Introduction
Overcoming the complexity of developing an AI-assisted model to mimic the principles of value investing can seem like an overwhelming task, especially given the complexities of today’s investment landscape. To successfully complete such a project, designers are recommended to utilize visual tools that will keep their projects within agreed budgets, timeframes, and quality (Verzuh, 2021).
Key Visual Tools
- - Gantt Chart: A Gantt chart helps stakeholders design schedules, track progress, and identify bottlenecks. By allocating horizontal bars against project schedules and individuals or teams responsible, Gantt charts provide a clear visualization of progress and time allocation.
- - Timelines: Timelines visually represent project timeframes, simplifying the design of sequences required to complete complex projects. Unlike Gantt charts, timelines emphasize a high-level overview from planning to completion.
- - Kanban Boards: A Kanban board tracks and optimizes workflows through columns like "To Do," "In Progress," and "Done." Tasks move from left to right based on progress, enhancing flexibility and adaptability.
- - RACI Charts: RACI charts assign project participants roles based on four criteria: Responsible, Accountable, Consulted, and Informed. This tool prevents confusion and clarifies accountability in complex projects.
AI-Aided Value Investing Modelling
A useful project management framework for AI-assisted value investing is CRISP-DM (Cross-Industry Standard Process for Data Mining), widely used in data science and AI development (López, 2021).
CRISP-DM Methodology
- - Business Understanding: Define project objectives, key metrics, and scope to align with value investing principles.
- - Data Understanding: Collect, explore, and assess financial and market data, including fundamental business metrics and market sentiment.
- - Data Preparation: Clean and structure data to ensure AI models function optimally, preventing inaccurate outputs.
- - Modeling: Train machine learning models such as Random Forest (RF) and Gradient Boosting Machines (GBM) for predictive analytics.
- - Evaluation: Assess model performance against business requirements, such as outperforming the S&P 500 (Greenwald et al., 2020).
- - Deployment: Integrate the AI model into fintech applications like robo-advisors and risk assessment tools for institutional investors.
Conclusion
Visual tools streamline AI-powered value investing projects by offering clear organization, tracking, and responsibility assignment. Implementing structured methodologies like CRISP-DM enhances efficiency and improves model accuracy, ensuring a systematic approach to tackling investment complexities.
References
Greenwald, B. C., Kahn, J., & Bellissimo, E. (2020). Value Investing: From Graham to Buffett and Beyond (2nd ed.). Hoboken, NJ: Wiley Finance.
López, C. P. (2021). DATA MINING. The CRISP-DM METHODOLOGY. The CLEM Language and IBM SPSS MODELER. Morrisville, NC: Lulu.com.
Verzuh, E. (2021). The Fast Forward MBA in Project Management: The Comprehensive, Easy-to-Read Handbook for Beginners and Pros (6th ed.). Hoboken, NJ: Wiley.
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