مدل‌سازی و تاثیر شوک‌های اقتصادی بر بازده سهام صنایع غذایی

نویسندگان
امیر دادرس مقدم 1
سید مهدی حسینی 2
محمد حسین کریم 3
Hossein Badi Farzin 4
محمد نوروزیان 5
1 استادیار گروه اقتصاد کشاورزی، دانشگاه سیستان و بلوچستان زاهدان، ایران
2 استاد یار گروه اقتصاد کشاورزی، دانشگاه سیستان و بلوچستان زاهدان، ایران
3 دانشیار گروه اقتصاد منابع، دانشگاه خوارزمی، تهران، ایران.
4 دانشجوی دکترا اقتصاد کشاورزی، دانشگاه سیستان و بلوچستان
5 دانشجوی دکترا اقتصاد کشاورزی، دانشگاه سیستان وبلوچستان(norozianali@pgs.usb.ac.ir)
10.52547/fsct.19.124.315
چکیده
هدف این پژوهش مدل­سازی و تأثیر شوکهای اقتصادی بر بازده سهام صنایع غذایی در طی دوره زمانی1390 تا 1398 است. در این پژوهش به مدل­سازی متغیرهای کلان اقتصادی بهینه بر بازده سهام شرکت‌های غذایی با استفاده از روش تابع تقریب الگوریتم ژنتیک پرداخته شده و سپس با روش خود رگرسیون برداری پانل تکانه‌ها و شوک­های متغیرهای کلان اقتصادی موثر بر بازده سهام صنایع غذایی تجزیه و تحلیل شده است. در ابتدا با روش الگوریتم تقریب تابع ژنتیک، چهار متغیر قیمت نفت اوپک، حجم نقدینگی، قیمت زمین و شاخص قیمت سهام از میان هشت متغیر کلان اقتصادی به عنوان متغیرهای تاثیرگذار در مدل رگرسیون بهینه شناسایی شدند. قیمت نفت اوپک و قیمت زمین تاثیر منفی و معناداری بر بازده سهام شرکت­های غذایی داشته در حالی که حجم نقدینگی و شاخص قیمت سهام تاثیر مثبت و معناداری بر بازده سهام شرکت­های غذایی دارند. با توجه به توابع عکس‌العمل آنی، واکنش بازده سهام نسبت به قیمت نفت اوپک و حجم نقدینگی در ابتدا مثبت بوده است. در روش تجزیه واریانس، بیشترین سهم ناشی از شوک بازده سهام صنایع غذایی به خودش بوده و بعد از آن مربوط به حجم نقدینگی است. با توجه به تاثیر مثبت حجم نقدینگی بر بازده سهام شرکت‌های صنایع غذایی، پیشنهاد می­شود سیاستگذاران و برنامه­ریزان به منظور توسعه سرمایه­گذاری صنایع غذایی، سیاست­های را اعمال کنند تا حجم نقدینگی را به سمت شرکتهایی صنایع غذایی افزایش دهند.
کلیدواژه‌ها
بازده سهام صنایع غذایی
حجم نقدینگی
تابع تقریب الگوریتم ژنتیک
خود رگرسیون برداری پانل

موضوعات

عنوان مقاله English

Modeling and the impact of economic shocks on food industry stock returns

نویسندگان English

amir dadrasmoghadam 1
Seyed Mahdi Hosseini 2
Mohammad Hossein Karim 3
حسین بدیع فرزین 4
Mohammad Norouzian 5
1 Assistant Professor, University of Sistan and Baluchestan
2 Assistant Professor, University of Sistan and Baluchestan
3 Associate Professor, Department of Resource Economics, Kharazmi University, Tehran, Iran.
4 PhD student in Agricultural Economics, University of Sistan and Baluchestan,
5 PhD student in Agricultural Economics, University of Sistan and Baluchestan, ، norozianali@pgs.usb.ac.ir
چکیده English

The purpose of this study is modeling and the effect of economic shocks on stock returns of food industry during the period 2009 to 2020. In this research, the optimal macroeconomic variables on the stock returns of food companies are modeled using the genetic algorithm approximation function method and then the impulses and shocks of macroeconomic variables affecting the stock returns of food industries are analyzed by Auto regression method has been analyzed. Initially, using the genetic function approximation algorithm, four variables of OPEC oil price, liquidity volume, land price and stock price index were identified among the eight macroeconomic variables as influential variables in the optimal regression model. OPEC oil prices and land prices have a negative and significant effect on the stock returns of food companies, while the volume of liquidity and stock price index have a positive and significant effect on the stock returns of food companies. Given the response impulse functions, the stock return reaction to OPEC oil prices and liquidity was initially positive. In the analysis of variance method, the largest share is due to the shock of the food industry stock returns to itself, followed by the volume of liquidity. Given the positive impact of liquidity on the stock returns of food industry companies, it is suggested that policy makers and planners to implement policies to increase the volume of liquidity to food industry companies in order to develop food industry investment.

کلیدواژه‌ها English

Food industry stock returns
liquidity volume
genetic algorithm approximation function
panel vector auto regression
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