An Active Factor Tilting Strategy Powered by AI

October 27, 2021 EDT

Factor based strategies have been in vogue in recent times, creating a proliferation of factor themed investment vehicles in the process. Many investors have been actively moving in and out of these vehicles, following the factor which at any given time is deemed to be offer more upside value.

Investors move in into these factors when they believe they are trading at a discount, creating the potential for large factor trends, and thereby chipping away at the premium of said factor. Once a factor is overbought it may result in an outflow in search of other factors are trading at a discount, creating a never-ending cycle of factor rotation, disputing the price discovery process.

It is therefore risky to put all of one’s eggs into one factor, as it happens with factor rotation strategies. This insight has given way to the development of multi-factor strategies that can reduce exposure to a single factor. There is enough back-tested research to support that factor rotation and factor investment does deliver performance over the market in the long run[1]. Accordingly, the market has caught on to the value of multi-factor strategies with the total assets held by multifactor ETFs increasing more than 30 times in the last 10 years from $2.5 billion in 2008 to $74 billion in 2018.[2]

With current market uncertainty and volatility, it is wise to invest in a product with flexibility that can react to market conditions through strategies such as factor tilting. Factor tilting allows for a reactive multi-factor strategy based on market conditions. The last few years have shown us the unpredictability of traditional factors and their influence on the market, especially with changing bond yields and an uncertain Fed. Even before COVID-19, in 2019 Blackrock noticed the effect that US-China trade relations were having on certain factors[3] and their relevance to market returns.

Additional research by Vanguard[4] has found that with traditional factors such as value, quality, momentum, size, and risk, the more active approach may yield the greatest factor premiums. It stated- “The findings may surprise some as it shows that, based on the last 30 years of factor-investing performances, factor funds that maintained a consistent factor exposure by rebalancing more frequently—on a daily basis instead of monthly or biannually—achieved significantly higher factor premiums, effectively doubling the historically observed premiums of many factors”. This suggests that as most current multi-factor ETFs rebalance on a quarterly or biannual basis, an investor may be leaving potential performance on the table pursuing products following the established multi-factor norm.

Qraft’s AI-powered QRFT represents an actively managed and monthly rebalanced, multi-factor approach. Using the benefits of artificial intelligence and machine learning it can calculate the relevance of each factor at a given time, testing the accuracy of such calculations through back-testing. This allows the QRFT to react to constantly changing market conditions.

The performance data quoted represents past performance. Past performance does not guarantee future results.  Current performance may be lower or higher than the performance data quoted. The investment return and  principal value of an investment will fluctuate so that an investor’s shares, when sold or redeemed, may be worth  more or less than their original cost.

Performance data current to the most recent month-end and quarter-end may be obtained by visiting

This approach is backed up by market results. QRFT has outperformed the MSCI USA Multi Factor Index and S&P 500 Index by more than 40% since its inception on May 21st of 2019.

The relatively high-frequency monthly rebalanced approach is feasible because AI technology is leveraged into Qraft’s management process. While the research states that a daily rebalance approach may provide maximum yields, that method is unlikely to be feasible at this time due to its resource intensity. Furthermore, the trading costs that may arise from such an approach may make it cost inefficient. Qraft’s vertically integrated technology allows a time and cost-efficient process in reflecting the latest news and data into its algorithms on a monthly basis. Without the use of AI to enable the process, monthly rebalancing would be difficult and re­­source intensive. This would be reflected within the fee scheme and is also shown by the actual dearth of more actively managed multi-factor ETFs in the market.

There is no guarantee that a factor-based investing strategy will enhance performance or reduce risk


[1] Hoffstein, Corey. “Factor Rotation: Possible, but Worth It?” Flirting with Models, Newfound Research, 10 Jan.2019,

[2] Pollock, Michael A. “What Are 'Multifactor' Etfs? and Do They Work?” The Wall Street Journal, Dow Jones & Company, 10 Dec. 2018, work-1544411340.

[3] “A Factor Rotation with Staying Power?: Blackrock Blog.” BlackRock,

[4] Picca, Antonio. Why Regular Rebalancing Is Key to Maximizing Factor Premiums, Vanguard,

[5] S&P 500 Index: The Standard & Poor's 500 Index, is a market-capitalization-weighted index of 500 leading publicly traded companies in the U.S

MSCI USA Diversified Multi Factor Index: Based on a market cap weighted parent index, the MSCI USA Index, which includes US large and mid cap stocks. Aims to maximize exposure to – Value, Momentum, Quality, Low Size Index performance is for illustrative purposes only. Indexes are unmanaged and one cannot invest directly in an index. Past performance does not guarantee future results.

[6] All data in this chart is from the year 2021 

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Investing involves risk, including loss of principal. The Funds are subject to numerous risks including but not limited to: Equity Risk, Sector Risk, Large Cap Risk, Management Risk, and Trading Risk. The Funds rely heavily on a proprietary artificial intelligence selection model as well as data and information supplied by third parties that are utilized by such model. To the extent the model does not perform as designed or as intended, the Fund’s strategy may not be successfully implemented and the Funds may lose value. Additionally, the funds are non-diversified, which means that they may invest more of their assets in the securities of a single issuer or a smaller number of issuers than if they were a diversified fund. As a result, each Fund may be more exposed to the risks associated with and developments affecting an individual issuer or a smaller number of issuers than a fund that invests more widely. A new or smaller fund's performance may not represent how the fund is expected to or may perform in the long term if and when it becomes larger and has fully implemented its investment strategies. Read the prospectus for additional details regarding risks.

While it is anticipated that the Adviser will purchase and sell securities based on recommendations by the U.S. Large Cap Database, the Adviser has full discretion over investment decisions for the Fund. Therefore, the Adviser has full decisionmaking power not only if it identifies a potential technical issue or error with the U.S. Large Cap Database, but also if it believes that the recommended portfolio does not further the Fund’s investment objective or fails to take into account company events such as corporate actions, mergers and spin-offs.

QRAFT AI-Enhanced U.S. Large Cap ETF: Companies in the health care sector are subject to extensive government regulation and their profitability can be significantly affected by restrictions on government reimbursement for medical expenses, rising costs of medical products and services, pricing pressure (including price discounting), limited product lines and an increased emphasis on the delivery of health care through outpatient services.

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QRAFT AI-Enhanced U.S. Next Value ETF: The value approach to investing involves the risk that stocks may remain undervalued, undervaluation may become more severe, or perceived undervaluation may actually represent intrinsic value. Value stocks may underperform the overall equity market while the market concentrates on growth stocks. The small- and mid-capitalization companies in which the Fund invests may be more vulnerable to adverse business or economic evens than larger, more established companies, and may underperform other segments of the market or the equity market as a whole. Securities of small- and mid-capitalization companies generally trade in lower volumes, are often more vulnerable to market volatility, and are subject to greater and more unpredictable price changes than larger capitalization stocks or the stock market as a whole.

Alpha – Alpha is a measure of the active return on an investment, the performance of that investment compared with a suitable market index.

AutoML – Short for Automated Machine Learning, AutoML is the automation of the machine learning process to make machine learning jobs simpler, easier, and faster.

Kirin API - Developed by Qraft’s data scientists, integrates multiple vendors to provide both macroeconomic and company fundamentals with the correct point-in-time data.