Research Framework for Comparative Study of Financial News Translation Strategies Based on Parallel Corpora

Abstract

This study investigates translation strategies in English-Chinese financial news through a corpus-based analysis, utilizing a parallel corpus comprising over 10,000 tokens sourced from the United Nations Parallel Corpus and bilingual reports from Bloomberg and Reuters. Focusing on specialized economic terminology and culturally loaded expressions, the research categorizes translation strategies into literal translation, free translation, and cultural adaptation. Statistical results reveal that literal translation predominates (70%), particularly in technical terms such as "quantitative easing" and "monetary policy," ensuring terminological precision in financial contexts. Free translation accounts for 20%, often applied to metaphorical expressions (e.g., "bull market" translated as "牛市"), while cultural adaptation (10%) addresses region-specific concepts like "Black Friday" localized as "双十一." The findings highlight the tension between accuracy and readability in financial news translation, emphasizing the need for context-aware strategies to balance specialized knowledge dissemination and audience accessibility. This research contributes to translation studies by providing empirical insights into economic text localization and offers practical implications for translators and machine translation systems operating in cross-cultural financial communication.