There’s no better way to say this — value sucks. Or it has been ever since the financial crisis hit in 2007–2008.
A careful examination of value and growth stocks over the last 13 years (January 2007-June 2020) shows that value has underperformed growth by at least 39%, according to data from Research Affiliate, a global leader in factor investing and asset allocation. Year after year, despite investors’ anticipation, expensive growth stocks have outperformed value by the widest margins since the dot-com era. That’s disconcerting for value investors — especially when value is meant for long-term strategy.
So does this mean it’s over for value? Absolutely not. Quite the opposite, actually.
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While no one knows what the future holds in the stock market, many investors believe that value will soon take over growth once again. In fact, just two months ago in September of this year, the large cap value stocks have outperformed large cap growth.
In addition, value favors a strong economic recovery. We saw this happen each time the U.S. went through a recession since 1929. And now, as the economy prepares to exit the pandemic slump, we believe value stocks are in a great position to outperform growth stocks.
Another point is that in the past decade, growth stocks have surged on the backs of large tech companies. Many fear that another tech bubble burst is imminent since we have found that in most market cycles, profitable growth companies have lost their momentum and eventually underperformed. While this may be true, many investors still believe that due to low interest rates and the high profitability of tech giants, growth stocks will likely continue to dominate the market.
Regardless, for value investors, a comeback is long overdue. Not just for the reasons mentioned above, but because there’s a new way to accurately measure a company’s book value. According to Baruch Lev, a professor at New York University Stern School of Business, our approach to calculating the value factor has been wrong for a while. In today’s financial markets, intangible assets are often overlooked but they play an integral role in book value calculations.
Lev’s conclusion is that if you are not measuring the assets properly, you can’t correctly determine that a company is undervalued or overvalued. For example, you shouldn’t just rely on tangible assets like factories or lands when companies like Amazon or Apple spends considerable amount of expenses on intangible assets like trademarks, patents, and branding.
Intangible assets have no physical presence, but they add long-term value to all businesses. You can divide the assets into two broad categories: intellectual property and goodwill. Intellectual property refers to any possession or product that is owned and created by the human mind. This may include trademarks, patents, or licensing agreements. Goodwill, on the other hand, refers to a company’s brand value. This includes employee relations, loyal customer base, brand identity, and proprietary technology.
As the economy tilts its value from factories, office buildings, and machineries to ideas, brands, and software, the need to shift from tangible capitals to intangibles will become more apparent than ever before. But according to Michael Mauboussin, the Head of Consilient Research at Counterpoint Global, measuring intangibles through the conventional accounting methods is extremely challenging. While some companies voluntarily share this information in their reports to give investors a better idea of their value, those numbers are mostly developed in-house and subject to pre-determined prices. In essence, intangibles have unclear boundaries and calculating a meaningful analysis can be an almost impossible task.
Bottom line is, when properly understood, the fundamental of value investing — buying stocks that fall below their intrinsic value, still holds true today. But the economy has changed drastically since the last dot-com crash, and we find the normal way of using tangible assets to calculate book value is no longer effective. The new way, however, has yet to be found. Or at least properly implemented. So then, if measuring intangible assets is a notoriously tricky task, would AI be able to handle it? That’s the challenge that Qraft Technologies has sought after and it’s been able to provide successful results thus far.
Our AI technology aims to learn relevant data to the future intangible assets (R&D costs, marketing costs, patent issuance, etc.) and properly measure a company’s book value. Then our AI will invest in stocks that have a higher ratio of adjusted book value to their market value.
By “relevant” data, we mean that AI is able to detect and take into account the differences between sectors. For instance, the R&D cost can be more expensive for a biotechnology sector than it is with an IT sector. With each industries, there are costs and patterns that are hard to calculate and analyze for intangible assets. You can’t just assume that Coca-Cola has the same branding costs than a company like Apple, where it spends more on patents and trademarks.
Looking at the image above, once a company’s intangible assets are accurately calculated and correct book value is measured, that could be the determining factor if a stock is either overvalued or undervalued.
An easy way to digest Qraft’s core AI technology is to refer to a traditional Chinese game of Go. From the surface, Go seems pretty simple; two players take turns placing stones to determine who occupies more space. But when observed more carefully, Go is actually a complicated board game that requires in-depth strategic knowledge. For example, the search universe for Go has nearly 1 trillion different ways to form a strategy, making it almost impossible for the players to calculate during a game. It’s also one of the reasons why computers had difficulty mastering the game early on.
In finance, the search space of an investment universe is similar to the search space of Go. Just as there are over trillions of ways Go players can form a strategy, a well-engineered deep learning model can survey the vast network of probable factor candidates and extract investment strategies that bring excess return potential. And just as AlphaGo was able to defeat Go champions across the world, we believe that Qraft AI can position itself to outperform the markets.
There’s no doubt that growth has had a tremendous run. But as AI learns and relearns ways to find the “correct” value, we believe it’s just a matter of time that value investors take the lead.
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.