慶應義塾大学 経済学部 PEARL入試 志望理由書 提出例(新井 拓児先生ゼミ向け)

Dr. Takuji Arai 

Professor

Department of Mathematical Finance / Economics

Keio University

Dear Professor Arai,

I am writing this letter to express my interest in attending Keio University, especially a position in your seminar. I would be more than grateful if you could give it a consideration.

Abstract – Finance experts often want to take the risk to seize good deal bounds in incomplete markets than exploiting existing models with trade restrictions. It used to be completely dependent on personal preference of individuals working at investment banks and many other organizations. In the modern days, those deal evaluations are done more and more by artificial intelligence to help decision makers evaluate, minimize risk and action. Artificial intelligence is built on machine learning which involves gaussian method that uses lazy learning with many variants. But how do we evaluate the AI helping us evaluate? Is expertise of human experts under threat? It makes me wonder what the value would be if I wanted to pursue my academic path within mathematical economics.

Question – How does gaussian method work in real life scenarios and how do we evaluate its involvement in risk minimization and decision making?

Methodology – Look into various scenarios this model would be useful and study how we can utilize it. 

Findings – Companies have adopted artificial intelligence in cyber security, market trend prediction and risk minimization such as venture capital, security companies, e-commerce and market research companies. And gaussian method becomes handy because of its strength for volumetric and random events that are common in real time market trends. What we need to remember here is that machine learning is great at processing vast amounts of complex data, while human experts are better at making intuitive decisions or interpreting data creatively. To me, it sounds like machine learning is still a tool for us to speed up data processing than doing the entire evaluation by itself. Here, there is a new field of study where engineers validate the tool functions and learning methods behind it. For example, whether a tool went through proper installation, tuning, administration, and configuration instead of needing to learn and absorb before full function.

Conclusion – Through reading various reports on what has happened in the industry, there seems to be a room for human experts and machine to co-exist. Local experts must look into opportunities and accelerate adoption of machine learning especially in finance field in the country to compete with the western and chinese organizations.

It would be very exciting for me to join your seminar and research on various methods that I don’t know about used in decision making. I very much look forward to hearing from you on this matter.

Sincerely yours,

*Good deal bounds with convex constraints, International Journal of Theoretical and Applied Finance, vol.20, 1750011, 2017. *Beyond Arbitrage: Good Deal Asset Price Bounds in. Incomplete Markets. John H. Cochrane and JesВus SaВa Requejo.|}{. January 26, 1999. Abstract. *ARTIFICIAL INTELLIGENCE IN FINANCE. Understanding how automation and machine learning is transforming the financial industry. Manju Kunwar. Theseus.fi. August 2019

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