Sectoral portfolio optimization by judicious selection of financial ratios via PCA

Dhingra, Vrinda; Sharma, Amita; Gupta, Shiv K.

doi:10.1007/s11081-023-09849-1

Cited by 1 publication

(1 citation statement)

References 53 publications

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“…Additionally, K. Sabbar et al [12] ventured into stock prediction in 2023, incorporating machine learning and deep learning models. Notably, V. Dhingra et al [13] in 2021 and K. Olorunnimbe et al [14] in 2023 delved into deep learning models for this purpose. Most of these studies center on technical and sentiment analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Stock Selection Using Machine Learning Based on Financial Ratios

Tsai,

Gao,

Yuan

2023

Mathematics

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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.

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Section: Literature Reviewmentioning

confidence: 99%