Prediction of Stock Price Based on Fundamental, Technical and Economic Factors

Subject Areas : Journal of Investment Knowledge

Mahdi Asghari ¹ , narges yazdanian ² , Bita Tabrizian ³ , Fraydoon Rahnamay Roodposhti ⁴

1 - Ph.D Student of financial engineering at Rudehen Islamic Azad University, Rudehen, Iran,
2 - Assistant Professor at Rudehen Islamic Azad University, Rudehen, Iran,
3 - Assistant Professor at Rudehen Islamic Azad University, Rudehen, Iran,
4 - Faculty member of Islamic azad University, branch of Researches and sciences (Tehran) , Department of Faculty of Education and Counseling & Accountancy College, Professor,

Received: 2020-10-05 Accepted : 2020-10-27 Published : 2024-09-22

Keywords: Stock Return, technical factors, economic factors, Fundamental Factors,

Abstract :

In the present study, stock price forecasts were evaluated based on fundamental, technical and economic factors. For this purpose, three groups of fundamental, technical and economic factors were studied. In data analysis, fitting of least squares error regressions was used for the share price data of 30 companies with more than 50% of the stock market value in 2020 and the stock prices of companies were analyzed unbalanced from 2002 to 2020. The results showed that each of the fundamental, technical and economic factors alone can predict stock returns, while the technical and economic factors did not have additional information content than the fundamental factors.

References:

• ایوانی، فرزاد (1395)، ارائه الگوی بهینه پیش‌بینی بازده سهام و انتخاب پرتفوی بر مبنای مدل ترکیبی درخت تصمیم و رگرسیون، دکتری تخصصی، دانشگاه بوعلی سینا، دانشکده علوم اجتماعی.


• جهانگیری کاپک، سیامک (1395)، پیش‌بینی بازده آتی با استفاده از پایداری عملکرد مالی، کارشناسی ارشد، دانشگاه تربیت مدرس، دانشکده مدیریت و حسابداری.


• حاج‌علیان، مهناز (1394)، بررسی تأثیر سبک سرمایه‌گذاری سهامداران بر پیش‌بینی بازده سهام شرکت‌ها در بورس تهران، کارشناسی ارشد، دانشگاه الزهرا (س)، دانشکده علوم اجتماعی و اقتصادی.


• سرور، جواد (1395)، پیش‌بینی بازده سهام شرکت‌های پذیرفته شده در بورس اوراق بهادار تهران، کارشناسی ارشد، دانشگاه صنعتی شیراز، دانشکده مهندسی صنایع.


• شیخی، حامد (1397)، پیش‌بینی قیمت سهام با استفاده از ترکیب تکنیک‌های شبکه عصبی و ژنتیک به منظور بهبود پیش‌بینی بازده سهام، کارشناسی ارشد، دانشگاه آزاد اسلامی‌واحد دماوند.


• Adebiyi AA, Oluinka A (2014) Comparision of ARIMA and artificial neural network models for stock market prediction. Journal of Applied Mathematics. https://doi.org/10.1155/2014/614342


• Almudhaf F (2018) Predictability, Price bubbles, and efficiency in the Indonesian stock-market. Bull Indones Econ Stud 54(1):113–124


• Aras S, Kocakoc ID (2016) A new model selection strategy in time series forecasting with artificial neural networks. IHTS Neurocomputing 174:974–987


• Box GEP, Jenkins GM (1970) Time series analysis: forecasting and control. Holden-Day, San Francisco


• Buncic, D., Tischhauser, M., (2017). Macroeconomic factors and equity premium predictability. International Review of Economics & Finance 51, 621–644.


• Chen, N.-K., Chen, S.-S., Chou, Y.-H., (2017). Further evidence on bear market predictability: The role of the external finance premium. International Review of Economics & Finance 50, 106–121


• Claeskens, G.; Magnus, J.; Vasnev, A.; Wang, W. (2016). The forecast combination puzzle: A simple theoretical explanation. Int. J. Forecast, 32, 754–762.


• Cochrane, J.H.(2011). Presidential Address: Discount Rates. J. Financ., 66, 1047–1108.


• Dai, Z., Zhou H., (2020). Prediction of Stock Returns: Sum-of-the-Parts Method and Economic Constraint Method, Sustainability, 12, 541; doi:10.3390/su12020541.


• Dangl, T.; Halling, M. (2012). Predictive regressions with time-varying coe_cients. J. Financ. Econ., 106, 157–181.


• Erdem E, Ulucak R (2016) Efficiency of stock exchange markets in G7 countries: bootstrap causality approach. Economics World 4(1):17–24


• Fama EF (1970) Efficient capital markets:a review of theory and empirical work. J Financ 25(2):383–417


• Fan, Y.; Zhang, Z.; Zhao, X.; Yin, H. (2018). Interaction between Industrial Policy and Stock Price Volatility: Evidence from China’s Power Market Reform. Sustainability, 10, 1719.


• Firat EH (2017) SETAR (self-exciting threshold autoregressive) non-linear currency Modelling in EUR/USD, EUR/TRY and USD/TRY parities. Mathematics and Statistics 5(1):33–55


• Garcia, R., (2012). Portfolio allocation decisions in the presence of regimes in asset returns. Investment and Pensions Europe 98, 6.


• Gebka B.,Wohar M.E., (2019). Stock return distribution and predictability: Evidence from over a century of daily data on the DJIA index. International Review of Economics and Finance 60, 1–25


• Golez, B., & Koudijs, P. (2014). Four centuries of return predictability. NBER working paper 20814. National Bureau of Economic Research, Inc.


• Gooijer DJ (1998) On threshold moving-average models. J Time Ser Anal 19(1):1–18


• Guptha SK, Rao RP (2018) The causal relationship between financial development and economic growth experience with BRICS economies. Journal of Social and Economic Development 20(2):308–326


• Hamilton JD (1989) A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57:357–384


• Hammerschmid R. & Lohre H., (2017). Regime shifts and stock return predictability, International Review of Economics and Finance (2017), doi: 10.1016/j.iref.2017.10.021


• Harrison B, Moore M (2012) Stock market efficiency, non-linearity, thin trading and asymmetric information in MENA stock markets. Economic Issues 17(1):77–93


• Heo, J and Yong, J, (2016), Stock Price Prediction Based on Financial Statements Using SVM, International Journal of Hybrid Information Technology, 9(2):57-66.


• Hsieh, S. (2014). The causal relationships between stock returns, trading volume, and volatility. International Journal of Managerial Finance. 10(2): 218 – 240.


• Huang, D.; Jiang, F.; Tu, J.; Zhou, G.(2015). Investor sentiment aligned: A powerful predictor of stock returns. Rev. Financ. Stud., 28, 791–837


• Khandelwal I, Adhikari R (2015) Time series forecasting using hybrid ARIMA and ANN models based on DWT decomposition. Procedia Computer Science 48:173–179


• Konak F, Seker Y (2014) The efficiency of developed markets: empirical evidence from FTSE 100. J Adv Manag Sci 2(1):29–32


• Li, G. (2016). Growth options, dividend payout ratios and stock returns. Studies in Economics and Finance. 33(4): 638 – 659.


• Mazuruse, P. (2014). Canonical correlation analysis. Journal of Financial Economic Policy, 6 (2): 179-196.


• Mostafa MM (2010) Forecasting stock exchange movements using neural networks: empirical evidence from Kuwait. Expert Syst Appl 37:6302–6309


• Neely, C.J.; Rapach, D.E.; Tu, J.; Zhou, G. (2014). Forecasting the Equity Risk Premium: The Role of Technical Indicators. Manag. Sci, 60, 1772–1791.


• Qiu M, Song Y, Akagi F (2016) Application of artificial neural network for the prediction of stock market returns the case of the Japanese stock market. Chaos, Solitons and Fractals 85:1–7


• Radikoko I (2014) Testing weak-form market efficiency on the TSX. J Appl Bus Res 30(3):647–658


• Robu, M.A and Robu, I.B, (2015), The Influence of the Audit Report on the Relevance of Accounting Information Reported by Listed Romanian Companies, Procedia Economics and Finance, 20:562-570.


• Said A (2015) The efficiency of the Russian stock market: a revisit of the random walk hypothesis. Academy of Accounting and Financial Studies Journal 19(1):42–48


• Shafana, M, Rimziya, A. F and A.I, (2013), Relationship between Stock Returns and Firm Size, and Book-To-Market Equity: Empirical Evidence from Selected Companies Listed on Milanka Price Index in Colombo Stock Exchange, Journal of Emerging Trends in Economics and Management Sciences, 4(2): 217- 225 .


• Tong H (1990) Non-Linear Time Series: A Dynamical System Approach. Oxford University Press, Oxford


• Tong T, Li B, Benkato O (2014) Revisiting the weak form efficiency of the Australian stock market. Corp Ownersh Control 11(2):21–28


• Tsay R (1989) Testing and modeling threshold autoregressive processes. Journal of American Statistical Association 84:231–240


• Welch, I.; Goyal, A. (2007). A Comprehensive Look at The Empirical Performance of Equity Premium Prediction. Rev. Financ. Stud. 21, 1455–1508


• Wieland OL (2015) Modern financial markets and the complexity of financial innovation. Universal Journal of Accounting and Finance 3(3):117–125


• Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159–175


• Zhang K., Zhong G., Dong J., Wang S., Wang Y., (2019). Stock Market Prediction Based on Generative Adversarial Network, Procedia Computer Science,Vol. 147: 400-406.


• Zhu, M. (2013). Return distribution predictability and its implications for portfolio selection. International Review of Economics & Finance, 27, 209–223.