The Application of ARIMA and Mean-variance Models on Financial Market
DOI:
https://doi.org/10.54691/bcpbm.v26i.2069Keywords:
Markowitz theory; Monte Carlo simulation; ARIMA model; return prediction; portfolio optimization; Chinese A-share marketAbstract
This study centers on forecasting return and constructing proper portfolios with 5 typical assets rarely focused on the Chinese A-share market. This paper applies the fittest ARIMA models for each of the selected stocks to predict their trend of returns in the next 20 days. Besides, we create the efficient frontier by Monte Carlo simulation under Markowitz’s Mean-Variance framework to focus on two portfolios, i.e., the maximum Sharpe ratio portfolio and the minimum volatility portfolio. The empirical results of the ARIMA model indicate a rational prediction of return for assets in the A-share market. The maximum Sharpe ratio portfolio and the minimum volatility portfolio show that stock of Foshan Haitian Flavouring and Food Company Ltd. and stock of China Merchants Bank Co., Ltd. account for the largest proportion in the two portfolios. Further empirical results show that returns for two portfolios are higher than the market index return, which illuminates the two portfolios outperform the market index. The results in this paper will surely benefit related investors in the financial market.
Downloads
References
Situmorang, R. E., Maruddani, D., & Santoso, R.. (2019). Formation of stock portfolio using markowitz method and measurement of value at risk based on generalized extreme value (case study: company’s stock the idx top ten blue 2017, period 2 january - 29 december 2017). Journal of Physics: Conference Series.
Markowitz, H. (1952). Portfolio selection. J. Finance. 7, 77-91
Sharpe, W. F. . (1964). Capital asset prices: a theory of market equilibrium under conditions of risk*. Journal of Finance, 19(3), 425-442.
CB Guran, Ugurlu, U. , & Tas, O. . (2019). Mean-variance portfolio optimization of energy stocks supported with second order stochastic dominance efficiency. Czech Journal of Economics and Finance (Finance a uver), 69.
Kiris, S. & Ustun, O. (2011). An integrated approach for stock evaluation and portfolio optimization. Optimization, 423-441
Thakur, G. M. , Bhattacharyya, R. , & Sarkar, S. . (2016). Stock portfolio selection using dempster–shafer evidence theory. Journal of King Saud University - Computer and Information Sciences, 30(2).
Rounaghi, Mahdi, M. , Zadeh, N. , & Farzaneh. (2016). Investigation of market efficiency and financial stability between s&p 500 and london stock exchange: monthly and yearly forecasting of time series stock returns using arma model. Physica A.
Challa, M. L. , Malepati, V. , & Kolusu, S. . (2020). S&p bse sensex and s&p bse it return forecasting using arima. Financial Innovation, 6.
Kumar, M. & Thenmozhi, M. (2012). Stock Index Return Forecasting and Trading Strategy Using Hybrid ARIMA-Neural Network Model. International Journal of Financial Management.
Qi, Y. , Peng, X. , & Li, M. . (2010). Removing the necessity of simplifications in large‐scale portfolio selection and implications to chinese portfolio theory and management. Nankai Business Review International, 1(1), 20-38.
Chen, J. , Jiang, F. , & Tu, J. . (2015). Asset allocation in chinese stock market: the role of return predictability. Social Science Electronic Publishing, 41(5), 71-83.
Jarrett, J. E. & Schilling, J. (2008). Daily Variation And Predicting Stock Market Returns For The Frankfurter Börse (stock market). Journal of Business Economics and Management
Siswanah, E. . (2020). Comparative analysis of mean variance efficient frontier and resampled efficient frontier for optimal stock portfolio formation. IOP Conference Series: Materials Science and Engineering, 846(1), 012065 (10pp).
Downloads
Published
Issue
Section
How to Cite
