Psychological Determinants of Investment Decisions: Analyzing Financial Behavior in Personal Investments
Main Article Content
Abstract
Understanding the psychological factors that influence investor behavior is critical in the dynamic world of financial markets. Financial conduct encompasses the decisions and behaviors individuals exhibit in managing their finances, including investments in various asset classes. Factors such as risk tolerance, cognitive biases, emotional influences, and financial knowledge significantly shape investment outcomes. Gaining long-term financial success requires mastery over these behavioral aspects. This study investigates the influence of three psychological factors—information asymmetry, problem framing, and risk propensity—on the investment decisions of 220 active investors trading on the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE). A quantitative research approach was employed, utilizing a structured questionnaire to collect data. Statistical analyses, including regression and correlation analysis, were used to assess the relationships between these psychological variables and investment behaviors. The results reveal significant correlations between psychological factors and investment decisions. Information asymmetry, problem framing, and risk propensity were found to strongly influence individual investor choices. These findings shed light on the intricate role that cognitive biases and psychological processes play in shaping financial decision-making. The study's findings offer valuable insights into the psychological drivers behind investment behavior. By highlighting the impact of these factors, the research contributes to both academic understanding and practical applications for financial professionals. The results underscore the importance of enhancing financial literacy and investor education, enabling more informed decision-making and promoting improved financial outcomes.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
How to Cite
References
Ahmad, A. Y. A. B., Gongada, T. N., Shrivastava, G., Gabbi, R. S., Islam, S., & Nagaraju, K. (2023). E-Commerce Trend Analysis and Management for Industry 5.0 using User Data Analysis. International Journal of Intelligent Systems and Applications in Engineering, 11(11).
Amine, O. M., Zineb, E., & Eddine, K. S. (2023). STUDY OF THE BEHAVIOURAL DETERMINANTS OF INVESTMENT IN THE ERA OF THE COVID-19 PANDEMIC AMONG SOCIALLY RESPONSIBLE INVESTORS IN MOROCCO. Economic Archive, 1(2).
Asmara, N., Lako, A., & Trimeiningrum, E. (2020). The Impact of Employee Characteristics in the Relation of Financial Knowledge, Financial Management Behavior and Personal Income with Investment Decision of Employee. Journal of Management and Business Environment (JMBE), 1(2). https://doi.org/10.24167/jmbe.v1i2.2408
Badru, A. F., Karadas, G., & Olugbade, O. A. (2024). Employee voice: the impact of high-performance work systems and organisational engagement climate. Service Industries Journal, 44(7–8). https://doi.org/10.1080/02642069.2022.2056163
Feroz, Q. S., & Asif, T. K. (2021). High-Performance Work System & Employee Performance in Public Sector: Testing the Mediating Effect of Job Engagement. European Scientific Journal ESJ, 17(12). https://doi.org/10.19044/esj.2021.v17n12p129
Girdher, S. (2019). Role of Artificial Intelligence in Transforming E-commerce Sector. Research Review Journals, 4(6).
Huang, Y., Ma, Z., & Meng, Y. (2018). High-performance work systems and employee engagement: empirical evidence from China. Asia Pacific Journal of Human Resources, 56(3). https://doi.org/10.1111/1744-7941.12140
Jasiniak, M. (2018). Determinants of Investment Decisions on the Capital Market. E-Finanse, 14(2). https://doi.org/10.2478/fiqf-2018-0007
Kshetri, N., Ahmad, N., & Chauhan, P. (2024). Generative Artificial Intelligence and E-Commerce. In Computer (Vol. 57, Issue 2). https://doi.org/10.1109/MC.2023.3340772
Kumar, V., & Shukla, K. (2024). Psychological Biases and Contextual factors as the determinants of Financial Satisfaction: An Evidence-Based Study on Individual Investment Decisions. Global Business and Economics Review, 1(1). https://doi.org/10.1504/gber.2024.10055094
Kumari, B. K., Sundari, V. M., Praseeda, C., Nagpal, P., John, E. P., & Awasthi, S. (2023). Analytics-Based Performance Influential Factors Prediction for Sustainable Growth of Organization, Employee Psychological Engagement, Work Satisfaction, Training and Development. Journal for ReAttach Therapy and Developmental Diversities, 6(8).
Lima, T. S., Mail, R., Karim, M. R. A., Ulum, Z. K. A. B., Mifli, M., & Jaidi, J. (2020). An investigation of financial investment intention using covariance-based structural equation modelling. Global Business and Finance Review, 25(2). https://doi.org/10.17549/gbfr.2020.25.2.37
M. Kumari, K. P. (2020). Psychological Determinants Affecting Investment Decision Behaviour of Millennial Investors. Psychology, Business.
Mishra, P. (2019). Modeling investment decision through perceived risk. International Journal of Engineering and Advanced Technology, 8(6 Special Issue 3). https://doi.org/10.35940/ijeat.F1306.0986S319
Occhipinti, J. A., Skinner, A., Iorfino, F., Lawson, K., Sturgess, J., Burgess, W., Davenport, T., Hudson, D., & Hickie, I. (2021). Reducing youth suicide: systems modelling and simulation to guide targeted investments across the determinants. BMC Medicine, 19(1). https://doi.org/10.1186/s12916-021-01935-4
Oppong, C., Salifu Atchulo, A., Akwaa-Sekyi, E. K., Grant, D. D., & Kpegba, S. A. (2023). Financial literacy, investment and personal financial management nexus: Empirical evidence on private sector employees. Cogent Business and Management, 10(2). https://doi.org/10.1080/23311975.2023.2229106
Potrich, A. C. G., Vieira, K. M., & Kirch, G. (2015). Determinantes da Alfabetização Financeira: Análise da Influência de Variáveis Socioeconômicas e Demográficas. Revista Contabilidade & Finanças, 26(69). https://doi.org/10.1590/1808-057x201501040
Rajput, N., Das, G., Shivam, K., Kumar Nayak, C., Gaurav, K., & Nagpal, P. (2023). An inclusive systematic investigation of human resource management practice in harnessing human capital. Materials Today: Proceedings, 80. https://doi.org/10.1016/j.matpr.2021.07.362
Shrivastava, A., Suji Prasad, S. J., Yeruva, A. R., Mani, P., Nagpal, P., & Chaturvedi, A. (2023). IoT Based RFID Attendance Monitoring System of Students using Arduino ESP8266 & Adafruit.io on Defined Area. Cybernetics and Systems. https://doi.org/10.1080/01969722.2023.2166243
Small, D. A., & Lerner, J. S. (2008). Emotional policy: Personal sadness and anger shape judgments about a welfare case. Political Psychology, 29(2). https://doi.org/10.1111/j.1467-9221.2008.00621.x
Sobaih, A. E. E., & Elshaer, I. A. (2023). Risk-Taking, Financial Knowledge, and Risky Investment Intention: Expanding Theory of Planned Behavior Using a Moderating-Mediating Model. Mathematics, 11(2). https://doi.org/10.3390/math11020453
Sood, K., Pathak, P., & Gupta, S. (2024). How do the determinants of investment decisions get prioritized? Peeking into the minds of investors. Kybernetes. https://doi.org/10.1108/K-04-2023-0662
Stack, S. W., Jagsi, R., Sybil Biermann, J., Lundberg, G. P., Law, K. L., Milne, C. K., Williams, S. G., Burton, T. C., Larison, C. L., & Best, J. A. (2019). Maternity Leave in Residency: A Multicenter Study of Determinants and Wellness Outcomes. Academic Medicine, 94(11). https://doi.org/10.1097/ACM.0000000000002780
Sun, L., & Bunchapattanasakda, C. (2019). Employee Engagement: A Literature Review. International Journal of Human Resource Studies, 9(1). https://doi.org/10.5296/ijhrs.v9i1.14167
William, P., Shrivastava, A., Chauhan, H., Nagpal, P., Vasantha Kumari, T. N., & Singh, P. (2022). Framework for Intelligent Smart City Deployment via Artificial Intelligence Software Networking. Proceedings of 3rd International Conference on Intelligent Engineering and Management, ICIEM 2022. https://doi.org/10.1109/ICIEM54221.2022.9853119
Wisandani, I., Iswati, S., Anshori, M., & Ismail, R. (2019). Investment decisions in the interbank money market based on islamic principles. Opcion, 35(20).
Yadav, S. K. S., & Mohsin Qureshi, M. (2021). Impacts of Covid-19 on Indian Travel & Tourism Industry. International Journal of Trade and Commerce-IIARTC, 9(2). https://doi.org/10.46333/ijtc/9/2/5