Design and development of an iOS application for tracking behavioural biases in investment decisions

Abstract

This thesis presents the implementation of a tool: the design and full development cycle of FOMONITOR - a mobile application for the iOS operating system designed to help beginners, amateurs, and even experienced investors recognize and manage emotional biases, in particular, the well-known psychological characteristic of the human brain – the fear of missing out (FOMO), which influences financial decision-making and significantly impacts overall investment performance. The app tracks and analyses user interaction patterns, such as the frequency and timing of checking market price changes, correlates them with market volatility, and supplements them with optional mood logs to calculate personalized behavioural indicators such as the FOMO Index and Emotional Risk Score. By visualizing this data for the user and providing emotion- and bias-free notifications, FOMONITOR motivates and helps users analyse their behavioural tendencies, reducing the risks of impulsive actions and helping them regulate their reactions to short-term market fluctuations.

The entire system was implemented using Swift and SwiftUI and follows the MVVM architectural pattern. Market data is integrated from multiple API providers, while long-term behavioural, mood, and portfolio information is stored locally through CoreData. The project combines behavioural finance, emotional psychology, and mobile software development to demonstrate how technologies available on any modern mobile device can foster emotional awareness in investing and money management. The results demonstrate that real-time behavioural tracking, mood correlation analysis, and personalized, visualized feedback can foster healthier and more disciplined trading habits.