Financial Forecasting for Informed Decisions in the Stock Exchange Market
In the ever-changing landscape of the stock exchange market, financial stakeholders heavily rely on accurate and insightful information for making informed decisions. Traditionally, investors turned to the equity research department for valuable reports on market insights and investment recommendations. However, these reports face several challenges, including the complexity of analyzing the volatile nature of market dynamics.
This article introduces a groundbreaking solution to address these challenges. A new interpretable decision-making model leveraging the SHAP-based explainability technique is proposed to forecast investment recommendations. This model not only offers valuable insights into the factors influencing forecasted recommendations but also caters to investors with different interests, from daily to short-term investment opportunities.
To validate the effectiveness of this model, a compelling case study is presented. The results showcase a remarkable enhancement in investors’ portfolio value when employing the proposed trading strategies. These findings emphasize the significance of incorporating interpretability in forecasting models, as it boosts stakeholders’ confidence and fosters transparency in the stock exchange domain.
Abstract:Financial forecasting plays an important role in making informed decisions for financial stakeholders, specifically in the stock exchange market. In a traditional setting, investors commonly rely on the equity research department for valuable reports on market insights and investment recommendations. The equity research department, however, faces challenges in effectuating decision-making due to the demanding cognitive effort required for analyzing the inherently volatile nature of market dynamics. Furthermore, financial forecasting systems employed by analysts pose potential risks in terms of interpretability and gaining the trust of all stakeholders. This paper presents an interpretable decision-making model leveraging the SHAP-based explainability technique to forecast investment recommendations. The proposed solution not only provides valuable insights into the factors that influence forecasted recommendations but also caters to investors of varying types, including those interested in daily and short-term investment opportunities. To ascertain the efficacy of the proposed model, a case study is devised that demonstrates a notable enhancement in investor’s portfolio value, employing our trading strategies. The results highlight the significance of incorporating interpretability in forecasting models to boost stakeholders’ confidence and foster transparency in the stock exchange domain.