosenstein提出的小数据量算法等计算最大李雅普诺夫指数以及其它混沌系统的特征量,对我国证券市场的混沌动力学结构作出了稳健的分析。结果表明中国股市具有显著的非线性混沌特征,这一结论将为金融理论的研究提供新的方向。     

Identifying Market Behaviours Using European Stock Index Time Series by a Hybrid Segmentation Algorithm

The discovery of useful patterns embodied in a time series is of fundamental relevance in many real applications. Repetitive structures and common type of segments can also provide very useful information of patterns in financial time series. In this paper, we introduce a time series segmentation and characterization methodology combining a hybrid genetic algorithm and a clustering technique to automatically group common patterns from this kind of financial time series and address the problem of identifying stock market prices trends. This hybrid genetic algorithm includes a local search method aimed to improve the quality of the final solution. The local search algorithm is based on maximizing a likelihood ratio, assuming normality for the series and the subseries in which the original one is segmented. To do so, we select two stock market index time series: IBEX35 Spanish index (closing prices) and a weighted average time series of the IBEX35 (Spanish), BEL20 (Belgian), CAC40 (French) and DAX (German) indexes. These are processed to obtain segments that are mapped into a five dimensional space composed of five statistical measures, with the purpose of grouping them according to their statistical properties. Experimental results show that it is possible to discover homogeneous patterns in both time series.     

Reverse Engineering Financial Markets with Majority and Minority Games Using Genetic Algorithms

Using virtual stock markets with artificial interacting software investors, aka agent-based models, we present a method to reverse engineer real-world financial time series. We model financial markets as made of a large number of interacting boundedly rational agents. By optimizing the similarity between the actual data and that generated by the reconstructed virtual stock market, we obtain parameters and strategies, which reveal some of the inner workings of the target stock market. We validate our approach by out-of-sample predictions of directional moves of the Nasdaq Composite Index.     

汽车跟驰状态下驾驶员反应时间研究

驾驶员反应时间是评价驾驶安全性的重要指标之一。本研究采用互相关分析法与灰色关联分析法相结合的方式对驾驶员反应时间进行标定,并结合简单反应时间、复杂反应时间双正态分布的理论假设,通过大量的实测数据对假设的合理性进行验证。结果表明,理论假设能够很好地解释反应时间的实测数据,反应时间的实测数据可以由这两个正态分布的混合分布来拟合。对跟驰状态下反应时间的深入研究,为驾驶员形成进一步的行动方案和提升驾驶安全性提供重要的理论参考。     

Swarm-based translation-invariant morphological prediction method for financial time series forecasting

In this paper, we present a method to overcome the random walk (RW) dilemma for financial time series forecasting, called swarm-based translation-invariant morphological prediction (STMP) method. It consists of a hybrid model composed of a modular morphological neural network (MMNN) combined with a particle swarm optimizer (PSO), which searches for the best time lags to optimally describe the time series phenomenon, as well as estimates the initial (sub-optimal) parameters of the MMNN (weights, architecture and number of modules). An additional optimization is performed with each particle of the PSO population (a distinct MMNN) using the back-propagation (BP) algorithm. After the MMNN parameters adjustment, we use a behavioral statistical test and a phase fix procedure to adjust time phase distortions observed in financial time series. Finally, we conduct an experimental analysis with the proposed method using four real world stock market time series, where five well-known performance metrics and a fitness function are used to assess the prediction performance. The obtained results are compared with those generated by classical models presented in the literature.     

Bridging the divide in financial market forecasting: machine learners vs. financial economists

Financial time series forecasting is a popular application of machine learning methods. Previous studies report that advanced forecasting methods predict price changes in financial markets with high accuracy and that profit can be made trading on these predictions. However, financial economists point to the informational efficiency of financial markets, which questions price predictability and opportunities for profitable trading. The objective of the paper is to resolve this contradiction. To this end, we undertake an extensive forecasting simulation, based on data from thirty-four financial indices over six years. These simulations confirm that the best machine learning methods produce more accurate forecasts than the best econometric methods. We also examine the methodological factors that impact the predictive accuracy of machine learning forecasting experiments. The results suggest that the predictability of a financial market and the feasibility of profitable model-based trading are significantly influenced by the maturity of the market, the forecasting method employed, the horizon for which it generates predictions and the methodology used to assess the model and simulate model-based trading. We also find evidence against the informational value of indicators from the field of technical analysis. Overall, we confirm that advanced forecasting methods can be used to predict price changes in some financial markets and we discuss whether these results question the prevailing view in the financial economics literature that financial markets are efficient.     

Evaluating machine learning classification for financial trading: An empirical approach

Technical and quantitative analysis in financial trading use mathematical and statistical tools to help investors decide on the optimum moment to initiate and close orders. While these traditional approaches have served their purpose to some extent, new techniques arising from the field of computational intelligence such as machine learning and data mining have emerged to analyse financial information. While the main financial engineering research has focused on complex computational models such as Neural Networks and Support Vector Machines, there are also simpler models that have demonstrated their usefulness in applications other than financial trading, and are worth considering to determine their advantages and inherent limitations when used as trading analysis tools. This paper analyses the role of simple machine learning models to achieve profitable trading through a series of trading simulations in the FOREX market. It assesses the performance of the models and how particular setups of the models produce systematic and consistent predictions for profitable trading. Due to the inherent complexities of financial time series the role of attribute selection, periodic retraining and training set size are discussed in order to obtain a combination of those parameters not only capable of generating positive cumulative returns for each one of the machine learning models but also to demonstrate how simple algorithms traditionally precluded from financial forecasting for trading applications presents similar performances as their more complex counterparts. The paper discusses how a combination of attributes in addition to technical indicators that has been used as inputs of the machine learning-based predictors such as price related features, seasonality features and lagged values used in classical time series analysis are used to enhance the classification capabilities that impacts directly into the final profitability.     

Topological mapping and assessment of multiple settlement time series in deep excavation: A complex network perspective

This study proposed a novel methodology that integrates complex network theory and multiple time series to enhance the systematic understanding of the daily settlement behavior in deep excavation. The original time series of ground surface, surrounding buildings, and structure settlement instrumentation data over an excavation time period were measured into a similarity matrix with correlation coefficients. A threshold was then determined and binarized into adjacent matrix to identify the optimal topology and structure of the complex network. The reconstructed settlement network has nodes corresponding to multiple settlement time series individually and edges regarded as nonlinear relationships between them. A deep excavation case study of the metro station project in the Wuhan Metro network, China, was applied to validate the feasibility and potential value of the proposed approach. Results of the topological analysis corroborate a small-world phenomenon with highly compacted interactions and provide the assessment of the significance among multiple settlement time series. This approach, which provides a new way to assess the safety monitoring data in underground construction, can be implemented as a tool for extracting macro- and micro-level decision information from multiple settlement time series in deep excavation from complex system perspectives.     

Automatic extraction and identification of chart patterns towards financial forecast

Technical analysis of stocks mainly focuses on the study of irregularities, which is a non-trivial task. Because one time scale alone cannot be applied to all analytical processes, the identification of typical patterns on a stock requires considerable knowledge and experience of the stock market. It is also important for predicting stock market trends and turns. The last two decades has seen attempts to solve such non-linear financial forecasting problems using AI technologies such as neural networks, fuzzy logic, genetic algorithms and expert systems but these, although promising, lack explanatory power or are dependent on domain experts. This paper presents an algorithm, PXtract to automate the recognition process of possible irregularities underlying the time series of stock data. It makes dynamic use of different time windows, and exploits the potential of wavelet multi-resolution analysis and radial basis function neural networks for the matching and identification of these irregularities. The study provides rooms for case establishment and interpretation, which are both important in investment decision making.