تأثیر کاهش ابعاد در انتخاب سهام
ترجمه نشده

تأثیر کاهش ابعاد در انتخاب سهام

عنوان فارسی مقاله: تأثیر کاهش ابعاد در انتخاب سهام با تجزیه و تحلیل خوشه ای در شرایط مختلف بازار
عنوان انگلیسی مقاله: Effect of dimensionality reduction on stock selection with cluster analysis in different market situations
مجله/کنفرانس: سیستم های خبره با برنامه های کاربردی - Expert Systems With Applications
رشته های تحصیلی مرتبط: اقتصاد
گرایش های تحصیلی مرتبط: اقتصاد مالی، توسعه اقتصادی و برنامه ریزی، برنامه ریزی سیستم های اقتصادی
کلمات کلیدی فارسی: انتخاب سهام، کاهش ابعاد، وضعیت بازار، استراتژی چرخش، یادگیری عمیق
کلمات کلیدی انگلیسی: Stock selection، Dimensionality reduction، Market situation، Rotation strategy، Deep learning
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.eswa.2020.113226
دانشگاه: School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, PR China
صفحات مقاله انگلیسی: 31
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 5/891 در سال 2019
شاخص H_index: 162 در سال 2020
شاخص SJR: 1/190 در سال 2019
شناسه ISSN: 0957-4174
شاخص Quartile (چارک): Q1 در سال 2019
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E14296
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

1- Introduction

2- Methodology

3- The effect of dimensionality reduction on stock selection with cluster analysis in different market situations

4- A stock-selection rotation strategy based on the effect of dimensionality reduction

5- Conclusions

References

بخشی از مقاله (انگلیسی)

Abstract

Dimensionality reduction is inevitable in stock selection with cluster analysis. Considering relations among dimensionality reduction, noise trading, and market situations, we empirically investigate the effect of dimensionality-reduction methods–principal component analysis, stacked autoencoder, and stacked restricted Boltzmann machine–on stock selection with cluster analysis in different market situations. Based on the index fluctuation, the market is divided into sideways and trend situations. For the CSI 100 and Nikkei 225 constituent stocks, experimental results show that: (1) In sideways situations, dimensionality reduction hardly improves the performance of stock selection with cluster analysis; (2) the advantage of dimensionality reduction is mainly reflected in trend situations, but whether it is in an up or down trend depends on the market analyzed. More importantly, according to the above findings and assuming that the dimensionality-reduction effect will continue, we propose a rotation strategy with and without dimensionality reduction. The results of experiments show that the proposed rotation strategy outperforms the stock market indices as well as the stock-selection strategies based on dimensionality reduction and cluster analysis. These findings offer practical insights into how dimensionality reduction can be efficiently used for stock selection.

Introduction

Stock selection is a crucial issue in investment management, which determines the return of stock investments (Markowitz, 1952; Ren et al., 2017). There are various stock-selection strategies, including multi-factor models (Carvalho et al., 2010; Fama and French, 2018), momentum and contrarian strategies (Grinblatt et al., 1995; Cooper et al., 2004), style rotation strategies (Lucas et al., 2002; Ahmed et al., 2002), volatility strategies (Chong and Phillips, 2012; Hsu and Li, 2013), and behavior biases strategies (Huang et al., 2011). Among these strategies, multi-factor models are the most studied, mainly including the Fama-French three-factor model (Fama and French, 1992), the Fama-French five-factor model (Fama and French, 2017), factor models based on investor attention (Li and Yu, 2012), and factor models based on fundamental and technical analysis (Peachavanish, 2016). Investors can use these models to analyze stock characteristics from different perspectives. If stock characteristics last for a period, investors would obtain a higher benefit from analyzing stock characteristics than from random selection.