بررسی حافظه درازمدت شاخص کل قیمت بورس اوراق بهادار تهران (مطالعه موردی: صنعت داروسازی)

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشیار حسابداری، عضو هیأت علمی دانشگاه پیام نور

2 مربی حسابداری، عضو هیأت علمی دانشگاه پیام نور

3 دانشجوی دکترای حسابداری دانشگاه تهران، مدرس دانشگاه پیام نور.

چکیده

مقدمه: پژوهش حاضر دو هدف را دنبال می‌کند: اول، بررسی وجود حافظه درازمدت در شاخص کل قیمت صنعت داروسازی بورس اوراق بهادار تهران، و دوم، ارزیابی دقت پیش‌بینی الگوهایی که حافظه درازمدت شاخص کل قیمت این صنعت را در نظر می‌گیرد.
روش پژوهش: در این پژوهش از روش‎های بیشینه درست‌نمایی، وایتل، جی. پی. اچ و اسپریو برای برآورد عامل انباشتگی کسری (حافظه بازار) استفاده شده است. در ابتدا، از بین چهار روش ذکرشده‌ی قبلی، دو روش بیشینه درست‌نمایی و وایتل توانستند بهترین الگوی ARFIMA را به داده‎ها برازش کنند. سپس، با استفاده‎ از آماره‎ها و معیارهای انتخاب بهترین الگو، الگوی به‌دست آمده با روش بیشینه درست‌نمایی به عنوان بهترین روش برآورد انتخاب شد.
یافته‌ها: شاخص کل قیمت صنعت داروسازی بورس اوراق بهادار تهران دارای حافظه با دامنه درازمدت است و بهترین الگو برای پیش‌بینی شاخص کل قیمت صنعت داروسازی الگوی ARFIMA (1,0.13,1) است.
نتیجه‌گیری: با اطمینان بالایی می‌توان ادعا کرد که سری زمانی شاخص کل قیمت صنعت داروسازی بورس اوراق بهادار تهران دارای حافظه درازمدت است. وجود این ویژگی، دلیلی بر رد شکل ضعیف فرضیه کارایی بازار است. مطابق فرضیه بازار کارا، قیمت دارایی‌ها نباید با استفاده از داده‌های گذشته قابل پیش‌بینی باشد. وجود حافظه درازمدت در شاخص کل قیمت صنعت داروسازی، بیانگر وجود خودهمبستگی میان مشاهدات با فاصله زمانی زیاد است. بنابراین، می‌توان از شاخص‌های گذشته به منظور پیش‌بینی شاخص‌های آینده استفاده کرد که این امر امکان استفاده از راهبردی سوداگرایانه را فراهم می‌کند.

کلیدواژه‌ها


عنوان مقاله [English]

Investigating the Long-Term Memory of Total Price Index of the Tehran Stock Exchange (A Case Study: Pharmaceutical Industry)

نویسندگان [English]

  • S. M. Mousavi Shiri 1
  • S. H. Vaghfi 2
  • M. Ahangary 3
1 Associate Professor, A Faculty Member of the Department of Accounting, Payame Noor University
2 Instructor, A Faculty Member of the Department of Accounting, Payame Noor University
3 Ph. D. Student in Accounting, Instructor, Payame Noor University
چکیده [English]

Introduction: This study meets two objectives: first, investigating the presence of long-term memory of total price index in the pharmaceutical industry of the Tehran Stock Exchange; second, evaluating the accuracy of predicting models which include the long-term memory of the total price index of this industry.
Method: In this research, the methods of Maximum Likelihood (MLE), Whittle, GPH, and Sperio have been used in order to evaluate the fractional integration parameter (market memory). In the beginning, among the four above-mentioned methods, two methods of MLE and Whittle could process the best pattern of ARFIMA to the data. Then, by using statistics and choosing criteria for the best one, this pattern was selected as the best for evaluating by MLE method.
Results:  The total price index in the pharmaceutical industry of the Tehran Stock Exchange has a long-term memory, and the best pattern for predicting the stock total price index of pharmaceutical industry is ARFIMA (1, 0.13, 1).
Conclusion: It can be claimed with high certainty that the time-series of total price index in the pharmaceutical industry of the Tehran Stock Exchange has a long-term memory. The existence of this feature is a good reason for rejecting the weak shape of the market efficiency assumption. According to this assumption, the price of assets should not be predictable by historical data. The existence of long-term memory of total price index in the pharmaceutical industry indicates self-correlation among evidence with high intervals. Thus, it is possible to use historical data in order to predict the future indices which will be provided by applying a profitable strategy.

کلیدواژه‌ها [English]

  • Predicting Total Price Index of Pharmaceutical Industry
  • Fractional Integration Parameter (Market Memory)
  • ARFIMAMethod
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