Select all the true sentences:

I Nonstationarity in financial data is a common issue when modeling with Machine Learning.
II It is possible to blend different machine learning paradigms, for instance, applying an unsupervised model to the input data before solving a regression problem through a supervised model.
III The problem of overfitting a model poses a severe risk to any financial-based machine learning application.
IV Changing the form of the label in supervised learning from real values to discrete values modifies the shape of the problem to solve.

Question

Select all the true sentences:

I	Nonstationarity in financial data is a common issue when modeling with Machine Learning.
II	It is possible to blend different machine learning paradigms, for instance, applying an unsupervised model to the input data before solving a regression problem through a supervised model.
III	The problem of overfitting a model poses a severe risk to any financial-based machine learning application.
IV	Changing the form of the label in supervised learning from real values to discrete values modifies the shape of the problem to solve.

BlackTom AI · Accepted Answer

The question asks us to select all true sentences among four statements labeled I through IV. Since the options list is not provided, I will evaluate each of the four statements on its own merits and note general considerations.

Statement I: Nonstationarity in financial data is a common issue when modeling with Machine Learning.
- Explanation: In finance, statistical properties such as mean, variance, and correlations can change over time due to regime shifts, shocks, or evolving market conditions. This nonstationarity can violate assumptions of many ML models and complicate training, validation, and deployment. Therefore, stating that nonstationarity is a common issue is accurate and consistent with real-world financial modeling challenges.

Statement II: It is possible to blend different machine learning paradigms, for instance, applying an unsupervised model to the input data before solving a regression problem through a supervised model.
- Explanation: Hybrid and layered approaches are widely used. Unsupervised techniques (e.g., clustering, dimensionality reduction like PCA, autoencoders) can be employed to extract meaningful features or representations from data, which are then fed into supervised models (e.g., regression) to predict continuous targets. This approach is common in practice and can enhance performance when labeled data are limited or when patterns are more easily captured in a reduced feature space.

Statement III: The problem of overfitting a model poses a severe risk to any financial-based machine learning application.
- Explanation: Overfitting—the model capturing noise rather than signal—can lead to poor out-of-sample performance, which is especially dangerous in finance where decisions have real monetary consequences. Given the typically limited and non-stationary nature of financial data, the risk of overfitting is indeed severe and a central concern in financial ML workflows.

Statement IV: Changing the form of the label in supervised learning from real values to discrete values modifies the shape of the problem to solve.
- Explanation: Yes, altering a regression target (real-valued) to a classification target (discrete) changes the problem type. Regression aims to predict continuous outputs, while classification predicts discrete categories. This shift changes the loss functions, evaluation metrics, model architectures, and potentially the data preprocessing steps required, thereby modifying the problem’s shape and the learning objective.

In summary, each statement presents a concept that aligns with established ideas in machine learning and its application to finance. The lack of provided answer options does not prevent evaluating the truth of each sentence based on standard ML knowledge. A careful reader would recognize all four statements as true given the described contexts.

FINTECH 540.01.Fa25 Final Exam

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