Designing the optimal investment model based on the parameters of predicting stock returns and the risk factors of disruptive traders

Document Type : Research Paper

Authors

1 Department of Industrial Management, Financial Orientation, Najafabad Branch, Islamic Azad University, Najafabad, Iran

2 Department of Management, Dehaghan Branch, Islamic Azad University, Isfahan, Iran

Abstract

Noise traders cause severe fluctuations and deviation of asset values from their intrinsic value; thus, this article designs an optimal investment model based on the parameters of predicting stock returns and noise trader risk factors. This is an applied post-event paper. In this article, first, the stock returns predicting parameters are obtained followed by the noise traders' risk factors obtained through behavioural error or the beta difference in transactions, according to the combined regression model or models, which are the results of the risk factors. Then, noise traders and stock return predictor variables were designed and tested using econometric software, including Eviews9 software and Matlab algorithmic models. The statistical population of this research includes all companies admitted to the Tehran Stock Exchange, whose shares were traded until March 19, 2020. Also, in this research, PCA, GSADF, and logit methods were used to determine the impact of noise traders in determining the incidence of the bubble used in the Tehran Stock Exchange. The research findings show that noise traders have a positive and significant effect on the occurrence of a bubble, and an increase of one unit of optimistic sentiments and optimistic sentiments with a break in the stock market increases the probability of a bubble occurrence.

Keywords

[1] E. Abbasian, E. Farzangan, and I. Nasirul Islami, Price bubble irregularity in Tehran stock exchange: limitation approach in arbitrage, Econ. Res. Policy Quart. 23 (2015), no. 76, 75–92.
[2] C. Aranha and H. Iba, The memetic tree-based genetic algorithm and its application to portfolio optimization, Memetic Comput. 1 (2009), no. 2, 139–151.
[3] M. Asghartabar Ledari and A. Jafari Samimi, An optimization of moving average stock price in Tehran stock exchange: meta-heuristic approach adaptive improved genetic algorithm, J. Invest. Knowledge 7 (2018), no. 25, 127–148.
[4] Z. Azarian and S.M. Homayouni, Forecasting stock price changes using technical analysis indicators and using the combined method of genetic algorithm and artificial neural network: a case study of Iran Khodro stocks, Appl. Econ. Quart. 7 (2017), no. 22.
[5] M. Baker and J. Wurgler, Investor sentiment in the stock market, J. Econ. Persp. 21 (2007), no. 2, 129–151.
[6] B.M. Barber, T. Odean and N. Zhu, Do noise traders move markets?, EFA 2006 Zurich Meetings Paper, 2006.
[7] J.B. Bayer, P. Trieu and N.B. Ellison, Social media elements, ecologies, and effects, Ann. Rev. Psycho. 71 (2020), 471–497.
[8] F. Black, Noise, J. Finance 3 (1986), 529–543.
[9] R. Bloomfield, M. O’Hara, and G. Saar, How noise trading affects markets: an experimental analysis, Rev. Financ. Stud. 22 (2009), no. 6, 2275–2302.
[10] G.W. Brown and M.T. Cliff, Investor sentiment and the near-term stock market, J. Empir. Finance 11 (2004), no. 1, 1–27.
[11] S. Chowdhury and M.M. Rahman, The potentiality of women entrepreneurs in Bangladesh with special reference o Sylhet region, Glob. Discl. Econ. Bus. 3 (2014), no. 2, 121–130.
[12] J.B. De Long, A. Shleifer, L. Summers, and J.R. Waldmann, Noise trader risks in financial markets, J. Politic. Econ. 4 (1990), 703–738.
[13] H. Ghalibaf Asl, R. Tehrani, M. Rostami, and A. Siyari, Designing a prediction model for long-term stock return with nonparametric simulation of debt security return, Financ. Manag. Persp. 8 (2019), no. 21, 133–155.
[14] F. Herve, M. Zouaoui and B. Belvaux, Noise traders and smart money: Evidence from online searches, Econ. Modell. 83 (2019), 141–149.
[15] O.A.H. Khasawneh, Noise trading in small markets: Evidence from Amman stock exchange (ASE), Res. Int. Bus. Finance 42 (2017), 422–428.
[16] S. Kholdy and A. Sohrabian, Noise traders and the rational investors: a comparison of the 1990s and the 2000s, J. Econ. Stud. 41 (2014), no. 6, 849–862.
[17] R.B. Kline, Principles and practice of Structural Equation Modeling, Guilford Publications, 2023.
[18] K.P. Lim and R.D. Brooks, The evolution of stock market efficiency over time: A survey of the empirical literature, J. Econ. Surv. 25 (2011), 69–108.
[19] D.C. Ling, A. Naranjo, and B. Scheick, Investor sentiment and asset pricing in public and private markets, RERI WP 170 (2010).
[20] B.W. Mazviona, Measuring investor sentiment on the Zimbabwe stock exchange, Asian J. Econ. Modell. 3 (2015), no. 2.
[21] M.R. Mehrabanpour, B. Ezzabadi and S. Jafakesh Konfgorabi, Investigate the role of types of investors in the formation of behavioral bubbles in the Tehran stock exchange using an autoregression model, Financ. Knowledge Secur. Anal. 10 (2017), no. 35.
[22] S. Mohammadi, R. Raei, H. Ghalibaf, and G.H. Gol Arzi, Analysis of herd behavior of investors in Tehran stock exchange using with state space model, J. Financ. Account. Res. 2 (2010), no. 2, 49–60.
[23] A. Mohtadi, R. Hejazi, S.A. Hosseini, and M. Momeni, Applying the technique of ”principal component analysis” in data collection of variables affecting stock returns, Financ. Account. Audit. Res. 10 (2017), no. 37.
[24] V. Nadi Qomi and N. Saif, Bubble analysis on stock returns, Invest. Knowledge 8 (2019), no. 29.
[25] A. Pakmaram, J. Bahri Sales, and M. Valizadeh, Selection and portfolio optimization by genetic algorithms using the mean semi-variance Markowitz model, Financ. Eng. Portfolio Manag. 8 (2017), no. 31.
[26] F. Palomino, Noise trading in small markets, J. Finance 51 (1996), no. 4, 1537–1550.
[27] F. Rahnemaie Rudpashti, Explanation of financial engineering and its application in venture capital industry, First Nat. Conf. Venture Capital Industry, Tehran, 2012.
[28] F. Rahnemaie Rudpashti, M. Maadanchi Zaj, and S. Babalouyan, Testing the informational efficiency and rational bubble in TSE and its subsections using variance ratio test and stationary test of price- dividend ratio, J. Financ. Knowledge Secur. Anal. 5 (2012), no. 2, 59–75.
[29] V. Ramiah and S. Davidson, Inefficiency of the Australian Stock Market, UK publisher, Edward Elgar, 2011.
[30] V. Ramiah and S. Davidson, Information-adjusted noise model: evidence of inefficiency on the Australian stock market, J. Behav. Financ. 8 (2007), no. 4, 209–224.
[31] A. Saranj, R. Tehrani, K. Abbasi Mosulu, and M. Nadiri, Identify trading behaviors and risk of noise traders in the Iranian stock market, Financ. Manag. Strat. 6 (2018), no. 22, 31–58.
[32] H. Shefrin and M. Statman, Behavioural capital asset pricing theory, J. Financ. Quant. Anal. 29 (1994), 323–349.
[33] A. Shleifer and L.H. Summers, The noise trader approach to finance, J. Econ. Persp. 4 (1990), no. 2, 19–33.
[34] M. Tohidi, Extracting composite sentiment index for Tehran stock exchange, J. Asset Manag. Financ. 8 (2020), no. 2.
[35] L. Wang, F. Ma, J. Liu, and L. Yang, Forecasting stock price volatility: New evidence from the GARCH-MIDAS model, Int. J. Forecast. 36 (2020), no. 2, 648–694.
[36] Y.-R. Wu and L.Y. Han, Imperfect rationality, sentiment and closed-end-fund puzzle, Econ. Res. J. 42 (2007), no. 3, 117–129.
[37] X. Xu, V. Ramiah, I. Moosa, and D. Sinclair, An application of the information-adjusted noise model to the Shenzhen stock market, Int. J. Manag. Finance 12 (2016), no. 1, 71–91.
[38] Z. Yu, L. Qin, Y. Chen, and M.D. Parmar, Stock price forecasting based on LLE-BP neural network model, Phys. A: Statist. Mech. Appl. 553 (2020), 124197.
[39] A. Zarei and R. Darabi, The impact of investors’ sentimental tendencies on optional disclosure in Iran’s capital market, Financ. Account. Audit. Res. 10 (2018), no. 37, 131–157.
[40] A. Zhouri, Trees and people: an anthropology of British campaigners for the Amazon rainforest, PhD diss., University of Essex, 1999.
Volume 16, Issue 2
February 2025
Pages 279-291
  • Receive Date: 03 May 2023
  • Revise Date: 28 June 2023
  • Accept Date: 17 July 2023