AI and ML for Finance Practitioners

Topic 1: Introduction to Data Science & Big Data

Topic 2: Machine Learning: Introduction to Algorithms

Topic 3: Machine Learning: Regression,Support Vector Machine & Time Series Models

Topic 4: Machine Learning: Regularization, Regression Trees, Random Forest & Overfitting

Topic 5: Machine Learning: Classification & Clustering

Topic 6: Machine Learning: Performance Evaluation, Backtesting & False Discoveries

Topic 7: Data Mining & Machine Learning: Naïve Bayes & Text Mining

Topic 8: Big Data & Machine Learning: Ethical & Privacy Issues

Topic 9: Big Data & Machine Learning in the Financial Industry

Quiz LO 9.2.1

Test your knowledge of LO 9.2.1

20%

Question 1 of 5

1. Is the following description about ‘the difference between machine programming and machine learning‘ generally correct?

Description:
“In machine programming, the analyst must formulate and code the association rules between input and output based on available information and observations. Machine learning is used in cases where association rules between input and output are not so obvious but can be learned by analyzing large amounts of data. Therefore, in machine learning, ML algorithms seek to learn the association rules directly from the data. “

Not correct

Correct

Question 1 of 5

Question 2 of 5

2. Which of these statements best defines why we use tree models to sort portfolio equity assets?

I) Tree models can be used to sort the portfolio equity assets into homogeneous groups (i.e. the leaves of the tree). Thus, the analyst can calculate the average return and other statistics of each homogeneous group.

II) Tree models can be used to sort the portfolio assets into random groups to expedite the calculations.Thus, the analyst can calculate the average return and other statistics of each random group.

III) Tree models can be used to sort the portfolio assets into non-homogeneous groups.Thus, the analyst can calculate the average return and other statistics of each non-homogeneous group.

I

II

III

Question 2 of 5

Question 3 of 5

3. Is the following description about ‘the difference between “regression” and “regression with transformed X’s”‘ generally correct?

Description:
“Our goal is to find the best relationship between a set of features X’s (i.e. the regressors or predictors) and the dependent variable Y. Sometimes it is opportune to transform the regressors X’s (e.g. scaling by volatility) in order to find a more meaningful relationship with the dependent variable Y. The problem is that you don’t know in advance which transformation of the regressors X’s will lead to the best relationship with the dependent variable Y. Machine learning algorithms, such as the Artificial Neural Network (ANN), would search for the best transformation of the regressors X’s in the hidden layer of the neural network. “

Not correct

Correct

Question 3 of 5

Question 4 of 5

4. Which of these is NOT one of the feature that makes finance different when it comes to the applications of machine learning ?

I) Low signal-to-noise ratio

II) Evolving markets

III) Inside trading

IV) Short Samples and Unstructured Data

V) Need for Interpretability

I

II

III

IV

V

Question 4 of 5

Question 5 of 5

5. Is the following statement about ‘ The importance of combining economic theory and machine learning ‘ generally correct?

Statement:
“Due to the low signal-to-noise ratio in finance, an effective ML predictive model needs an extra boost to filter out the prevalent noise and capture the feeble predictive signal in financial data. This boost can be attained by incorporating well-accepted economic theory into the ML model structure, which facilitates the detection of valuable signals in the dataset.”

Not correct

Correct

Question 5 of 5

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