Optimizing Risk and Return of Sustainable Equity Investment in Indonesia with Machine Learning

Investors are eagerly seeking the most efficient and enduring digital investment solutions. This research paper delves into the realm of optimal sustainable digital investment in Indonesia, with a focus on optimizing risk and return using machine learning. By examining automation and identifying sustainable investment strategies, the study aims to explore ideas related to risk and return optimization. The research problem seeks what are the key factors influencing risk and return in the investment environment of Indonesia and in what approaches can be utilized to enhance predictive accuracy through the application of machine learning techniques. Leveraging a comprehensive literature evaluation and employing a quantitative research strategy, investment data is analyzed to construct predictive models. Machine learning algorithms, including Moving Average Convergence, Stochastic Indicators, ANN and SVM are utilized to achieve this objective. Empirical analysis involves evaluating Indonesia's investment landscape and applying machine learning models. The findings underscore the success of these models in optimizing risk and return in digital investment environments. Moreover, the research emphasizes the importance of Indonesia's stock exchange adopting sustainable digital investment approaches. By providing insights into how technology enhances investment decision-making processes in emerging countries, this study contributes significantly to existing knowledge. Future research could explore the application of these models in different economic contexts or integrate additional data sources, such as global sustainability indices, to improve predictive accuracy. Expanding the scope of analysis to include other emerging markets would also strengthen the findings and broaden the applicability of the models.