On Constructing a Financial Decision Support System

Financial decision support system development usually involves a number of recognizable steps: data preparation – cleaning, selecting, making data suitable for the predictor; prediction algorithm development and tuning – for performance on the quality of interest and evaluation – to see if indeed the system performs on unseen data. But since financial prediction is very difficult, extra insights are needed. This work is twofold aiming to provide data enhancing techniques, performance improvements, evaluation hints and pitfalls to avoid as well as to exploit them by using a prototype tool that can automate the financial decision support process.