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

Paper: arXiv 2412.05297

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.

Complexity vs Empirical Score

• Math Complexity: 2.0/10
• Empirical Rigor: 6.0/10 (capped from 7.0: transaction costs not modelled; see penalty flags)
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper focuses on applied machine learning with fundamental data and a full decision support system, relying on practical implementation rather than advanced mathematics, and includes evaluation on a real dataset with comparisons to baselines.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Develop an integrated DSS for stock selection<br>and asset allocation using fundamental data"] --> B
  
  subgraph B ["Key Methodology & Data Inputs"]
      direction LR
      B1["Financial Data<br>Balance Sheet, Income Statement"] --> B3["Data Processing<br>Cleaning, Feature Engineering"]
      B2["Macroeconomic Data<br>Inflation, GDP, Interest Rates"] --> B3
  end

  B3 --> C["Computational Process<br>Machine Learning Models for Prediction"]
  C --> D{"Stock Selection<br>Predict Mid-Long Term Returns"}
  D --> E
  
  subgraph E ["Key Findings & Outcomes"]
      direction LR
      E1["High Predictive Accuracy<br>Superior to Novel Research"]
      E2["Adaptive Asset Allocation<br>Strategies for: Stocks, Bonds, Commodities"]
      E3["Robust DSS for<br>Mid-Long Term Investors"]
  end

  E1 & E2 & E3 --> F["Final Outcome<br>End-to-End Decision Support System<br>Evaluates Tehran Stock Exchange Market"]

Cited References (50)

1. Understanding inflation-indexed bond markets (2009)
2. The intelligent investor (2005)
3. Stock markets, banks, and economic growth (1998)
4. Bond markets and economic growth (2003)
5. Institutional and individual investors: Saving for old age (2018) — link
6. Financial applications of machine learning: A literature review (2023) — link
7. Stock market prediction using artificial intelligence: A systematic review of systematic reviews (2024) — link
8. Evaluating multiple classifiers for stock price direction prediction (2015) — link

1 of 50 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.