Microprudential Stabilizer: Machine Learning

Microprudential Stabilizer : Machine Learning

Machine Learning has been utilized in several sectors for some time now. Previously the domain of programmers, it has found many use cases in many major fields such as logistics, production, and finance. Machine Learning is a subfield within the application of artificial intelligence (AI). Due to the nature of ML and AI which tries to identify market trends and make predictions.

ML’s contributions

ML and AI have also caused several employment movements in the finance industry. Over the past few years, financial firms such as Bloomberg has expanded its Machine Learning group in order to help capitalize on the need to aggregate data while providing models in order to predict market behavior. It tries to provide reports to clients through market indicators and make recommendations. Machine Learning thrives as it acknowledges the open endedness of market behavior and attempts to reconcile it using AI and data engines. Most recently Deutsche Bank’s CEO John Cryan said “We’re too manual, which can make you error-prone and it makes you inefficient. There’s a lot of machine learning and mechanization that we can do” (Noonan, Jenkins, & Storbeck, 2017). Cryan has also vowed to cut at least 45,000 jobs of its current 90,000 strong roster.

Role of Machine Learning in Cryptoeconomics

Considering the role of Machine Learning in economic models and was met with several issues. Firstly, econometric models are used to have advanced or assist with proving theories. This act of providing confirmations does not allow for ML to have a role in econometrics. In addition to this, since the arguments on the Efficient Market Hypothesis is contested by behaviors of economic agents, which are irrational or rational. It limits the usefulness of any prediction engines such as fundamental or technical analysis. This too includes Machine Learning. There remains a strong need to reconcile the theory/data-driven econometrics which is more “hindsight” driven by prediction/data-driven machine learning environments which are more “forward” driven. It is, however, important to acknowledge that both fields draw on probability and statistics which form its shared core which often results in statistical models using real-world data.

General Updates

Machine Learning applications today are especially focused on unsupervised ML, this is important as huge amounts of data must be sorted by the AI continuously, and it is useful if it is able to do it without any human intervention. When explained simply, the application of ML at all stages of finance in the middle office/back office results in higher perceived returns for clients.

Leveraging on networks, sharing of progress within ML will cause all vested interests in the field to be literally on the same frontier as they push boundaries of applications in finance. Unlike humans, ML through AI can remain unbiased as they select or bootstrap the required components for their task. This is huge as the fundamental goal is an attempt to use data and all available information to provide any extraordinary excess returns or optimize existing invested funds. Although it is important to point out that the perimeters of an AI are always firstly defined by a human, thus bias cannot be completely explained away. Through this ML is attempting to smooth EMH. There is more to ML however that is beyond the scope of the paper.

Flip side

The flip side of the growth of FinTech attempts to address this issue through another avenue. While ML tries to sort the market by consuming and utilizing massive amounts of technology and processing power. The blockchain retains the processing power as well as the software programmers behind it. Blockchain technology proposes a brave new solution. Sort out the whole financial system instead of trying to optimize all inefficiencies.

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