Is REAL trading more suitable than HALO for the Asia-Pacific stock market?

The impact of AI is reshaping the logic of valuations in global stock markets. Given the structural peculiarities of the Asia-Pacific markets, Bank of America Securities believes that the crowded "HALO" (heavy assets, low attrition rate) trades have obvious limitations, and identifying investment opportunities using the REAL (Regulatory barriers, Enduring cycles, Asset heavy, Local services) framework may be more appropriate—especially in markets where capacity is abundant and heavy industry lacks scarcity-driven moats, the protective effects of the HALO strategy may be at risk of overestimation.

According to ZF Trading Desk, BofA analyst Winnie Wu's team wrote in a report on the 16th that the U.S. software sector has lost over $2 trillion in market capitalization over the past five months, India's IT sector has fallen more than 40% since its high in December 2024, and Asia-Pacific media, e-commerce, and fintech stocks have also been massively sold. Meanwhile, funds have been quickly rotating into heavy asset sectors like semiconductors, capital goods, energy, and utilities, boosting the popularity of "HALO" trades.

However, BofA warns that the HALO strategy has limitations in markets with abundant industrial capacity. In heavy industries lacking regulation or a scarcity moat, heavy assets may turn into liabilities, since AI can shorten R&D cycles and create alternative technology paths, intensifying overcapacity and competition.

Therefore, the REAL framework has been introduced to reassess business survival risks. This framework screens companies through four dimensions: regulatory barriers, enduring cycles, asset-heavy characteristics, and local service intensity. BofA’s research shows that even in software and consumer internet sectors most severely affected by AI, leading firms with REAL characteristics still show remarkable resilience to declines and long-term investment value.

Limitations of the HALO Strategy: Heavy Assets Are Not a Universal Moat

The core logic behind the market chasing HALO sectors is that tangible assets take a long time to build and are difficult for AI to quickly replace. This logic is reasonable in fields where asset scarcity is secure, but its limitations are evident in markets with abundant engineering talent and industrial capacity.

BofA points out that heavy industries without regulatory constraints or scarcity moats (such as automobiles, solar, steel, and cement) can have supply easily outpace demand, leading to fierce price competition. In these areas, heavy assets not only fail to provide protection but may even become burdens—especially when AI further shortens R&D cycles and opens alternative technology routes. Conversely, some light asset industries that highly rely on human services (such as healthcare and dining) may have more enduring resistance to AI substitution.

BofA also notes that the REAL framework does not imply these companies are immune to AI. AI can expand their addressable market, reduce operating costs, and even compress innovation cycles and lower industry barriers. The key judgment is: Leading companies in high REAL moat industries face significantly lower existential threat than their counterparts in low REAL sectors, especially in an environment of rapid AI-driven disruption, where the valuation compression of low REAL sectors may be more persistent and deeper.

The Four REAL Moats: Redefining Defensiveness

BofA defines the four dimensions of the REAL framework as follows:

Regulatory Barriers (Regulatory Critical): Systemically important banks, telecom operators, power and energy suppliers, and defense-related industries. These sectors relate to social stability and national security, with regulators imposing strict requirements on licensing, foreign ownership ratios, and critical infrastructure operations. The introduction of AI often results in higher monitoring and supervision requirements, increasing compliance costs instead of lowering barriers.

Enduring Cycles: Semiconductors, capital goods, aerospace and shipbuilding, pharmaceuticals/biotech, and game IP. Barriers in these industries are due to the accumulation of time in the real world—advanced chip processes rely on multi-generational knowhow and EUV equipment; new wafer fab construction takes years; aircraft must go through complex certification; drugs undergo multi-stage clinical and regulatory review; and game copyrights usually last 50–70 years, supporting sustained IP monetization. AI can optimize some processes but cannot bypass lengthy certification and verification requirements.

Asset Heavy: Natural resources and commodities, power grids and utilities, railways and ports, and livestock farming. Scarcity here is driven by physical resource constraints—mineral reserves are limited, permit processes are lengthy, infrastructure costs are high, and new entrants struggle to economically replicate existing assets.

Local Services: Hotels, catering and property management, childcare, eldercare and pet care, as well as on-site IT deployment and maintenance. These jobs involve non-standardized environments and require high adaptability, with low tolerance for errors—AI and robots currently cannot offer economical substitutes here.

REAL Distribution Across Markets: ASEAN’s Short-Term Advantage and Long-Term Concerns

From the sector composition of the MSCI Asia Pacific Index, each market's exposure to AI risk varies greatly. Southeast Asian markets are highly tilted towards high REAL sectors: about 79% in Singapore, 87% in Malaysia, and 94% in Indonesia are concentrated in these sectors, with banks as the core weight. This structure gives them relative resilience in an AI narrative-dominated market—Thailand has risen 14.6% year-to-date, Malaysia is up 5.1%, both outperforming India (-10.6%).

However, BofA also warns that the short-term resilience of Southeast Asian markets may turn into long-term fragility. If AI-driven automation lowers manufacturing costs in developed economies and makes nearshoring more attractive, demand for offshore labor-intensive production will fall. Vietnam, Malaysia, and Thailand have high FDI-to-GDP ratios and export dependence, and without sufficient digital infrastructure and AI talent, they face structural challenges like widening technology gaps and derailed global supply chain integration.

AI Impact and Population Aging: Dual-Axis Analysis of Sector Structure

BofA combines the impact of AI disruption with Asia’s low birth rates and accelerating population aging, creating a dual-axis matrix to assess the structural positioning of each sector in the mid to long term.

Healthcare, semiconductors, capital goods, and insurance are in the best position: They enjoy high short-term AI moats and benefit from aging-driven demand for automation and increased spending by wealthier elderly populations, making them "structural opportunity" sectors with defensive traits and long-term growth prospects.

Real estate, utilities, and banks have high REAL moats sufficient to buffer AI-driven valuation shocks in the short term, but all face long-term pressure from aging on housing formation and discretionary consumption. BofA concludes these sectors "offer short-term protection against AI volatility, but limited upside for long-term revaluation." Consumer durables, media and entertainment, retail distribution, and automobiles face the most pronounced pressure—low AI moats and aging drag on discretionary spending combine as dual negatives, making these sectors currently the most concentrated areas of risk.

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The above content is from ZF Trading Desk.

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