سامانه مدیریت نشریات علمی دانشگاه شهید بهشتی

عزیزی, صدیقه, محمدی, حسین. (1402). مدل‌سازی متغیرهای اثرگذار بر کارایی سرمایه‌گذاری بر مبنای ترکیب شبکه‌های عصبی مصنوعی و الگوریتم بهینه‌سازی حرکت تجمعی ذرات. , 13(42), 35-62. doi: 10.48308/jfmp.2023.103949
صدیقه عزیزی; حسین محمدی. "مدل‌سازی متغیرهای اثرگذار بر کارایی سرمایه‌گذاری بر مبنای ترکیب شبکه‌های عصبی مصنوعی و الگوریتم بهینه‌سازی حرکت تجمعی ذرات". , 13, 42, 1402, 35-62. doi: 10.48308/jfmp.2023.103949
عزیزی, صدیقه, محمدی, حسین. (1402). 'مدل‌سازی متغیرهای اثرگذار بر کارایی سرمایه‌گذاری بر مبنای ترکیب شبکه‌های عصبی مصنوعی و الگوریتم بهینه‌سازی حرکت تجمعی ذرات', , 13(42), pp. 35-62. doi: 10.48308/jfmp.2023.103949
عزیزی, صدیقه, محمدی, حسین. مدل‌سازی متغیرهای اثرگذار بر کارایی سرمایه‌گذاری بر مبنای ترکیب شبکه‌های عصبی مصنوعی و الگوریتم بهینه‌سازی حرکت تجمعی ذرات. , 1402; 13(42): 35-62. doi: 10.48308/jfmp.2023.103949

مدل‌سازی متغیرهای اثرگذار بر کارایی سرمایه‌گذاری بر مبنای ترکیب شبکه‌های عصبی مصنوعی و الگوریتم بهینه‌سازی حرکت تجمعی ذرات

چشم انداز مدیریت مالی
دوره 13، شماره 42، شهریور 1402، صفحه 35-62    اصل مقاله (702.61 K)
نوع مقاله: علمی - پژوهشی
شناسه دیجیتال (DOI): 10.48308/jfmp.2023.103949
نویسندگان
صدیقه عزیزی* 1؛ حسین محمدی2
1استادیار، گروه حسابداری، دانشگاه آزاد اسلامی، واحد کرمان، کرمان، ایران.
2دانشجوی دکتری، گروه حسابداری، دانشگاه آزاد اسلامی، واحد جیرفت، جیرفت، ایران.
چکیده
هدف این پژوهش الگویی برای اندازه‌گیری کارایی سرمایه‌گذاری در شرکت‌های پذیرفته‌شده در بورس اوراق بهادار تهران بر مبنای ترکیب شبکه‌های عصبی مصنوعی و الگوریتم بهینه‌سازی حرکت تجمعی ذرات است. بدین منظور، نمونه‌ای متشکل از 124 شرکت پذیرفته‌شده در بورس اوراق بهادار تهران طی سال‌های 1386-1400 بررسی شده‌است.  در راستای دستیابی به اهداف پژوهش، ابتدا با مطالعه پژوهش‌های پیشین در زمینه کارایی سرمایه‌گذاری 17 متغیر اثرگذار بر کارایی سرمایه‌گذاری انتخاب و با استفاده از روش داده‌های ترکیبی، مدل بهینه کارایی سرمایه‌گذاری بر مبنای جریان وجه نقد ناشی از فعالیت‌های سرمایه‌گذاری برآورد گردیده است. نتایج برآورد مدل نشان داد 8 متغیر فرصت رشد، کیفیت گزارشگری مالی، رشد فروش، نرخ بازده دارایی‌ها، اهرم مالی، جریآن‌های نقد عملیاتی، سود سهام تقسیمی، سود عملیاتی به کل دارایی‌ها با میزان وجه ‌نقد صرف شده در فعالیت‌های سرمایه‌گذاری دارای رابطه‌ای معنادار هستند. در نهایت، نتایج مقایسه مدل پژوهش بر مبنای ترکیب شبکه‌های عصبی مصنوعی و الگوریتم بهینه‌سازی حرکت تجمعی ذرات با سایر مدل‌ها نشان داد مدل پژوهش، خطای آموزش شبکه عصبی با الگوریتم حرکت تجمعی ذرات را بـه مقدار 0560/0 کاهش داد. همچنین، با توسعه مدل پژوهش، از طریق وارد کردن متغیرهای حسابداری، سطح زیرمنحنی راک به 6164/0 افزایش می‌یابد و در نتیجه، بر دقت مدل پژوهش تا 602/63 درصد افزوده می‎شود. این نتیجه، مؤثر بودن ورود متغیرهای محیطی به مدل پژوهش را نشان می‌دهد.
کلیدواژه‌ها
کارایی سرمایه‌گذاری؛ نسبت‌های مالی؛ داده‌های ترکیبی؛ شبکه‌های عصبی مصنوعی؛ الگوریتم بهینه‌سازی حرکت تجمعی ذرات
عنوان مقاله [English]
Modeling variables affecting investment efficiency based on the combination of artificial neural networks and the optimization algorithm of cumulative movement of particles
نویسندگان [English]
Sedighe Azizi1؛ Hossein Mohammadi2
1Assistant Prof., Department of Accounting, Islamic Azad University, Kerman Branch, Kerman, Iran.
2Ph.D. Candidate, Department of Accounting, Islamic Azad University, Jiroft branch, Jiroft, Iran.
چکیده [English]
The purpose of this study is a model for measuring investment efficiency in companies listed on the Tehran Stock Exchange based on the combination of artificial neural networks and the cumulative particle motion optimization algorithm. For this purpose, samples consisting of 124 companies listed on the Tehran Stock Exchange during the years 2008-2019 have been studied. In order to achieve the goals of the research, first by studying the previous researches in the field of investment efficiency, 17 variables affecting investment efficiency were selected and using the combined data method, the optimal model of investment efficiency was estimated based on the cash flow resulting from investment activities. The results of the model estimation showed that 8 variables of growth opportunity, quality of financial reporting, sales growth, rate of return on assets, financial leverage, operating cash flows, dividends, operating profit on total assets have a significant relationship with the amount of cash spent on investment activities. Finally, the results of comparing the research model based on the combination of artificial neural networks and particle cumulative motion optimization algorithm with other models showed that the development of the research model reduced the neural network training error with particle cumulative motion algorithm by 0.0560. Also, with the development of the research model, by entering accounting variables, the rock curvature level increases to 0.6164 and as a result, the accuracy of the research model increases to 63.602%.
کلیدواژه‌ها [English]
Investment Efficiency, Financial Ratios, Hybrid Data, Artificial Neural Networks, Optimization Algorithm of Cumulative Particle Motion
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