کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
376848 658324 2015 20 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Moving average reversion strategy for on-line portfolio selection
ترجمه فارسی عنوان
حرکت استراتژی بازاریابی به طور متوسط ​​برای انتخاب نمونه کارها بر روی خط
کلمات کلیدی
انتخاب نمونه کارها، آموزش آنلاین، معکوس متوسط حرکت متوسط ​​به عقب
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

On-line portfolio selection, a fundamental problem in computational finance, has attracted increasing interest from artificial intelligence and machine learning communities in recent years. Empirical evidence shows that stock's high and low prices are temporary and stock prices are likely to follow the mean reversion phenomenon. While existing mean reversion strategies are shown to achieve good empirical performance on many real datasets, they often make the single-period mean reversion assumption, which is not always satisfied, leading to poor performance in certain real datasets. To overcome this limitation, this article proposes a multiple-period mean reversion, or so-called “Moving Average Reversion” (MAR), and a new on-line portfolio selection strategy named “On-Line Moving Average Reversion” (OLMAR), which exploits MAR via efficient and scalable online machine learning techniques. From our empirical results on real markets, we found that OLMAR can overcome the drawbacks of existing mean reversion algorithms and achieve significantly better results, especially on the datasets where existing mean reversion algorithms failed. In addition to its superior empirical performance, OLMAR also runs extremely fast, further supporting its practical applicability to a wide range of applications. Finally, we have made all the datasets and source codes of this work publicly available at our project website: http://OLPS.stevenhoi.org/.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Artificial Intelligence - Volume 222, May 2015, Pages 104–123
نویسندگان
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