Google's Gemini AI model, while impressive in many areas, has demonstrated significant shortcomings when tasked with stock market prediction. To evaluate Gemini's capabilities, the model was asked to identify the top 10 gainers and losers from the NASDAQ-100 Index over a 100-day period.

Gemini's responses were riddled with errors, including:

Listing fewer than 10 stocks.

Repeating stocks within a list.

Including stocks not belonging to the NASDAQ-100.

Naming the same stock as both a gainer and a loser.

Producing three inconsistent lists for each question.

While Gemini's stock selections occasionally outperformed the NASDAQ-100 Index, the overall results were inconsistent and puzzling. For example, stocks predicted to be the biggest losers often showed gains, highlighting Gemini's difficulty in distinguishing between winners and losers.

Gemini's performance on similar tests using the CAC 40 and AEX indices further underscored its limitations in stock prediction.

Gemini is not a reliable stock advisor due to its technical errors and questionable financial results.

Interestingly, Gemini seems to have acknowledged its shortcomings. When asked the same questions today, it provides a detailed explanation of its limitations and why it cannot provide definitive answers.

In a response to the research, Gemini acknowledges the complexity of financial markets and its own limitations as a language model. It emphasizes its primary function as processing and generating text and its unsuitability for real-time financial analysis. Gemini also expresses confidence in its continuous improvement and the potential for future iterations to handle complex tasks like financial forecasting.