金声玉亮2.0模型评测报告

问题

There is a single choice question about econometrics. Answer the question by replying A, B, C or D.
Question: Which one of the following statements is true concerning alternative forecast accuracy measures?
A. Mean squared error is usually highly correlated with trading rule profitability
B. Mean absolute error provides a quadratic loss function
C. Mean absolute percentage error is a useful measure for evaluating asset return forecasts
D. Mean squared error penalises large forecast errors disproportionately more than small forecast errors
Answer:

答案

模型	模型答案	正确答案
金声玉亮2.0	D. Mean squared error penalises large forecast errors disproportionately more than small forecast errors.	D
deepseekr1	D. Mean squared error penalises large forecast errors disproportionately more than small forecast errors Explanation: - Option D is correct because Mean Squared Error (MSE) squares forecast errors, magnifying larger errors quadratically. For example, an error of 10 becomes 100, while an error of 2 becomes 4, disproportionately penalizing larger errors. - Option B is incorrect because Mean Absolute Error (MAE) uses absolute values, resulting in a linear loss function, not quadratic. - Option C is problematic for asset returns, as MAPE struggles with zero or negative actual values (common in returns), making it less suitable. - Option A is false because MSE focuses on error magnitude, not directional accuracy critical for trading profitability. Answer: D	D
gpto1	D	D
GPT-4o	D	D
llama3.1-70B-Instruct	A	D
llama3.1-405B-Instruct	D. Mean squared error penalises large forecast errors disproportionately more than small forecast errors.	D
DeepSeek-Coder-V2	D. Mean squared error penalises large forecast errors disproportionately more than small forecast errors	D
Qwen2-72B-Instruct	D	D
baichuan4	D	D
doubao	D	D
minimax	D. Mean squared error penalises large forecast errors disproportionately more than small forecast errors	D
kimi	D. Mean squared error penalizes large forecast errors disproportionately more than small forecast errors. Mean squared error (MSE) is a common measure of forecast accuracy that squares the differences between forecasted and actual values, which means that larger errors are penalized more heavily than smaller ones. This is because the square of a larger number is much larger than the square of a smaller number. The other options are incorrect for the following reasons: A. Mean squared error is not necessarily correlated with trading rule profitability. While it is a measure of forecast accuracy, it does not directly translate to profitability in trading because it does not consider transaction costs, risk, or other factors that affect trading performance. B. Mean absolute error (MAE) provides a linear loss function, not a quadratic one. It measures the average magnitude of the errors in a set of forecasts, without considering their direction. C. Mean absolute percentage error (MAPE) is not a particularly useful measure for evaluating asset return forecasts because it can be misleading, especially when the actual values are close to zero, which can result in very high MAPE values even for small errors.	D
wenxinyiyan	D Mean squared error (MSE) penalises large forecast errors disproportionately more than small forecast errors because it squares the differences between actual and forecasted values. This means that larger errors have a greater impact on the overall measure of forecast accuracy when using MSE. A. Mean squared error is not necessarily highly correlated with trading rule profitability. The correlation depends on the specific context and may vary. B. Mean absolute error (MAE) provides a linear loss function, not a quadratic one. MAE measures the average magnitude of the errors without squaring them. C. Mean absolute percentage error (MAPE) can be a useful measure for evaluating certain types of forecasts, but it has limitations and may not be the best choice for evaluating asset return forecasts in all cases. For example, it can be sensitive to outliers and may not perform well when actual values are close to zero. Therefore, the correct answer is D.	D
zhipu	D Explanation: - A. Mean squared error is usually highly correlated with trading rule profitability: This is not necessarily true. Mean squared error (MSE) measures the average of the squares of the errors, but it doesn't directly correlate with trading rule profitability, which depends on many other factors. - B. Mean absolute error provides a quadratic loss function: This is incorrect. Mean absolute error (MAE) provides a linear loss function, not quadratic. The quadratic loss function is associated with mean squared error (MSE). - C. Mean absolute percentage error is a useful measure for evaluating asset return forecasts: This is not necessarily true. Mean absolute percentage error (MAPE) can be misleading for evaluating asset return forecasts, especially when returns are close to zero or negative. - D. Mean squared error penalises large forecast errors disproportionately more than small forecast errors: This is true. MSE squares the errors, so larger errors are penalized more heavily than smaller ones, making it sensitive to outliers.	D
gpto1	D	D

模型

模型答案

正确答案

金声玉亮2.0

D. Mean squared error penalises large forecast errors disproportionately more than small forecast errors.

deepseekr1

D. Mean squared error penalises large forecast errors disproportionately more than small forecast errors

**Explanation**:
- **Option D** is correct because Mean Squared Error (MSE) squares forecast errors, magnifying larger errors quadratically. For example, an error of 10 becomes 100, while an error of 2 becomes 4, disproportionately penalizing larger errors.
- **Option B** is incorrect because Mean Absolute Error (MAE) uses absolute values, resulting in a linear loss function, not quadratic.
- **Option C** is problematic for asset returns, as MAPE struggles with zero or negative actual values (common in returns), making it less suitable.
- **Option A** is false because MSE focuses on error magnitude, not directional accuracy critical for trading profitability.

**Answer**: D

gpto1

GPT-4o

llama3.1-70B-Instruct

llama3.1-405B-Instruct

D. Mean squared error penalises large forecast errors disproportionately more than small forecast errors.

DeepSeek-Coder-V2

D. Mean squared error penalises large forecast errors disproportionately more than small forecast errors

Qwen2-72B-Instruct

baichuan4

doubao

minimax

D. Mean squared error penalises large forecast errors disproportionately more than small forecast errors

kimi

D. Mean squared error penalizes large forecast errors disproportionately more than small forecast errors.

Mean squared error (MSE) is a common measure of forecast accuracy that squares the differences between forecasted and actual values, which means that larger errors are penalized more heavily than smaller ones. This is because the square of a larger number is much larger than the square of a smaller number. The other options are incorrect for the following reasons:

A. Mean squared error is not necessarily correlated with trading rule profitability. While it is a measure of forecast accuracy, it does not directly translate to profitability in trading because it does not consider transaction costs, risk, or other factors that affect trading performance.

B. Mean absolute error (MAE) provides a linear loss function, not a quadratic one. It measures the average magnitude of the errors in a set of forecasts, without considering their direction.

C. Mean absolute percentage error (MAPE) is not a particularly useful measure for evaluating asset return forecasts because it can be misleading, especially when the actual values are close to zero, which can result in very high MAPE values even for small errors.

wenxinyiyan

D

Mean squared error (MSE) penalises large forecast errors disproportionately more than small forecast errors because it squares the differences between actual and forecasted values. This means that larger errors have a greater impact on the overall measure of forecast accuracy when using MSE.

A. Mean squared error is not necessarily highly correlated with trading rule profitability. The correlation depends on the specific context and may vary.

B. Mean absolute error (MAE) provides a linear loss function, not a quadratic one. MAE measures the average magnitude of the errors without squaring them.

C. Mean absolute percentage error (MAPE) can be a useful measure for evaluating certain types of forecasts, but it has limitations and may not be the best choice for evaluating asset return forecasts in all cases. For example, it can be sensitive to outliers and may not perform well when actual values are close to zero.

Therefore, the correct answer is D.

zhipu

D

Explanation:
- **A. Mean squared error is usually highly correlated with trading rule profitability**: This is not necessarily true. Mean squared error (MSE) measures the average of the squares of the errors, but it doesn't directly correlate with trading rule profitability, which depends on many other factors.

- **B. Mean absolute error provides a quadratic loss function**: This is incorrect. Mean absolute error (MAE) provides a linear loss function, not quadratic. The quadratic loss function is associated with mean squared error (MSE).

- **C. Mean absolute percentage error is a useful measure for evaluating asset return forecasts**: This is not necessarily true. Mean absolute percentage error (MAPE) can be misleading for evaluating asset return forecasts, especially when returns are close to zero or negative.

- **D. Mean squared error penalises large forecast errors disproportionately more than small forecast errors**: This is true. MSE squares the errors, so larger errors are penalized more heavily than smaller ones, making it sensitive to outliers.

gpto1