Constructing ETFs with AI

January 04, 2021 EST


To create a high-quality quant fund with robust alpha factors, Qraft Technologies goes through three distinct steps: data pre-processing, factor research, and strategy extraction. Understanding these steps is critical to getting a comprehensive overview of how Qraft AI ETFs are made and executed. This in turn will provide you with a better sense of the “why” you should invest in our ETFs.

We hope that by explaining our process with simplicity and conviction, we can bring you closer to integrating any one of our NYSE-listed AI ETFs (ticker: QRFT, AMOM, HDIV) into your portfolio.

At Qraft Technologies, our goal is to allow AI systems to automate the traditional process of producing active index ETFs with high level of alpha at low cost.


Data Pre-Processing with Kirin API

The first step in trying to form an excess return focused ETF with AI technology is taking on the right source of datasets. Without a proper and clean data, the outcomes may eventually lead to inaccurate results. Qraft’s in-house Kirin API, which is a comprehensive data platform that integrates multiple vendors to provide both macroeconomic and company fundamentals with the correct point-in-time data, can do just that. To learn more about Kirin API, please read our article published on Medium here.

What Kirin API essentially does is it eliminates any inaccurate data that leads to look-ahead and/or survivorship biases. In layman’s terms, Kirin API ensures that the release of a company’s fundamental data matches the official end or start of a quarter. For example, Q4 ended on 12/31/19 and let’s suppose that official documents didn’t get released until 01/15/2020. That means if you try to formulate a model before the release date, then your data will be flawed because you are using a future date. And the problem with future data is that it can cause biases which will lead to overfitting of the model.

Big-sized global data vendors like S&P Global and Refinitiv overlook the importance of point-in-time data. With Qraft’s data processing system, however, not only is the correct records available, but investors can avoid making bias decisions that might eventually hurt their portfolios.


Researching Robust Factors

The core function of Qraft’s AI system lies with automatically finding high alpha factors and coming up with new investment strategies to implement. In short, with the right datasets provided by Kirin API, the AI technology navigates through the vast investment universe to narrow the probable candidates for a strategy. To better understand this, let’s consider a typical Go game.

If you are unfamiliar with Go, it’s a Chinese-originated abstract strategy board game that aims to declare more territory than the opponent. Two players with black or white stones will take turns placing the stones on an empty intersection of a board. Once the stone gets surrounded by the opponent, then it gets removed. At the end of the game, the players will count one point for each stone they’ve captured and one point for each vacant territory. A winner is declared when he/she has more captured stones and territory than the opponent.


Go looks simple, but it is a complicated board game that requires in-depth strategic knowledge, creativity, and intuition. There are literally billions of possible board configurations available and for nearly a decade, AI wasn’t able to overcome this hurdle due to the qualitative and mysterious nature of the game. That is, until March 2016 when AlphaGo famously defeated Lee Sedol, a world champion with more than 18 titles to his name, by 4:1 in a historic match held in Seoul, South Korea.

AlphaGo, developed by Google’s DeepMind Technologies, uses both advanced search tree and deep neural networks to increase its learning capability by playing different versions of itself thousands of times. Eventually, AlphaGo became an expert at learning what works and what doesn’t. AlphaGo is considered the world’s greatest Go player, defeating champions several times over.

So why bring this up? And how does this connect to Qraft’s AI technology?

Similar to a Go game, the search space for the investment universe is massive. In fact, it may even be bigger. In such a vast search universe, a well-engineered deep learning model can exhibit consistent results in narrowing the probable candidates and automatically back/forward testing the candidates to extract an investment strategy. In other words, just as AlphaGo can strategize and predict which outcomes work best, Qraft’s AI system can formulate effective strategies by finding which factors potentially provide the highest returns.

The initial part of this process is finding the factors and the latter part is automatically extracting strategies. Factor Factory is Qraft’s core research technology that intuitively finds factors that may bring excess returns. With the correct data processed by Kirin API, Factor Factory can explore hundreds of market anomalies in a single day by applying AutoML technology.


Extracting New Investment Strategies

After finding robust factors, Strategy Factory constructs an investment strategy that applies to our ETFs. There are currently four main components that make up the extract framework: Data Loading, Processing, Splitting, and Backtest Simulation.

Just as there are different types of strategies that Go players can implement for optimal play, there are countless ways that Strategy Factory can make the best use of the factors found by Factor Factory. Let’s briefly discuss the four main components:

*Heavy technical terms are applied.

1. Data Loading: Fetches data from Kirin API and adds other layer of necessary operations to properly process data. There are two main types of data that we focus on: data with different values for individual stocks (RCA factors) and data with only one value at a specific time (GDP, S&P 500 Index).

2. Processing: Applies cross-sectional Z-score to the data and divides the quartile based on the value to convert it into One Hot. One Hot from data is as follows:

  • Convert data to percentile
  • Convert percentile to classification according to given intervals ((0~0.3) : 0, (0.3 ~ 0.7) : 1, (0.7 ~ 1) : 2)
  • Convert class number to one hot vector (0: [1,0,0], 1 : [0,1,0], 2: [0,0,1])

3. Splitting: Data is truncated according to validation time points to avoid look-ahead bias. For example, when validating model performance for May 2015, you need to train and infer using only data up to April 2015.

The remaining data is then transferred to the model training and inference stage, where it’s able to train multiple models with different goals. The series of models trained are stored for each time point and the prediction is inferred from models trained with that time point data.

4. Backtest Simulation: Performs a simulation based on the predictions at each time point to check the portfolio components, weights of each stock, dividend yield, turnover rate, capital gains, etc. The number of portfolio items is set at this point and how they will be distributed within the ETF.


Wrapping Up

Simply put, our ETFs are created by processing clean data, finding high alpha factors through AI, and forming an investment strategy that brings the potential for excess returns. Since inception, Qraft AI ETFs have brought incredible results and outperformed several benchmark indices — you can view our performances here.

Qraft AI ETFs trade on the New York Stock Exchange and they are available to purchase on various brokerage accounts, including Fidelity, Charles Schwab, TD Ameritrade, E-Trade, and Robinhood. Please note that you cannot invest directly through Qraft Technologies, Inc.

If you are looking to actively invest in high-growth ETFs, Qraft Technologies, Inc. is devoted to helping you achieve alpha potential by leveraging the latest AI technology.

Artificial intelligence selection models are reliant upon data and information supplied by third parties that are utilized by such models. To the extent the models do not perform as designed or as intended, the strategy may not be successfully implemented. If the model or data are incorrect or incomplete, any decisions made in reliance thereon may lead to the inclusion or exclusion of securities that would have been excluded or included had the model or data been correct and complete. Service providers may experience disruptions that arise from human error, processing and communications error, counterparty or third-party errors, technology or systems failures, any of which may have an adverse impact.

— — — — — — — — — — — — — — —

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

AutoML technology is short for Automated Machine Learning. It’s essentially the automation of the machine learning process to make machine learning jobs simpler, easier, and faster.

RCA Factors, otherwise known as Root Cause Analysis, represents a systematic process to understand and perform a comprehensive, system-wide review of significant problems.

Z Score gives you an idea of how far from the mean a data point is. In statistics, Z scores are measured in standard deviation units.

One Hot is a process by which categorical variables are converted into a form that could be provided to Machine Learning algorithms to do a better job in prediction.

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 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.