| تعداد نشریات | 60 |
| تعداد شمارهها | 2,199 |
| تعداد مقالات | 17,837 |
| تعداد مشاهده مقاله | 54,835,358 |
| تعداد دریافت فایل اصل مقاله | 28,700,120 |
Prediction of Time Series of Financial Information Based on Lyapunov View of Information Using Chaos Theory | ||
| International Journal of Digital Content Management | ||
| مقاله 1، دوره 5، شماره 9، مهر 2024، صفحه 1-16 اصل مقاله (1.08 M) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22054/dcm.2022.70147.1158 | ||
| نویسندگان | ||
| Mostafa Sohoulivahed1؛ Mohammadali Aghaei* 2؛ Fariborz Avazzadeh Fath3؛ Ali Pirzad4 | ||
| 1Ph.D. Candidate in Accounting, Department of Accounting, Yasuj Branch,Islamic Azad University, Yasuj, Iran | ||
| 2Associate Professor, Department of Accounting,Tarbiat Modares University,Tehran,Iran | ||
| 3Assistant Professor, Department of Accounting, Gachsaran Branch, Islamic Azad University , Gachsaran, Iran | ||
| 4Assistant Professor, Department of Management, Yasuj Branch, Islamic Azad University, Yasuj, Iran | ||
| چکیده | ||
| Purpose: The purpose of this research was to provide a model for predicting time series of financial information based on the Lyapunov representation of information using chaos theory. Method: This research is applied in its purpose, which is conducted using a quantitative approach. The research ranks as descriptive-causal accounting research based on actual information in companies' financial statements. The research method is the "post-event" type and was carried out using chaos theory and Saida's method based on the Lyapunov view. Findings: The findings showed that during the ADF test, the null hypothesis was rejected at a level of less than 5% type 1 error and 95% confidence, and it shows that the data is not static. During the substitution analysis test and its significance level, the behavior of the time series of the main financial information is significantly different compared to their substitutes. The obtained value was calculated to describe the production process of all data sets for μ = 2, ApEnMax equal to 0.65 and rMax equal to 0.32, and for μ = 3, ApEnMax equal to 0.6 and rMax equal to 0.44. The value of the Lyapunov profile in stability at a certain point is less than zero and in the limited cycle of stability is equal to zero and in chaos, it is greater than zero and smaller than ∞, and in noise it is equal to ∞. Conclusion: The results show that higher returns, encourage investors to invest and increase the flow of capital. It is believed that companies' stock returns are a function of systematic risk, and systematic risk represents the changes in the return rate of a share compared to the changes in the return rate of the entire stock market. | ||
| کلیدواژهها | ||
| Chaos Theory؛ Financial Information؛ Lyapunov View؛ Optimal Stock Portfolio؛ PSO Algorithm | ||
| مراجع | ||
|
Abbaszadeh, M., Jabbari Nooghabi, M., & Rounaghi, M. (2020). Using Lyapunov’s method for analysing of chaotic behaviour on financial time series data: a case study on Tehran stock exchange. National Accounting Review,2(3): 297-308
Aladag, C.H, Yolcu, U., Egrioglu, E., & Dalar, A.Z.(2012). "A new time invariant fuzzy time series forecasting method based on particle swarm optimization", Appl. Soft Computer,12(2) : 3291–3299.
Amiri, M., Shariat Panahi, M., & Banakar, M. H. (2019). "Choosing the optimal stock portfolio using multi-criteria decision making", Tehran Stock Exchange Quarterly, 3(11): 24-25.
Bates, J.M., Granger, & C.W.J. (1969)." The combination of forecasts". Oper. Res. Q. No.20:89-98.
Coleman R.T.(1989). "Combining forecasts: A review and annotated bibliography". Int. J. Forecast, 5 : 559–583.
Donate, J., Cortez, P., Sanchez, G.G. & Migue, A.(2013)."Time sereis forecasting using a weighted cross-validation evolutionary artificial neural network ensemble". Neurocomputing, 109 (3): 27–32.
Huang, S.-C., Chuang, P.-J., Wu, C.-F., & Lai, H.-J.(2010)." Chaos-based support vector regressions for exchange rate forecasting". Expert Syst. 37 : 8590 8598.
Hussain, A.J., Al-Jumeily, D., Al-Askar, H., & Radi, N.(2016)." Regularized dynamic selforganized neural network inspired by the immune algorithm for financial time series prediction". Neurocomputing , 188 : 23–30.
Li, C., Lin, C. W., & Huang, H. (2011)." Neural Fuzzy Forecasting of the China Yuan to US Dollar Exchange Rate – A Swarm Intelligence Approach". In: Proceedings of the Second International Conference, ICSI 2011, LNCS 6728, Chongqing, China, June 12-15, 2011, pp. 616–625.
Makridakis, S., Wheelwright, S., & Hyndman, R.(1998). Forecasting: methods and applications, edition 3 ed., New York: John Wiley & Sons,
Mitsa, T.(2010). Temporal Data Mining, Chapman & Hall/ CRC Data mining and knowledge discovery series, CRC Press, US, 2010.
Pavlidis, N.G., Tasoulis, D.K., & Vrahatis, M.N. (2003)."Financial forecasting through unsupervised clustering and evolutionary trained neural networks". In: Proceedings of 2003 Congress on Evolutionary Computation, CEC'03, volume 4, Canberra, Australia, 8–12 Dec, 2003, pp. 2314–2321.
Pelikan,E., De Groot, C., & Wurtz, D.(1992)."Power consumption in West-Bohemia: improved forecasts with decorrelating connectionist networks". Neural Netw. 2(4) : 701–712.
Pradeepkumar,D., & Ravi,V.(2017). "Forex rate prediction: A hybrid approach using chaos theory and multivariate adaptive regression splines", in: Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications, Springer, Singapore, Bhubaneshwar, Odisha, India, 16-17 September 2016, pp. 219–227.
Pulido, M., Melin, P., & Castillo, O.(2014)."Optimization of ensemble neural networks with fuzzy integration using the particle swarm algorithm for the US Dollar/MX time series prediction". In: 2014 IEEE Conference on Norbert Wiener in the 21st Century (21CW), Boston, MA, USA, 24-26 June 2014, pp. 1–7.
Ravi, V., Lal, R., & Kiran, N. (2012)."Foreign Exchange Rate Prediction using Computational Intelligence Methods".International Journal of Computer information System, 4 : 659–670.
Reid, D. J.(1968). "Combining three estimates of gross domestic product". Economica, No. 35:68-86.
Rout, M., Majhi, B., Majhi, R., & Panda, G.(2014)." Forecasting of currency exchange rates using an adaptive ARMA model with differential evolution based training". J. King Saud. Univ. - Comput. Inf. Sci. 26 (4) :7–18.
Sermpinis, G., Stasinakis, C., Theofilatos, K., & Karathanasopoulos, A.(2015)."Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms-Support vector regression forecast combinations". Eur. J. Oper. Res. No.247 : 831–846.
Sermpinis, G., Theofilatos, K., Karathanasopoulos, A., Georgopoulos, E., & Dunis, C.(2012)." A Hybrid Radial Basis Function and Particle Swarm Optimization Neural Network Approach in Forecasting the EUR/GBP Exchange Rates Returns", in: Proceedings of the 13th International Conference on Engineering Applications of Neural Networks, EANN 2012, CCIS 311, London, UK, 20-23 September 2012, pp. 413– 422.
Shen, F., Chao, J., & Zhao, J.(2015)." Forecasting exchange rate using deep belief networks and conjugate gradient method". Neurocomputing, No.167 : 243–253.
Svitlana, G. (2016)." Neural networks performance in exchange rate prediction". Neurocomputing, 172(3): 446–452. | ||
|
آمار تعداد مشاهده مقاله: 586 تعداد دریافت فایل اصل مقاله: 383 |
||