Investment decisions play a crucial role in ensuring the financial well-being and long-term economic security of salaried individuals. Employees differ considerably in terms of income levels, expenditure patterns, financial goals, and investment awareness, which significantly influence their investment behaviour. This study aims to examine the investment preferences of salaried employees in Chennai and identify the key factors influencing their investment choices. The research adopts a descriptive research design and focuses on salaried employees working across diverse sectors, including Information Technology, Education, Banking and Financial Services, Healthcare, and Manufacturing. Primary data were collected through a structured questionnaire, while secondary data were gathered from academic journals, research articles, conference proceedings, books, and published reports to support the conceptual framework and empirical analysis. A Convenience Sampling technique was employed, and data were collected from 60 respondents. The study is grounded in Modern Portfolio Theory (MPT), which provides a framework for understanding investment decisions based on risk-return trade-offs and diversification principles. Data analysis was carried out using both quantitative statistical techniques and qualitative methods to gain comprehensive insights into investment behaviour. The findings indicate that salaried employees in Chennai predominantly prefer low-risk and tax-efficient investment avenues. Furthermore, investment decisions are significantly influenced by factors such as income level, risk perception, financial literacy, and investment objectives. The study also highlights the growing importance of informed financial planning among salaried individuals. The insights derived from this research can assist financial planners, policymakers, and investment service providers in designing suitable investment products, financial literacy initiatives, and awareness programmes tailored to the needs of salaried employees in Chennai.
Samuel et al. (Tue,) studied this question.