Stock Price Pattern Prediction Based on Complex Network and Machine Learning
المؤلفون المشاركون
Cao, Hongduo
Lin, Tiantian
Li, Ying
Zhang, Hanyu
المصدر
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-05-28
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
Complex networks in stock market and stock price volatility pattern prediction are the important issues in stock price research.
Previous studies have used historical information regarding a single stock to predict the future trend of the stock’s price, seldom considering comovement among stocks in the same market.
In this study, in order to extract the information about relation stocks for prediction, we try to combine the complex network method with machine learning to predict stock price patterns.
Firstly, we propose a new pattern network construction method for multivariate stock time series.
The price volatility combination patterns of the Standard & Poor’s 500 Index (S&P 500), the NASDAQ Composite Index (NASDAQ), and the Dow Jones Industrial Average (DJIA) are transformed into directed weighted networks.
It is found that network topology characteristics, such as average degree centrality, average strength, average shortest path length, and closeness centrality, can identify periods of sharp fluctuations in the stock market.
Next, the topology characteristic variables for each combination symbolic pattern are used as the input variables for K-nearest neighbors (KNN) and support vector machine (SVM) algorithms to predict the next-day volatility patterns of a single stock.
The results show that the optimal models corresponding to the two algorithms can be found through cross-validation and search methods, respectively.
The prediction accuracy rates for the three indexes in relation to the testing data set are greater than 70%.
In general, the prediction ability of SVM algorithms is better than that of KNN algorithms.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Cao, Hongduo& Lin, Tiantian& Li, Ying& Zhang, Hanyu. 2019. Stock Price Pattern Prediction Based on Complex Network and Machine Learning. Complexity،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1131705
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Cao, Hongduo…[et al.]. Stock Price Pattern Prediction Based on Complex Network and Machine Learning. Complexity No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1131705
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Cao, Hongduo& Lin, Tiantian& Li, Ying& Zhang, Hanyu. Stock Price Pattern Prediction Based on Complex Network and Machine Learning. Complexity. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1131705
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1131705
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر