AI & Big Data, now on SGX: Gain global exposure from just SGD 1

• Why AI & Big Data: AI adoption is accelerating across industries, driving significant investment in semiconductors, data centres, cloud infrastructure and cybersecurity and creating a broader ecosystem of potential beneficiaries. 

• Patent-based approach: The Nasdaq Global Artificial Intelligence and Big Data Index (NYGBIG) uses approved patent activity to identify companies driving innovation across eight AI and Big Data sub-themes, with its methodology assessing both the breadth and depth of companies’ technological exposure. 

• Beyond traditional tech: The index looks beyond conventional technology companies, allowing investors to gain exposure to businesses across sectors that are increasingly adopting and benefiting from AI and Big Data. 

• Strong long-term performance: NYGBIG has delivered strong returns since its 2018 launch, outperforming the Nasdaq-100 and several AI-focused peers over longer time horizons. 

• Accessible through SGX: The iFAST Xtrackers Artificial Intelligence & Big Data UCITS Index ETF is traded in SGD, has a one-share board lot and an initial issue price of SGD 1, with SRS eligibility after listing. The Initial Offer Period runs from 1 to 14 October 2026, with listing targeted for 22 October 2026.

Artificial intelligence (AI) is rapidly becoming part of everyday life, while businesses are increasingly integrating the technology into their operations. This broadening adoption is creating growing demand for the technologies and infrastructure needed to develop and deploy AI, with nearly nine in ten respondents in McKinsey's 2026 State of AI survey saying their organisations regularly use AI in at least one business function.

Supporting this growing use of AI requires substantial investment across the technology ecosystem. Gartner expects worldwide AI spending to reach about USD 2.7 trillion in 2026, up almost 50% from a year earlier, with more than half expected to go towards AI infrastructure such as servers and data centres. Semiconductors sit at the heart of this build-out, as every AI model depends on advanced chips to train and run, alongside the memory and networking hardware needed to support them. Hyperscalers are also estimated to spend approximately USD 800 billion on capital expenditure in 2026, much of which is going towards the computing infrastructure required for AI.

The opportunity, however, extends beyond companies developing AI models and the infrastructure that supports them. AI adoption is spreading across industries such as financial services, retail and telecommunications, while the increasing reliance on data and cloud infrastructure is also driving greater demand for cybersecurity to protect sensitive data and systems. This creates a broad ecosystem of companies that can benefit as AI adoption continues to deepen.

For investors, this represents an opportunity to gain exposure to a structural growth theme spanning multiple technologies and industries. However, identifying the companies best positioned to benefit is not straightforward. Technologies are evolving rapidly, and companies' exposure to them can shift as new applications emerge. A systematic approach based on technological innovation can therefore help investors identify companies with meaningful exposure to these trends.

Introducing the iFAST Xtrackers Artificial Intelligence & Big Data UCITS Index ETF

Scheduled to list on SGX on 22 October 2026, the iFAST Xtrackers Artificial Intelligence & Big Data UCITS Index ETF (SGX:XBG) is one of four new ETFs launched by iFAST in collaboration with DWS Asset Management. It will be the first ETF on SGX to offer direct exposure to global technology companies, complementing the exchange’s existing lineup of Asian tech ETFs.

Investors can subscribe to the ETF during its Initial Offer Period (IOP) from 1 October to 14 October 2026 via iFAST.

The ETF aims to track as closely as possible, before fees and expenses, the performance of the Nasdaq Global Artificial Intelligence and Big Data Index (NYGBIG), by investing in shares of the Xtrackers Artificial Intelligence and Big Data UCITS ETF (LSE: XAID).

Table 1: Key information about the ETF

ETF Details
Underlying index	Nasdaq Global Artificial Intelligence and Big Data Index
Issue Price	SGD 1 per share
Initial Offer Period (IOP)	1 Oct to 14 Oct 2026
Target Listing Date	22 Oct 2026
Base Currency	SGD
Trading Currency	SGD
SGX Code	XBG
Trading Board Lot Size	1 Share
Management Fee	0.25% p.a.
Total Expense Ratio	0.63% p.a.
Distribution Policy	NIL
Subscription Mode	Cash is eligible during the IOP, while SRS eligibility will commence after listing.
Source: iFAST Compilations Data as of 30 Sept 2026

NYGBIG is designed to capture companies at the forefront of AI and Big Data. It selects companies active across eight sub-themes, based on the relevance of their approved patent activity:

Table 2: NYGBIG's AI and Big Data sub-themes

Theme	Sub-themes
Artificial Intelligence	Deep Learning, Image Recognition, Natural Language Processing (NLP), Speech Recognition & Chatbots
Data Computing & Processing	Big Data, Cloud Computing, Cyber Security, Quantum Computing
Source: DWS. Data as of July 2026.

Why patents?

A key feature of the index is its use of patent activity to identify companies driving technological innovation, which has become an increasingly important driver of corporate earnings and share price performance. Research by Nasdaq found that, among global large caps, the top quartile of companies by R&D as a percentage of sales delivered substantially stronger sales growth and share price performance than both the remaining three quartiles and companies with no R&D spending.

Figure 1: Companies with the highest R&D intensity have delivered the strongest returns

Patent filings tell a similar story. When Nasdaq grouped global large caps by their 2013 patent activity, companies that filed patents went on to achieve markedly faster sales growth over the following decade than the broader market, while companies with no patents saw their sales decline.

Figure 2: Patent filings have been a leading indicator of sales growth 

While R&D spending and patent filings both point to the value of innovation, NYGBIG uses patents rather than R&D or other conventional measures to identify its constituents, for several reasons.

R&D spending is a useful gauge of how much a company invests in innovation, but it offers investors little edge in the market, as it is reported on companies’ financial statements and is readily available to all market participants. R&D is also a broad measure. It covers a wide range of activities, from product and software development to hiring researchers and acquiring data, and does not show how much of that spending is directed towards a specific technology such as AI.

Patents, by contrast, offer a more direct and potentially more forward-looking indication of innovation in a specific technology, often before that innovation translates into meaningful revenue. However, patent filings are unstructured, text-heavy documents that are challenging and costly for investors to collect, classify and analyse at scale. This is where NYGBIG stands out: Nasdaq’s in-house AI team uses a natural language processing model to read and classify millions of approved patents. By systematically analysing this alternative data, the index can identify companies with significant patent activity in specific emerging technologies, potentially providing earlier exposure to innovation trends than measures based on reported financial results.

Other measures also have their limitations. Revenue is inherently backward-looking. A company may have significant exposure to an emerging technology without yet generating substantial revenue from it, and by the time the technology shows up clearly in its sales, much of the early high-growth phase may have passed. Counting mentions of AI in news coverage, earnings calls or annual reports is another approach, but management may have an incentive to talk up its involvement in a popular theme. Such signals can therefore reflect aspirations about future opportunities rather than tangible evidence of technological development.

How NYGBIG selects its constituents

The selection process begins with the parent index, the Nasdaq Global Disruptive Technology Benchmark2™ Index (NYDTB2), which tracks a broad universe of companies engaged in disruptive technological themes. These include artificial intelligence, robotics, automotive innovation, healthcare innovation, new energy and environment, electrification, the internet of things, future mobility, and data computing and processing.

As of the latest reconstitution in July 2026, the parent index comprised 3,286 securities, of which 1,181 held patents related to at least one of the eight NYGBIG sub-themes, based on rolling two-year patent data.

For each sub-theme in which a company has patent activity, it receives two key scores: a Pure Score and a Contribution Score.

The Pure Score measures how strongly a company's patent activity is concentrated in a particular AI and big data sub-theme relative to its overall patent portfolio. A higher Pure Score indicates that a greater proportion of the company's patent activity is focused on that particular technology.

The Contribution Score, meanwhile, measures a company's relative importance within a particular sub-theme by assessing the company's share of recently filed approved patents relative to other companies active in the same sub-theme. A higher Contribution Score therefore indicates that a company makes a greater contribution to innovation within that technology area.

The two scores are complementary. The Pure Score identifies where a company is focusing its innovation efforts, while the Contribution Score indicates how significant the company is within that technology area.

Rather than ranking all companies against one another, the index divides securities into Comparison Groups based on their market capitalisation segment (large, mid or small cap).

This makes comparisons more meaningful, as each company is assessed against peers of a similar size that are active in the same technology area.

Within each Comparison Group, an existing constituent must have both its Pure Score and Contribution Score at or above the 50th percentile to remain eligible. A potential new entrant faces a higher bar: both scores must be at or above the 65th percentile (top 35%).

Each sub-theme in which a company qualifies adds one point to its Intensity Score, which can range from one to eight. For instance, if a company has patents in three sub-themes but qualifies in only one based on its Pure and Contribution Scores, it will have an Intensity Score of one. The Intensity Score therefore rewards breadth: companies that clear the bar across more sub-themes score higher.

Figure 3: Screening companies by Pure Score and Contribution Score percentiles

Source: DWS, as of end July 2026.

Companies that remain eligible are then separated into two pools: Primary Subsector securities and High Relevance securities.

Primary Subsector securities are companies belonging to selected Industry Classification Benchmark (ICB) subsectors considered particularly relevant to AI and big data. These include Computer Services, Software, Consumer Digital Services, Semiconductors, Electronic Components, Production Technology Equipment, Computer Hardware, Telecommunications Equipment, and Telecommunications Services.

These Primary Subsector companies form the core of the index. If there are more than 100 eligible Primary Subsector securities, they are ranked first by Intensity Score, then by average Contribution Score percentile, and finally by six-month average daily traded value. This favours companies with broader involvement across the relevant technologies, stronger contributions within those technologies and sufficient liquidity.

However, the index is not restricted solely to companies classified within these traditional technology-related subsectors. This is where High Relevance securities come into play.

Companies outside the Primary ICB subsectors can still qualify if their patent activity demonstrates sufficiently strong and broad relevance to the AI and big data themes. To be considered, a High Relevance security must have a higher Intensity Score than the lowest-scoring Primary Subsector security.

This allows the index to capture companies that are highly relevant to AI and big data despite falling outside conventional technology classifications. In other words, the methodology is designed to follow where technological innovation is taking place, rather than simply restricting the index to companies that happen to carry a technology-related industry classification.

The final index can contain up to 100 securities, of which no more than five can be High Relevance securities. Constituents are weighted by float-adjusted market capitalisation, with individual securities capped at 4.5%, and the index is reconstituted and rebalanced twice a year, in January and July.

The resulting portfolio reflects the diverse range of sectors participating in the AI and big data ecosystem. While technology naturally accounts for the majority of the portfolio at 72.0% as of 30 September 2026, the index also offers exposure to other sectors such as telecommunications (10.7%), consumer discretionary (10.6%) and financials (3.1%).

The inclusion of Amazon and Walmart, which are classified as consumer discretionary rather than technology companies, shows that the index looks beyond conventional industry classifications. Walmart, for instance, is integrating AI across its retail operations, from customer-facing applications to supply chain management. Similarly, Bank of America, which was among the index’s top 10 holdings at the July 2026 rebalancing, reflects the growing adoption of AI in financial services, in areas such as customer service and fraud detection.

Geographically, the index is heavily weighted towards the US, which accounts for 84.3% of the portfolio. It also provides exposure to Asian markets, with South Korea, China, Taiwan and Japan collectively accounting for 13.2% of the portfolio.

Figure 4: Sector breakdown

Table 3: Top 10 holdings

Company	Weight	Market	Sector
Microsoft	5.8%	United States	Technology
Meta Platforms	5.4%	United States	Technology
Apple	4.8%	United States	Technology
NVIDIA	4.8%	United States	Technology
Advanced Micro Devices	4.4%	United States	Technology
Amazon	4.4%	United States	Consumer Discretionary
Alphabet	4.0%	United States	Technology
Micron Technology	3.9%	United States	Technology
Samsung Electronics	3.9%	South Korea	Telecommunications
Walmart	3.9%	United States	Consumer Discretionary
Source: DWS. XAID ETF is used as a proxy for the NYGBIGN Index. Weights may drift between rebalancing dates, causing some holdings to exceed 4.5%. Data as of 30 September 2026

Figure 5: Geographical breakdown

Strong performance and continued index enhancement

The effectiveness of this approach has been reflected not only in the index's ability to identify innovative companies, but also in its long-term performance. Since its launch on 12 November 2018, the Nasdaq Global Artificial Intelligence and Big Data Index Net Total Return (NTR) has delivered a total return of approximately 383%, equivalent to an annualised return of 22.1%, as of 30 September 2026.

The index has also outperformed the tech-heavy Nasdaq-100 index as well as the majority of its AI peers across longer-time horizons, delivering three- and five-year annualised returns of 35.8% and 20.6% respectively.

Table 4: NYGBIG has delivered strong long-term performance

ETFs/Index	Annualised Total Returns (SGD Terms)
	1Y	3Y	5Y
Nasdaq Global AI and Big Data Index NTR	41.2%	35.8%	20.6%
Robo Global Artificial Intelligence ETF	50.6%	38.8%	16.5%
Global X Artificial Intelligence & Technology ETF	30.8%	31.5%	15.3%
WisdomTree Artificial Intelligence UCITS ETF	46.9%	29.6%	13.4%
iShares Future AI & Tech ETF	69.6%	34.3%	12.7%
Nasdaq-100 Index	22.9%	25.5%	15.1%
Source: Bloomberg Finance L.P., iFAST Compilations. Data as of 30 September 2026

Importantly, NYGBIG is not a static index. As innovation cycles accelerate and new technologies emerge, the methodology is periodically enhanced to keep the index aligned with evolving sources of future growth.

The July 2026 methodology update introduced four key changes designed to strengthen the index’s ability to identify companies demonstrating sustained innovation, broaden its investment universe, improve its representation of emerging technologies and capture new innovators sooner.

First, the index extended its patent assessment window from one year to a rolling two-year period. Patent activity can fluctuate from year to year due to differences in filing schedules, research cycles and the timing of patent approvals. By assessing a longer period, the methodology seeks to provide a more comprehensive view of a company's ability to consistently generate intellectual property and identify businesses contributing to longer-term technological advancement.

Second, the index replaced the Nasdaq Global Disruptive Technology Benchmark™ Index (NYDTB™) with the broader Nasdaq Global Disruptive Technology Benchmark2™ Index (NYDTB2™) as its parent universe. The updated universe can also incorporate eligible China A-shares accessible through the Shanghai-Hong Kong and Shenzhen-Hong Kong Stock Connect programmes, broadening the pool of companies from which NYGBIG can select its constituents.

Third, quantum computing was added as a dedicated sub-theme. The addition expands the index's coverage of emerging technologies and reflects growing research and investment activity in quantum computing, as well as its potential applications alongside AI, advanced analytics and next-generation computing.

Finally, qualifying initial public offerings (IPOs) can now be added to the index between its semi-annual reconstitutions, during dedicated windows in April and October, rather than having to wait until the next scheduled reconstitution. This reduces the lag between a company’s public listing and its potential inclusion, allowing the index to reflect emerging innovators more quickly.

Together, these enhancements allow NYGBIG to evolve alongside the technologies and companies shaping the AI and Big Data ecosystem, rather than relying on a fixed definition of today's AI leaders.

The case for an SGX-listed global AI & Big Data ETF

Beyond its exposure to the AI and Big Data themes, the iFAST Xtrackers Artificial Intelligence & Big Data UCITS Index ETF offers several practical benefits for Singapore investors.

First, as the ETF is traded in SGD, investors do not need to convert currencies when entering or exiting their positions.

Second, the ETF makes it easier to start small or invest regularly. With a one-unit board lot and an initial issue price of just SGD 1, investors can build their exposure gradually without having to commit a large amount upfront. By comparison, a single share of the London Stock Exchange-listed Xtrackers Artificial Intelligence and Big Data UCITS ETF (XAID) costs around USD 250, as of 1 October 2026.

Finally, the ETF will be eligible for investment using Supplementary Retirement Scheme (SRS) funds after listing. With uninvested SRS balances earning just 0.05% p.a. in interest, investors can put their SRS funds to work in pursuit of long-term capital growth for their retirement.

Declaration:

This research report was prepared with the assistance of artificial intelligence (AI) tools. iFAST Financial Pte Ltd does not rely exclusively on AI for content generation; the content of this report – including all investment theses, ratings, price targets and conclusions – has been independently reviewed and verified by the research analyst(s) to ensure accuracy and professional integrity.

For specific disclosure, at the time of publication of this report, IFPL (via its connected and associated entities) and the analyst who produced this report hold a NIL position in the abovementioned securities.