طراحی مدل ارزش گذاری سهام بر مبنای نماگرهای بازار سرمایه با بهره گیری از روش های داده کاوی | ||
| سیاست گذاری اقتصادی | ||
| دوره 17، شماره 34، مهر 1404، صفحه 86-116 اصل مقاله (835.95 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22034/epj.2024.20845.2526 | ||
| نویسندگان | ||
| حسین اشرفی سلطان احمدی1؛ سعید جبارزاده* 2؛ جمال بحری ثالث3؛ علی آشتاب4 | ||
| 1دانشجوی دکترا، رشته حسابداری، دانشکده حسابداری، دانشگاه آزاد اسلامی، واحد ارومیه، ارومیه، ایران | ||
| 2دانشیار گروه حسابداری، دانشکده حسابداری، دانشگاه آزاد اسلامی، واحد ارومیه، ارومیه، ایران | ||
| 3دانشیار گروه حسابداری، دانشکده حسابداری، دانشگاه آزاد اسلامی، واحد ارومیه، ارومیه، ایران. | ||
| 4استادیار گروه حسابداری، دانشکده حسابداری، دانشگاه آزاد اسلامی، واحد ارومیه، ارومیه، ایران. | ||
| چکیده | ||
| هدف مطالعه طراحی مدل ارزش گذاری سهام بر مبنای نماگرهای بازار سرمایه با بهره گیری از روش داده کاوی است. در این مقاله به منظور بررسی تأثیر نماگرهای بازار سرمایه بر ارزش سهام از مدلسازی معادلات ساختاری بهره برده شد. در راستای تجزیه و تحلیل نتایج از اطلاعات آماری گردآوری شده در سال 1401 استفاده گردید. نتایج بدست آمده از این مطالعه نشان میدهد که نماگرهای بازار سرمایه تأثیر معناداری بر ارزش سهام دارند و 7/52 % از ارزش سهام توسط نوسانات نماگرهای بازار سرمایه توضیح داده شده است. به منظور پاسخ به سوال پژوهش ابتدا به مقایسه دقت 32 مدل یادگیری ماشین به منظور پیش بینی ارزش سهام پرداخته شد و نتایج نشان میدهد که مدل C5 دارای دقت پیش بینی بالاتری نسبت به سایر مدل های یادگیری ماشین دارد. در ادامه و به منظور افزایش دقت و کاهش خطای مدل C5 از تکنیک های اعتبارسنجی بوستینگ و انتخاب ویژگی استفاده شد و نتایج بیانگر آن بود که مدل برتر (C5) دارای افزایش قابل ملاحظهای در دقت و کاهش محسوسی در خطا شده است. همچنین مدلسازی به تفکیک برای نماگرهای بازار سرمایه انجام گرفت و نتایج نشان میدهد که نماگرهای نقدی نسبت به سایر نماگرها دارای دقت بالاتری به منظور پیش بینی ارزش سهام داشته است. | ||
| کلیدواژهها | ||
| ارزش گذاری سهام؛ داده کاوی؛ بازار سرمایه؛ پیش بینی | ||
| عنوان مقاله [English] | ||
| Designing a stock valuation model based on capital market indicators using data mining methods | ||
| نویسندگان [English] | ||
| Hosein Ashrafi Soltan Ahmadi1؛ Saeid Jabarzade2؛ Jamal Bahri Sales,3؛ Ali Ashtab4 | ||
| 1Ph.D. student, Accounting Department, Faculty of Accounting, Islamic Azad University, Urmia Branch, Urmia, Iran. | ||
| 2Associate Professor, Department of Accounting, Faculty of Accounting, Islamic Azad University, Urmia branch, Urmia, Iran | ||
| 3Associate Professor, Department of Accounting, Faculty of Accounting, Islamic Azad University, Urmia branch, Urmia, Iran. | ||
| 4Assistant Professor, Department of Accounting, Faculty of Accounting, Islamic Azad University, Urmia branch, Urmia, Iran | ||
| چکیده [English] | ||
| Purpose: The purpose of the study is to design a stock valuation model based on capital market indicators using data mining method. In most of the studies on capital market indicators, the indicators have been separated into structural, cash flow and expectation categories. In the existing research, these indicators and their effects on the price are presented at the same time, or several indicators are mentioned as influencing variables on the price. In his book entitled “Stock Market Indicators”, William Gordon (2000) defined and classified the main indicators of capital market and studied their effects on the Dow Jones Index in a period of 70 years. He divided the market indicators into those related to the profitability of companies, cash flow indicators and structural market and stated the time series of decisions to buy and sell shares. Yardani (2003) investigated the basis of valuation in different periods by classifying cash and expectation indicators. Van Scheulein (2003), Fernandez (2004) and Ignacio (2003) investigated the effects of cash and discount indices on stock prices and compared different value-forecasting methods. Lina Dajilin and colleagues investigated the factors affecting stock valuation in terms of accounting and market factors. Duran (2005) tested the method of Conslim presented by O’Neill and explained the role of expectation and structural indicators in the valuation process. Roeback (2005, 2002 and 2004) presented the basics of value extraction. Kenny also divided the market indicators into structural, cash and expected indicators. This research examines the relationship between stock valuation and financial ratios by using the financial information of the companies listed in Tehran Stock Exchange. It is expected that the investigation of this relationship and the clarification of ambiguous cases will increase the level of transparency of the market through increasing the knowledge of investors and better allocations. Therefore, the question the research seeks to answer is ‘What is the effect of financial ratios and market indicators on share price valuation?’ Methodology: Since the current research sought to design a model of capital market indicators using a data mining method and the experts in this field were limited, the statistical population of the research included all the companies in which investors generally have the opportunity to participate in shareholding processes. The data needed to analyze the relationship between the research variables were obtained from the Kodal system. They were collected from audited financial statements, explanatory notes, and the companies admitted to Tehran Stock Exchange. The new Rahvard software was used for data analysis. Finally, the panel data method was used to test the hypotheses. The independent variable in this research was the structural indicators of the capital market, the cash flow indicators of the capital market, and the expected indicators of the capital market. The stock value was the dependent variable. The information related to 157 companies admitted to Tehran Stock Exchange during a period of 11 years from 2012 to 2023 was collected in the form of daily, monthly and quarterly data. The collected data were analyzed according to the purpose of the research, which was to provide a comprehensive model regarding the main indicators of the capital market with a new method. Findings and Discussion: In this research, structural equation modeling was used to investigate the effect of capital market indicators on the stock value. The results showed that those indicators have significant effects on the stock value; 52.7% of the stock value was explained by the fluctuations of the capital market indicators. In the next step, in order to test the hypothesis of the research, 32 machine learning models were first compared in terms of accuracy in order to predict the stock value. The results showed that the C5 model has higher prediction accuracy than the other machine learning models. Furthermore, in order to increase the accuracy and reduce the error of the C5 model, boosting and feature selection validation techniques were used. The results showed that the superior model (C5) has significantly increased accuracy and a noticeable decrease in error. Also, modeling was done separately for the capital market indicators. Conclusions and Policy Implications: It was shown that cash indicators are more accurate than the other indicators in predicting the stock value. Based on the results, the following suggestions are made: a) It is suggested that managers and investors use machine learning models and cash indicators, along with other relevant variables, to predict the value of company shares and make the necessary investment decisions. b) Potential and actual investors should make investment decisions based on the purchase and sale of shares, managers are suggested to take preventive measures in order to prevent the occurrence of financial risk that may ultimately lead to the financial distress of the company, analysts should consider the information of ordinary investors (non-experts), the stock exchange is recommended to the stock exchange organization for the acceptance and evaluation of companies, and investment companies are advised to develop and expand their activities (through the purchase and merger of other companies). To predict the value of stocks, the C5 decision tree model is recommended. | ||
| کلیدواژهها [English] | ||
| stock valuation, data mining, capital market, forecasting | ||
| مراجع | ||
|
Achelist, S. B. (2003). Technical Analysis from A to Z: Covers Every Trading Tool from the Absolute Breadth to the Zig Zag. Chicago, IL: Probus Publisher. Alizadeh, S. & Malek Mohammadi, S. (2014). Data Mining and Knowledge Discovery Step by Step with Clementine Software. Tehran. Khwaja Nasiruddin Tusi University Publications (In Persian). Arabsalehi, M. & Kamali Dehkordi, A. (2021). Comparative Investigating of Stock Valuation Discount Models in Companies Listed in Tehran Stock Exchange. Financial Management Perspective, 11(33): 113-138 (In Persian). Ashtab, A. (2016). Development of Financial Crisis Prediction Models and its Impact on Profit Management Tools. PhD Dissertation, Imam Khomeini International University (In Persian). Delshad, A. & Sadeqi Sharif, S. J. (2018). Investigating the Reaction of Capital Market on Managerial Myopia in Companies Listed on Tehran Stock Exchange. Financial Research Journal, 20(1): 91-106 (In Persian). Gudarzi Farahani, Y. Baratinia, M. & Abniki, M. (2023). Determining the Effect of Economic and Financial Variables on the Ratio of Bank Facilities and Deposits using Smooth Transition Regression. The Journal of Economic Policy, 14(28): 75-100 (In Persian). Hu, H. Tang, L. Zhang, S. & Wang, H. (2018). Predicting the Direction of Stock Markets using Optimized Neural Networks with Google Trends. Neurocomputing, 285(2): 21-38. Khaleghi Moghadam, H. & Talaneh, A. (2006). Ownership Valuation Models: Theories and Applications. Journal of Certified Public Accountants, 8(2): 38-81 (In Persian). Kumbure, M. M. Lohrmann, C. Luukka, P. & Porras, J. (2022). Machine Learning Techniques and Data for Stock Market Forecasting: A Literature Review. Expert Systems with Application, 34(2): 58-69. Lee, B. S. & Rui, O. M. (2002). The Dynamic Relationship between Stock Return and Trading Volume: Domestic and Cross-Country Evidence. Journal of Banking and Finance, 26(2): 51-78. Li, X. & Wu, P. (2022). Stock Price Prediction Incorporating Market Style Clusteri. Cognitive Computation, 14(1): 149-166. Park, J. Ko, H. & and Lee, J. (2023). Exploring Generative AI for Modeling the Dynamics of Asset Price Process. Available at SSRN: https://ssrn.com/abstract=4491342 or http://dx.doi.org/10.2139/ssrn.4491342. Rostami, A. Falamarzi, E. & Faroughi, S. (2017). Neuro-Genetic Structure to Valuation of Initial Public Offering. Financial Engineering and Portfolio Management, 8(33): 183-206 (In Persian). Rostami, M. & Makiyan, S. N. (2022). Is the Stock Market in Iran Efficient? A Residual-based Co-integration Test with the Partial Bayesian Approach. The Journal of Economic Policy, 13(26): 197-221 (In Persian). Sadorsky, P. (2022). Forecasting Solar Stock Prices using Tree Based Machine Learning Classification: How Important are Silver Prices?. The North American Journal of Economics and Finance, 23(4): 45-69 Sanii Abadeh, M. Mahmoudi, S. & Tahir Parvar, M. (2013). Applied Data Mining. Tehran, Niaz Danesh Publications, Second Edition (In Persian). Soni, P. Tewari, Y. & Krishnan, D. (2022). Machine Learning Approaches in Stock Price Prediction: A Systematic Review. In Journal of Physics, 2161(1): 35-48. Thaqfi, A. & Taslimi, M. (2016). Basic Variables of Accounting and Stock Returns. Special Accounting Letter, 43(1): 11-25 (In Persian). Wu, L. & Xu, S. (2023). A Capital Asset Pricing Model Based on the Value at Risk under Asymmetric Laplace Distribution. Kybernetes, 11(2): 34-51.
| ||
|
آمار تعداد مشاهده مقاله: 648 تعداد دریافت فایل اصل مقاله: 190 |
||