Can We Optimize Stock Price?—A Mathematical Driven Stock Price Optimization Model in Finance Based on Desirability Function

The research employs response surface analysis utilizing the desirability function approach for stock price optimization. It builds upon prior work introducing a data-driven analytical model to forecast the weekly closing price of MSFT stock, a key entity in the Information Technology Sector of the S&P 500. Central to this study are crucial financial and economic indicators, and their interactions identified through our analytical model, which constitute the foundational elements of our refined predictive framework. By establishing target values for these indicators, our optimization method aims to maximize the weekly closing price of the stock with heightened predictive precision and confidence intervals. Our findings underscore the significance of vigilant monitoring and managing statistically significant financial indicators via predictive modeling to achieve desired stock prices, thereby enhancing investment returns. This research yields valuable insights for investors and firms, aiding in strategic planning by elucidating the nuanced impact of financial and economic indicators on optimizing stock returns. The stock price optimization model based on desirability function can effectively be applied to other individual stocks or sets of stocks to construct an optimal portfolio tailored to achieve desired returns, considering specified risk factors.