Analyzing the Impact of Analytical Paralysis on Investors' Decisions in the Tehran Stock Exchange

Document Type : Research Paper

Authors

1 Associate Prof., Department of Accounting, Faculty of Management and Economic, Lorestan University, Khoramabad, Iran.

2 Ph.D. Candidate, Department of Accounting, Faculty of Human Sciences, Khomein Branch, Islamic Azad University, Arak, Iran.

Abstract

Objective
An investment-making decision is a multifaceted process that requires the investor's precision, punctuality, and timely decision. This process is influenced by future expectations and the analysis of several factors the investor must consider to achieve a specific goal. Paralysis in the analysis of these factors causes a delay in obtaining profits or the realization of unwanted losses. Therefore, the main goal of the current study is to investigate the effect of the dimensions of the analysis paralysis on investors’ decisions in the Tehran Stock Exchange.
Methods
To collect data and information, libraries, interviews, and field methods were used simultaneously. For data analysis in the qualitative section of the research, the Fuzzy Delphi method, Excel, and MaxQDA software were employed. In the quantitative section, structural equation modeling and Smart PLS software were utilized. The Fuzzy Delphi method was used to analyze the qualitative data. After extracting the indices and influential variables in the model and thematic analysis, the initial influential variables were identified and presented to the experts in the form of a questionnaire to gather their opinions. Then, their answers were classified and the lack of agreement was announced. This process was repeated until a consensus was reached and the way of quantifying and measuring specific research variables in four political, social, cultural and economic dimensions were examined and analyzed.
Results
According to the results of the Delphi section, the final research model was approved with two variables of analytical paralysis and investors’ decisions. The results of path coefficients showed a negative and significant relationship between analysis paralysis and investors' decisions, although there was a negative and significant relationship with investors' short-term decisions, with no significant relationship with investors' long-term decisions. Among the dimensions of analysis paralysis, the political dimension has the greatest impact on the analytical paralysis of investors' decision-making. Then the economic, social and cultural dimensions have respectively the greatest effect on the analytical paralysis of investors' decision-making.
Conclusion
The findings showed that analytical paralysis caused by abundant and fleeting data and political, economic, social and cultural instability will cause different behaviors among investors and these behaviors will affect people's investment decisions. In other words, analytical paralysis will increase the instability of investors in the decision-making process. Faced with such factors, investors will not be able to react appropriately and avoid deviations in decision-making in the short term. For this reason, in the conditions of abundant and unstable data affecting decision-making, changing from short-term decisions to long-term decisions can have fewer negative effects. Therefore, it is suggested that the trustees of the capital market reduce the analytical paralysis of the investors by informing and providing reliable and timely information to the society and help them in the proper management of their capital.

Keywords

Main Subjects


 
Adil, M., Singh, Y. & Ansari, M. S.  (2021). How financial literacy moderate the association between behaviour biases and investment decision? Asian Journal of Accounting Research, 7(1), 17-30.
Aggarwal, D. & Damodaran, U. (2020). Ambiguity attitudes and myopic loss aversion: Experimental evidence using carnival games. Journal of Behavioral and Experimental Finance, 25, 1-9.
Aghajani, V., Pakmaram, A., Rasoul, A. & Narimani, M. (2021). Examining the mediating role of self-efficacy in the relationship between behavioral types and investment decisions of investors in the TSE. Journal of Modern Psychological Research, 15(60), 115- 130.
(in Persian)
Alkaraan, F. & Northcott, D. (2013). Strategic investment decision-making processes: the influence of contextual factors. Meditari Accountancy Research, 21(2), 117-143.
Alleyne, P. & Broome, T. (2011). Using the theory of planned behaviour and risk propensity to measure investment intentions among future investors. Journal of Eastern Caribbean Studies, 36(1), 1-21.
Atif Sattar, M., Toseef, M. & Fahad Sattar, M. (2020). Behavioral Finance Biases in Investment Decision Making, International Journal of Accounting, Finance and Risk Management, 5(2), 69-75.
Babaei, M. & Afsharnejad, A. (2020). The influence of behavioral biases on the inclination to invest in the foreign exchange market. 2nd National Conference on Science and Technology of the Third Millennium in Iran: Economics, Management, and Accounting. (in Persian)
Bagheri, M., Azkia, M. & Mousai, M. (2023). Sociological Analysis of Socio-economic Factors Affecting the Investment Behavior of Shareholders in Tehran Stock Market, (43), 351-381. (in Persian)
Byrne, B.M. (2001). Structural equation modeling with AMOS, EQS, and LISREL: Comparative approaches to testing for the factorial validity of a measuring instrument. International journal of testing, 1(1), 55-86.
Carr, C., Kolehmainen, K. & Mitchell, F. (2010). Strategic investment decision making practices: A contextual approach. Management Accounting Research, 21(3), 167- 184.
Cheng, C.H. & Lin, Y. (2002). Evaluating the Best Main Battle Tank Using Fuzzy Decision Theory with Linguistic Criteria Evaluation. European Journal of Operational Research, 142(1), 174-186.
Chernev, A. (2003). When more is less and less is more: The role of ideal point availability and assortment in consumer choice. Journal of consumer Research, 30(2), 170-183.
Chua, C. T., Goh, J., & Zhang, Z. (2020). Expected volatility, unexpected volatility, and the cross-section of stock returns. The Journal of Financial Research, 33(2), 103–123.
Dluhošová, D., Richtarová, D. & Čulík, M. (2011). Multi factor sensitivity analysis in the investment decision-making. Borno., 2-12
Gallagher, M. (2008). Foucault, power and participation. The International Journal of Children's Rights, 16(3), 395-406.
Habibi, A., & Sarbandi, M., (2022). SPSS practical training, Tehran, Narvan. (in Persian)
Hall, J. L. & Tacon, P. B. (2010). Forecast accuracy and stock recommendations. Journal of Contemporary Accounting & Economics, 6(1), 18- 33.
Hassanzad Salmani, A., Dehghan, A. & Alikhani, M. (2020). The influence of financial literacy and risk perception on investment choices in the stock market. Journal of Financial Engineering and Securities Management, 10(41), 98-108. (in Persian)
Haynes, G. A. (2009). Testing the boundaries of the choice overload phenomenon: The effect of number of options and time pressure on decision difficulty and satisfaction. Psychology & Marketing, 26(3), 204-212.
Hood, M., Nofsinger, J., & Varma, A. (2014). Conservation, discrimination, and salvation: Investors’ social concerns in the stock market. Journal of Financial Services Research, 45(1), 5–37.
Iyengar, S. S. & Lepper, M. R. (2000). When choice is demotivating: Can one desire too much of a good thing? Journal of personality and social psychology, 79(6), 995.
Iyengar, S., Jiang, W. & Huberman, G. (2004). How Much Choice Is Too Much? Contributions to 401(K) Retirement Plans. Pension Design and Structure: New Lessons from Behavioral Finance, 83-96.
Jain, J., Walia, N. & Gupta, S. (2020). Evaluation of behavioral biases affecting investment decision making of individual equity investors by fuzzy analytic hierarchy process. Review of Behavioral Finance, 12(3), 297–314.
Jak, S., Jorgensen, T. D., Verdam, M. G., Oort, F. J. & Elffers, L. (2020). Analytical power calculations for structural equation modeling: A tutorial and Shiny app. Behavior Research Methods, 53, 1–22.
Jamshidi, M. (2019). Investigating behavioral biases and influential factors in creating them among the Fereshteh Neighberhood investors. 5th International Conference on Management and Accounting Techniques in Tehran. (in Persian)
Kabir, M. H. & Shakur, S. (2018). Regime-dependent herding behavior in Asian and Latin American stock markets. Pacific-Basin Finance Journal, 47(1), 60–78.
Kartini, K. & Katiya, N. (2021), Behavioral Biases on Investment Decision: A Case Study in Indonesia. The Journal of Asian Finance, Economics and Business, 8 (3), 1231-1240.
Kempf, A. & Ruenzi, S. (2006). Status quo bias and the number of alternatives: An empirical illustration from the mutual fund industry. The journal of behavioral finance, 7(4), 204-213.
Khan, N., Latif, K., Sohail, N. & Zahid, Z. (2019). Web disclosure as mediating role in the relationship between paradox of choice, investor experience, financial literacy, and investment decision making: evidence from China. Journal of Accounting and Finance in Emerging Economies, 5(1), 83-92.
Krishnan, R. & Booker, D. M. (2002). Investors' use of analysts' recommendations. Behavioral Research in Accounting, 14(1), 129-156.
Kurien, R., Paila, A. R. & Nagendra, A. (2014). Application of paralysis analysissyndrome in customer decision making. Procedia Economics and Finance, 11, 323-334.
Laschinger, H. K. S. & Leiter, M. P. (2006). The impact of nursing work environments on patient safety outcomes: The mediating role of burnout engagement. Journal of Nursing Administration, 36(5), 259-267.
Liu, J. & Pang, D. (2009). Financial factors and company investment decisions in transitional China. Managerial and Decision Economics, 30(2), 91-108.
Lutfi, A., Al-Okaily, M., Alsyouf, A. & Alrawad, M. (2022). Evaluating the D&M IS Success Model in the Context of Accounting Information System and Sustainable Decision Making. Sustainability, 14(13), 8120.
Madsen, J. B. (2002). The causality between investment and economic growth. Economics Letters, 74(2), 157-163.
Malhotra, N. K. & Peterson, M. (2006). Basic marketing research: A decision-making approach. Prentice hall.
Manolica, A., Guta, A. S., Roman, T. & Dragan, L. M. (2021). Is Consumer Overchoice a Reason for Decision Paralysis? Sustainability, 13(11), 1-16.
Martínez-Noya, A. & García-Canal, E. (2011). Technological Capabilities and the Decision to Outsource/ Outsource Off shore R&D Services. International Business Review, 20(3), 264-277.
Mirmohammadi, M. & Shakarian, H. (2020). Examining the role of financial literacy and financial knowledge in investors' investment decision-making. Journal of Business Management, 11(43), 311-355. (in Persian)
Moqrab, A., Garami, A., & Mousavi, Z. (2021). The concept of risk management and its application in the capital market. International Conference on Management and Human Sciences Research in Iran. SID. https://sid.ir/paper/901965/fa. (in Persian)
Niyozovna, N. I. (2021). The role of investment and modernization in the development of the uzbek economy. Research Jet Journal of Analysis and Inventions, 2(06), 140-145.
Patil, S. & Bagodi, V. (2021). A study of factors affecting investment decisions in India: The KANO way. Asia Pacific Management Review, 26(4), 197-214.
Pinglu, C., Ullah, S. & Ullah, A. (2022), Behavioral Biases in Investment Decision Making and Moderating Role of Investor’s Type. Intellectual Economics, 14(1), 87-105.
Rahman, M., & Gan, S. S. (2020). Generation Y investment decision: An analysis using behavioral factors. Managerial Finance, 46(8), 1023–1041. https://doi. org/10. 1108/MF-10-2018-0534
Rahmani. A., Vaziri Nezhad R., Ahmadinia H. & Rezaeian, M. (2020). Methodological Principles and Applications of the Delphi Method: A Narrative Review. JRUMS, 19 (5), 515-538. (in Persian)
Sachdeva, M., Lehal, R., Gupta, S., & Garg, A. (2023). What make investors herd while investing in the Indian stock market? A hybrid approach. Review of Behavioral Finance. Issue ahead of print, 19-37.
Samal, A. & Mohapatra, K. (2020). Impact of behavioral biases on investment decisions: a study on selected risk averse investors in India. International Journal of Advanced Science and Technology, 29 (6), 2408-2425.
Satish, B. & Padmasree, K., (2018). An empirical analysis of herding behaviour in Indian stock market. International Journal of Management Studies, 3(3), 124–132.
Schwartz, B. (2015). The paradox of choice. Positive psychology in practice: Promoting human flourishing in work, health, education, and everyday life, 121- 138.
Shah, S. Z. A., Ahmad, M. & Mahmood, F. (2022). Heuristic biases in investment decision-making and perceived market efficiency: A survey at the Pakistan stock exchange. Qualitative Research in Financial Markets, 10(1), 85–110.
Sharma, M. R., Stadler, W. M. & Ratain, M. J. (2011). Randomized phase II trials: a long-term investment with promising returns. Journal of the National Cancer Institute, 103(14), 1093-1100.
Silva, P. V. J. D. G., Klotzle, M. C., Pinto, A. C. F. & Gomes, L. L. (2019). Herding behavior and contagion in the cryptocurrency market. Journal of Behavioal and Experimental Finance, 22, 41–50.
Son, N. T. & Nguyen, N. M. (2022). Prospect theory value and idiosyncratic volatility: Evidence from the Korean stock market. Journal of Behavioral and Experimental Finance, 21, 113-122.
Trejos, C., Deemen, A.V., Rodríguez, Y.E. & Gomez, M.(2022).Overconfidence and disposition effect in the stock market: A micro world-based setting. Journal of Behavioral and Experimental Finance, 21, 61–69.
Ullah, S., Jamali, D., & Harwood, I. A. (2014). Socially responsible investment: insights from Shari'a departments in Islamic financial institutions. Business Ethics: A European Review, 23(2), 218-233.
Virlics, A. (2013). Investment decision making and risk. Procedia Economics and Finance, 6, 169-177.
 Wetzels, M., Schroder, G. & Van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration. Management Information Systems Quarterly, 33(1), 177-195.
Zeinivand, M., Janani, M., Hematfar, M. & Setayesh, M. (2022). Investigating behavioral biases and investment decisions of individual and institutional investors based on technical information in the Tehran Stock Exchange. Quarterly Journal of Financial Economics, 15(57), 233-258. (in Persian)