Answer-first summary for fast verification
Answer: Regression
## Explanation **Correct Answer: A (Regression)** This is a **regression problem** because: - We are predicting a continuous numerical value (next quarter's return) - The output is a quantitative measure that can be ranked - The goal is to estimate numerical returns to identify the lowest performers **Why other options are incorrect:** - **B (Classification)**: Would be used if we were categorizing stocks into discrete classes (e.g., buy/hold/sell) - **C (K-means)**: A clustering algorithm for grouping similar items, not for predicting continuous values - **D (PCA)**: A dimensionality reduction technique, not a prediction method The problem involves predicting continuous returns and then ranking them, which is characteristic of regression analysis.
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We apply ML techniques to a model including fundamental and technical variables (features) to predict next quarter's return for each of the 100 stocks currently in our portfolio. Then, the 20 stocks with the lowest estimated return are identified for replacement. The machine learning techniques appropriate are most likely to be based on:
A
Regression
B
Classification
C
K-means
D
PCA
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