دانشگاه اصفهان مدیریت دارایی و تامین مالی 2383-1189 11 2 2023 06 22 The Relationship Between Financing Restrictions and Financing Strategies: An Emphasis on the Mediating Role of Corporate Governance محدودیت‌های مالی در شرکت‌ها و رابطۀ آن با استراتژی‌های تأمین ‌مالی با تأکید بر نقش تعدیل‌گر ‌حاکمیت ‌شرکتی 1 18 27546 10.22108/amf.2023.135738.1769 FA ماندانا طاهری استادیار، گروه حسابداری، دانشکدۀ مدیریت و حسابداری، دانشگاه علامه طباطبایی، تهران، ایران Journal Article 2022 11 22 The purpose of this study is to investigate whether the increase in financing restrictions of companies has an effect on their financing strategies. Based on this, the strategies of company managers for financing were formulated in the form of their desire to finance internally instead of externally and through transactions with related parties and financing from debt instead of issuing shares in the capital market. In addition, this research, emphasizing the role of corporate governance and its effect on defined relationships, seeks to provide a reasonable answer in relation to the effect of corporate governance on reducing the adverse effect of financing restrictions on the financing strategies of companies. In this regard, the data of 150 companies listed in the Tehran Stock Exchange during 2015-2019 were selected, and according to the Kaplan/Zengales indices as a measure of supplier criteria governing the companies, research hypotheses were examined. The results of this study showed that financing restrictions have a negative and significant relationship with transactions with related parties and a positive and significant relationship with capital structure. In addition, corporate governance only has a moderating effect on the relationship between financing constraints and capital structure. Keywords: Financing Restrictions, Financial Strategies, Related-party Transactions, Capital Structure, Corporate Governance. Introduction Firms need financial resources to realize their activities and maximize the wealth of shareholders. Deciding on the choice between internal and external sources, the combination of capital structure and generally their strategies for financing is one of the most important decisions related to the growth and survival of the company. The concept of financing is explained by cash flows entering the firm, and any change and fluctuation is an indication of potential risk in financing activities, investment, and future operations of firms. In lack of proper financing and fewer cash flows, the firm faces financial restrictions and this problem affects the reduction of their investments and consequently the incoming cash flows to the firm. On the other hand, the existence of asymmetric information and agency problems can lead firms to not invest due to financial restrictions. Uninformed investors have less information about the net present value of their investment, while increasing the levels of information transparency and increasing the cash flow in firms lead to decreasing the lack of investment. According to this, in this research, we examined the relationship between financial restrictions and financing strategies. We also answered the question of whether corporate governance can lead to reducing the adverse effects of financial restrictions in adopting financing strategies. In this research, financing strategies will be discussed in the form of two categories of strategies, including transactions with related parties and capital structure. Methods and Data To test the hypotheses, we used simple regression and multivariate regression models. For financial data and information, we collected the financial statements of firms listed on the Tehran Stock Exchange and databases such as Rahvard Navin. We examined the firm’s data for 5 years from 2015 to 2019. Based on this, 150 companies listed on the Tehran Stock Exchange were selected as a sample to investigate the research hypotheses. Findings The results of the estimation of the first model of the research indicated that at a significance level of 99%, financial constraints based on the Kaplan and Zangales’ (1997) index have a negative and significant relationship with transactions with related parties. Therefore, the first hypothesis of the study regarding the relationship between financial constraints and transactions with related parties is confirmed. The results of the estimation of the second research model indicated that financial constraints have a positive and significant relationship with capital structure. Therefore, the second hypothesis of the research about the relationship between financial constraints and capital structure is confirmed. The results of the estimation of the third model of the research indicated that there was a negative relationship between corporate governance and financial constraints. Therefore, the third hypothesis of the research is confirmed. The results of the estimation of the fourth research model indicated that the moderator variable (corporate governance) did not have a significant effect on the relationship between financial restrictions and transactions with related parties. Therefore, the fourth hypothesis of the research was rejected. The results of the estimation of the fifth research model indicated that the the moderator variable (corporate governance) has a significant effect on the relationship between financial constraints and capital structure. Discussion and Conclusions This research was done with two main goals and several sub-goals. One of the main goals of this study was to investigate the effects of financial restrictions on financing strategies. Another main goal of this study was to provide a solution to reduce the negative effect of financial constraints on the mentioned strategies. For this purpose, corporate governance was used. According to the theory of hierarchy, it was predicted that firms facing financial constraints are likely to have a much greater desire for debt than issuing shares in external financing, and also to use intragroup transactions tend to occur. Also, the results of this research in the Iranian capital market showed that the financial constraints of firms have a significant effect on the financing strategies, and it is necessary to consider them in the examination of the situation of firms. For this reason, investors estimate the risk of their investments higher. In addition, the results of this research confirmed the effect of corporate governance in controlling the negative effects of financial restrictions on firms. In other words, by establishing the principles of corporate governance, firms can reduce the problems of information asymmetry and representation caused by financial constraitns. Therefore, the present study can be effective in better understanding the effect of financial constraints on the selected strategies of firms in the capital market of Iran and lead to the development and enrichment of research literature in this field. اهداف: بررسی این مسئله است که آیا افزایش ‌محدودیت‌های مالی شرکت‌ها بر استراتژی‌های تأمین ‌مالی آنها اثرگذار است. بر این اساس، استراتژی‌های مدیران شرکت‌ها برای تأمین ‌مالی در قالب تمایل آنها به تأمین ‌مالی داخلی به‌جای بیرونی و از طریق معامله با اشخاص وابسته و تأمین‌ مالی از محل بدهی به‌جای انتشار سهام در بازار سرمایه تدوین شد. این پژوهش، با تأکید بر نقش ‌حاکمیت ‌شرکتی و اثر آن بر روابط تعریف‌شده به‌دنبال ارائۀ پاسخ منطقی در ارتباط با اثر ‌حاکمیت ‌شرکتی بر کاهش اثر نامساعد ‌محدودیت‌های مالی بر استراتژی‌های تأمین ‌مالی شرکت‌هاست. روش: داده‌های 150 شرکت فعال در بورس اوراق بهادار تهران طی بازۀ زمانی 1395 تا 1399 انتخاب شد و با تأکید بر شاخص کاپلان و زانگالس (1997) به‌عنوان معیار سنجش ‌محدودیت‌های مالی حاکم بر شرکت‌ها فرضیه‌های پژوهش بررسی شد. نتایج: نتایج این بررسی نشان‌دهندۀ آن بود که ‌محدودیت‌های مالی رابطۀ منفی و معناداری با معامله با اشخاص وابسته و رابطۀ مثبت و معناداری با ساختار سرمایه دارد. علاوه بر آن، ‌حاکمیت‌ شرکتی تنها بر رابطۀ بین ‌محدودیت‌های مالی با ساختار سرمایه اثر تعدیلی دارد.

https://amf.ui.ac.ir/article_27546_5d2fe96f62545e73fd87f9906b333c5b.pdf

دانشگاه اصفهان مدیریت دارایی و تامین مالی 2383-1189 11 2 2023 06 22 Investigation of the Effects of Price Limit Changes on the Intraday Volatility of Iran’s Stock Market Using Realized Variance (RV) and District Fourier Transform (DFT) بررسی اثر تغییر دامنه نوسان مجاز قیمت بر نوسان‌پذیری روزانه بورس در ایران به کمک واریانس به وقوع پیوسته و بسط فوریه 19 34 27544 10.22108/amf.2023.134778.1755 FA محمد حسن نژاد استادیار، گروه مدیریت مالی و بیمه، دانشکدۀ مدیریت و حسابداری، دانشگاه شهید بهشتی، تهران، ایران مریم دولو دانشیار، گروه مدیریت مالی و بیمه، دانشکدۀ مدیریت و حسابداری، دانشگاه شهید بهشتی، تهران، ایران 0000-0003-3321-1165 فرید شعبانی کارشناسی ارشد، گروه مدیریت مالی و بیمه، دانشکدۀ مدیریت و حسابداری، دانشگاه شهید بهشتی، تهران، ایران Journal Article 2022 08 27 In this paper, the effect of a change in price limit on Iran’s stock market and its volatility was studied by applying two methods. One was Realized Variance (RV) before and after the price change was applied and the other was District Fourier Transform (DFT), which was applied to intraday price changes before and after changes occurred to the price limits so that the volatility could be studied at different frequencies. Using RV, it was found that the effects of a price limit change on all the markets and industries were not the same. As we witnessed, a change from a limit of ±10% to ±3% in the yellow market actually resulted in a more volatile market, while a change from a limit of ±10% to ±2% in the orange market did not result in a significantly more volatile market. The results of DFT showed that a tighter price limit increased the volatility; however, the effects were not the same at different frequencies. In conclusion, narrowing the price limit did not necessarily result in a decline in intraday volatility. Even in some cases, severe narrowing of the price limit could lead to an increase in the intraday volatility of stock prices. Keywords: Price Limit, Intraday Trades, Trade Halts, Realized Variance (RV), District Furrier Transform (DFT). Introduction In inefficient markets, prices are not always a true reflection of value. Therefore, law-makers and some scholars suggest using price limits and trade halts as a method of controlling severe volatilities and preserving a fair trend in the market. It has always been a challenge for law-makers whether or not to use price limits and no consensus is present among professionals and law-makers about the usage of these tools. Price limits are set in a way to prevent trades outside of a predetermined price. Price limits have two main characteristics that make them effective, firstly, by imposing a legal limitation on trading on prices outside a limit and secondly, by creating a lag, in which investors can have a time to reassess their decisions. As there is no consensus on the effects of price limits, there have been numerous changes in the policy. When the price limits of different markets in Iran’s Fara Bourse (IFB) was changed from 10% to tighter 2 and 3%, the unique chance of studying the effects of price limits on volatility was presented. In this paper, we tried to shed light on the relationship between price limits and intraday volatility in Iran’s stock exchange and investigated whether tightening the price limit would lead to a less volatile market or not. Method and data In this study, intraday trade data for a last price of 1-minute intervals was obtained in a two-year time period consisting of one year before and one year after the change in the price limit. Then, the new price limit was set in September of 2018 so that the dataset ranged from September 2017 to September 2019. This study used two methods to analyze the effect of a change in price limit on market volatility. In one analysis, Realized Variance (RV) for each day of each stock was calculated before and after the change in the price limit and then, changes in the RV were investigated by using a T-test. In the second method, to further study the changes in volatility, District Fourier Transform (DFT) was applied to the time series before and after the change in the price limit, resulting in an amplitude vector with 105 elements for each stock. Each element presented the amplitude of a specific frequency of volatility. By comparing the changes in each frequency before and after the price change, the effect of the price limit change on volatility could be further studied. Findings The results from both methods showed a similar pattern suggesting that a narrower price limit did not always result in a change in market volatility. A reduction from a symmetric price limit of 10% to 3% actually resulted in a more volatility market; however, further narrowing of the price limit to a symmetric price limit of 2% did not result in a significant change in the market volatility. Contribution To reach a consensus on the matter of effectiveness of price limits and trade halts, this research focused on how these tools could affect intraday volatility. As a more volatile market with narrower price limits could have negative effects, such as weakening the effects of market makers, this research showed how policy makers had to take a possibly more volatile market into account when deciding to impose a narrower price limit, thus paving the way for further studying the negative effects of price limits, especially in the case of narrowing them. Conclusion and discussion It was witnessed that a decrease in price limit to a symmetric limit of 3% from a symmetric limit of 10% resulted in a more volatile market. Further narrowing of the price limit to a symmetric limit of 2% did not indicate a significant change in volatility. This finding was especially notable when confirming that imposing price limits did not always result in the intended outcomes expected by the law-makers. This increase in volatility could be considered as an undesirable consequence of narrowing the price limit, which had to be taken into consideration by the law-makers before imposing such changes. To evade such effects, law-makers could consider applying methods like smart price limits. Accordingly, it can be suggested that future studies be conducted to investigate whether or not the undesired effects of price limits get more intense as the price limits get narrower. Also, after this study was performed, Tehran’s Stock Exchange (TSE) experienced a gradual expansion of the price limit from a symmetric limit of 5% to 7%. Each of these changes suggest an opportunity for conducting further research on the effects of price limit changes. اهداف: کارکرد اصلی بازار سهام، تخصیص بهینۀ منابع است؛ با این حال، نوسان قیمت سهام به دلایل مختلف همیشه در پاسخ به اطلاعات، واقعی و متناسب با آن‌ نیست. برخی سیاست‌گذاران و پژوهشگران استفاده از حد نوسان قیمت به‌عنوان سازوکار کنترل نوسان‌های قیمتی برای حفاظت از روند کشف قیمت پیشنهاد می‌کنند. هدف این پژوهش بررسی اثرات قوانین محدودکنندۀ دامنۀ نوسان بر نوسان‌پذیری بازار سرمایۀ ایران است. روش: پژوهش پیش رو به کمک محاسبۀ واریانس به‌وقوع‌پیوسته و محاسبۀ بسط فوریۀ گسسته برای بازۀ زمانی یک‌ساله پیش و پس از تغییر دامنۀ نوسان مجاز بازارهای پایۀ فرابورس در سال 1398 صورت گرفته است. یافته‌ها: نتایج حاصله هم از روش واریانس به‌وقوع‌پیوسته و هم با روش بسط فوریۀ گسسته هم‌سو بوده است؛ همچنین نشان‌دهندۀ آن است که کاهش دامنۀ نوسان تا سطح دامنۀ نوسان بازار پایۀ زرد یعنی دامنۀ 6 درصد میان 3- تا 3+ درصد نوسان‌پذیری درون روز را افزایش می‌دهد. در صورتی که گفته نمی‌شود، کاهش بیشتر تا سطح دامنۀ نوسان تابلوی نارنجی یعنی دامنۀ 4 درصد بین 2- و 2 درصد نوسان‌پذیری را افزایش می‌دهد. نتایج: کاهش دامنۀ نوسان فقط منتهی به کاهش نوسان‌های درون روز سهام نمی‌شود؛ حتی گاهی کاهش‌های شدید دامنۀ نوسان مجاز تشدید‌کنندۀ پدیده‌هایی است که نوسان‌های درون روز سهام را افزایش می‌دهد.

https://amf.ui.ac.ir/article_27544_d87903e1be7b550580b5d43e6438eeec.pdf

دانشگاه اصفهان مدیریت دارایی و تامین مالی 2383-1189 11 2 2023 06 22 Investigating the Causal Relationship Between Stocks’ Initial Public Offerings and Macroeconomic Variables بررسی رابطۀ علیت بین عرضه اولیۀ سهام و عوامل کلان اقتصادی 35 52 27643 10.22108/amf.2023.136359.1779 FA وحید نیک پی پسیان دانشجوی دکتری توسعه-مالی اقتصادی، گروه اقتصاد و مدیریت، دانشکده اقتصاد و مدیریت، دانشگاه ارومیه، ارومیه، ایران علی رضازاده دانشیار گروه اقتصاد، دانشکده اقتصاد و مدیریت، دانشگاه ارومیه، ارومیه، ایران حسین احمدی نژاد دانش‌آموخته‌ی کارشناسی ارشد علوم اقتصادی، گروه اقتصاد و مدیریت، داتشکده علوم اداری و اقتصاد، دانشگاه مازندران (بابلسر)، مازندران، شکوفه احمدوند دانش آموخته‌ی کارشناسی ارشد توسعه اقتصادی و برنامه‌ ریزی، گروه اقتصاد و مدیریت، دانشکده علوم اقتصادی و اجتماعی، دانشگاه بوعلی Journal Article 2023 01 14 Initial public offerings (IPO) is considered an important turning point in the investment literature. This type of stock not only removes the constraints of resources and capital limitations faced by companies and provides the necessary basis for providing liquidity to companies, but also offers a wide range of investment strategies to companies. Therefore, the main goal of the present study is to investigate the Granger causality relationship between the number of IPO and macroeconomic variables including industrial production, the interest rate on government debt, stock market, and stock market volatility using the Vector Auto Regression (VAR) model during the period 1991-2019 for the Tehran Stock Exchange. The results showed that the number of IPOs is the reason for the growth of industrial production and the fluctuations of the stock index. It was also found that the number of IPOs was the reason for the growth of the stock index and the fluctuations of the stock market and interest rate due to the government's debt. This means that macroeconomic variables such as industrial production, interest rates on government debt, and the stock market are sensitive to small impulses from the number of IPOs and can have macroeconomic consequences in Iran. Based on the results, it can be acknowledged that the financial resources obtained through the number of IPOs of the companies should not be spent at the expense of the government and it is better to spend the financial resources on the development of these companies and the stock market. Keywords: Granger Causality, Number of Initial public offerings, Industrial Production, the Interest Rate on Government Debt, Stock Market, Stock Market Volatility, Vector Auto Regression (VAR). Introduction One of the important factors in achieving economic growth and development is benefiting from developed financial markets. So, today the importance of financial markets is clear. The securities market is considered one of the most important factors in the development of financial markets and economic development because it plays a valuable role in directing stagnant savings and small capital towards active industries in an economy and allocates resources in this field. IPO is considered as one of the most well-known ways of collecting and directing stray capital and stagnant savings to the productive sector of an economy. At the same time that the stock market is affected by IPO, it is strongly influenced by macroeconomic variables, and IPO is no exception to this issue (Angelini & Foglia, 2018). By providing liquidity, reducing the cost of transactions by reducing the cost of searching, and reducing the cost of information, financial markets are a suitable place to move people's stagnant savings towards the production and provision of capital for companies and economic institutions and one of the basic tools in providing capital. Stock markets are among the most important and popular financial markets in most countries, but the fluctuating behavior of this market has always been discussed and investigated. Due to the importance of stock markets in attracting small and large savings, the discussion about determining the factors affecting stock market fluctuations has always been of interest because these fluctuations can be the basis for changes in important macroeconomic variables. In the stock market, wide fluctuations always cause the entry and exit of capital, and the effects of this movement on the economy can be extremely risky (Hsing, 2011). Materials and Methods In this study, the Granger and the Toda-Yamamoto causality tests are used to investigate the causality between the number of IPOs and macroeconomic variables. First, to check the stationarity of the variables, the generalized Dickey-Fuller test is used for the time series data from 1991 to 2019. In the following, the Toda-Yamamoto approach and Granger causality are used to investigate the causality between the variables. Finally, instantaneous reaction functions are used to investigate shocks to each of the variables. Research Findings The results of the causality tests show that there is no causal relationship between the ratio of interest rate to government debt and the growth of the index and stock market fluctuations to IPOs. In addition, the results indicate the absence of a causal relationship between the ratio of interest rates to government debt, stock market fluctuations, the number of IPOs, and industry with the growth of the stock market index. In addition to this, the causal relationship of the stock market growth rate, the ratio of interest rate to government debt, the number of IPOs, and the industry with stock market fluctuations were not found, and the null hypothesis that there is no causal relationship is confirmed. The results of the causality tests show that there is no causal relationship between the ratio of interest rate to government debt and the growth of the stock index fluctuations to IPOs. There is also no causal relationship between the ratio of interest rate to government debt, stock market volatility, the number of IPOs, and the industry with the growth of the stock index. In addition, the causal relationship of the stock market growth rate, the ratio of interest rate to government debt, the number of IPOs, and the industry with stock market fluctuations were not found, and the null hypothesis that there is no causal relationship is confirmed. The results of this research about the impact and influence between the variables of industry growth and IPOs, IPO and interest rates, and stock market index and industry growth with the contents raised in the studies of Fou et al. (2022), Amorim et al. (2021), Barva and Echter (2021), Idres and Glavina (2020), Carnagia et al. (2019), Hong et al. (2019), Angelini and Foglia (2018), Keong (2019), and Penn and Mishra (2018). Discussion of Results and Conclusions IPO and its impact on macroeconomic variables can be a breakthrough in planning and policy-making in various programs of economic development. Because according to the above issue, it can be acknowledged that the financial resources obtained through the IPO should not be spent on the government expenses, and it is better to spend it on the development and progress of these companies and the stock market. All government companies should offer all their shares in the stock market in the form of an IPO and the government should use the resources to strengthen and improve the productivity of these companies. To know the fluctuations of the stock market, small investors are suggested to have a complete understanding of how macroeconomic variables affect the volume of the IPO before deciding to invest in the IPO. This is because correct timing when there is a high volume of IPOs can allow retail investors to earn more profits. Careful examination and complete understanding can prevent small investors from making wrong decisions and incurring losses. In the following, the partners who want to issue the IPO are suggested to use their professional knowledge and experience before issuing the initial offering to investigate the causal relationship between IPO and macroeconomic variables. This is because the effect of macroeconomics on the low volume of IPO may indicate that the company is not willing to go public in those economic conditions. After all, it cannot attract enough capital, and fewer investors are willing to invest in that period. Finally, it is recommended to investigate the impact of foreign stock offerings on macroeconomic variables and to investigate the impact of other macroeconomic variables on IPO of research proposals for future studies. اهداف: عرضه‌های اولیه نقطۀ عطف مهمی در بحث سرمایه‌گذاری محسوب می‌شوند. این نوع از سهام نه‌تنها کمبود منابع و محدودیت سرمایه‌ای پیش ‌روی شرکت‌ها را برداشته و زمینۀ لازم را برای تأمین نقدینگی شرکت‌ها فراهم می‌کند، بلکه مجموعه‌ای گسترده از استراتژی‌های سرمایه‌گذاری را پیش روی شرکت‌ها قرار می‌دهد؛ از این رو، هدف اصلی بررسی رابطۀ علیت گرنجری بین تعداد عرضه‌های اولیه سهام با فاکتورهای کلان اقتصادی ازجمله تولیدات صنعتی، نرخ بهره به بدهی دولت، بازار سهام و نوسان‌ها بازار سهام ایران است. روش: به‌منظور دستیابی به هدف فوق از الگوی خودرگرسیون برداری (VAR) طی دورۀ زمانی 1398-1370 برای بررسی رابطۀ علیت گرنجری بین تعداد عرضه‌های اولیۀ سهام با متغیرهای کلان اقتصادی استفاده شده است. نتایج: یافته‌ها حاکی است که تعداد عرضه‌های اولیۀ علیت گرنجری رشد تولیدات صنعتی و نوسان‌های شاخص کل است و مشخص شد، تعداد عرضه اولیه، علت رشد شاخص کل و نوسانات بازار سهام و نرخ بهره به بدهی دولت بوده است. به این معنا که عوامل کلان اقتصادی ازجمله تولیدات صنعتی، نرخ بهره به بدهی دولت و بازار سهام به تکانۀ کوچک از تعداد عرضه‌های اولیۀ حساس است و پیامدهای کلان اقتصادی در ایران دارد. براساس نتایج حاصل‌‌شده ادعا می‌شود، منابع مالی به‌دست‌آمده از طریق تعداد عرضه‌های اولیۀ شرکت‌ها همه نباید صرف هزینۀ دولت شود و بهتر است، منابع مالی به‌دست‌آمده صرف توسعه و پیشرفت این شرکت‌ها و بازار سهام شود.

https://amf.ui.ac.ir/article_27643_41abd12b83d6e5ccc34d2ae522b93916.pdf

دانشگاه اصفهان مدیریت دارایی و تامین مالی 2383-1189 11 2 2023 06 22 Comparing the Efficiency of Statistical Models and Machine-Learning Models and Choosing the Optimal Model for Predicting Net Profit and Operating Cash Flows مقایسۀ کارایی مدل‌‌های آماری و یادگیری ‌‌ماشین و انتخاب مدل بهینه در پیش‌‌بینی سود خالص و جریان‌های نقدی عملیاتی 53 74 28111 10.22108/amf.2023.136720.1784 FA سجاد میرزائی کارشناس ارشد مدیریت مالی، دانشکده اقتصاد و مدیریت، دانشگاه ارومیه، ارومیه، ایران علی آشتاب استادیار، گروه حسابداری، دانشکدۀ اقتصاد و مدیریت، دانشگاه ارومیه، ارومیه، ایران اکبر زواری رضائی استادیار، گروه حسابداری، دانشکدۀ اقتصاد و مدیریت، دانشگاه ارومیه، ارومیه، ایران 0009-0005-1999-3676 Journal Article 2023 02 05 The present study compared the predictive performance of machine-learning models and statistical models for forecasting profit and operational cash flow by using a combination of accrual and cash variables. The research method encompassed 3 main stages: data set and variable selection, modeling, and estimation. The study focused on companies listed on the Tehran Stock Exchange (TSE), analyzing data from 184 companies over the period of 2012-2021. The findings indicated that accrual variables exhibited greater explanatory power than cash variables in predicting net profit and future operating cash flow. Furthermore, the comparison of machine-learning and statistical models for forecasting net profit and future operating cash flow revealed that the artificial intelligence approach exhibited superior capability. Specifically, symbolic regression among the machine-learning models and the probit model among the statistical models demonstrated higher performance. Additionally, the results indicated that certain statistical models outperformed some machine-learning models while, on average, machine-learning models outperformed statistical models. Keywords: Classification, Data Mining, Machine Learning, Net Profit Forecasting, Operating Cash Flow Forecasting. Introduction In the current intensely competitive business environment, precise prediction of financial outcomes has emerged as a pivotal element in organizational triumph. Projecting crucial financial indicators, such as net profit and operating cash flows, equips businesses with the insight needed to make well-informed choices regarding investment strategies, resource distribution, and comprehensive financial strategizing. The capacity to anticipate future financial performance enables organizations to streamline operations and mitigate risks. Consequently, there is an escalating need for effective forecasting models. This study had two primary objectives: firstly, assessing the predictive capability of accrual and cash variables for forecasting profit and future cash flows and secondly, comparing the efficacy of statistical models and machine-learning models in predicting net profit and operating cash flows. Statistical models seek to scrutinize historical data patterns and underlying relationships to anticipate future financial outcomes. Conversely, machine-learning models have emerged as a potent alternative, employing advanced computational techniques to glean insights from data and make predictions without explicit programming. This research was guided by four hypotheses: First hypothesis: The predictive capability of accrual ‎variables for future net profit significantly exceeds that of cash variables.‎ Second hypothesis: The predictive capacity of accrual ‎variables for future operational cash flow significantly surpasses that of cash variables.‎ Third hypothesis: Machine-learning models outperform statistical ‎models significantly in predicting net profit.‎ Fourth hypothesis: Machine-learning models outperform statistical ‎models significantly in predicting operational cash flows.‎ Materials & Methods This study utilized the Bourseview software database, Rahavard Novin, and the Codal website for analyzing and drawing conclusions regarding the hypotheses. Additionally, data-mining software, such as Weka, SPM, RapidMiner, SPSS Modeler, and Eureqa, were employed for modeling, while Stata econometric and statistical software was used for the Vuong test, EViews for descriptive statistics, SPSS for mean comparison test, and Excel for data sorting and categorization. Following the application of these specified tools, 184 companies listed on the Tehran Stock Exchange (TSE) were examined. Initially, the study investigated the ability to explain each category of cash and accrual variables for net profit and future operating cash flow through special regression estimation of panel data and the Vuong test. Subsequently, the superior model was utilized for modeling and the average performance of the machine-learning models was compared with that of statistical models. Findings The significance of Vuong statistic in predicting net profit at a 1% significance level suggested a notable difference in the explanatory power of the two models with the model of accrual variables demonstrating higher explanatory power than that of the cash flow statement variables. Conversely, the non-significance of the Vuong statistic at the 5% significance level for predicting operational cash flow indicated no significant difference in the explanatory power of the two models. The performance results of both statistical and machine-learning models indicated that the symbolic regression classifier, utilizing the genetic algorithm to predict net profit, exhibited the best overall performance and provided valuable results in the longitudinal test sample. Following symbolic regression, the linear support vector machine and MARS ranked second and third, respectively, in overall performance. Similarly, the symbolic regression classifier, employing the genetic algorithm to predict operating cash flow, demonstrated the best overall performance in the longitudinal test samples. After symbolic regression, the deep learning classifier and MARS ranked second and third, respectively, in overall performance. Discussion & Conclusions In accordance with testing of the first and second hypotheses of the research, which posited that accrual variables have a greater explanatory capacity for net profit and future operating cash flow compared to cash variables, the coefficients of determination of the models were compared after estimating the appropriate panel data approach. The investigation results indicated that accrual variables indeed possessed greater explanatory power for net profit, thus providing no grounds for rejecting the first hypothesis of the study. However, in the case of operating cash flow, while the explanatory value of accrual variables surpassed that of cash variables, there was no statistically significant difference in the explanation between accrual and cash variables. Consequently, the second hypothesis of the research was rejected. In accordance with testing of the third and fourth hypotheses of the current study, which posited that machine-learning models outperform statistical models in predicting net profit and operating cash flow, the AUC criterion was derived through the implementation of both statistical and machine-learning models. By comparing the success rates of the statistical and machine-learning models, it was observed that the machine-learning models significantly outperformed statistical models in predicting net profit and operational cash flow. Therefore, there was no basis for rejecting the third and fourth hypotheses of the study. هدف: در پژوهش حاضر، مقایسۀ عملکرد مدل‌‌های یادگیری ماشین و مدل‌‌های آماری در پیش‌‌بینی سود و جریان نقد عملیاتی با استفاده از مجموعه متغیر‌‌های تعهدی و نقدی بررسی شده است. روش: روش‌‌شناسی پژوهش به سه مرحلۀ گزینش مجموعه داده و متغیرها، مدل‌‌سازی و قیاس تقسیم‌‌بندی می‌شود. جامعۀ آماری پژوهش حاضر، شرکت‌‌های بورس اوراق بهادار تهران و داده‌‌های 184 شرکت‌‌ طی بازۀ زمانی 1391 تا 1400 بررسی شده است. یافته‌‌ها: نتایج این پژوهش نشان‌دهندۀ آن بود که متغیرهای تعهدی توان تبیین بیشتری نسبت به متغیر‌‌های نقدی برای پیش‌‌بینی سود خالص و جریان نقد عملیاتی آتی دارد. علاوه بر این، مقایسۀ عملکرد مدل‌‌های یادگیری ماشین و آماری در پیش‌‌بینی سود خالص و جریان نقد عملیاتی آتی نشان‌دهندۀ آن بود که رویکرد هوش مصنوعی توانایی بیشتری دارد و بین مدل‌‌های یادگیری ماشین، رگرسیون نمادین و مدل‌‌های آماری، مدل پروبیت از عملکرد بیشتری برخوردار است؛ همچنین نتایج نشان‌دهندۀ آن بود که اگرچه به‌‌طور میانگین مدل‌‌های یادگیری ماشین عملکرد بیشتری نسبت به مدل‌های آماری دارد، مدل‌های آماری نیز عملکرد بیشتری از برخی مدل‌‌های یادگیری ماشین ارائه می‌‌دهد.

https://amf.ui.ac.ir/article_28111_442bb731e0c76dd0b5003afe5f8c817f.pdf

دانشگاه اصفهان مدیریت دارایی و تامین مالی 2383-1189 11 2 2023 06 22 Providing a Framework for Crowdfunding in the Film Industry of the Islamic Republic of Iran ارائۀ چارچوب تأمین مالی جمعی در صنعت فیلم‌سازی جمهوری اسلامی ایران 75 94 27908 10.22108/amf.2023.137075.1786 FA امیر تاجیک دکتری، گروه مدیریت بازرگانی، دانشکده مدیریت و حسابداری، پردیس فارابی دانشگاه تهران، قم، ایران. حمیدرضا یزدانی استادیار، گروه مدیریت بازرگانی، دانشکده مدیریت و حسابداری، پردیس فارابی دانشگاه تهران، قم، ایران. Journal Article 2023 05 14 The present study compared the predictive performance of machine-learning models and statistical models for forecasting profit and operational cash flow by using a combination of accrual and cash variables. The research method encompassed 3 main stages: data set and variable selection, modeling, and estimation. The study focused on companies listed on the Tehran Stock Exchange (TSE), analyzing data from 184 companies over the period of 2012-2021. The findings indicated that accrual variables exhibited greater explanatory power than cash variables in predicting net profit and future operating cash flow. Furthermore, the comparison of machine-learning and statistical models for forecasting net profit and future operating cash flow revealed that the artificial intelligence approach exhibited superior capability. Specifically, symbolic regression among the machine-learning models and the probit model among the statistical models demonstrated higher performance. Additionally, the results indicated that certain statistical models outperformed some machine-learning models while, on average, machine-learning models outperformed statistical models. Keywords: Classification, Data Mining, Machine Learning, Net Profit Forecasting, Operating Cash Flow Forecasting. Introduction In the current intensely competitive business environment, precise prediction of financial outcomes has emerged as a pivotal element in organizational triumph. Projecting crucial financial indicators, such as net profit and operating cash flows, equips businesses with the insight needed to make well-informed choices regarding investment strategies, resource distribution, and comprehensive financial strategizing. The capacity to anticipate future financial performance enables organizations to streamline operations and mitigate risks. Consequently, there is an escalating need for effective forecasting models. This study had two primary objectives: firstly, assessing the predictive capability of accrual and cash variables for forecasting profit and future cash flows and secondly, comparing the efficacy of statistical models and machine-learning models in predicting net profit and operating cash flows. Statistical models seek to scrutinize historical data patterns and underlying relationships to anticipate future financial outcomes. Conversely, machine-learning models have emerged as a potent alternative, employing advanced computational techniques to glean insights from data and make predictions without explicit programming. This research was guided by four hypotheses: First hypothesis: The predictive capability of accrual ‎variables for future net profit significantly exceeds that of cash variables.‎ Second hypothesis: The predictive capacity of accrual ‎variables for future operational cash flow significantly surpasses that of cash variables.‎ Third hypothesis: Machine-learning models outperform statistical ‎models significantly in predicting net profit.‎ Fourth hypothesis: Machine-learning models outperform statistical ‎models significantly in predicting operational cash flows.‎ Materials & Methods This study utilized the Bourseview software database, Rahavard Novin, and the Codal website for analyzing and drawing conclusions regarding the hypotheses. Additionally, data-mining software, such as Weka, SPM, RapidMiner, SPSS Modeler, and Eureqa, were employed for modeling, while Stata econometric and statistical software was used for the Vuong test, EViews for descriptive statistics, SPSS for mean comparison test, and Excel for data sorting and categorization. Following the application of these specified tools, 184 companies listed on the Tehran Stock Exchange (TSE) were examined. Initially, the study investigated the ability to explain each category of cash and accrual variables for net profit and future operating cash flow through special regression estimation of panel data and the Vuong test. Subsequently, the superior model was utilized for modeling and the average performance of the machine-learning models was compared with that of statistical models. Findings The significance of Vuong statistic in predicting net profit at a 1% significance level suggested a notable difference in the explanatory power of the two models with the model of accrual variables demonstrating higher explanatory power than that of the cash flow statement variables. Conversely, the non-significance of the Vuong statistic at the 5% significance level for predicting operational cash flow indicated no significant difference in the explanatory power of the two models. The performance results of both statistical and machine-learning models indicated that the symbolic regression classifier, utilizing the genetic algorithm to predict net profit, exhibited the best overall performance and provided valuable results in the longitudinal test sample. Following symbolic regression, the linear support vector machine and MARS ranked second and third, respectively, in overall performance. Similarly, the symbolic regression classifier, employing the genetic algorithm to predict operating cash flow, demonstrated the best overall performance in the longitudinal test samples. After symbolic regression, the deep learning classifier and MARS ranked second and third, respectively, in overall performance. Discussion & Conclusions In accordance with testing of the first and second hypotheses of the research, which posited that accrual variables have a greater explanatory capacity for net profit and future operating cash flow compared to cash variables, the coefficients of determination of the models were compared after estimating the appropriate panel data approach. The investigation results indicated that accrual variables indeed possessed greater explanatory power for net profit, thus providing no grounds for rejecting the first hypothesis of the study. However, in the case of operating cash flow, while the explanatory value of accrual variables surpassed that of cash variables, there was no statistically significant difference in the explanation between accrual and cash variables. Consequently, the second hypothesis of the research was rejected. In accordance with testing of the third and fourth hypotheses of the current study, which posited that machine-learning models outperform statistical models in predicting net profit and operating cash flow, the AUC criterion was derived through the implementation of both statistical and machine-learning models. By comparing the success rates of the statistical and machine-learning models, it was observed that the machine-learning models significantly outperformed statistical models in predicting net profit and operational cash flow. Therefore, there was no basis for rejecting the third and fourth hypotheses of the study. هدف: نرخ شکست و فقدان گرایش سرمایه‌گذاران به پروژه‌های تأمین مالی جمعی در ایران زیاد است. یکی از مهم‌ترین دلایل شکست این پروژه‌ها، فقدان چارچوب مناسب تأمین مالی جمعی است؛ بنابراین هدف از پژوهش حاضر، ارائۀ چارچوب تأمین مالی جمعی در صنعت فیلم‌سازی جمهوری اسلامی ایران است. روش: این پژوهش با استفاده از روش کیفی فراترکیب انجام شده است. ابزار گردآوری داده‌ها و اطلاعات در پژوهش حاضر، اسناد و مدارک گذشته در این زمینه است که به‌طور کلی شامل 45 مقاله می‌شود. شیوۀ تحلیل داده‌ها براساس کدگذاری باز است. یافته‌ها: چارچوب تأمین مالی جمعی مستخرج از پژوهش حاضر در برگیرندۀ سه مرحله قبل، حین و پسا سرمایه‌گذاری است. مرحلۀ قبل سرمایه‌گذاری در برگیرندۀ عوامل مربوط به فرهنگ، صنعت فیلم‌سازی، اجتماعی، اقتصادی، محیط قانونی و حمایتی، زیرساخت ‌فناورانه و امکان‌سنجی اجرای طرح است. مرحلۀ حین سرمایه‌گذاری در برگیرندۀ عوامل مربوط به پلتفرم و سایت‌های ارائه‌دهندۀ خدمات، مدیریت ریسک، توسعه و بهبود ارتباطات و بانک اطلاعاتی سرمایه‌گذاران است. مرحلۀ پسا سرمایه‌گذاری نیز شامل کنترل و نگهداشت سرمایه‌گذاران می‌شود.

https://amf.ui.ac.ir/article_27908_3adb9f1fe4af676df8af2a65a08969e8.pdf

دانشگاه اصفهان مدیریت دارایی و تامین مالی 2383-1189 11 2 2023 06 22 A Survey of Factors Affecting Financing of Small and Medium-Sized Businesses in the Tehran Stock Exchange (TSE) بررسی عوامل مؤثر بر تأمین ‌‌مالی شرکت‌‌های کوچک و متوسط در بورس اوراق بهادار تهران 95 114 28039 10.22108/amf.2023.137262.1790 FA لیلا میرزائی کارشناسی ارشد گروه مدیریت، دانشکده علوم اجتماعی و اقتصادی، دانشگاه الزهرا، تهران، ایران ابراهیم عباسی دانشیار گروه مدیریت مالی دانشکده علوم اجتماعی و اقتصادی دانشگاه الزهرا ، تهران ، ایران فرهاد ترحمی پژوهشگر پسا دکتری گروه اقتصاد، دانشکده علوم اجتماعی و اقتصادی، دانشگاه الزهرا، تهران، ایران Journal Article 2023 05 07 The purpose of this research was to identify the factors affecting financing of small and medium-sized enterprises in the Tehran Stock Exchange (TSE). The method of this study was to use the multiple regression model and panel data to test the hypotheses. The statistical population included 63 small and medium-sized companies admitted to the TSE, which were tested for the period of 2006 to 2021. The contribution was the use of market cap as a criterion for determining small and medium-sized companies. According to the findings, company size has a significant effect on internal financing. In addition, company size had a significant relationship with external financing through debt and share issuance. Also, there was a significant relationship between intangible assets and internal financing, while the ages of the small and medium sized enterprises did not have a significant relationship with external financing. It is suggested that small and medium-sized enterprises pay more attention to the significant variables for financing. Keywords: Financing, Debt, Equity, Intangible Assets, Small and Medium-Sized Enterprises. Introduction The primary focus of this study was to investigate the financing of the capital structure of Small and Medium-sized Enterprises (SMEs) in Iran. SMEs play a crucial role in the economies of both developed and developing countries. According to the theory posed by Schumacher, a renowned German economist, as presented in the book "Small is Beautiful", creating job opportunities in rural areas and small towns can be achieved by making modest investments to generate employment, utilizing relatively simple production methods and leveraging local resources to establish small industries. SMEs serve as the backbone of the developing world's economy (Memarnejad, 2019). In today's world, financing has become a significant concern for countries, whether they are developing or developed. A well-designed capital structure possesses the potential and capacity to adapt to changes in the surrounding environment and, in turn, influences its surroundings by generating appropriate returns. SMEs play a crucial role in poverty alleviation, wealth creation, and fostering greater participation of marginalized sections of society, such as youth and women, in the economic development of nations. The growth of these enterprises strengthens the democratic ethos and civil society, while also encouraging entrepreneurs to actively engage in the economic, political, and social fabric of their countries. In fact, in most nations, the majority of employment opportunities are generated by SMEs. For instance, in the 30 high-income countries belonging to the Organization for Economic Cooperation and Development, two-thirds of the total workforce can be attributed to SMEs (Memarnejad, 2019). This study aimed to highlight the significance and role of SMEs in Iran's economy. However, certain selection criteria were applied, such as: a) selecting companies with fiscal years ending in March and no changes in their fiscal year, b) encountering incomplete data for some companies, and c) excluding banks, financial institutions, and financial investment companies due to their distinct nature of operations. Consequently, the number of companies studied was reduced to 63. Therefore, caution should be exercised when generalizing the findings of this study to other entities within the industry under consideration. Moreover, it is important to note that financing is influenced by various macroeconomic factors, including the inflation rate, gross domestic product, interest rates on facilities, and exchange rates. However, these factors were not incorporated into this study, and consequently, might impact the results. Various factors, such as asset structure, age, profitability, growth, and industry, have been identified as key determinants that can significantly influence the capital structure (Hall, 2002). Indeed, a wide range of variables have been found to impact the choice of an appropriate capital structure (Chen, 2004; Çekrezi, 2013). Additionally, this study examined factors that could potentially affect both the capital structure and profitability of companies. Recognizing that the capital structure can impact the overall value of a company, it is crucial to investigate the factors that effectively and predictably influence it. Numerous authors have conducted studies in this area, leading to the development of theories, such as the static equilibrium theory, the pecking order theory, and the agency theory. The static equilibrium theory emphasizes the balance between the tax shield of interest rate and the costs associated with debt issuance. According to this theory, a company should strive to achieve an optimal level of debt that maximizes its profitability. When the value of the tax benefit exceeds the present value of the costs associated with debt issuance, the company is considered to be at an optimal equilibrium point. Therefore, a manager aiming to maximize shareholders' wealth should carefully select a level of debt for the company that ensures the resulting tax shield outweigh the current value of the costs associated with debt creation (Rasiah & Kim, 2011). Another prominent theory of capital structure is the pecking order theory, initially proposed by Myers and Majluf. This theory suggests a preference for financing investment projects using internal funds, such as retained earnings (internal financing), rather than relying on external resources obtained through equity issuance and debt issuance. According to this theory, managers prioritize utilizing retained earnings for funding their projects. Once the accumulated earnings are depleted, they turn to debt issuance as a source of financial resources. Finally, when it becomes impractical to take on additional debt, they resort to share issuance to meet their financial needs (Rasiah & Kim, 2011). On the other hand, the agency theory posits that the optimal capital structure is achieved by minimizing the costs arising from conflicts of interest between stakeholders (Jensen and William, 1976). In this context, agency costs play a significant role in funding decisions due to the potential conflicts that may arise between shareholders and debt holders. The size of an enterprise has a profound impact on its capital structure (Rajan & Zingales, 1995; Titman & Wessels, 1988). Small firms, in particular, face unique challenges compared to larger businesses as they have often limited access to external sources of capital, such as debt. Consequently, they are compelled to make alternative financing decisions (Ang, 1991). This supports the notion that SMEs are more susceptible to financial difficulties and confront higher levels of uncertainty and risk compared to newer, smaller firms (Engel & Stiebale, 2013; Rosenbusch Brinckmann & Müller, 2013). Based on the proposed conceptual framework, the following hypotheses were put forth: Hypothesis 1: The size of small and medium-sized enterprises exhibits a significant relationship with internal financing. Hypothesis 2: The size of small and medium-sized enterprises demonstrates a significant relationship with external financing in the form of debt. Hypothesis 3: The size of small and medium-sized enterprises displays a significant relationship with external financing through equity issuance. Intangible assets possess the potential to create valuable knowledge-based competitive advantages, thereby fostering future growth (Barney, 1991; Hitt et al., 2001). However, these assets are often challenging to transfer to other businesses, making it difficult to secure external funding sources (Brierley, 2001; Revest and Sapio, 2012). Firms with intangible assets face a greater problem of asymmetric information as these assets are difficult to value. This, in turn, reduces their opportunities to obtain external financing (Clarysse et al., 2003; Harris et al., 1991). Based on the above, the following hypothesis was proposed: Hypothesis 4: Intangible assets exhibit a significant relationship with internal financing in small and medium-sized enterprises. The age of a company also plays a crucial role in determining its capital structure. Faulkender (2005) highlights an interesting point, suggesting that younger firms have less established track records and may not be as recognized by their more experienced competitors. Consequently, small and medium-sized enterprises often struggle to secure sufficient financial resources (Demirel & Parris, 2015). The pecking order theory further supports the notion that internal financing should be prioritized followed by debt financing (Myers & Majluf, 1984). Based on the aforementioned cases, the following hypotheses were proposed: Hypothesis 5: The age of small and medium-sized enterprises exhibits a significant relationship with external financing through equity issuance. Hypothesis 6: The age of small and medium-sized enterprises demonstrates a significant relationship with external financing in the form of debt. Materials & Methods The aim of this study was to examine the impact of size, age, and intangible asset variables on the dependent funding variable. Additionally, control variables, such as the operating cash, operating income ratio, current account ratio, fixed asset ratio, and working capital, were included. This study was conducted through a literature review, analyzing relevant literature and employing descriptive and inferential analyses of the data. The statistical population for this study consisted of small and medium-sized collected listed in the Tehran Stock Exchange (TSE). A sample of 63 companies was selected for the period of 2006-2021. The hypotheses were based on the models proposed by Neville & Lucy (2022) and Aghaei (2015). Regression analysis was employed to test the effect of factors on the models of internal financing, external financing, and ownership ratio. Three regression models were utilized and their definitions and methods of obtaining the variables were explained as follows: INTRNLit=β0+β1INTANGPERCit+β2CURRENTRATIOit+β3FIXEDASSETit+β4SIZEit+β5OPERATINGCASHTOINCOMEit+β6WORKINGCAPITALit+eit Model (1) Model 2 was employed to test the hypotheses regarding the factors influencing external financing (debt). In this model, the following variables were considered: INTRNL is internal financing represented as a percentage of the total capital. It is calculated by dividing the capital increase from reserves, cash inflows, and current receivables by the total capital. INTANGPER is intangible asset ratio determined by dividing the value of intangible assets by the total assets listed on the balance sheet. CURRENTRATIO is current ratio calculated by dividing current assets by current liabilities. FIXEDASSETRATIO is fixed asset ratio obtained by dividing fixed assets by total assets. SIZE is size of the enterprises measured by using the logarithm of the book value of assets. OPERATINGCASHBYINCOME is the relationship between operating cash and operating profit calculated by dividing operating cash by operating profit. WORKINGCAPITAL is net working capital calculated as the difference between current assets and liabilities. These variables were analyzed in Model 2 to assess their impacts on external financing (debt) and test the hypotheses. DEBTit=β0+β1AGEit+β2CURRENTRATIOit+β3FIXEDASSETit+β4SIZEit+β5OPERATINGCASHTOINCOMEit+β6WORKINGCAPITALit+eit Model (2) In the above model, DEBT represents the proportion of total debt to total assets, indicating the extent to which the company is financed through debt. AGE refers to the age of the enterprises calculated based on the logarithm of the number of years of activity. In addition to these variables, other control variables, such as the capital ratio, current ratio, operating cash ratio, and working capital were included. Model 3 was developed to test and validate the assumptions regarding the factors influencing the ownership ratio. The aim of this model was to investigate the variables that contributed to determining the ownership structure of the sample enterprises. EQUITYit=β0+β1AGEit+β2WORKINGCAPITALit+β3CURRENTRATIOit+β4FIXEDASSETit+β5SIZEit+β6OPERATINGCASHTOINCOMEit +eit Model (3) EQUITY represents the shareholder ratio, which is calculated by dividing the total funding by the total capital. Selection of the dependent and independent variables was based on the study conducted by Neville and Lucy (2022). Findings The data used in this study were combined at the enterprise-year level and econometric diagnostic tests were conducted. Based on the evidence, Hypothesis 1, which posited a significant relationship between the size of SMEs and internal financing, was confirmed. Additionally, Hypothesis 4, which suggested a significant relationship between intangible assets and internal financing, was also supported. The results of Model 1 can be observed in Table 1. Table 1: The results of estimating model 1 Variable Coefficient t statistic Significance level OPERATINGCASHTOREVENUE -0.73 -2.00 0.04 SIZE 0.03 3.01 0.00 WORKINGCAPITAL -4.18 -1.39 0.16 CURRENTRATIO -0.04 -1.13 0.25 FIXEDASSETRATIO -0.14 -0.94 0.34 INIBLETANGIBLEASSETRATIO 7.46 2.19 0.03 C -0.76 -2.34 0.02 AR(1) 0.01 0.47 0.63 F statistic probability 0.00 4.27 Durbin Watson statistics 2.39 Coefficient of Determination 0.58 Adjusted coefficient of determination 0.44 According to the Table 1, the coefficient of the variable of working capital is found to be significant at the given significance level, indicating a direct relationship with external financing (debt). On the other hand, the variables, such as size, operating cash ratio, current ratio, and fixed asset ratio, exhibit a significant and inverse relationship with external financing. Based on the evidence, Hypothesis 2, which suggested a significant relationship between the size of small and medium-sized enterprises and external financing (debt), was confirmed. However, Hypothesis 6, which proposed a significant relationship between the age of small and medium-sized enterprises and external financing (debt), was not supported. The results of Model 2 are presented in Table 2. Table 2: The results of estimating Model 2 Variable Coefficient t statistic Significance level AGE 0.03 1.57 0.11 SIZE -0.24 -15.09 0.00 OPERATINGCASHTOINCOME -0.043 -2.40 0.01 CURRENTRATIO -0.07 -11.68 0.00 FIXED ASSETRATIO -0.14 -4.14 0.00 WORKINGCAPITAL 1.91 2.11 0.03 C -51.90 -1.53 0.12 AR(1) 0.74 21.90 0.00 F statistic probability 0.00 69.31 Durbin Watson statistics 2.11 Coefficient of Determination 0.89 Adjusted coefficient of determination 0.87 Based on the Table 2, the variables of size, fixed asset ratio, current ratio, and operating cash ratio are found to be significantly and positively associated with the ownership ratio, while the working capital ratio exhibits a significant and negative relationship. Based on the evidence, Hypothesis 3, which suggested a significant relationship between the size of small and medium-sized enterprises and external financing (proprietary rights), was confirmed. However, Hypothesis 5, which proposed a significant relationship between the age of SMEs and external financing (proprietary rights), was not supported. The results of Model 3 are presented in Table 3. Table 3: The results of Hypothesis Test Model 3 Variable Coefficient t statistic Significance level AGE -0.02 -0.24 0.80 SIZE 0.21 15.41 0.00 WORKING CAPITAL -2.00 -3.14 0.00 FIXED ASSET RATIO 0.15 4.45 0.00 CURRENT RATIO 0.07 12.42 0.00 OPERATING CASH TO INCOME 0.03 1.97 0.04 C 29.80 0.24 0.81 AR(1) 0.71 22.70 0.00 F statistic probability 0.00 0.01 Durbin Watson statistics 2.01 Coefficient of Determination 0.87 Adjusted coefficient of determination 0.85 Discussion & Conclusions The findings of this study supported the 1st and 3rd hypotheses, which suggested a positive and significant relationship between company size and the dependent variables of internal financing and ownership ratio, respectively. Conversely, company size exhibited a negative and significant relationship with debt, in line with the second hypothesis. Additionally, the results indicated a significant positive relationship between intangible assets and internal financing, aligning with the 4th hypothesis. These findings suggested that small and medium-sized companies relied more on internal financing and utilize less debt, which aligned with the pecking order theory. This is consistent with the study conducted by O'Brien (2003). Furthermore, the study did not find a significant relationship between the age of SMEs and internal and external financing (capital structure), contradicting the 5th hypothesis. In conclusion, the results of this study highlighted the importance of company size and intangible assets in determining the financing choices of SMEs. These findings contributed to our understanding of the capital structure decisions made by SMEs. Regarding the relationship between the size of small and medium-sized enterprises and their internal and external financing, the findings align with the studies conducted by Neville and Lucy (2022), Sunaina (2020), and Aghaei et al. (2014). However, the results differ from those of Ozkan (2001), which can be attributed to variations in the economic structure, such as inflation rate and exchange rate, of the countries. Furthermore, the results support the findings of Neville and Lucy (2022) and O'Brien (2003), regarding the relationship between intangible assets, such as ideas, intellectual property, brands, business methods, and internal financing. It was confirmed that companies with a higher proportion of intangible assets faced more challenges and barriers when seeking external financing, which is consistent with the hierarchical theory. Regarding the relationship between the age of small companies and external financing, specifically through debt and ownership rights, the findings of this study are consistent with the studies conducted by Gregory (2005), Neville and Lucy (2022), and Wasiuzzaman and Nurdin (2019). However, the results differ from the study conducted by Faulkner et al. (2006), which focused on credit limits and the distinction between the public debt market (bonds) and the private debt market (banks). In their study conducted in England, they found a negative relationship between debt and age of company. The disparity in findings could be attributed to the different economic structures of the countries. This variation highlighted the importance of considering the specific context and economic conditions when analyzing the relationship between company age and external financing. هدف ‌‌از این پژوهش شناسایی عوامل مؤثر بر تأمین ‌‌مالی شرکت‌‌های کوچک و متوسط در بورس اوراق بهادار تهران است. روش این پژوهش استفاده از ا‌لگوی رگرسیون چندگانه و داده‌‌های تابلویی به‌منظور آزمون فرضیه‌‌ها است. جامعۀ آماری، 63 شرکت کوچک و متوسط پذیرفته شده در بورس اوراق بهادار تهران است که برای دورۀ 1385 تا 1400، آزموده شده‌اند. نوآوری پژوهش استفاده از ارزش بازار به‌صورت معیاری برای تعیین شرکت‌های کوچک و متوسط است. طبق یافته‌‌های به‌دست‌آمده اندازۀ شرکت بر تأمین ‌‌مالی داخلی تأثیر معناداری دارد. به‌علاوه اندازۀ شرکت با تأمین‌‌مالی خارجی ازطریق بدهی و انتشار سهام در شرکت‌‌های آزمون‌شده رابطۀ معنادار دارد. همچنین رابطۀ معناداری بین دارایی نامشهود و تأمین‌‌مالی داخلی وجود دارد، درحالی‌که سن شرکت‌‌های کوچک و متوسط رابطۀ معناداری با تأمین‌‌مالی خارجی ندارد. پیشنهاد می‌‌شود شرکت‌‌های کوچک و متوسط برای تأمین‌‌مالی به متغیرهای معنادار توجه بیشتری داشته باشند. طبقه‌بندی موضوعی G32, D25, D24, F34, D63

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