  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 do 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 the 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.
