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 7.1.5

Test your knowledge of LO 7.1.5

33%

Question 1 of 3

1. Which one of these statements is not part of the simplifying assumptions required to make the problem more tractable?

I) It is difficult to predict the effect of news far in advance. Therefore, we’ll try to predict what effect a news story will have on stock price the same day.

II) It is difficult to predict large changes in stock price, so instead, we’ll predict relatively small changes.

III) It is difficult to predict exactly what the stock price will be. Instead, we will be satisfied with the direction of movement: up, down, or no change.

IV) It is difficult to associate a specific piece of news with a price change. We will assume that only news stories mentioning a specific stock will affect that stock’s price.

I

II

III

IV

Question 1 of 3

Question 2 of 3

2. Which one of these reflections is not typically part of the required considerations for data preprocessing?

I) How to compute a percentage price change.

II) How to time-tag stories.

III) How to reduce a story to a TFIDF representation.

IV) How to tag a story containing Material information.

V) How to tag a story indicating price change

I

II

III

IV

V

Question 2 of 3

Question 3 of 3

3. Is the following statement about ‘ ‘ generally correct?

Statement:
For instance, by analyzing the lift curves shown in the figure below, we can infer that at the point x = 0.2, the lifts of Logistic Regression and Naive Bayes are both approximately 2.0. This means that, if you were to score all the news stories and take the top 20% (x = 0.2), you’d have twice the precision (lift of two) of finding a positive story in that group than in the population as a whole.
715C

715C

Source: Assigned reading

Not correct

Correct

Question 3 of 3

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