مقاله پژوهشی: بررسی قابلیت پیش بینی پذیری نقاط شروع و پایان روند کوتاه مدت قیمت سهام با استفاده از شبکه احتمالات بیزین

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مدیریت مالی، واحد علی‌آباد کتول،دانشگاه آزاد اسلامی،علی‌آباد کتول،ایران

2 گروه مهندسی صنایع، واحد علی‌آباد کتول، دانشگاه آزاد اسلامی، علی‌آباد کتول، ایران

3 گروه مهندسی صنایع، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران

4 دانشیار دانشگاه الزهرا

چکیده

هدف اصلی از این پژوهش بررسی قابلیت پیش بینی پذیری نقاط شروع(کف) و پایان(سقف) روند کوتاه مدت قیمت سهام با استفاده از مدل نایبویز جهت ارایه یک سیستم پشتیبان تصمیم می باشد.در این تحقیق از دو دسته متغیر شامل متغیرهای تقویمی و تکنیکی جهت مدل سازی استفاده گردید.نتایج این پژوهش نشان داد که مدل مورد استفاده در شناسایی و پیش بینی نقاط پایان(سقف) در نمودار قیمت سهام از عملکرد بهتری نسبت به سایرنقاط برخوردار است و همچنین دقت مدل مورد استفاده در سی وشش صنعت مورد بررسی دارای تفاوت های قابل تامل می باشد که این موضوع تعمیم پذیری نتایج این پژوهش و استفاده از مدل نایبویز برای پیش بینی نقاط مذکور در صنایع مختلف را دچار خدشه می کند . ما با اطمینان زیاد می توانیم در مورد رفتار آشوب گونه نقاط شروع و پایان در نمودار قیمت سهام اظهار نظر کنیم اما در مورد رفتار احتمالی می بایست دقت نظر به خرج دهیم.

کلیدواژه‌ها


عنوان مقاله [English]

Investigating the Predictability of Starting Point and Ending Short-Term Trend of Stock Price Using the Bayesian Likelihood Network

نویسندگان [English]

  • Mohammad Moshari 1
  • Hosein Didehkhani 2
  • Kaveh Khalili dameghani 3
  • Ebrahim Abbasi 4
1 Department of Financial Management, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran
2 Industrial Engineering Department, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran
3 Industrial Engineering Department, South Tehran Branch, Islamic Azad University, Tehran, Iran
4 Associate Professor, AlZahra University
چکیده [English]

The main objective of this research is to investigate the predictability of the starting points (floor) and the end (roof) of the short-term stock price trend using the Naibouz model for providing a decision support system. In this research, two variables including calendar and technical variables for modeling were used. The results of this study showed that the model used to identify and predict the end points (roof) in the stock price chart has a better performance than the other, and also the accuracy of the model used in the thirty of the industry survey has discrepancy differences. This is a generalization of the results of this research and the use of the Naibouz model to predict the points in the various industries. We can confidently comment on the turbulence behavior of the starting and ending points in the stock price chart, but we need to pay careful attention to probable behavior.

کلیدواژه‌ها [English]

  • Decision support system
  • Baysian network
  • Prediction
  • Stock market
 
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