The dividend is one of the issues that has long been considered by financial researchers and continues to be one of the controversial issues in the field of financial management. The aim of this study was to model confirmatory factor analysis to investigate the effect of risk management on corporate dividends. For this reason, we have used some systematic risk components such as exchange rate risk, interest rate risk, and financial risk to measure risk management. In order to investigate the dividend of companies, we have used the variables of earnings per share, dividends, earnings growth per share, and dividend ratio. The research tool was a questionnaire whose reliability was confirmed based on the opinion of a group of faculty members and related experts and Cronbach's alpha coefficient was used to determine the reliability of different departments (0.78-0.92). Data were analyzed using Lisrel8.8 software. To analyze the collected data, first, descriptive statistics describing the main variables and demographic characteristics are examined. Then inferential statistics are presented. In inferential statistics, confirmatory factor analysis (CFA) with covariance-based approach was used by Lisrel8.8 software to assess the occasion of the measurement tool (questionnaire). Also, skolgi and elongation coefficients were used to investigate the type of data distribution. According to the type of questions raised in the questionnaire, an independent sample T-test was used to investigate the assumptions. The results of the hypothesis test indicate that risk management is directly related to corporate dividends. تفاصيل المقالة

پژوهش حاضر با هدف بکارگیری شبکه عصبی کانولوشن برای پیش بینی ورشکستگی شرکتها با استفاده از نسبتهای مالی تصویری انجام می‌گیرد. در دنیای به سرعت در حال تغییر، برای شناسایی تصاویر، شبکه‌های عصبی کانولوشن در مسائل مربوط به بسیاری از رشته‌ها بکار می‌روند. دوره زمانی پژوهش 1388 تا 1397 و شرکتهای نمونه از بین شرکتهای پذیرفته شده در بورس اوراق بهادار تهران و در قالب دو گروه شامل 66 شرکت ورشکسته و 66 شرکت غیر ورشکسته انتخاب شدند. از آنجا که کار شبکه عصبی کانولوشن شناخت تصاویر از بین تصاویر موجود است لذا ابتدا نسبتهای مالی به عنوان داده های پژوهش از طریق نرم افزار متلب 2019 به تصویر تبدیل و سپس با کمک شبکه عصبی کانولوشن و تحت معماری گوگل نت اقدام به تشخیص و پیش بینی وضعیت شرکتهای نمونه گردید. نتایج حاصل از تحلیل یافته‌ها نشان داد که مدل شبکه‌های عصبی کانولوشن در پیش بینی و شناخت از روی تصاویر، پیش بینی درستی با دقت 50 درصد از بین شرکتها انجام داد. به عبارتی در پیش بینی وضعیت تداوم فعالیت شرکتها 50 درصد از شرکتهای ورشکسته و 50 درصد از شرکتهای غیر ورشکسته را درست تشخیص داد. تفاصيل المقالة

Objectives: The purpose of this study is to investigate the role of company ownership in investment efficiency, focusing on business strategy.

Design/methodology/approach: This research is applied, with a causal correlation methodology. The statistical population of the study included all firms listed on the Tehran Stock Exchange. Using a systematic sampling method, 135 companies were selected as the sample and examined over an 8-year period from 2015 to 2022. Three types of institutional ownership—management, family ownership—were considered.

Results: The results of testing the research hypotheses indicated that institutional ownership, as well as family ownership, have a direct and significant relationship with the investment efficiency of the company. However, managerial ownership showed no relationship with the efficiency of the company's investment. The interaction of family ownership with business strategy impacted investment efficiency. However, the interaction of institutional ownership and managerial ownership with business strategy did not affect the efficiency of the company's investment.

Innovation: The current research provides evidence that the types of company ownership have an impact on investment efficiency and emphasizes that the role of business strategy cannot be overlooked.

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