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성노윤(Nohyoon Seong),남기환(Kihwan Nam) 한국지능정보시스템학회 2018 지능정보연구 Vol.24 No.2
Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company’s news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a
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Kihwan Nam,Hyelin Oh,Wonseok Oh,Nohyoon Seong 한국경영정보학회 2019 한국경영정보학회 학술대회논문집 Vol.2019 No.11
Recommender systems reduce the information overload by providing users’ “top-pick” recommendations. However, selecting the “best” out of good options can be more challenging than separating good and bad ones. Based on the of attraction effects frameworks, we examine the effectiveness of recommender systems including “worstpicks”. The addition of an unfavored item may alleviate consumers’ cognitive load and ease comparisons—all of which means the improved performance of recommendation structures. For empirical validation, we employed a randomized field experiment involving 475,339 unique users, 93,282 fashion products and 25,854,168 total instances of exposure to recommendations. The findings show that the attraction effect (AE) model outperformed the rational choice (RC) equivalent. AE model was more effective on PCs than over mobile phones. For male consumers, the AE model outcompeted the RC equivalent, but such difference was not detected among female shoppers. Based on these findings, we discuss the theoretical and practical implications.
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