Model Portfolios

Grounded in tried-and-true principles, supercharged with machine learning.

Kavout has developed model portfolios based on vetted and established trading strategies, augmented with AI algorithms.

Kai Enhanced Sector Portfolio

Figure: Kai Enhanced Healthcare Sector portfolio has consistently outperformed the healthcare sector ETF (XLV) since 2014.

Kai Enhanced Sector Portfolio

We screen all stocks within a sector to select a subset of best stocks according to our K scores, quantamental analytics, and machine learning models. Our portfolio gives full sector exposure and concentrates on the stocks with the highest potential. As our AI engine updates the stocks ratings and predictive analytics, our strategy dynamically rebalances and optimizes holdings.

Our methodology applies to all sectors. In the following example, we illustrate the performance of an enhanced healthcare sector portfolio and compare it to the healthcare sector ETFs.

Multi-Layer Tactical Asset Allocation

Figure: Multi-layer Tactical Asset Allocation portfolio (Two-tier Sector) has significantly outperformed the benchmark SPY since 2014.

Multi-Layer Tactical Asset Allocation

Our Kai engine dynamically selects top stocks based on price momentums, volatilities, correlations, and other factors. Stocks have to go through multiple rounds of comparison and selection in order to be included in the portfolio.

We analyze hundreds of stocks included in major ETFs and constantly update our results. This ensures a balanced exposure between top-quality assets that ride on short-term momentum and assets that seek long-term growth.

Smart Thematic Portfolio

Figure: Growth of Smart Thematic – Obesity portfolio (KSLIM) from has significantly outperformed SPY since 2012.

Smart Thematic Portfolio

We conduct research to identify investable themes and find related assets using our AI asset selector. Once we obtain a collection of stocks, we perform portfolio optimization to determine the best strategy. The portfolio is also rebalanced regularly, adaptive to price behaviors and market conditions.

As an example, we selected 10 companies related to obesity care. We determined the portfolio weights based on price behaviors and their dependency using ML algorithms. For a performance comparison, our benchmark is the Obesity ETF (SLIM) as well as the S&P 500 ETF (SPY)

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