Answer-first summary for fast verification
Answer: Regression
## Explanation This is a **regression** problem because: - **Continuous Target Variable**: The goal is to predict "next quarter's return" which is a continuous numerical value (e.g., 5.2%, -3.1%, etc.) - **Ranking Purpose**: After predicting returns for all 100 stocks, we identify the 20 with the lowest estimated returns for replacement, which requires ranking based on predicted continuous values **Why not the other options:** - **Classification (B)**: Would be used if we were predicting discrete categories (e.g., "buy/hold/sell" or "positive/negative return") - **K-means (C)**: A clustering technique for grouping similar data points, not for predicting continuous values - **PCA (D)**: A dimensionality reduction technique, not a prediction method **Machine Learning Context**: - Regression algorithms (linear regression, random forest regression, gradient boosting regression, etc.) are designed to predict continuous outcomes - The features (fundamental and technical variables) serve as inputs to predict the continuous target variable (stock returns) - The ranking and selection of worst-performing stocks is a natural application of regression predictions
Author: LeetQuiz .
Ultimate access to all questions.
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
No comments yet.