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The authors investigate the role of financial literacy in influencing the relationship between human capital and social capital, with access to finance of micro, small and medium enterprises (MSMEs).

Data were gathered from 337 MSMEs in Brunei Darussalam, and analysis on the data was carried out using a number of statistical methods. The relationships between human capital, social capital, financial literacy and access to finance were analyzed using PLS-SEM.

The results show that human capital does influence access to finance but contrary to previous studies, the influence is negative. Financial literacy is an important element in the relationship between human capital, social capital and access to finance, although it plays a greater role in the relationship between social capital and access to finance. Further analysis shows that financial knowledge is significant in moderating the relationships between human and social capital with access to finance. Financial skills is found to only moderate the relationship between social capital and access to finance.

To the authors' knowledge, this study is the first that integrates the human capital, social capital, financial literacy and access to finance in a single model. The authors also highlight the importance of enhancing the financial literacy of MSMEs so that the problem of access to finance can be alleviated, especially in developing countries.

The study aims to explore how integrating recent fundamental values (RFVs) from conventional accounting studies enhances the accuracy of a machine learning (ML) model for predicting stock return movement in Indonesia.

The study uses multilayer perceptron (MLP) analysis, a deep learning model subset of the ML method. The model utilizes findings from conventional accounting studies from 2019 to 2021 and samples from 10 firms in the Indonesian stock market from September 2018 to August 2019.

Incorporating RFVs improves predictive accuracy in the MLP model, especially in long reporting data ranges. The accuracy of the RFVs is also higher than that of raw data and common accounting ratio inputs.

The study uses Indonesian firms as its sample. We believe our findings apply to other emerging Asian markets and add to the existing ML literature on stock prediction. Nevertheless, expanding to different samples could strengthen the results of this study.

Governments can regulate RFV-based artificial intelligence (AI) applications for stock prediction to enhance decision-making about stock investment. Also, practitioners, analysts and investors can be inspired to develop RFV-based AI tools.

Studies in the literature on ML-based stock prediction find limited use for fundamental values and mainly apply technical indicators. However, this study demonstrates that including RFV in the ML model improves investors’ decision-making and minimizes unethical data use and artificial intelligence-based fraud.