Introduction
Selecting the appropriate research methodology is crucial in ensuring the effectiveness and validity of any scientific study. Researchers may choose between qualitative, quantitative, or mixed-methods approaches depending on the nature of the investigative questions, the stated objectives, and the type of data needed in conducting the research (Creswell & Creswell, 2018).
Qualitative Research
Qualitative research is most appropriate when exploring complex, multi-faceted issues relating to human experiences, cultural influences, social phenomena, or subjective perceptions. Often, research questions involve open-ended inquiries requiring descriptive and interpretive analysis. Fields like anthropology, sociology, psychology, and education often use qualitative research, employing methods such as interviews, focus groups, ethnographic studies, thematic analysis, and observations.
Quantitative Research
Quantitative research is used when testing a theory or hypothesis that involves numerical proof or statistical evidence. Researchers aim to find patterns, connections, and cause-and-effect relationships with numerical support. Quantitative research is often utilized in fields such as medicine, economics, psychology, and business, using techniques like surveys with closed-ended questions, experiments, statistical modeling, and longitudinal and cross-sectional studies.
Mixed-Methods Research
Mixed-methods research is employed when combining both qualitative and quantitative data provides a more comprehensive and holistic understanding of the research question. Researchers may utilize a mixed-methods approach when both qualitative and quantitative approaches support each other, validating qualitative findings with quantitative data or vice versa. Mixed-methods approaches are used in public health, education, marketing, and social sciences to provide a multidimensional perspective on complex issues.
Application to Value Investing AI-Modelling
Selecting the appropriate research methodology is crucial in developing a stock market investing machine learning model. Within the value investing approach, several challenges emerge due to the interdisciplinary nature of the analysis. Value investing requires thorough analysis of numerical metrics reflecting business performance. This analysis aligns with a quantitative approach, focusing on metrics and ratios such as Return on Assets (ROA), Return on Equity (ROE), debt-to-assets ratio, and cash flows.
However, value investing also emphasizes competitive advantage, a more subjective factor requiring qualitative methods to evaluate aspects like brand strength, market positioning, patents, and legal considerations. Furthermore, value investing emphasizes purchasing stocks at a discount, often during economic downturns and market pessimism, which involves assessing market sentiment—a qualitative endeavor.
Conclusion
The design of a machine learning model within value investing principles necessitates a mixed-methods approach. This ensures the thoroughness and rigor of financial metrics while also capturing the subjectivity and complexity of market positioning and sentiment. By combining both approaches, researchers can create a value investing model that successfully replicates the nuances of successful value investing strategies.
References
Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). Sage Publications.
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