Journal of Systems and Information Technology

Hu, Zhongyi; Bao, Yukun; Jiang, Wu

2022 Journal of Systems and Information Technology

doi: 10.1108/jsit-05-2022-265

Huang, Yanmei; Deng, Changrui; Zhang, Xiaoyuan; Bao, Yukun

2022 Journal of Systems and Information Technology

doi: 10.1108/jsit-12-2019-0262

Despite the widespread use of univariate empirical mode decomposition (EMD) in financial market forecasting, the application of multivariate empirical mode decomposition (MEMD) has not been fully investigated. The purpose of this study is to forecast the stock price index more accurately, relying on the capability of MEMD in modeling the dependency between relevant variables.Design/methodology/approachQuantitative and comprehensive assessments were carried out to compare the performance of some selected models. Data for the assessments were collected from three major stock exchanges, namely, the standard and poor 500 index from the USA, the Hang Seng index from Hong Kong and the Shanghai Stock Exchange composite index from China. MEMD-based support vector regression (SVR) was used as the modeling framework, where MEMD was first introduced to simultaneously decompose the relevant covariates, including the opening price, the highest price, the lowest price, the closing price and the trading volume of a stock price index. Then, SVR was used to set up forecasting models for each component decomposed and another SVR model was used to generate the final forecast based on the forecasts of each component. This paper named this the MEMD-SVR-SVR model.FindingsThe results show that the MEMD-based modeling framework outperforms other selected competing models. As per the models using MEMD, the MEMD-SVR-SVR model excels in terms of prediction accuracy across the various data sets.Originality/valueThis research extends the literature of EMD-based univariate models by considering the scenario of multiple variables for improving forecasting accuracy and simplifying computability, which contributes to the analytics pool for the financial analysis community.

Yuan, Gui; Huang, Shali; Fu, Jing; Jiang, Xinwei

2022 Journal of Systems and Information Technology

doi: 10.1108/jsit-03-2020-0040

This study aims to assess the default risk of borrowers in peer-to-peer (P2P) online lending platforms. The authors propose a novel default risk classification model based on data cleaning and feature extraction, which increases risk assessment accuracy.Design/methodology/approachThe authors use borrower data from the Lending Club and propose the risk assessment model based on low-rank representation (LRR) and discriminant analysis. Firstly, the authors use three LRR models to clean the high-dimensional borrower data by removing outliers and noise, and then the authors adopt a discriminant analysis algorithm to reduce the dimension of the cleaned data. In the dimension-reduced feature space, machine learning classifiers including the k-nearest neighbour, support vector machine and artificial neural network are used to assess and classify default risks.FindingsThe results reveal significant noise and redundancy in the borrower data. LRR models can effectively clean such data, particularly the two LRR models with local manifold regularisation. In addition, the supervised discriminant analysis model, termed the local Fisher discriminant analysis model, can extract low-dimensional and discriminative features, which further increases the accuracy of the final risk assessment models.Originality/valueThe originality of this study is that it proposes a novel default risk assessment model, based on data cleaning and feature extraction, for P2P online lending platforms. The proposed approach is innovative and efficient in the P2P online lending field.

SadighZadeh, Saeid; Kaedi, Marjan

2022 Journal of Systems and Information Technology

doi: 10.1108/jsit-12-2019-0264

Online businesses require a deep understanding of their customers’ interests to innovate and develop new products and services. Users, on the other hand, rarely express their interests explicitly. The purpose of this study is to predict users’ implicit interest in products of an online store based on their mouse behavior through various product page elements.Design/methodology/approachFirst, user mouse behavior data is collected throughout an online store website. Next, several mouse behavioral features on the product pages elements are extracted and finally, several models are extracted using machine learning techniques to predict a user’s interest in a product.FindingsThe results indicate that focusing on mouse behavior on various page elements improves user preference prediction accuracy compared to other available methods.Research limitations/implicationsUser mouse behavior was used to predict consumer preferences in this study, therefore gathering additional data on user demography, personality dimensions and emotions may significantly aid in accurate prediction.Originality/valueMouse behavior is the most repeated behavior during Web page browsing through personal computers and laptops. It has been referred to as implicit feedback in some studies and an effective way to ascertain user preference. In these studies, mouse behavior is only assessed throughout the entire Web page, lacking a focus on different page elements. It is assumed that in online stores, user interaction with key elements of a product page, such as an image gallery, user reviews, a description and features and specifications, can be highly informative and aid in determining the user’s interest in that product.

Nanath, Krishnadas; Kaitheri, Supriya; Malik, Sonia; Mustafa, Shahid

2022 Journal of Systems and Information Technology

doi: 10.1108/jsit-11-2020-0257

The purpose of this paper is to examine the factors that significantly affect the prediction of fake news from the virality theory perspective. The paper looks at a mix of emotion-driven content, sentimental resonance, topic modeling and linguistic features of news articles to predict the probability of fake news.Design/methodology/approachA data set of over 12,000 articles was chosen to develop a model for fake news detection. Machine learning algorithms and natural language processing techniques were used to handle big data with efficiency. Lexicon-based emotion analysis provided eight kinds of emotions used in the article text. The cluster of topics was extracted using topic modeling (five topics), while sentiment analysis provided the resonance between the title and the text. Linguistic features were added to the coding outcomes to develop a logistic regression predictive model for testing the significant variables. Other machine learning algorithms were also executed and compared.FindingsThe results revealed that positive emotions in a text lower the probability of news being fake. It was also found that sensational content like illegal activities and crime-related content were associated with fake news. The news title and the text exhibiting similar sentiments were found to be having lower chances of being fake. News titles with more words and content with fewer words were found to impact fake news detection significantly.Practical implicationsSeveral systems and social media platforms today are trying to implement fake news detection methods to filter the content. This research provides exciting parameters from a viral theory perspective that could help develop automated fake news detectors.Originality/valueWhile several studies have explored fake news detection, this study uses a new perspective on viral theory. It also introduces new parameters like sentimental resonance that could help predict fake news. This study deals with an extensive data set and uses advanced natural language processing to automate the coding techniques in developing the prediction model.