Which of the following is an example of sequential data that RNNs can process effectively?

Recurrent Neural Networks (RNNs) are specifically designed to handle sequential data, which is characterized by the order and context of the data points. The correct choice refers to stock prices increasing over time, as this data set is fundamentally sequential. In this case, the price of a stock at one moment can influence its price at a later moment. RNNs excel at capturing this temporal dependency due to their architecture, which allows them to maintain a hidden state that can carry information from previous time steps forward to inform predictions about future steps.

In contrast, image files being classified do not involve sequential relationships, as the data is two-dimensional and processed in parallel rather than sequentially. Text documents without order also do not qualify because the lack of sequence means there's no context for RNNs to draw from. Similarly, static numerical datasets where the information is presented without any inherent sequence would not benefit from the recurrent structure of RNNs, which thrive on finding patterns over time. This is why the example of stock prices is the most appropriate demonstration of RNN capabilities.