Smart Finance: Evaluating AI and Machine Learning's Impact on Investment Strategies and Financial Management

A significant technological advancement is artificial intelligence (AI), which also encompasses machine learning (ML) and algorithmic languages. The 2 main considerations in organizational investment choices are optimizing profitability and/or optimizing market price. The primary goal of this study is to create a sustained statistical stock investing framework depending on ML and Financial Value-Added (FVA) methodologies for optimizing investment strategies and financial management. The framework has two characteristics: statistical choice of stocks and algorithmic trade. Principal component evaluation and FVA parameters are employed in statistical stock frameworks to effectively choose equities, which can consistently choose lucrative stocks. The FVA measures, which were one of the earliest initiatives, are utilized to rate equities in this research. Also, the use of FVA in stock choices is demonstrated. The results of the suggested framework's demonstration on the Indian stock industry indicate that Long-Short Term Memory systems are superior at predicting subsequent stock levels. The suggested approach yields a return that is far higher than the marketplace return and is viable in every scenario of the market. This means that the suggested strategy can help investors achieve important returns that are both reasonable and worthwhile in addition to helping the marketplace revert to reasonable investing.