CFA level3, 레벨3 Portfolio Management Pathway (전과목 보유)

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1. Benchmarks

벤치마크는 1) 규칙 기반, 2) 투명성, 3) 투자 가능해야 합니다. 1. 규칙 기반: 포트폴리오에 주식을 포함/제외하는 규칙, 가중치 체계, 재균형 빈도 등이 일관적이고 객관적이며 예측 가능해야 복제가 가능합니다. 2. 투명성: 규칙이 공개되어 있고 명확하게 설명되어 있어 이해할 수 있어야 합니다. 3. 투자 가능: 복제가 가능해야 합니다. 벤치마크 선택 시 고려사항: 1. 시장 및 위험 노출 결정: 어떤 시장에 투자할 것인지, 위험이 무엇인지(IPS의 위험-수익 목표 및 제약 고려) 2. 지수 구축/유지에 사용되는 방법론 식별: 포함할 주식을 식별하는 방법, 지수 가중치 부여 방법

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1. Benchmarks

Benchmarks are an important tool for evaluating the performance and capabilities of artificial intelligence (AI) systems. They provide a standardized way to measure and compare the performance of different AI models, algorithms, and architectures across a range of tasks and datasets. Benchmarks can help researchers and developers identify areas for improvement, track progress over time, and make informed decisions about which AI technologies to invest in or deploy. However, benchmarks also have their limitations and challenges. Designing effective benchmarks that accurately capture the complexity and diversity of real-world AI applications can be difficult. Benchmarks may not fully reflect the nuances and context-dependent nature of many AI tasks, and they can be susceptible to overfitting or gaming by researchers and developers. Additionally, the choice of benchmark tasks, datasets, and evaluation metrics can significantly influence the results and rankings of AI systems. Overreliance on a narrow set of benchmarks can lead to a skewed understanding of AI capabilities and may not translate well to practical applications. To address these challenges, it is important to develop a diverse and comprehensive set of benchmarks that cover a wide range of AI tasks and domains. Benchmarks should also be regularly updated and refined to keep pace with the rapid advancements in AI technology. Furthermore, it is crucial to consider the broader context and real-world implications of AI performance on benchmarks, rather than treating them as the sole measure of success. Ultimately, benchmarks are a valuable tool for the AI community, but they should be used judiciously and in conjunction with other forms of evaluation and assessment to gain a more holistic understanding of AI capabilities and limitations.

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