Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
By predicting stock prices based on historical data trends
B
By generating static reports on annual financial performance
C
By creating fixed investment strategies without adjustment
D
By designing user interfaces for financial applications
Understanding the Answer
Let's break down why this is correct
Answer
Recurrent Neural Networks can follow a stream of financial data, remembering past prices and news to predict the next value. By feeding each new tick into the RNN, the model updates its hidden state and instantly produces a forecast, making it ideal for high‑frequency trading or fraud alerts. The network learns patterns such as sudden drops or spikes by weighting recent events more heavily, so it adapts to market shifts. For example, an RNN can take the last ten minutes of stock prices and output a 1‑minute‑ahead price, allowing traders to react before the market moves. This real‑time processing lets firms act quickly on signals that would be missed by static models.
Detailed Explanation
RNNs remember past values, so they can look at a history of stock prices and guess the next price. Other options are incorrect because People might think RNNs can just hand out yearly financial reports; A misconception is that RNN can lock in an investment plan forever.
Key Concepts
applications in finance
real-time data processing
Topic
Recurrent Neural Networks (RNN)
Difficulty
medium level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1In the context of financial forecasting, how can Recurrent Neural Networks (RNNs) be effectively utilized for sequence prediction in business intelligence applications?
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2
Question 2What is the primary reason recurrent neural networks (RNNs) are particularly suited for sequence modeling tasks?
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Practice
3
Question 3How can Recurrent Neural Networks (RNN) be effectively utilized in the finance sector for real-time data processing?
mediumComputer-science
Practice
4
Question 4In the context of financial forecasting, how can Recurrent Neural Networks (RNNs) be effectively utilized for sequence prediction in business intelligence applications?
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Practice
5
Question 5What is the primary reason recurrent neural networks (RNNs) are particularly suited for sequence modeling tasks?
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Practice
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