AI big data and other disruptors are driving tech stock dispersion

Tech stocks have historically generated a greater spread of returns than any other sector in the market.¹ Constant changes in the industry give certain tech companies an advantage over those not equipped to compete successfully for customers. The bigger the change, the more likely new industry leaders will emerge, while other businesses will be left behind. For many of the same reasons, this division of extremes may also apply to the analysis of tech stocks, with generalist investors at a potential disadvantage relative to career technology sector specialists who have the ability and expertise to take both long and short positions.

Consider, for example, artificial intelligence, or AI—the science of teaching machines how to think and solve problems. Advancements in machine learning and the ways that computers process information along with the collection and storage of unprecedented amounts of data—called big data—are leading to a revolution in AI that will affect nearly all sectors of the global economy. AI is already being used in a wide range of day-to-day applications, including web searches, directed online advertising, and financial trading programs, as well as self-driving cars, virtual reality devices, and disease diagnosis.

Big data analysis with parallel processing, which was previously just used for gaming applications, makes use of graphics processing units (GPUs) instead of the central processing units (CPUs) typically used in servers and PCs. GPUs create neural networks, which can perform many complex calculations, learning from each task to become smarter. This use of parallel processing poses a direct competitive challenge to traditional x86 processors.

Online search and social networking companies are using the big data they collect from users to deliver customized advertiser content and extract higher ad rates. Companies with the most information about their users’ interests are the most likely to attract advertisers, and companies with inferior data are at risk of losing out on ad revenue.

Software companies are developing applications to help customers analyze and better understand their big data and use it to retain their customers and run their businesses more efficiently. Traditional information technology consulting firms are being displaced by these software technologies. We expect AI to cause this same trend to play out across other sectors of the economy, as machines replace humans to perform a growing range of job functions.

We believe that the most successful tech investors will be those who are able to identify the long-term trends that will change the way we live our lives. That said, there are several shorter-term factors that can lead to volatility in the tech sector, resulting in opportunities for long/short equity managers specializing in information technology.

• Spending initiatives in key end markets can influence near-term demand patterns. A supplier of telecom equipment may see a surge in orders driven by a major project on network infrastructure upgrades. Such high order volume might be sustained for a few quarters, or a few years, but it can also be lumpy and end abruptly at any time. As a result, a series of better-than-expected quarterly earnings reports for our hypothetical telecom equipment company could be followed by a series of disappointments, a pattern that generalist investors may not be expecting to see.
• Product cycles can represent an important demand driver for a tech company, as well as its suppliers. A new smartphone launch by a major handset supplier would be supported by a combination of semiconductor manufacturers, screen suppliers, distributors, assembly partners, and contract manufacturers. The success or failure of a particular product launch can result in unexpected estimate revisions, positive or negative, throughout the entire supply chain, which career tech sector specialists may have more insight to recognize.
• Inventory cycles can create opportunities. Tech supply chains gear up production according to an anticipated level of demand for an end product. When actual demand differs from expectations, suppliers may be caught with too much or too little inventory. Changes in order patterns caused by inventory adjustments can happen fast, resulting in upside or downside earnings surprises. Here again, the tech specialist may have an advantage over the generalist investor who may be unaware of certain linkages in the inventory cycle.

Inconsistent operating results tied to such short-term factors can lead to large and sudden changes in stock prices. Generalists sometimes expect—erroneously—a continuation of near-term trends, whether they’re positive or negative. We believe this can provide scope for sector specialists with deep subindustry expertise to identify and exploit inefficiencies in the tech sector.

Companies with the ability to collect and analyze big data and pair it with AI to drive cost savings or new sources of revenue will find themselves with a distinct competitive advantage, but companies unable to adapt risk irrelevance. By extension, we expect long/short tech sector specialists to find themselves with a distinct competitive advantage relative to generalist investors, at least when it comes to distinguishing—and exploiting the differences between—the winners and losers in information technology.

1 Wellington Management, Standard & Poor’s, 2017.