بسط مدل‌هاي عاملي قيمت‌گذاري q فاكتور و q فاكتور تعديل‌شده با عامل رشد ـ سرمايه‌گذاري مورد انتظار با استفاده از عامل بازده مورد انتظار

Developing Q-factor and Adjusted Q-factor Pricing Models by the Expected Investment Growth Factor using an Expected Return Factor

هدف شناسايي مدل درست قيمت گذاري دارايي، نه تنها بازده سهام را توضيح مي دهد، بلكه باعث افزايش توانايي پيشبيني بازده غيرعادي خواهد شد. هدف پژوهش حاضر، ارزيابي امكان بهبود عملكرد مدل هاي مورد بررسي پژوهش با اضافه كردن عامل بازده مورد انتظار است. روش در اين پژوهش از داده هاي 345 شركت پذيرفته شده در بورس اوراق بهادار تهران و فرابورس طي بازه زماني 1385 تا 1398 استفاده شد. سپس، بر پايه نظريه مصرف و اصول و مفروضات حسابداري، ويژگي هاي حسابداري اثرگذار بر رشد سود و بازده سهام شناسايي و به دنبال آن به صورت تجربي آزمون شدند. در ادامه، ويژگي هاي مربوطه در يك عامل تحت عنوان عامل بازده مورد انتظار خلاصه شد تا در بسط مدل هاي چندعاملي قيمت گذاري استفاده شود. در نهايت، دارايي هاي آزمون در دو دسته، با لحاظ كردن ويژگي بازده مورد انتظار شركت و بدون لحاظكردن ويژگي بازده مورد انتظار شركت، طبقه بندي شدند. يافته ها: موارد متعددي از هر دو گروه دارايي هاي آزمون نشان مي دهد، بسط مدل هاي مورد بررسي پژوهش با عامل بازده مورد انتظار، موجب افزايش ارزش احتمال آماره GRS، كاهش آماره GRS و افزايش ضريب تعيين تعديل شده آنها مي شود كه اين موضوع حاكي از بهبود عملكرد و افزايش شايان توجه قدرت توضيح دهندگي مدل هاي حاوي عامل بازده مورد انتظار نسبت به مدل هاي متناظر آنها است. نتيجه گيري: نتايج پژوهش نشان مي دهد اضافه شدن عامل بازده مورد انتظار به مدل هاي مورد بررسي پژوهش باعث شده تا عملكرد اين مدل ها در توضيح الگوهاي مختلف بازده سهام بهبود يابد كه اين تفاوت عملكرد از لحاظ قدرت توضيح دهندگي براي دارايي هاي آزموني كه با استفاده از ويژگي بازده مورد انتظار تشكيل شده، مطلوب تر است.

Objective: Identifying the correct asset pricing model has long been an important topic in the thematic literature of financial economics. Such a model not only explains stock returns but also increases the ability to predict abnormal returns. The first models for estimating returns date back to the 1960s, when Markowitz (1952)'s new theory of securities attracted the attention of researchers. The first model for estimating returns was The capital asset pricing model (CAPM) which was presented by William Sharp (1964). In his research, William Sharp showed that return on asset was a function of line of market risk premium. But from 1975 to 1990, deviations and anomalies related to the CAPM model gradually became apparent. Following the recognition of these anomalies in accounting, in this study, based on the research of Penman and Zhou (2018), a expected return factor based on accounting characteristics is introduced. The purpose of this research is to evaluate the possibility of improving the performance of q-factor and adjusted q-factor with the expected investment growth factor’s models for explaining the difference between stock returns by adding the expected return factor based on accounting characteristics. Methods: This is an applied research in terms of purpose and an inferential and descriptive research, in terms of method. To achieve the research objective, data of 345 companies enlisted in the Tehran Stock Exchange (TSE) and Iran Farabourse market, from 2006 to 2020, were gathered. Then based on consumption theory and accounting principles and assumptions, accounting characteristics affecting earnings growth and expected return were identified. After, the data was experimentally tested. In the following, the relevant characteristics in a factor are summarized as the expected return factor based on accounting characteristics to be used in the development of multi-factor pricing models. Finally, to evaluate the performance of multi-factor asset pricing models, test assets were classified into two categories (once considering the expected return, and once without considering the company’s expected return factor). Results:Numerous cases from both groups of test assets showed that the expansion of q-factor and adjusted q-factor with the expected investment growth factor’s models with the expected return factor based on accounting characteristics increases the probability value of GRS statistic, decreases GRS statistic, and increases their adjusted coefficient of determination. This indicated an improvement in performance and a considerable increase in the explanatory power of models containing the expected return factor based on accounting characteristics compared to their respective models. Conclusion: The results of this research showed that the added expected return factor based on accounting characteristics to q-factor and adjusted q-factor with the expected investment growth factor’s models improves their performance in explaining the stock returns. Also, the test assets that considered the company’s expected return characteristic performed better compared to those that did not.