Given the current trend, hedge funds are likely working diligently to leverage artificial intelligence in their stock selection processes. It seems prudent to apply similar approaches to democratized index strategies for exchange-traded funds (ETFs). A pioneer in this field, Qraft Technologies, is dedicated to democratizing quantitative investing by utilizing AI models for portfolio construction and providing these capabilities through accessible investment products such as ETFs.

The company offers several AI-enhanced exchange-traded funds (ETFs), including QRFT, AMOM, NVQ, and HDIV, which are listed on the NYSE. These ETFs use AI to select and weight portfolios of companies.

Qraft’s AI ETFs dynamically shift among five proven factors to optimize investment returns. These factors are: Quality: Focuses on companies with strong financial health, profitability, and stable earnings. Size: Involves investing in companies based on their market capitalization, typically favoring larger firms. Value: Targets stocks that appear undervalued based on fundamental analysis metrics such as price-to-earnings ratios. Momentum: Invests in stocks that have shown an upward price trend, capitalizing on the tendency of winning stocks to continue performing well. Low Volatility: Seeks companies with lower price fluctuations, aiming for more stable returns.

Qraft’s AI ETFs use a variety of data sources to run their investment strategies. Here are the key types of data utilized:

Data Sources

1. Macroeconomic Data: The AI models analyze macroeconomic variables to understand broader economic trends and their potential impact on stock performance3.
2. Company Fundamentals: Fundamental financial reports, such as earnings, revenue, and other financial metrics, are used to assess the financial health and potential of individual companies3.
3. Market Price Data: Historical and current market price data help in analyzing the momentum and volatility of stocks, which are crucial for making investment decisions3.
4. Structured and Unstructured Data: The AI processes both quantitative (structured) and qualitative (unstructured) data from numerous sources to enhance the investment process3.
5. Kirin API: Developed by Qraft’s data scientists, this API integrates multiple vendors to provide both macroeconomic and company fundamentals with the correct point-in-time data

Their strategy library that incorporates various methodologies to optimize their investment processes. Here are the key components of their strategy library:

Strategy Library Components

1. Proprietary AI Engine: Developed in collaboration with LG AI Research, Qraft’s AI engine uses deep neural networks to generate signals and analyze the relative strength of individual stocks within a given universe1.
2. Factor-Based Approach: The strategy dynamically shifts among five proven factors—quality, size, value, momentum, and low volatility—allowing the ETFs to adapt to changing market conditions and optimize risk-adjusted returns14.
3. Data-Driven Analysis: Qraft’s AI system processes large amounts of structured and unstructured data to identify investment opportunities. This includes macroeconomic trends and fundamental company data3.
4. Automated Portfolio Management: The AI engine automatically evaluates and filters data to support specific investment theses, selecting and weighting portfolios to provide balanced exposure to market factors13.
5. Continuous Learning: The AI models continuously learn from historical data to refine their predictions and improve decision-making processes over time5.
6. Risk Management: Qraft’s AI includes a risk indicator model that helps in understanding market risks better than traditional tools like the VIX index. This model is used by financial institutions for portfolio adjustments and risk mitigation5.

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the website: Qraft AI ETFs | AI-Enhanced Exchange Traded Funds