With the increasing global need for precise and timely financial news translation, this study explores the potential of designing effective prompts for GPT-4 to address this challenge. Through a detailed error analysis, the study identifies five primary types of mistakes commonly made by GPT-4: word choice inaccuracies, syntactic disarray, inappropriate style, cultural mismatches, and omissions. Based on these findings, it proposes a three-tiered strategy to enhance prompt design: macro-level contextual framing, meso-level logical segmentation, and micro-level lexical clarification. Using BLEU, chrF++, and BERTScore to evaluate lexical, character-level, and semantic accuracy, the research compares pre- and post-prompt engineering outputs. Results reveal that well-crafted prompts significantly enhance translation quality, aligning machine-generated translations more closely to human standards. This highlights prompt engineering as a viable and promising method for refining machine translation in high-stakes domains like financial news.