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The Data Sets Used in Our Quantitative Strategies

 

Since 1998, Optimized Investments, Inc. (publisher of ETFOptimize.com) has developed and marketed sophisticated, quantitative investment strategies for individual investors and investment advisors. Each of our investment models is configured with the objective of attaining the highest Risk-Adjusted Return (RAR)  – an investment model's return relative to its inherent volatility – which allows our models to to avoid losses and provide robust profits every year consistently.

To achieve this objective, seasoned strategy designers – with 50+ combined years of experience – select from proprietary composites of macroeconomic, market-internal, stock-fundamental, and technical data sets so that all aspects of an investment strategy function synchronously – with a level of sophistication never previously offered to individual investors.

To our knowledge, none of the investment brokerages, investment advisories, hedge funds, nor mutual funds provide investment strategies as sophisticated as those offered by ETFOptimize.com – made available to you for as little as $14 per month – just 46¢ cents/day. Our investment strategies provide consistent annual returns that range from 12% to 65%, and have never incurred a money-losing year – collectively that's 66 of 66 profitable years!



38 Data Sets in Multiple Configurations for Timing and Asset Selection

What makes the ETFOptimize strategies unique is that they utilize as many as 38 proven drivers of investment performance to calculate the appropriate market exposure and asset selection at any given time to attain their exceptional returns. Most ETF strategy builders take the most accessible approach and use Momentum or Relative Strength technical indicators because an ETF's price is the only data that is readily available.

The problem with using a single or minimal number of indicators (as almost all other designers do) to determine exposure and asset selection is that no gauge is ever 100% correct, 100% of the time. And all it requires is for a quantitative strategy's indicator to make the wrong selection at the wrong time and a significant portion of a portfolio can be wiped out.

The problem with using a single or minimal number of indicators (as almost all other designers do) to determine exposure and asset selection is that no gauge is ever 100% correct, 100% of the time. And all it requires is for a quantitative strategy's indicator to make the wrong selection at the wrong time and a significant portion of a portfolio can be wiped out.

The other consideration is that individual indicators are notoriously context-driven. For example, if a strategy design is backtested from 1990 to present, while it's a significant portion of time with a considerable number of data samples, that doesn't mean that every possible market scenario in the future is covered. Also, when there are too many quantitative strategies relying on the same indicator set (such as momentum), not only is it difficult to beat the market, there's a good possibility that indicator will fail in the future. That is just the nature of crowded trades.

Inconveniently, because ETFs are based on market indices (i.e., the SPDR 'SPY' ETF is based upon the S&P 500 large-cap US stock index), individual ETFs are not accompanied by the plethora of data and statistics that are available for individual stocks. Therefore, most ETF strategies use basic technical (price-based) measures – such as comparing the relative strength of one ETF to the relative strength of others or the market as a whole.

However, ETFOptimize goes the extra mile, and each weekend we calculate billions of bits of data, compiling key indicators, ratios, and data sets for each of the stocks that comprise each index upon which individual ETFs are based. After our systems construct this robust set of custom measures for every available ETF each weekend, our proprietary algorithms make decisions based upon those measures.

For example, if a strategy uses valuation as a key component for its selections, we may calculate the Enterprise Value-to-Operating-Income (EV/OpInc) ratio for every stock in every index of each ETF that is in the strategy's Universe. For our Adaptive Equity Rotation (2 ETF) Strategy, this is 206 ETFs.

All of this detailed calculation requires not just an enormous amount of computer horsepower, but very high-quality, point-in-time data sets, which have been arranged with data providers over the last 20 years. We believe the end result of the extra effort required to build all of these custom measures is well worth it because we achieve highly accurate signals driving the performance of each strategy. We think you'll agree that the performance record of each of the ETFOptimize strategies is a testament to the payoff for this effort.





COMPONENTS OF CUSTOM INDICATORS AND COMPOSITES

Depending on the strategy, we may use a combination of the following components in our proprietary Composite Indicators, consisting of a total of 38 different data sets (as of April 2018) used to determine the market environment and the optimum ETF selection...

 • MacroEconomic Composite Indicator that includes the Unemployment Rate and Trend, the Yield Curve status and trend, Consumer Price Index trend, US Dollar Relative Strength, Relative Profit Trend for the Nine S&P Sectors, Wage Growth Rate and Trend, Consumer Sentiment level and trend, and others….

Fundamental Ratio Composite (all ratios by sector/segment) includes Price/Sales ratio, Progressive-Blend Earnings Composite (PBEC) indicator, EV/OpInc ratio, EV/CashFlow Ratio, Price/Free Cash Flow Ratio, Earnings Yield, Revenue Growth, Earnings Growth, Debt/Equity Ratio, Margin Ratio, Debt Service-to-Cash Flow Ratio, Advisor Expectations Index, and more

Market Regime Indicator includes S&P 500 Earnings Trend, Relative Earnings Trend (by market segment and sector), Relative Strength (by market segment and sector), Market Breadth (status and trend), ETF Sector/Segment Fund Flow trends, Relative Volatility (by market segment), High-Yield Bond Trend, Analyst Estimate Trend, High-Beta/Low Beta Index, Sector Relative Growth status, and more…

Quantitative Technical Composite includes 'ETFOptimize Market Trend Indicator' status, Relative Strength (by market segment), Breadth 1 (New Highs-New Lows), Breadth 2 (Advancing-Declining Issues), Breadth 3 (% above 100-Day MA), S&P 500 Bullish Percent Index trend, Stocks Above 100 and 250-day EMA %, S&P 500 RSI trend, Momentum Index trend, Accumulation-Distribution Index, Overbought-Oversold Index, and others...

ETFOptimize selects the appropriate combination of components and indicators from the data sets listed above to determine market-exposure and ETF-selection for our strategies, with each indicator carefully selected to work synergistically to achieve the highest performance for each approach. We have decades of experience with using quantitative investment systems to determine the proper asset configuration and market exposure to achieve the maximum return with the least amount of drawdown – and least amount of stress for you. Appealing to investors rather than fast-paced, speculative traders, our strategies have an average positions hold time of nearly 4 months, with a range from 1.99 months to 7.26 months.

For the first time ever, this wide variety of fundamental, breadth, technical, and economic indicators are being utilized to generate robust, high-performance investment signals using ETFs that deliver consistent outperformance of their closest market benchmarks in 96.5% of all years since inception –with 100% of years profitable (66 of 66 total years without a loss).



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