A SVM Stock Selection Model within PCA

Yu, Huanhuan; Chen, Rongda; Zhang, Guoping

doi:10.1016/j.procs.2014.05.284

Cited by 77 publications

(30 citation statements)

References 6 publications

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“…To capture specific features in the financial market, PCA is used to extract the low-dimensional and efficient feature information. The empirical results show that the training accuracy and efficiency have been improved [17]. Although numerous successful applications have been reported, PCA performs poorly in dealing with nonlinear processes because it characterizes only the linear correlation among variables and does not explore the nonlinear relationships.…”

Section: Introductionmentioning

confidence: 99%

Sustainable Visual Analysis for Bank Non-Performing Loans and Government Debt Distress

Liu

Kim

2019

Sustainability

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This article visualizes bank non-performing loans (NPLs) and government debt distress data integration and an outcome classification after the outbreak of European sovereign debt. Linear and functional principal component analysis (FPCA) and biclustering are used to show the clustering pattern of NPLs and government debt for 25 EU and BRICS countries (Brazil, Russia, India, China and South Africa) during the period of 2006 to 2017 through high-dimensional visualizations. The results demonstrate that the government debt markets of EU countries experienced a similar trend in terms of NPLs, with a similar size of NPLs across debt markets. Through visualization, we find that the government debt and NPLs of EU and BRICS countries increased drastically after the crisis, and crisis countries are contagious. However, the impact of the Greek debt crisis is lower for non-crisis countries, because the debt markets of these countries are decoupled from the Greek market. We also find that sovereign debtors in the EU countries have much closer fiscal linkages than BRICS countries. The level of crisis in the EU countries will be higher than that in the BRICS countries if crisis is driven by the common shocks of macroeconomic fundamentals.

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Section: Introductionmentioning

confidence: 99%

Sustainable Visual Analysis for Bank Non-Performing Loans and Government Debt Distress

Liu

Kim

2019

Sustainability

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“…These types of methods make the assumption that 'normal' data occurs in dense neighborhoods while anomalies happen far from these. There are of course other methods which can be of some value, such as artificial neural networks [87,69], SVM [103], Bayesian Networks [45] or Decision Trees [53]. In the case of unsupervised learning methods, outliers are considered part of no cluster, while normal instances of data are part of a cluster.…”

Section: 12mentioning

confidence: 99%

“…Identifying the most important features in a particular data set can help prevent potentially large execution times of data mining algorithms as well as costly data storage and transfer operations [15]. Dimensionality reduction methodologies that are good candidates for further investigations and potentially further improvements are Principal Component Analysis [5,103], Wrapper Methods [90,40,47] and others [101,43,102]. The idiosyncrasies of each method have to be taken into consideration in order to effectively work on exascale systems.…”

Section: 12mentioning

confidence: 99%

On Processing Extreme Data

Petcu¹,

Iuhasz²,

Pop³

et al. 2016

SCPE

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Abstract. Extreme Data is an incarnation of Big Data concept distinguished by the massive amounts of data that must be queried, communicated and analyzed in near real-time by using a very large number of memory or storage elements and exascale computing systems. Immediate examples are the scientific data produced at a rate of hundreds of gigabits-per-second that must be stored, filtered and analyzed, the millions of images per day that must be analyzed in parallel, the one billion of social data posts queried in real-time on an in-memory components database. Traditional disks or commercial storage nowadays cannot handle the extreme scale of such application data.Following the need of improvement of current concepts and technologies, we focus in this paper on the needs of data intensive applications running on systems composed of up to millions of computing elements (exascale systems). We propose in this paper a methodology to advance the state-of-the-art. The starting point is the definition of new programming paradigms, APIs, runtime tools and methodologies for expressing data-intensive tasks on exascale systems. This will pave the way for the exploitation of massive parallelism over a simplified model of the system architecture, thus promoting high performance and efficiency, offering powerful operations and mechanisms for processing extreme data sources at high speed and/or real time.

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“…Wang et al (2013) proposed fuzzy time series for stock market prediction where the data are fuzzified to the cluster centers. Yu et al (2014) suggested that the selection of the representative features in creation of the rules is the governing factor for better forecasting results. Korol (2014) designed a fuzzy logic system that creates a knowledgebase that contains fuzzy rules.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

A novel fuzzy document-based information retrieval scheme (FDIRS)

Roy

2016

Appl Inform

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BackgroundPrediction or forecasting is both an art as well as science. The process and outcome of forecasting have long been a matter of research and still are in its childhood state. We can devise numerous ways of modeling a phenomenon and predict its outcome, but there are no universal methods using which we can model every phenomena. Modeling of linear systems is comparatively simpler than dynamical systems. Stock markets are completely chaotic and dynamic systems which are both time and sentiment driven. The time series generated through stock market data can only represent a financial time series of prices but cannot represent the overall sentiment of the market players who trade and invest in the stock markets. Hence modeling of stock market data is one of the toughest as it should incorporate not only data but market sentiment also. The stock market data are a series of prices that are observed in a series of certain time intervals (minutes, hours, days, or weeks etc.). Data mining is a very effective tool using which the Abstract Information retrieval systems are generally used to find documents that are most appropriate according to some query that comes dynamically from the users. In this paper, a novel fuzzy document-based information retrieval scheme (FDIRS) is proposed for the purpose of Stock Market Index forecasting. The novelty of the proposed approach is the use of a modified tf-idf scoring scheme to predict the future trend of the stock market index. The contribution of this paper has two dimensions: (1) In the proposed system, the simple daily time series data are converted to an enriched fuzzy linguistic time series with a unique approach of incorporating information about the manner in which the OHLC (open, high, low, and close) price formation took place at every instance of the time series, and (2) A unique approach is followed while modeling the information retrieval (IR) system which converts a simple IR system into a forecasting system. The modified IR system provides us with a trend forecast and after which a crisp value is generated that becomes the forecast value that can be achieved in next few trading sessions. From the performance comparison of FDIRS with standard benchmark models, it can be affirmed that the proposed model has a potential of becoming a good forecasting model. Transaction data of CNX NIFTY-50 index of National Stock Exchange of India are used to experiment and validate the proposed model. Roy Appl Inform (2016) past behavior of the price movement can be modeled to predict the future. Fuzzy logic is a very effective tool using which the market sentiment can be captured and modeled. By adopting a hybrid approach of combining time series, data mining, and fuzzy logic, an effective system can be built to model the stock market price data that can not only give information about price but also the market sentiment or the mood of the market participants. Keywords RESEARCHThe stock market gives facilities to gain both from rising prices as well as from falling prices....

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A SVM Stock Selection Model within PCA

Cited by 77 publications

References 6 publications

Sustainable Visual Analysis for Bank Non-Performing Loans and Government Debt Distress

Sustainable Visual Analysis for Bank Non-Performing Loans and Government Debt Distress

On Processing Extreme Data

A novel fuzzy document-based information retrieval scheme (FDIRS)

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