Stock Selection Using Machine Learning Based on Financial Ratios

Abstract

Stock prediction has garnered considerable attention among investors, with a recent focus on the application of machine learning techniques to enhance predictive accuracy. Prior research has established the effectiveness of machine learning in forecasting stock market trends, irrespective of the analytical approach employed, be it technical, fundamental, or sentiment analysis. In the context of fiscal year-end selection, the decision may initially seem straightforward, with December 31 being the apparent choice, as discussed by B. Kamp in 2002. The primary argument for a uniform fiscal year-end centers around comparability. When assessing the financial performance of two firms with differing fiscal year-ends, substantial shifts in the business environment during non-overlapping periods can impede meaningful comparisons. Moreover, when two firms merge, the need to synchronize their annual reporting often results in shorter or longer fiscal years, complicating time series analysis. In the US S&P stock market, misaligned fiscal years lead to variations in report publication dates across different industries and market segments. Since the financial reporting dates of US S&P companies are determined independently by each listed entity, relying solely on these dates for investment decisions may prove less than entirely reliable and impact the accuracy of return prediction models. Hence, our interest lies in the synchronized fiscal year of the TW stock market, leveraging machine learning models for fundamental analysis to forecast returns. We employed four machine learning models: Random Forest (RF), Feedforward Neural Network (FNN), Gated Recurrent Unit (GRU), and Financial Graph Attention Network (FinGAT). We crafted portfolios by selecting stocks with higher predicted returns using these machine learning models. These portfolios outperformed the TW50 index benchmarks in the Taiwan stock market, demonstrating superior returns and portfolio scores. Our study’s findings underscore the advantages of using aligned financial ratios for predicting the top 20 high-return stocks in a mid-to-long-term investment context, delivering over 50% excess returns across the four models while maintaining lower risk profiles. Using the top 10 high-return stocks produced over 100% relative returns with an acceptable level of risk, highlighting the effectiveness of employing machine learning techniques based on financial ratios for stock prediction.

A Novel Proposal for Improving Economic Decision-Making Through Stock Price Index Forecasting

“…Through the utilization of specific classification metrics, they arrived at conclusions regarding the effectiveness of their predictive models. Tsai et al [25] examined the interest of investors in stock forecasting, focusing on the recent application of ML to enhance precision. They deliberated on fiscal year-end selection and the impact that misaligned reporting periods have on investment decisions and comparability.…”

Prediction of Financial Markets Utilizing an Innovatively Optimized Hybrid Model: A Case Study of the Hang Seng Index

“…By employing particular classification metrics, they derived conclusions concerning the efficacy of their predictive models. Tsai et al [18] discussed investors' interest in stock prediction, especially with the recent use of machine learning to improve accuracy. Machine learning works in technical, fundamental, and sentiment analysis, according to prior research.…”

“…These portfolios outperformed TW50 index benchmarks in returns and portfolio scores, according to their study. Machine learning models were beneficial for stock market analysis and investment decisionmaking, according to Tsai et al [18]. Ardakani et al [19] proposed a federated learning framework for stock market prediction using Random Forest, Support Vector Machine, and Linear Regression models.…”

Directional Stock Price Forecasting Based on Quantitative Value Investing Principles for Loss Averted Bogle-Head Investing using Various Machine Learning Algorithms

“…The emphasis is based on the idea that the feature-engineering process greatly improves the prediction performance. Further, Tsai et al (2023) the use of machine learning methodologies for predicting stock returns in Taiwan financial markets using financial ratios. The authors found that the portfolios that are made up of only those top stocks selected by the models outperform the benchmark TW50 index, thus confirming the effectiveness of using the aligned financial ratios when making investment decisions with a mid-to long-term scope.…”