دانشگاه آزاد اسلامی واحد علوم و تحقیقات

 آمار

تعداد نشریات50
تعداد شماره‌ها2,168
تعداد مقالات20,045
تعداد مشاهده مقاله23,636,654
تعداد دریافت فایل اصل مقاله21,723,312
Zamanpour, Alireza, Zanjirdar, Majid, Davodi nasr, Majid. (1401). Analyzing Stock in a Complex Market in accordance with the Portfolio Optimization Using Network Architecture. , 7(26), 101-113. doi: 10.30495/ijfma.2022.19527
Alireza Zamanpour; Majid Zanjirdar; Majid Davodi nasr. "Analyzing Stock in a Complex Market in accordance with the Portfolio Optimization Using Network Architecture". , 7, 26, 1401, 101-113. doi: 10.30495/ijfma.2022.19527
Zamanpour, Alireza, Zanjirdar, Majid, Davodi nasr, Majid. (1401). 'Analyzing Stock in a Complex Market in accordance with the Portfolio Optimization Using Network Architecture', , 7(26), pp. 101-113. doi: 10.30495/ijfma.2022.19527
Zamanpour, Alireza, Zanjirdar, Majid, Davodi nasr, Majid. Analyzing Stock in a Complex Market in accordance with the Portfolio Optimization Using Network Architecture. , 1401; 7(26): 101-113. doi: 10.30495/ijfma.2022.19527

Analyzing Stock in a Complex Market in accordance with the Portfolio Optimization Using Network Architecture

International Journal of Finance & Managerial Accounting
دوره 7، شماره 26، مهر 2022، صفحه 101-113  XML اصل مقاله (756.71 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.30495/ijfma.2022.19527
نویسندگان
Alireza Zamanpour1؛ Majid Zanjirdar email 2؛ Majid Davodi nasr3
1Ph.D. Candidate in finance-financial engineering Islamic Azad University -Arak.
2Associated Professor, Financial Management Department, Arak Azad Islamic university, Arak , Iran.
3Assistant Professor, Accounting Department , arak azad islamic university , Arak , Iran.
چکیده
The present research was carried out aiming at analyzing stock in a complex market on the basis of portfolio optimization using network architecture. The statistical population of the study included 50 top stock firms in the last three months of 2019-2020 (first three months 2020) and the financial information of these firms was analyzed. The present study calculated the centrality measures of each firm and then ranked them based on those results in regard to the total performance difference of each firm in comparison to all top firms including the performance of the firm under assessment and by emphasizing the standardized integrated performance criteria. Thus, the yield spread of the assessed item was used in making an investment decision in comparison to all other justified options. With reference to the centrality measure ranks, investing in Bandar Abbas Oil Refining Company with the first rank centrality measure was considered to be the best investment option, and investing in Glucosan company with the rank of 50 was the last choice for investment. In accordance with the former studies, the variables of profit volatility, capital return, firm value, market risk premium, stock profitability, financial structure, liquidity, and survival index were used in the model of the present research as the important factors affect stock portfolio optimization.
کلیدواژه‌ها
Portfolio Optimization؛ Risk؛ Return؛ Network Architecture؛ Centrality
مراجع
  • Alireza Zamanpour (2020), Stock analysis in a complex market based on portfolio optimization using network architecture, Ph.D. Thesis, Islamic Azad University-Arak-Iran
  • Amihud, Y. (2002). Illiquidity and stock returns: cross-section and timeseries effects. Journal of Financial Markets 5. pp 31–56.
  • Dai, Z., Zhu, H., & Wen, F. (2020). Two nonparametric approaches to mean absolute deviation portfolio selection model. Journal of Industrial & Management Optimization, 16(5), 2283.
  • DeMiguel, V., Plyakha, Y., Uppal, R., & Vilkov, G. (2013). Improving portfolio selection using option-implied volatility and skewness. Journal of Financial and Quantitative Analysis, 1813-1845.
  • Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25(2), 383-417.
  • Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. The journal of finance, 51(1), 55-84.
  • Gopalan, R., Kadan, O., & Pevzner, M. (2009). Managerial decisions, asset liquidity, and stock liquidity. Asset Liquidity, and Stock Liquidity (February 1, 2009).
  • Han, B., & Wong, H. Y. (2020). Mean–Variance Portfolio Selection Under Volterra Heston Model. Applied Mathematics & Optimization, 1-28.
  • Iorio, C., Frasso, G., D’Ambrosio, A., & Siciliano, R. (2018). A P-spline based clustering approach for portfolio selection. Expert Systems with Applications, 95, 88-103.
  • Li, B., Zhu, Y., Sun, Y., Aw, G., & Teo, K. L. (2018). Multi-period portfolio selection problem under uncertain environment with bankruptcy constraint. Applied Mathematical Modelling, 56, 539-550.
  • Lintner, J. (1956). Distribution of incomes of corporations among dividends, retained earnings, and taxes. The American economic review, 46(2), 97-113.
  • Ma, J., Harstvedt, J. D., Jaradat, R., & Smith, B. (2020). Sustainability driven multi-criteria project portfolio selection under uncertain decision-making environment. Computers & Industrial Engineering, 140, 106236.
  • Mansour, N., Cherif, M. S., & Abdelfattah, W. (2019). Multi-objective imprecise programming for financial portfolio selection with fuzzy returns. Expert Systems with Applications, 138, 112810.
  • Markowitz, H. (1952). The utility of wealth. Journal of political Economy, 60(2), 151-158.
  • Peralta, G., & Zareei, A. (2016). A network approach to portfolio selection. Journal of Empirical Finance, 38, 157-180.
  • Ohlson, J. A. (1995). Earnings, book values, and dividends in equity valuation. Contemporary accounting research, 11(2), 661-687.
  • Ross, J. R. (1967). Constraints on variables in syntax.
  • Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The journal of finance, 19(3), 425-442.
  • Zhang, Y., Zhao, P., Li, B., Wu, Q., Huang, J., & Tan, M. (2020). Cost-sensitive portfolio selection via deep reinforcement learning. IEEE Transactions on Knowledge and Data Engineering.
  • Zhong, X., & Enke, D. (2017). Forecasting daily stock market return using dimensionality reduction. Expert Systems with Applications, 67, 126-139.
 

 

آمار
تعداد مشاهده مقاله: 159
تعداد دریافت فایل اصل مقاله: 153


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب