Credit scoring algorithm based on link analysis ranking with support vector machine

Xu, Xiujuan; Zhou, Chunguang; Wang, Zhe

doi:10.1016/j.eswa.2008.01.024

Cited by 76 publications

(39 citation statements)

References 20 publications

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“…For a detailed introduction to the subject, [3] and [4] The main idea of SVMs is to construct a hyperplane as the decision surface so that the margin of separation between positive and negative examples is maximised [5]; it is called the Optimum Separation Hyperplane (OSH), as shown in Fig. 1.…”

Section: Literature Review Of Svmmentioning

confidence: 99%

Different Stock Market Models Using Support Vector Machines

Rosillo¹,

Giner²,

Puente³

et al. 2013

IJTEF

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Abstract-The goal of this research is to analyse the different results that can be achieved using Support Vector Machines to forecast the weekly change movement of the different simulated markets. The data cover 3000 daily close for each simulated market. The main characteristic of these markets are: high volatility, bearish movement, bullish movement and low volatility. The inputs of the SVM are the Relative Strength Index (RSI) and the Moving Average Convergence Divergence (MACD). SVM-KM is used by Matlab in order to design the algorithm. The outputs of the SVM are the degree of set membership and the market movement (bullish or bearish).The configuration for the SVM shows that results are better in high volatility markets or low volatility markets than trend markets.Index Terms-Support vector machines, quantitative trading, stock market models, technical analysis. I. INTRODUCTIONDifferent market situations such us high volatility, low volatility, bullish movements and bearish movements are shown in this paper. The SVM helps to investor in the quantitative decision making choosing a weekly forecast (bullish or bearish). We analyse in which market situation the SVM can achieve the best results.The rest of the paper is structured as follows. Section II, the literature review of SVM is presented. Section III explains the design of the trading rule. The results are shown in section IV. Finally section V provides some concluding remarks. with extremely promising results. In this study, we apply SVM in the classification way. II. LITERATURE REVIEW OF SVM III. TRADING RULEThe design of the trading rule is presented in this section. A. Simulated MarketsThe simulated markets are 4 in this experiment. An especially characteristic has been chosen in order to differentiate each series. We want to analyse how SVM help to investors in bullish trend (Fig. 2), bearish trend (Fig. 3), high volatility (Fig. 4) and low volatility (Fig. 5). B. InputsThe inputs of the SVM are the quantitative analysis indicators RSI and MACD. In [10] it is explained that RSI gets good profits in blue chips and Momentum indicator gets good profits in Small Caps, MACD and Stochastic indicators have been analysed over the Spanish Continuous Market too.  Relative Strength Index (RSI)It was designed by [11]. A brief explanation of this indicator is shown below in equation 1. If more details are needed it can be seen in [11].The RSI is an oscillator that shows the strength or speed of the asset price by means of the comparison of the individual upward or downward movements of the consecutive closing prices.For each day, an upward change (U) or downward change (D) is calculated. "Up days" are characterised by the daily close St being higher than the close of previous day St-1 (Ut = St -St-1, Dt = 0). "Down days" are characterised by the daily close being lower than the close of the previous day (Ut = 0, Dt = St-1 -St).The average Ut and Dt are calculated using an n-period exponential moving average (EMAn).Relative Strength Index at time t (RSIt) is t...

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Section: Literature Review Of Svmmentioning

confidence: 99%

Different Stock Market Models Using Support Vector Machines

Rosillo¹,

Giner²,

Puente³

et al. 2013

IJTEF

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“…In (Wang & Huang, 2009), a back propagation based neural network was used to classify credit applicants. In (Xu, Zhou, & Wang, 2009), a credit scoring algorithm-based on support vector machines, was proposed to decide whether a bank should provide a loan to the applicant.…”

Section: Introductionmentioning

confidence: 99%

Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes

Khashman¹

2010

Expert Systems with Applications

198

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“…In addition, HITS has been used in different fields. For example, Xiujuan et al in [20] proposed a credit scoring algorithm based on link analysis ranking with a support vector machine. Their algorithm decides automatically whether a bank should provide a loan to an applicant.…”

Section: Hitsmentioning

confidence: 99%

New use of the HITS algorithm for fast web page classification

Meadi¹,

Babahenini²,

Ahmed³

2017

Turk J Elec Eng & Comp Sci

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Abstract:The immense number of documents published on the web requires the utilization of automatic classifiers that allow organizing and obtaining information from these large resources. Typically, automatic web pages classifiers handle millions of web pages, tens of thousands of features, and hundreds of categories. Most of the classifiers use the vector space model to represent the dataset of web pages. The components of each vector are computed using the term frequency inversed document frequency (TFIDF) scheme. Unfortunately, TFIDF-based classifiers face the problem of the large-scale size of input data that leads to a long processing time and an increase in resource requests. Therefore, there is an increasing demand to alleviate these problems by reducing the size of the input data without influencing the classification results. In this paper, we propose a novel approach that improves web page classifiers by reducing the size of the input data (i.e. web pages and feature reduction) by using the hypertext induced topic search (HITS) algorithm.We employ HITS results for weighting remaining features. We evaluate the performance of the proposed approach by comparing it with the TFIDF-based classifier. We demonstrate that our approach significantly reduces the time needed for classification.

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Credit scoring algorithm based on link analysis ranking with support vector machine

Cited by 76 publications

References 20 publications

Different Stock Market Models Using Support Vector Machines

Different Stock Market Models Using Support Vector Machines

Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes

New use of the HITS algorithm for fast web page classification

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