Citation: Alsulmi, M. From Ranking
Search Results to Managing
Investment Portfolios: Exploring
Rank-Based Approaches for Portfolio
Stock Selection. Electronics 2022, 11,
4019. https://doi.org/10.3390/
electronics11234019
Academic Editors: Phivos Mylonas,
Katia Lida Kermanidis and
Manolis Maragoudakis
Received: 24 October 2022
Accepted: 30 November 2022
Published: 4 December 2022
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Article
From Ranking Search Results to Managing Investment
Portfolios: Exploring Rank-Based Approaches for Portfolio
Stock Selection
Mohammad Alsulmi
Department of Computer Science, College of Computer and Information Sciences, King Saud University,
Riyadh 11451, Saudi Arabia; malsulmi@ksu.edu.sa
Abstract:
The task of investing in financial markets to make profits and grow one’s wealth is not a
straightforward task. Typically, financial domain experts, such as investment advisers and financial
analysts, conduct extensive research on a target financial market to decide which stock symbols are
worthy of investment. The research process used by those experts generally involves collecting a
large volume of data (e.g., financial reports, announcements, news, etc.), performing several analytics
tasks, and making inferences to reach investment decisions. The rapid increase in the volume of data
generated for stock market companies makes performing thorough analytics tasks impractical given
the limited time available. Fortunately, recent advancements in computational intelligence methods
have been adopted in various sectors, providing opportunities to exploit such methods to address
investment tasks efficiently and effectively. This paper aims to explore rank-based approaches,
mainly machine-learning based, to address the task of selecting stock symbols to construct long-term
investment portfolios. Relying on these approaches, we propose a feature set that contains various
statistics indicating the performance of stock market companies that can be used to train several
ranking models. For evaluation purposes, we selected four years of Saudi Stock Exchange data
and applied our proposed framework to them in a simulated investment setting. Our results show
that rank-based approaches have the potential to be adopted to construct investment portfolios,
generating substantial returns and outperforming the gains produced by the Saudi Stock Market
index for the tested period.
Keywords:
rank-based systems; machine learning; stock selection and recommendation; financial
analytics; learning to rank
1. Introduction
Nowadays, financial markets (e.g., stock exchanges, currency markets, and commodity
exchanges) play a major role in the global economy by reflecting countries’ economic
growth and stability [
1
,
2
]. The stock market is a type of financial market that provides an
effective platform for listed companies and investment institutions to trade and exchange
various types of securities (e.g., stocks, derivatives, and options). For listed companies in
particular, stock markets can provide a way to realize fair share value, increase the potential
of growing a company’s capital, and provide liquidity for shareholders. For investors (both
individuals and investment firms), stock markets provide a set of tangible opportunities to
diversify investment portfolios and produce financial gains, while keeping a transparent
environment [3].
However, making investments in financial markets is not an easy or straightforward
task; it requires tremendous effort from financial analysts and investment advisers to study
a target stock exchange in search of investment opportunities. Generally, domain experts
perform extensive research on stock markets for companies, which involves collecting
a large volume of data (e.g., periodic financial reports, announcements, news, etc.), per-
forming several data analytics tasks, and making inferences to reach investment decisions.
Electronics 2022, 11, 4019. https://doi.org/10.3390/electronics11234019 https://www.mdpi.com/journal/electronics
