طراحی و تبین مدل نقد شوندگی سهام با توان رقابت بازار محصول براساس رویکرد تطبیقی مدل اقتصادسنجی و منطق فازی
محورهای موضوعی : مهندسی مالیحسن حیدری سلطان ابادی 1 , حسین پناهیان 2
1 - گروه حسابداری ،واحدکاشان،دانشگاه آزاد اسلامی ، کاشان ، ایران
2 - گروه حسابداری ،واحد کاشان ، دانشگاه آزاد اسلامی ، کاشان ، ایران
کلید واژه: نقدشوندگی سهام, منطق فازی, رقابت بازار,
چکیده مقاله :
یکی از ریسک های مربوط به سهام شرکت، نقد شوندگی سهام است. سهام دارای نقد شوندگی بالا برای سهامداران و سرمایه گذاران جذاب بوده و تقاضای آن را افزایش می دهد.نقدشوندگی سهام به عنوان یکی از معیارهای موثر بر کارایی بازار، به خصوص به لحاظ اطلاعاتی است و به شکل گسترده در بررسی عوامل موثر بر ارایه اطلاعات مفید به کار گرفته می شود. در این پژوهش به طراحی و تبین مدل نقدشوندگی سهام با توجه به مولفه رقابت محصول پرداخته شد. برای انتخاب نمونه، تمام شرکت‌های پذیرفته شده در بورس اوراق بهادار تهران از سال 1386 تا سال 1397 بررسی شده است .ده متغیر اصلی تاثیرگذار بر نقدشوندگی وارد مدل پژوهش شدند و فرضیه‌های پژوهش با استفاده از مدل رگرسیونی مورد آزمون قرار گرفت، سپس از طریق آموزش و یادگیری در شبکه عصبی فازی که یک روش پژوهش تحلیلی- ریاضی است، مدل نقدشوندگی سهام ارائه گردید. پیش‌بینی حاصل از شبکه عصبی فازی بدلیل روش جستجوی فراگیر از دقت بالایی برخوردار و مدل نهایی با ضریب تعیین تعدیل شده 77 درصد تائید گردید. بر اساس نتایج بدست آمده، نقدشوندگی به پنج عامل تاثیرگذار شامل بازده سهام، اندازه شرکت، فرصت سرمایه گذاری، شاخص هرفیندال هیرشمن، شاخص لرنر بستگی دارد.
One of the risks associated with a company's stock is liquidity. Stock with high liquidity are attractive to shareholders and investors and increase demand. Stock liquidity is one of the most effective measures of market efficiency, especially in terms of information and is widely used in examining the factors affecting the supply of useful information. in this study, we designed and explained the stock liquidity model with respect to the product competition component. To select the sample, all companies listed in Tehran Stock Exchange from 2007 to 2019 are surveyed. not eligible were removed. Ten main variables affecting liquidity based on previous research were entered into the research model and the hypotheses were tested using regression model. Then, stock liquidity model was introduced through teaching and learning in Fuzzy neural network, which is an analytical-mathematical research method. The prediction obtained from fuzzy neural network is very accurate due to the comprehensive search method and the final model is confirmed with adjusted coefficient of determination of 77%. According to the results, liquidity depends on five influential factors including stock return, firm size, investment opportunity, Herfindahl Hirschman index and Lerner index
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