The pursuit of above-average returns is one that many have undertaken over the years, with most failing to do it consistently. With the rise of quant models in finance, there seemed to be a glimmer of hope in finding strategies that consistently worked. While these worked for some time, their effectiveness began to deteriorate as more people implemented them and it became harder to find alpha.
Marcus Kim, the founder of Qraft Technologies, was one such trader that saw this trend and decided AI was the best answer to this. Qraft Technologies, founded in 2016, is the product of Marcus’s dream. Qraft’s AI is the solution that powers four ETFs, QRFT, AMOM, HDIV, and NVQ. They have even garnered headlines with timely trades of Tesla, the addition of meme stocks, and positioning the weights of the portfolio to reduce drawdowns.
Inside The Supposed Black Box
While it would have been easier to use existing AI platforms and just apply them to the trading strategy, Qraft built the entire AI from scratch. By doing this, Qraft created an AI specifically tailored for investments. Therefore, the team is intimately aware of the capabilities and the improvements that the model needs.
Data is the fuel of AI. Just like a car engine, you put bad fuel in, and you can get bad results. AI is no different and, like a high-performance engine, it needs high-performance fuel. Financial data can be quite messy and cause problems with statistical models. Kirin API is Qraft’s in-house built solution created to make sure that the data is pre-processed in attempting to back test accurately without biases. Once the data is ready, it then goes into the AI model. This can seem mysterious and can lead to some skepticism. However, Qraft’s AI is not doing much different than a human would. The difference is that it can process trillions of possibilities within a few hours. It can take in both traditional structured data (macro data, price, factors, etc.) as well as non-structured data such as patents and sector labels.
Unstructured data is illustrated by Qraft’s experimentation of sector labels. Current sector labels do not account for modern corporations that do business in multiple sectors. For example, is Amazon in the logistics, software, data, or retail industry? Determining the correct sector, or a combination of sectors could lead to more accurate multiples and valuations. To do this, Qraft AI can use NLP (natural language processing) in an effort to accurately label which sector or industry a firm may belong to. In addition, it allows for blended sector labels, which can allow for weighted combinations of sector labels for a firm. We believe this approach would be beneficial as the importance of certain assets, such as intangible assets, may be more important in the context of valuing a tech firm than a retail company. While this labeling is possible with humans, the sheer volume of data that needs to be sourced and processed can make it difficult to do it in a timely and cost-efficient manner.
Structured data can be filtered by the Kirin API, which attempts to accurately determine the quality of the data, excluding data that is deemed irrelevant. This “high-quality data” is then added into the Alpha Factory platform, which is composed of the Factor Factory and Strategy Factory. The Factor Factory uses data from Kirin API to determine the factors that are relevant to the custom parameters set by the engineers. These factors include traditional linear factors such as P/B ratio, market capitalization, and other frequently used financial metrics, as well as nonlinear factors such as intangible assets. We believe nonlinear factors offer an advantage over traditional linear factors because of its ability to price in factors such as intangible value, macro trends, and various additional factors. These nonlinear strategies can be combined with traditional linear strategies to seek to find a more accurate value for securities, creating a comprehensive strategy that balances both nonlinear and linear factors.
Identified alpha factors in the Factor Factory are then assembled into a portfolio in the Strategy Factory, which can run through all the possible combination of the factors that have been identified in the Factor Factory. Then it seeks to create the best weighting for each factor and to identify stocks with these properties. The valuation of individual stocks considers the sector labeling through NLP identified above. Factor Factory produces a final list of stocks, given the identified factors are expected to yield alpha value.
After these processes, the AI puts together what it believes is the best potential portfolio. This includes the portfolio of stocks it feels best along with the weights of each stock. That portfolio gets updated into the ETFs during the monthly rebalancing. While the model, constraints, and choice of when to rebalance are all made by humans, the AI is the final decision maker for portfolio.
While it is anticipated the Adviser, Exchange Traded Concepts LLC, will purchase and sell securities based on recommendations of QRAFT AI, the Adviser has full discretion over investment decisions for the Fund.
More than an ETF Provider
While the ETFs provide visibility and transparency to what the proprietary AI can do, it is not the only tool in the toolbox for Qraft. Qraft offers institutional investors an AI order execution product called AXE, which has been in use since March 2020. The API and a B2B Robo advisory are also products currently available. Overall, Qraft has over 1.5B in assets under AI. These products and services have allowed Qraft to show what AI can do and have attracted the gaze of both retail investors and larger financial institutions. From ETFs to customized solutions, Qraft sees a future of democratizing opportunities in the market and more efficient asset management.
 Drawdowns - A drawdown is a peak-to-trough decline during a specific period for an investment, trading account, or fund. A drawdown is usually quoted as the percentage between the peak and the subsequent trough.
 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.
 Alpha Factory – Qraft’s proprietary strategy extraction system composed of two models, Factor Factory and Strategy Factory.
 Factor Factory - Factor Factory – Qraft’s core AI technology that automatically finds factors that could bring excess returns. Factor Factory can produce at least 10 factors per day without any human intervention.
 Strategy Factory - Strategy factory extracts the investment strategy with a nonlinear asset price model by nonlinearly combining factors extracted automatically from the factor factory.
 P/B - Price to book ratio, compares a company’s current market value to its book value
 Market Capitalization - Market capitalization refers to the total dollar market value of a company's outstanding shares of stock.
 Alpha – Alpha is a measure of the active return on an investment, the performance of that investment compared with a suitable market index.
 API - An application programming interface (API) is a set of programming code that queries data, parses responses, and sends instructions between one software platform and another. APIs are used extensively in providing data services across a range of fields and contexts.
 Robo Advisor - Robo-advisors (also spelled robo-adviser or rob advisor) are digital platforms that provide automated, algorithm-driven financial planning services with little to no human supervision.
Investors should consider the investment objectives, risks, charges and expenses carefully before investing. For a prospectus or summary prospectus with this and other information about the Fund, please call 1-855-973-7880 or visit our website at www.qraftaietf.com. Read the prospectus or summary prospectus carefully before investing.
The Funds are distributed by Foreside Fund Services, LLC
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.
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.
QRAFT AI-Enhanced U.S. Large Cap Momentum ETF: The Fund is subject to the risk that market or economic factors impacting technology companies and companies that rely heavily on technology advances could have a major effect on the value of the Fund’s investments. The value of stocks of technology companies and companies that rely heavily on technology is particularly vulnerable to rapid changes in technology product cycles, rapid product obsolescence, the loss of patent, copyright and trademark protections, government regulation and competition, both domestically and internationally, including competition from foreign competitors with lower production costs. Technology companies and companies that rely heavily on technology, especially those of smaller, less-seasoned companies, tend to be more volatile than the overall market.
QRAFT AI-Enhanced US High Dividend ETF: Securities that pay dividends, as a group, may be out of favor with the market and underperform the overall equity market or stocks of companies that do not pay dividends. In addition, changes in the dividend policies of the companies held by the Fund or the capital resources available for such company’s dividend payments may adversely affect the Fund. In the event a company reduces or eliminates its dividend, the Fund may not only lose the dividend payout but the stock price of the company may also fall.
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.