Learning Objectives: Describe the use of AI and machine learning in the following cases: operations focused uses; trading and portfolio management in financial markets; and uses for regulatory. Describe the possible effects and potential benefits and risks of AI and machine learning on financial markets and how they may affect financial stability.
Questions:
905.1. Consider the following three companies, each with an intended use case for artificial intelligence and machine learning (AI&ML):
I. In the category of operations-focused uses, Acme Trading Inc wants to score the liquidity of individual bonds by comparing them to similar bonds (similar in features such as duration) but there is no labeled training dataset
II. In the category of SupTech, a National Supervisor wants to incorporate sentiment derived from Twitter posts which themselves are unstructured data (note that tweets are semi-structured because structured JSON objects contain unstructured "tweets" as themselves text)
III. In the category of operations-focused uses, Bland Financial Corp wants to teach artificial intelligence tools to react to order imbalance and queue position in the limit order book by feeding non-labeled data to an algorithm that chooses an action and learns by receiving feedback (sometimes the feedback is human).
Which solutions, respectively, are probably best for each of the above use cases?
a. I. Classification trees, II. Cluster analysis, III. Ridge Regression
b. I. Random forests, II. Support vector machines, III. Supervised learning
c. I. Regression, II. Penalized regression, III. Natural Language Processing (NLP)
d. I. Cluster analysis, II. Natural Language Processing (NLP), and III. Reinforcement learning
905.2. The Financial Stability Board's Financial Innovation Network (FSB FIN, November 2017) says that "From a micro-financial point of view, the application of AI and machine learning to financial services may have an important impact on financial markets, institutions and consumers." Specifically, in regard to this micro-financial point of view, each of the following is true EXCEPT which statement is inaccurate?
a. In regard to consumers, AI&ML could enable wider access to financial services that are more personalized/customized
b. In regard to financial institutions, AI&ML can be used for risk management through earlier and more accurate estimation of risks
c. In regard to consumers, AI&ML guarantees the avoidance of discrimination by excluding sensitive features (e.g., race, religion, gender) from the dataset
d. In regard to financial markets, AI&ML is likely to enable participants to collect and analyze information on a greater scale which should (i) reduce information asymmetries and contribute to market efficiency; and (ii) lower trading costs
905.3. With respect to a macro-financial analysis, the Financial Stability Board's Financial Innovation Network (FSB FIN, November 2017) argues that "widespread adoption of AI and machine learning could impact the financial system in a number of ways, depending on the nature of the application." From a macro perspective, which of the following statements about the potential implications of artificial intelligence and machine learning (AI&ML) is TRUE?
a. A key vulnerability of AI&ML is its tendency to constrain economies of scope; that is, to promote dis-economies of scope
b. In insurance markets, although AI&ML is likely to increase the degree of moral hazard and adverse selection, it should create larger, fewer risk pools
c. Robo-advisors are likely to increase market liquidity but at the risk of higher volatility and less stability as more participants are exposed to the same correlated common factors
d. A concern is that AI&ML might favor a greater concentration of fewer, larger organizations including advanced third-party AI&ML providers, owners of proprietary sources of big data, and those able to afford heavy investments in such innovative technologies
Answers here:
Questions:
905.1. Consider the following three companies, each with an intended use case for artificial intelligence and machine learning (AI&ML):
I. In the category of operations-focused uses, Acme Trading Inc wants to score the liquidity of individual bonds by comparing them to similar bonds (similar in features such as duration) but there is no labeled training dataset
II. In the category of SupTech, a National Supervisor wants to incorporate sentiment derived from Twitter posts which themselves are unstructured data (note that tweets are semi-structured because structured JSON objects contain unstructured "tweets" as themselves text)
III. In the category of operations-focused uses, Bland Financial Corp wants to teach artificial intelligence tools to react to order imbalance and queue position in the limit order book by feeding non-labeled data to an algorithm that chooses an action and learns by receiving feedback (sometimes the feedback is human).
Which solutions, respectively, are probably best for each of the above use cases?
a. I. Classification trees, II. Cluster analysis, III. Ridge Regression
b. I. Random forests, II. Support vector machines, III. Supervised learning
c. I. Regression, II. Penalized regression, III. Natural Language Processing (NLP)
d. I. Cluster analysis, II. Natural Language Processing (NLP), and III. Reinforcement learning
905.2. The Financial Stability Board's Financial Innovation Network (FSB FIN, November 2017) says that "From a micro-financial point of view, the application of AI and machine learning to financial services may have an important impact on financial markets, institutions and consumers." Specifically, in regard to this micro-financial point of view, each of the following is true EXCEPT which statement is inaccurate?
a. In regard to consumers, AI&ML could enable wider access to financial services that are more personalized/customized
b. In regard to financial institutions, AI&ML can be used for risk management through earlier and more accurate estimation of risks
c. In regard to consumers, AI&ML guarantees the avoidance of discrimination by excluding sensitive features (e.g., race, religion, gender) from the dataset
d. In regard to financial markets, AI&ML is likely to enable participants to collect and analyze information on a greater scale which should (i) reduce information asymmetries and contribute to market efficiency; and (ii) lower trading costs
905.3. With respect to a macro-financial analysis, the Financial Stability Board's Financial Innovation Network (FSB FIN, November 2017) argues that "widespread adoption of AI and machine learning could impact the financial system in a number of ways, depending on the nature of the application." From a macro perspective, which of the following statements about the potential implications of artificial intelligence and machine learning (AI&ML) is TRUE?
a. A key vulnerability of AI&ML is its tendency to constrain economies of scope; that is, to promote dis-economies of scope
b. In insurance markets, although AI&ML is likely to increase the degree of moral hazard and adverse selection, it should create larger, fewer risk pools
c. Robo-advisors are likely to increase market liquidity but at the risk of higher volatility and less stability as more participants are exposed to the same correlated common factors
d. A concern is that AI&ML might favor a greater concentration of fewer, larger organizations including advanced third-party AI&ML providers, owners of proprietary sources of big data, and those able to afford heavy investments in such innovative technologies
Answers here:
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