- 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.
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2؛ بیتا تبریزیان3؛ فریدون رهنمای رودپشتی4
