Predicting risk/reward ratio in financial markets for asset management using machine learning

TL;DR

This paper presents a new machine learning algorithm that predicts profit and loss outcomes in financial trading, improving the effectiveness of trading strategies by integrating these forecasts with market trend predictions.

Contribution

The study introduces a novel algorithm for forecasting trading outcomes and an innovative method for combining these forecasts with trend predictions to enhance trading performance.

Findings

Significant improvement in traditional trading strategy performance

Enhanced accuracy in profit/loss outcome predictions

Potential for more profitable algorithmic trading systems

Abstract

Financial market forecasting remains a formidable challenge despite the surge in computational capabilities and machine learning advancements. While numerous studies have underscored the precision of computer-generated market predictions, many of these forecasts fail to yield profitable trading outcomes. This discrepancy often arises from the unpredictable nature of profit and loss ratios in the event of successful and unsuccessful predictions. In this study, we introduce a novel algorithm specifically designed for forecasting the profit and loss outcomes of trading activities. This is further augmented by an innovative approach for integrating these forecasts with previous predictions of market trends. This approach is designed for algorithmic trading, enabling traders to assess the profitability of each trade and calibrate the optimal trade size. Our findings indicate that this method significantly improves the performance of traditional trading strategies as well as algorithmic trading systems, offering a promising avenue for enhancing trading decisions.