Kernel-Based Aggregating Learning System for Online Portfolio Optimization
المؤلفون المشاركون
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-01-28
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
Recently, various machine learning techniques have been applied to solve online portfolio optimization (OLPO) problems.
These approaches typically explore aggressive strategies to gain excess returns due to the existence of irrational phenomena in financial markets.
However, existing aggressive OLPO strategies rarely consider the downside risk and lack effective trend representation, which leads to poor prediction performance and large investment losses in certain market environments.
Besides, prediction with a single model is often unstable and sensitive to the noises and outliers, and the subsequent selection of optimal parameters also become obstacles to accurate estimation.
To overcome these drawbacks, this paper proposes a novel kernel-based aggregating learning (KAL) system for OLPO.
It includes a two-step price prediction scheme to improve the accuracy and robustness of the estimation.
Specifically, a component price estimator is built by exploiting additional indicator information and the nonstationary nature of financial time series, and then an aggregating learning method is presented to combine multiple component estimators following different principles.
Next, this paper conducts an enhanced tracking system by introducing a kernel-based increasing factor to maximize the future wealth of next period.
At last, an online learning algorithm is designed to solve the system objective, which is suitable for large-scale and time-limited situations.
Experimental results on several benchmark datasets from diverse real markets show that KAL outperforms other state-of-the-art systems in cumulative wealth and some risk-adjusted metrics.
Meanwhile, it can withstand certain transaction costs.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wang, Xin& Sun, Tao& Liu, Zhi. 2020. Kernel-Based Aggregating Learning System for Online Portfolio Optimization. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1196915
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wang, Xin…[et al.]. Kernel-Based Aggregating Learning System for Online Portfolio Optimization. Mathematical Problems in Engineering No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1196915
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wang, Xin& Sun, Tao& Liu, Zhi. Kernel-Based Aggregating Learning System for Online Portfolio Optimization. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1196915
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1196915
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