A Probability-Driven Decision Support Tool for Forex Trading: A Binomial Distribution Analysis of Winning Trades and Profit Outcomes

Abstract

Foreign exchange trading is characterized by high uncertainty, nonlinear price movements, and asymmetric risk exposure, posing persistent challenges for institutional decision-makers. This study develops and evaluates a probability-driven decision support framework grounded in binomial distribution theory to examine how probabilistic modeling of winning trade occurrences relates to perceived profit outcomes and decision quality among professional foreign exchange practitioners. Using a quantitative explanatory research design, cross-sectional survey data were collected from 214 institutional practitioners, including chief investment officers, chief equity strategists, chief investment strategists, forex fund managers, and professional traders employed in Philippine banks, securities firms, and financial institutions. The proposed framework models trading outcomes as binary events and estimates winning trade probabilities using binomial probability principles to support disciplined decision-making and risk calibration. Multiple regression analysis indicates that the probability-driven decision support approach is positively associated with winning trade probability assessment (β = 0.61, p < .001), trade execution discipline (β = 0.56, p < .001), and perceived profit outcomes (β = 0.59, p < .001). These findings suggest that institutional practitioners who incorporate probability-based analytical frameworks report greater consistency in trade evaluation and execution decisions. The study contributes to the literature by extending the application of binomial probability modeling to institutional foreign exchange decision support and by providing an interpretable analytical framework that complements existing quantitative trading approaches in emerging market financial institutions.