TITLE
FROM INDICATORS TO DECISIONS: EVALUATING STOCK SELECTION MODEL COMPLETENESS
AUTHOR(S)
Rima Tamošiūnienė, Julija Mosina
ABSTRACT
In stock selection research, the boundary between technical indicator development and full decision-making model construction is often unclear. Many studies introduce new indicators and present them as models. This paper reviews 22 studies published between 2013 and 2025 to evaluate whether they propose complete decision frameworks or remain at the indicator or selection stage. Three categories are distinguished: (i) indicator or selection papers, which focus on predictive signals without implementation rules; (ii) partial decision-making models, which define some trading logic or portfolio structure but omit key elements such as cost modeling and risk constraints; and (iii) full trading systems, which integrate signals with decision rules and aim to evaluate performance under realistic validation procedures. Using a qualitative classification across seven structural elements, we assess each study’s methodological completeness and scope. A concise reporting checklist is proposed to help authors and readers state clearly what is included and what remains beyond the study’s scope. The review shows that while many studies define predictive logic, few extend to complete systems with realistic validation or real-time testing. The study contributes a clear framework for distinguishing indicators from decision models and for improving transparency and practical relevance in future research.
DOI
How to cite this article:
Rima Tamošiūnienė, Julija Mosina, Rima Tamošiūnienė*, FROM INDICATORS TO DECISIONS: EVALUATING STOCK SELECTION MODEL COMPLETENESS’ PERFORMANCE, UNITECH – SELECTED PAPERS - 2025