Investment Strategy Research in the New Energy Vehicle Industry Based on Google Trends: A Case Study of Tesla - Ye Guangcheng (SHS Web of Conferences, 2025)
This paper investigates a sentiment-based trading strategy in the context of the new energy vehicle industry, using Tesla (TSLA) as a representative case. Using Google Trends search volume data as a tool to observe public attention, we construct a simple momentum-style signal to evaluate the effectiveness of market sentiment in guiding trading decisions. The study compares the performance of the sentiment strategy with a traditional buy-and-hold strategy across four market regimes, including two bull markets and two bear markets. Our results suggest that the sentiment- based strategy significantly outperformed in bear markets, but counterintuitively underperformed in bull markets. This indicates that Google Trends data may serve as a useful complementary indicator in volatile or downward-trending environments. The paper contributes to the literature by extending sentiment momentum research from cryptocurrencies and broad indices to a major individual stock in the clean tech sector, Tesla, which is also a highly sentiment-driven stock.