Global Dynamic Allocation
Investor Profile
This systematic investment strategy has been designed for investors seeking a long-only, highly liquid, globally diversified exposure to equities, fixed income and currencies.
Strategy Fundamentals
This strategy is powered by a Neural-Q system. This system is composed of 6 teams with 118 AI analysts that quantitatively identify the most attractive investment opportunities within the defined investment universe, aligning the portfolio with the global macroeconomic landscape.
Base Currency
The base currency of this strategy is the EURO (EUR).
Benchmark
Benchmarked against the global 60% equity (MSCI ACWI), 40% bond (Bloomberg Global Agg) portfolio.
Key Statistics
Annualized Return
Profitable Years
Years Track Record
Annualized Alpha
Investment Universe
The investment universe includes global sector and regional equity ETFs, global bond ETFs, and a range of currencies, including gold. This diverse mix provides investors with a globally diversified, multi-asset portfolio, offering downside protection through assets like gold, the Swiss franc (CHF), and the US dollar (USD).
To achieve the desired exposure, client assets are invested in simple financial instruments such as spot currencies and ETFs, leveraging market-cap-weighted passive strategies from leading providers like BlackRock, State Street, and DWS.
While these providers handle allocation at the Tactical Asset Allocation (TAA) level, Neural-Q dynamically adjusts the Strategic Asset Allocation (SAA) to reflect the current global macroeconomic environment. This method gives investors peace of mind by eliminating the need to time market entries and exits, ultimately improving returns and reducing drawdowns compared to traditional passive strategies.
Test Our Strategy
Compare our investment strategy with others in the market while visualizing your investment's potential growth over time using a Dollar-Cost Averaging (DCA) investment plan. To calculate the most effective reinvestment frequency for this strategy, click here.
Invest in our strategy
We partner with qualifying investment management institutions interested in executing and commercializing our investment strategies. We already have a partner set to commercialize and execute this strategy and it will soon be available for investment. Contact us for more information.
Contact UsFrequently Asked Questions
What is a systematic investment strategy?
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Rule-Based:
The strategy follows a specific set of rules or criteria, ensuring decisions are consistent and not influenced by human emotions like fear or greed.
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Data-Driven:
Systematic strategies often rely on quantitative data, in this case, transforming macroeconomic fundamentals into numerical values.
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Reproducible:
Since decisions are based on predefined rules, the strategy can be tested on historical data, known as backtesting, to evaluate its past performance. This process also helps to optimize future performance.
How does the macroeconomic cycle influence this strategy?
The macroeconomic cycle represents the fluctuations in the overall economy, encompassing phases such as growth, recession, and recovery. Each phase presents distinct opportunities. Our strategies are designed to capture these opportunities by interpreting signals from macroeconomic data, ensuring that investments are quantitatively aligned with the current economic environment.
Is the strategy's historical performance data reliable?
Backtesting involves using historical data to test how a strategy would have performed.
To ensure its accuracy, we:
- Employ high-quality, unbiased, point in time data.
- Account for all transaction costs and slippages.
- Regularly update and cross-verify our data sources.
- Conduct out-of-sample tests to confirm our findings.
Will the strategy perform in future market conditions?
While our strategies are founded on robust fundamental research and thorough testing, it's crucial to recognize that markets aren't always rational. Nevertheless, we believe that, over the long term, fundamentals prevail. Thus, this strategy is well-positioned to continue outperforming.
In addition, as time progresses, more data is generated, which can improve the precision of the asset allocation. This is likely to result in enhanced performance.
How do you account for model overfitting?
Overfitting occurs when a model is too closely tailored to historical data and may not perform well on new data. We address this by:
- Ensuring our systems make fundamental sense. We avoid black-box models.
- Keeping our models parsimonious, focusing on essential variables.
- Employing out-of-sample validation.
- Our propietary analyst traning process includes assessing their performance on different subsets of data.
Why does this strategy show a significantly lower drawdown than its benchmark?
Our strategy's investment universe includes gold, USD, and CHF. These assets have a history of appreciating during turbulent market conditions, thereby reducing drawdowns. Additionally, our systems are trained to anticipate the likelihood of such market disruptions and adjust the portfolio accordingly to mitigate potential declines.