مقاله انگلیسی رایگان در مورد پیش بینی روند بازار – الزویر ۲۰۱۹

elsevier

 

مشخصات مقاله
ترجمه عنوان مقاله تجزیه و تحلیل فنی و تعبیه تمایلات برای پیش بینی روند بازار
عنوان انگلیسی مقاله Technical analysis and sentiment embeddings for market trend prediction
انتشار مقاله سال ۲۰۱۹
تعداد صفحات مقاله انگلیسی ۱۱ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه الزویر
نوع نگارش مقاله
مقاله پژوهشی (Research Article)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) Scopus – Master Journals List – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۵٫۸۹۱ در سال ۲۰۱۸
شاخص H_index ۱۶۲ در سال ۲۰۱۹
شاخص SJR ۱٫۱۹۰ در سال ۲۰۱۸
شناسه ISSN ۰۹۵۷-۴۱۷۴
شاخص Quartile (چارک) Q1 در سال ۲۰۱۸
مدل مفهومی ندارد
پرسشنامه ندارد
متغیر ندارد
رفرنس دارد
رشته های مرتبط مدیریت
گرایش های مرتبط بازاریابی
نوع ارائه مقاله
ژورنال
مجله / کنفرانس سیستم های خبره با کابردهای مربوطه – Expert Systems with Applications
دانشگاه  DIBRIS, University of Genova, Via Opera Pia 11A, Genova I-16145, Italy
کلمات کلیدی تجزیه و تحلیل فنی، تعبیه تمایلات، پیش بینی روند بازار، یادگیری نظارتی، تجزیه و تحلیل سری زمانی، تجزیه و تحلیل تمایلات
کلمات کلیدی انگلیسی Technical analysis، Sentiment embeddings، Market trend prediction، Supervised learning، Time series analysis، Sentiment analysis
شناسه دیجیتال – doi
https://doi.org/10.1016/j.eswa.2019.06.014
کد محصول  E13553
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
دانلود رایگان مقاله دانلود رایگان مقاله انگلیسی
سفارش ترجمه این مقاله سفارش ترجمه این مقاله

 

فهرست مطالب مقاله:
Abstract
۱٫ Introduction
۲٫ Problem formalization
۳٫ Methodology
۴٫ Available data
۵٫ Experiments discussion
۶٫ Conclusion and future directions
Declaration of Competing Interest
CRediT authorship contribution statement
References

 

بخشی از متن مقاله:
Abstract

Stock market prediction is one of the most challenging problems which has been distressing both researchers and financial analysts for more than half a century. To tackle this problem, two completely opposite approaches, namely technical and fundamental analysis, emerged. Technical analysis bases its predictions on mathematical indicators constructed on the stocks price, while fundamental analysis exploits the information retrieved from news, profitability, and macroeconomic factors. The competition between these schools of thought has led to many interesting achievements, however, to date, no satisfactory solution has been found. Our work aims to combine both technical and fundamental analysis through the application of data science and machine learning techniques. In this paper, the stock market prediction problem is mapped in a classification task of time series data. Indicators of technical analysis and the sentiment of news articles are both exploited as input. The outcome is a robust predictive model able to forecast the trend of a portfolio composed by the twenty most capitalized companies listed in the NASDAQ100 index. As a proof of real effectiveness of our approach, we exploit the predictions to run a high frequency trading simulation reaching more than 80% of annualized return. This project represents a step forward to combine technical and fundamental analysis and provides a starting point for developing new trading strategies.

Introduction

Stock market prediction is a challenging problem to solve and its complexity is strictly related to multiple factors which could affect price changes. Researchers and practitioners coming from different fields have taken the challenge, so that research units composed of mathematicians, data scientists, philosophers, and financial analysts are widely common. The heterogeneity of this environment has led to important steps forward the market theory. In fact, two theoretical hypotheses have been built to explain the market behavior: Efficient Market Hypothesis (EMH) and Adaptive Market Hypothesis (AMH). The EMH (Fama, 1991) states that the current market price fully reflects all the recently published news. This results in the past and current information being immediately incorporated into stock prices. Thus, price changes are merely due to new information or news, and independent of existing information. Since news is unpredictable in nature, in theory, stock prices should follow a random walk pattern and the best bet for the next price is the current price. In practice, the EMH states that it is not possible to ‘beat the market’ because stocks are always traded at their fair value, thus, buying of undervalued stocks or selling them for exaggerated prices should be impossible. However, the AMH (Lo, 2004) tries to connect the rational EMH, with the irrational behavioral finance principles. The AMH applies the principles of evolution and behavior to financial interactions. Behavioral finance attempts to explain stock market anomalies through psychology-based theories. According to AMH, it is possible to exploit weaknesses in the market efficiency to obtain positive returns from a portfolio of stocks.

ارسال دیدگاه

نشانی ایمیل شما منتشر نخواهد شد.