Capturing Nonlinear Value Factor Strategy Using AI ETF

August 27, 2021 EDT

In our view, traditional asset managers have long sought to create excess returns with linear value factor strategies. Many of these conventional value factor strategies apply linear functions[1] using valuation ratios such as P/E[2]P/B[3], and EV/CFO[4].

However, we find two drawbacks of such an approach – 1) intangible assets tend to be under-appreciated in the traditional P/B ratio, and 2) a linear function may not be fully optimized to measure the relative cheapness of stock prices.

According to a study conducted by Aon[5], in 2018, S&P 500 firms had intangible assets valued nominally at $21.03 trillion versus tangible assets of $4 trillion.

Moreover, a poll conducted by investment firm Columbus Threadneedle found that 88% of investors answered with the response 'Agree or Strongly Agree' when asked if "Conventional valuation methods such as discounted cash flow are inadequate without thorough consideration of intangible assets"[6], further highlighting the importance of measuring intangibles.

With the increasing value of intangible assets held by S&P 500 firms, asset managers may turn their eyes to unconventional solutions that can capture a company's intrinsic value, applying nonlinear functions[7] by artificial intelligence. In short, an alternative or complementary approach may be of value.

Qraft provides that approach, as exemplified by its AI ETF, NVQ. The Qraft AI-Enhanced U.S. Next Value ETF (NYSE: NVQ) has shown so far that Qraft’s technology can calculate the P/B ratio more accurately by including intangible assets value based on R&D costs, marketing costs, patent issuance, etc. After reassessing the P/B ratio, Qraft AI finds a nonlinear function that seeks to build an optimized value stock portfolio every month.

It can be time-consuming and costly to create a nonlinear function of multiple variables via human’s numerous trials and errors. With deep learning techniques, we believe Qraft AI can continuously adapt to a rapidly changing market and search for the best-optimized strategy.

Human managers, however, do not have to feel as if AI is replacing them, as nonlinear and linear (or conventional) value factor strategies can be complementary. A portfolio utilizing both linear and nonlinear methods could be a balanced approach to value investing.

NVQ has been showing attractive performances since its inception on 12/02/20.

[1] Linear function: a function that describes a straight-line relationship between two variables

[2] P/E: Price to earnings ratio, values a company by measuring its current share price to its per-share earnings (EPS)

[3] P/B: Price to book ratio, compares a company’s current market value to its book value

[4] E/V: Enterprise value, measures the market value of a firm using sum of claims of both creditors and shareholders

[5] Intangible Assets Strategy, Capital Markets and Risk Management. (2020). AON.   

[6] “Grasping The Intangible: How Intangible Assets Reveal Latent Value.” Columbia Threadneedle Investments,  

[7] Nonlinear function: a function where there is no straight-line or direct relationship between independent and dependent variables. In a nonlinear function, changes in output do not change in direct proportion to changes in any of the inputs

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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.