A Decision Support System for Stock Selection and Asset Allocation Based on Fundamental Data Analysis

TL;DR

This paper presents an end-to-end decision support system that leverages fundamental data analysis and machine learning to predict stock returns and optimize asset allocation in diverse economic environments, demonstrated on the Tehran Stock Exchange.

Contribution

It introduces a robust predictive model for mid- to long-term stock returns and a comprehensive DSS considering diverse economic features, with evaluation on the Tehran Stock Exchange.

Findings

The DSS outperforms recent models in predicting stock returns.

It effectively generates asset allocation strategies for different economic scenarios.

The system is tailored for investors, not traders.

Abstract

Financial markets are integral to a country's economic success, yet their complex nature raises challenging issues for predicting their behaviors. There is a growing demand for an integrated system that explores the vast and diverse data in financial reports with powerful machine-learning models to analyze financial markets and suggest appropriate investment strategies. This research provides an end-to-end decision support system (DSS) that pervasively covers the stages of gathering, cleaning, and modeling the stock's financial and fundamental data alongside the country's macroeconomic conditions. Analyzing and modeling the fundamental data of securities is a noteworthy method that, despite its greater power, has been used by fewer researchers due to its more complex and challenging issues. By precisely analyzing securities' fundamental data, the proposed system assists investors in predicting stock future prices and allocating assets in major financial markets: stock, bond, and commodity. The most notable contributions and innovations of this research are: (1) Developing a robust predictive model for mid- to long-term stock returns, tailored for investors rather than traders, (2) The proposed DSS considers a diverse set of features relating to the economic conditions of the company, including fundamental data, stock trading characteristics, and macro-economic attributes to enhance predictive accuracy, (3) Evaluating the DSS performance on the Tehran Stock Exchange that has specific characteristics of small to medium-sized economies with high inflation rates and showing the superiority to novel researches, and (4) Empowering the DSS to generate different asset allocation strategies in various economic situations by simulating expert investor decision-making.