AI-Powered ETF Trails S&P 500

The video tutorial discusses AIQ's comprehensive approach to stock selection, leveraging unstructured data, sentiment, and intent analysis. It explains how AIQ combines this information with macro and technical data to predict price appreciation for US publicly traded companies. The tutorial also covers the high turnover rate of AIQ's ETF, its implications on transaction costs and taxes, and how these factors are integrated into AIQ's decision-making models.

What types of data does AIQ analyze to determine the best stock picks?

• Only technical data

• Only macroeconomic data

• Unstructured data like news articles and social media posts

• Only fundamental data

What is the approximate turnover rate of the AIQ ETF?

• 1000%

• 500%

• 1700%

• 2000%

Why did AIQ choose an ETF vehicle for their strategy?

• To focus solely on technical data

• To decrease turnover rate

• To avoid capital gains distributions

• To increase transaction costs

How are transaction costs considered in AIQ's decision-making models?

• They are integrated into the models

• They are added after decisions are made

• They are ignored completely

• They are only considered for large trades

What benefit does institutional pricing provide in the context of ETFs?

• More complex decision-making

• Increased turnover rate

• Lower transaction costs

• Higher transaction costs