A Strategic Management Perspective on Risk and Alignment in Crypto Markets Using Deep Reinforcement Learning and Real-Time Sentiment Analysis

This study aims to develop an advanced management strategy for the cryptocurrency market This study aims to develop an advanced management strategy for the cryptocurrency market using a deep reinforcement learning framework that integrates real-time sentiment analysis and technical indicators. Beyond improving trading performance, the study frames its approach within a strategic management perspective by emphasizing the alignment of decision-making processes with dynamic market conditions and the proactive handling of financial risks. The core objective is to enhance trading profits while minimizing losses caused by market volatility and emotional bias. Sentiment analysis is conducted using the pre-trained FinRL NLP model to classify market sentiment as positive, negative, or neutral. Historical market data obtained via the Binance API is analyzed in Python, and the models are trained using PPO, A2C, and DQN algorithms. These algorithms incorporate sentiment and technical indicators into the state space. Results show that integrating sentiment analysis improves the effectiveness of decision-making under uncertainty particularly with the A2C algorithm providing more robust performance than traditional strategies. The findings highlight the value of combining sentiment-aware machine learning with strategic risk management to support better-aligned, adaptive investment decisions in volatile environments such as cryptocurrency markets.