기계학습과 금융을 주제로 한 보고서 셋

1.
The Future of Trading: Technology in 2024에 이어서 기계학습과 인공지능 기술이 현재 어느 수준으로 금융산업과 결합하고 있는지를 분석하는 보고서를 소개합니다.

AI Pioneers in Investment Management은 2019년 봄에 조사한 결과를 토대로 만들어진 보고서입니다. 보고서는 조사를 통하여 AI의 가능성뿐 아니라 현재 단계의 한계를 지적하면서 AI와 HI(Human Intelligence)이 결합한 모델을 제안합니다.

Their use cases are illuminating. Among other things, they underscore the opportunities but also the limitations of AI and the continued important role of human judgment in investment processes. We ascribe to the power of the “AI + HI” model: AI techniques can augment human intelligence to enable investment professionals to reach a higher level of performance, freeing them from routine tasks and enabling smarter decision making that leverages the collective intelligence of machines and humans.

이 보고서의 매력은 사례입니다. 보고서가 다루고 있는 사례들입니다.

1. Enhancing Trading Strategy and Execution with Machine Learning: Man AHL 2. Generating Signals for Quant Models with Machine Learning: New York Life Investments 3. Refining Equity Trading Volume Prediction with Deep Learning: State Street Corporation 4. Leveraging AI/Alternative Data Anaysis in Sell-Side Research: Goldman Sachs 5. Dissecting Earnings Conference Calls with AI and Big Data: American Century 6. AI and Big Data Assist in Debt Portfolio Management: China Life Asset Management and China Securities Credit Investment 7. Applying AI and Big Data Technologies in the Filing and Processing of Insurance Claims and Assessing Corporate Risk: Ping An 8. Sentiment Analysis: Bloomberg 9. Building the Data Science Team: Schroders 10. Special Focus: Enhancing the MPT Efficient Frontier with Machine Learning 11. Special Focus: Using Intelligent Searches to Collect and Process Information

[gview file="http://smallake.kr/wp-content/uploads/2019/10/AI-Pioneers-in-Investment-Management.pdf"]

두번째 보고서는 영란은행이 2019년 10월에 발표한 Machine learning in UK financial services입니다.

The Bank of England (BoE) and Financial Conduct Authority (FCA) have a keen interest in the way that ML is being deployed by financial institutions. That is why we conducted a joint survey in 2019 to better understand the current use of ML in UK financial services. The survey was sent to almost 300 firms, including banks, credit brokers, e-money institutions, financial market infrastructure firms, investment managers, insurers, non-bank lenders and principal trading firms, with a total of 106 responses received.

조사한 결과중 도입현황 및 단계입니다. 제가 관심을 가지는 Asset Management와 Trading이 초기단계인 점이 새롭습니다.

기계학습기술을 도입하면 아래와 같이 다양한 프로세스들의 변화가 필요합니다.

이 보고서의 핵심은 각각의 프로세스에서 어떤 점을 고려할지를 상세히 설명하고 있다는 점입니다.

5 How machine learning works 5.1 Machine learning applications consist of a pipeline of processes 5.2 Data acquisition and feature engineering are evolving with the advent of machine learning 5.3 Model engineering and performance evaluation decide which models are deployed 5.4 Model validation is key to ensuring machine learning models work as intended 5.5 Complexity can increase due to deployment of machine learning 5.6 Firms use a range of safeguards to address risks

[gview file="http://smallake.kr/wp-content/uploads/2019/10/machine-learning-in-UK-financial-services.pdf"]

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Posted from my blog with SteemPress : http://smallake.kr/?p=28078

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