ICE

Capturing Asian and U.S. tech giants

ICE Asia Tech 30 and NYSE® FANG+™ Indices

Howard L. Simons,
President, Rosewood Trading, Inc.
Published
November 2021

The technology sector has changed to the point where the lines between it and other sectors, such as media & communications and consumer discretionary, have become blurred. The somewhat grandiose statement that “all firms are technology firms” is not as facile as it may seem: how would you have explained to an auto industry executive in 1971 that one of the industry’s leading problems 50 years later would be a shortage of semiconductor chips and that the leading producer of these chips would be Taiwan Semiconductor? Amazon.com may be classified as a consumer discretionary stock, but its Amazon Web Services division has a decidedly “tech” feel to it. The same can be said for media & communications firms such as Facebook, Alphabet and Comcast.

Just as firms and industries change dramatically over time, so too do their domiciles. The largest technology firms globally include China’s Tencent Holdings and Alibaba Group, South Korea’s Samsung Electronics, Japan’s Sony and Taiwan’s Hon Hai Precision Industry. No one today finds this Asian concentration remarkable. Taken in combination with several significant macroeconomic and investment themes discussed below, this makes a new benchmark and trading instrument, the ICE Asia Tech 30 Index and the accompanying futures contract, a powerful tool for traders and asset managers alike, both by itself and, as we shall see below, in relation to other technology-based indices such as the NYSE FANG+™ Index.

An Industry Like No Other

A little perspective is in order. First, the pace with which the technology sector, however broadly defined, has been changing life globally has been accelerating for so long that we forget how young many of its leading firms are. Leading U.S. and Chinese social media firms Facebook and Tencent Holdings were founded in 2004 and 1998, respectively. Similarly, online e-commerce giants Amazon.com and Alibaba Group date back to 1994 and 1999, respectively. While some of these new corporate success stories such as Twitter created their own industries, others such as Netflix displaced established firms. Creative destruction may be the driving engine of a market economy, but as economists note drily, it is not without frictional costs and negative externalities.

Second, these frictions combined with the speed of technological penetration have been stressing sociopolitical systems. What we recognize as “mass culture” created by radio and movies is scarcely a century old. It retained a business model of a small number of people controlling the publication of information for consumption by a large number of people. Today’s technologies allow everyone to publish on a largely unfiltered basis and what had been a largely unified mass culture has been fragmenting into an aggregation of self-reinforcing opinion clusters often derided as “echo chambers”. Political structures are trying to adapt on the fly to corporations whose business models depend on the rapid and unfettered flow of information. This change has affected the operating environments for technology firms on both sides of the Pacific Ocean.

Third, the technology industry has and, hopefully, will continue to enable the greatest ever expansion of wealth, defined here by the capacity to consume. If humans as economic animals began their existence as hunter-gatherers, their capacity to consume literally was limited by their capacity to eat more and maybe climb the dietary curve a little. By the time a global industrial economy rose in the late Nineteenth Century, consumers could be sold on the concept of buying more than the necessities of life and on replacing those goods as they become outdated. Still, this process was limited by physical constraints, even allowing for conspicuous consumption.

The current technology-based economy has removed many of those physical constraints. The demand for access to information, social connection and improved efficiency is relatively unbounded and is available to nearly all at a low fixed cost and very low marginal costs. In addition, information technologies are highly scalable and have a much lower set of production costs than do industrial processes. While an automobile manufacturer required large supply chain investments in steel, rubber, glass, carpeting and the like, the value-added in Microsoft’s software or Apple’s phones is skewed toward design as opposed to manufacturing. If we combine this with network effects, we find firms capable of extraordinary profitability and concentrated market share. As modern societies seem to have an innate aversion toward size and concentration, we find yet another source of political stress globally even as many are loathe to damage what are both the crown jewels of the economy and the cornerstone of investment portfolios.

Fourth and finally for this purpose, the issues of size and concentration have an interesting outcome. Industrial economies are replete with naturally substituting competitors. Very simply, if you buy an airplane from Boeing, you are not buying an airplane from Airbus and vice-versa. With notable exceptions such as Apple, Samsung Electronics and Xiaomi in the telephone handset business, which itself has challenges due to different operating software, the technology industry has far fewer of these natural substitutions and is laden with network effects that drive buyers toward single suppliers. If that supplier falls out of favor as Myspace did relative to Facebook, it heads toward oblivion. In this sector, high levels of volatility are simply part of the investing landscape.

The implications of this volatility for investors are simple and clear: You may know that a business sector will be successful but you do not know which firm will emerge as dominant. Therefore, it is incumbent upon you to diversify your holdings and allow the eventual winner to produce long-term gains regardless of how many non-winners you hold. Indices such as the ICE Asia Tech 30 Index and the NYSE® FANG+™ Index facilitate this portfolio-based strategy. Critically, given the technology sector’s dynamic nature, the quarterly rebalancing process of these indices aims to represent the industry’s most important firms.

ICE Asia Tech 30 and NYSE® FANG+™ Indices

Just as the Micro NYSE® FANG+™ Index Futures allow investors and traders simple access to a portfolio of ten technology and tech-related giants through futures contracts, Micro Asia Tech 30 Index Futures offer a simple way to trade a basket of the top thirty technology and tech-related firms listed in the Asia-Pacific region. Currently, Chinese, Japanese, Taiwanese and South Korean-headquartered firms are represented. A discussion of the two indices’ composition and specifications for the ICE futures contracts referencing those indices is included in the Appendix.

If we compare the incremental total returns in USD terms of the ICE Asia Tech 30, NYSE® FANG+™, NASDAQ 100® and Hang Seng TECH Indices to the MSCI World Index since the January 2015 starting date of the Hang Seng TECH Index, we see just how strong the performance of the NYSE® FANG+™ Index has been.

Incremental Total Return Vs. MSCI World Index

(USD Terms)

Source: ICE

An initial glance at the chart above might suggest that the NYSE® FANG+™ and ICE Asia Tech 30 Indices move on such a different scale they should not be considered as having any sort of trading relationship. However, if we strip out the NASDAQ 100® and Hang Seng TECH Indices and plot the NYSE® FANG+™ and ICE Asia Tech 30 Indices on separate scales, a more intriguing picture emerges.

Incremental Total Return Vs. MSCI World Index

Source: ICE

Here we see sustained periods of both relatively strong and weak performance by the ICE Asia Tech 30 Index. As discussed above, the nature of issues facing technology firms increases their stock-specific risk relative to their market beta risk. For example, much of the variation in the ICE Asia Tech 30 Index’s performance has been driven by Tencent Holding and Alibaba Group, both to the upside in 2016 to 2017 and then to the downside in 2021. Another example is the strong rally in Tesla combining with the sharp decline in Meituan to widen the relative performance gap of the NYSE® FANG+™ Index relative to the ICE Asia Tech 30 Index in 2021.

As technology stocks are inherently volatile for the reasons discussed above, there is reason to believe that the spreads between technology-based indices will continue to be driven by the idiosyncratic performance of their components. The growth of newer technology businesses such as 5G wireless networks, online education, remote work, online gaming, cloud computing and data centers, artificial intelligence, electric vehicles and renewable energy will create both winners and losers, which will be just as impossible to predict today as were the eventual winners and losers of the 1990s dotcom era.

Macroeconomic factors intervene as well. Both NYSE® FANG+™ Index and ICE Asia Tech 30 Index incremental performance peaked in mid-February 2021 when rising U.S. inflation raised speculation that the Federal Reserve would start to raise short-term interest rates. Equity markets have historically rewarded fast-growing firms with higher multiples during periods when strong monetary accommodation is expected as it lengthens the effective duration of those equity investments.

A casual glance at the chart immediately above may suggest the ICE Asia Tech 30 Index is more volatile than the NYSE® FANG+™ Index, but this is not the case when we examine the distribution of returns between January 2015 and September 2021. Not only does the NYSE® FANG+™ Index have a higher standard deviation of returns during the period, 1.64% versus 1.21%, it has a far more pronounced negative skew of those returns, -0.81 versus -0.25, and much fatter tails along the distribution of those returns with a coefficient of kurtosis of 6.02 versus 2.03.

ICE Asia Tech 30
NYSE FANG+
Mean0.05%0.11%
Std. Deviation1.21%1.64%
Skew

(0.25)

(0.81)

Kurtosis2.036.02

NYSE FANG+ Index Has Fatter-Tailed Distribution of Returns Than Does ICE Asia Tech 30 Index

Source: ICE

As discussed below, the more volatile nature of the NYSE® FANG+™ Index is consistent with investors’ anecdotal sense that large moves made during U.S. trading hours reverberate globally far more than do large moves made during Asian trading hours. However, this leading relationship is not particularly strong when expressed on a daily percentage basis. If we map the daily returns of the ICE Asia Tech 30 Index against the previous day’s returns, we get the following relationship:

NYSE FANG+ Index Leads ICE Asia Tech 30 Index Weakly

Source: ICE

For the sake of completeness, a contemporaneous relationship produces a flatter slope coefficient of 0.17 and a lower R-squared of 0.05, while a reversed relationship of Asian markets leading the U.S. index produces a slope coefficient and R-squared of 0.03 and 0.002, respectively. Restated, the NYSE® FANG+™ Index leads the ICE Asia Tech 30 Index, but neither a contemporaneous nor reversed relationship of the ICE Asia Tech 30 Index leading the NYSE® FANG+™ Index is visible in the data.

Now let’s construct a simple linear model of trading the ICE Asia Tech 30 Index as a function of the NYSE® FANG+™ Index lagged one day. The USD value for each futures contract is derived by multiplying each index value by their respective futures contract size, $2 for the Micro Asia Tech 30 Index Futures and $5 for the Micro NYSE® FANG+™ Index Futures.

We should expect this ordinal, dollar-value relationship to be strong, and it is:

AT30 = 0.22 * FANGt-1 + 2723.43, r2 = 0.94.

Critically for investing and trading purposes there is strong serial correlation in this model’s residuals as evidenced by its low Durbin-Watson statistic of 0.02. Divergences in the relationship between the ICE Asia Tech 30 Index and the NYSE® FANG+™ Index are persistent, trending and thus may be suitable for longer-term investment as well as for shorter-term trading strategies. These trends are quite visible in the map of the model’s residuals.

Residuals of ICE Asia 30 Tech Index = f(NYSE FANG+ Index) Have Strong Serial Correlation

Source: ICE

Analysis of the residuals from September 2014 to June 2021 in the linear model above suggest two straightforward trading approaches:

  • If a simple technical model of the NYSE® FANG+™ Index shows an uptrend/downtrend and the residuals of the linear model are rising/falling, then consider taking a long/short position in the Micro Asia Tech 30 Index Futures. This is a risk-augmentation strategy as it would leave you with two long or short positions simultaneously.
  • Conversely, a risk-neutral strategy in such an environment would have you take opposite positions in a 9:2 ratio of Micro Asia Tech 30 Index Futures relative to Micro NYSE® FANG+™ Index Futures.

The following conclusions are neat, simple and inescapable: business, and indeed all aspects of life, is increasingly technology-based. Simple economics make it so as the efficiency of technology as a production factor can be improved in a way that the efficiency of labor, capital and land cannot be. Further, due to the advances in telecommunications the real domicile of technology does not lie in Asia, the United States or any other geographic region but rather in the human mind.

However, as the world continues to spin in a 24-hour cycle and as we create different legal and regulatory environments for securities, it seems quite natural to aggregate technology-related issues into separate but strongly related indices such as the ICE Asia Tech 30 Index and NYSE® FANG+™ Index, even allowing for the inclusion of Alibaba Group and Baidu in both indices. The highly tradeable differences based on past performance between the two index futures not only allow professional investors and asset allocators to shift funds in both market directions quickly and with great capital efficiency for purposes of investment and risk management, but they also encourage opportunistic traders to provide the market liquidity necessary for thriving futures contracts.

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Appendix: Specifications of the ICE Asia Tech 30 and NYSE® FANG+™ Indices

Per the comments made above, the thirty stocks are distributed across the technology, media & communications and consumer discretionary sectors as definitional overlap is unavoidable. As an important aside, as we look at comparative performances, two of the NYSE® FANG+™ Index components, Alibaba and Baidu, are members of the ICE Asia Tech 30 Index as well. Complete details of the ICE Asia Tech 30 and NYSE FANG+™ Indices’ composition and construction methodology are available in the index methodology documents found at: https://www.theice.com/equity-derivatives/ice-asia-tech-30-index and https://www.theice.com/fangplus, respectively.

The table presents the composition of the ICE Asia Tech 30 Index as of its June 2021 reconstitution:

The number of constituents is fixed at 30 with a modified float-adjusted market-capitalization weighting scheme. The five largest components, Taiwan Semiconductor, Tencent Holdings, Alibaba Group, Samsung Electronics and Meituan, account for just under 50% of the index weight giving the index a large exposure to the Chinese e-commerce sector. The index is reconstituted after the close of the third Friday in the March/June/September/December quarterly cycle.

The Micro Asia Tech 30 Index Futures contracts are based on a $2 multiplier to the cash index and trade in half-point increments for a $1 tick. Contracts are listed on the first and second contract months as well as the following two quarters. The last trading day is the business day preceding the last business day of the contract month; trading stops at 4:00 PM Singapore time. The contract is cash-settled to the nearest 0.5 index point.

The NYSE® FANG+™ Index is comprised at present of Facebook, Apple, Alphabet, Amazon.com, Alibaba Group, Netflix, NVIDIA, Baidu, Tesla and Twitter. It is an equal-weighted index reconstituted on the third Friday of the March/June/September/December quarterly cycle. The Micro NYSE® FANG+™ Index Futures contracts are based on a $5 multiplier to the cash index and trade in index points to two decimal places for a 20 index points = $1 tick size. The last trading day is the third Friday of the expiration month; trading stops at 9:30 AM Eastern time. Contracts are cash-settled and are listed on the March/June/September/December quarterly cycle.

Howard L. Simons is president of Rosewood Trading, Inc. (AKA Simons Research). He has served as an economist, oil trader, trading systems designer, director of research, consultant to exchanges, broker-dealers, private banks and hedge funds and professor of finance. Simons writes on macroeconomic and financial market topics including fixed-income, commodities, currencies, derivatives, and equity markets.

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