The improved Semi-parametric Markov switching models for predicting Stocks Prices

Advances in Mathematical Finance and Applications

مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 08 تیر 1402

نوع مقاله: Research Paper

شناسه دیجیتال (DOI): 10.22034/amfa.2021.1923297.1565

نویسندگان

Mehrdad Ghanbari^{* 1}؛ Hossein Naderi²؛ Babak Jamshidi Navid¹؛ Arash Nademi³

¹Department of Accounting, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.

²Department of Accounting, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

³Department of Statistics, Ilam Branch, Islamic Azad University, Ilam, Iran.

چکیده

The modelling of strategies for buying and selling in Stock Market Investment have been the object of numerous advances and uses in economic studies, both theoretically and empirically. One of the popular models in economic studies is applying the Semi-parametric Markov Switching models for forecasting the time series observations based on stock prices. The Semi-parametric Markov Switching models for these models are a class of popular methods that have been used extensively by researchers to increase the accuracy of fitting processes. The main part of these models is based on kernel and core functions. Despite of existence of many kernel and core functions that are capable in applications for forecasting the stock prices, there is a widely use of Gaussian kernel and exponential core function in these models. But there is a question if other types of kernel and core functions can be used in these models. This paper tries to introduce the other kernel and core functions can be offered for good fitting of the financial data. We first test three popular kernel and four core functions to find the best one and then offer the new strategy of buying and selling stocks by the best selection on these functions for real data.

کلیدواژه‌ها

Strategies for buying and selling؛ Kernel function؛ EM algorithm؛ Markov Switching Models