Asia Capital Markets Report 2026

Annex A. Methodology for data collection and classification

Regional classifications

Table A A.1. Mutually exclusive regional classification used in the report

Region	Jurisdictions
Asia excl. China & Japan	Bangladesh, Cambodia, Hong Kong (China), India, Indonesia, Korea, Lao PDR, Malaysia, Mongolia, Pakistan, the Philippines, Singapore, Sri Lanka, Chinese Taipei, Thailand and Viet Nam.
Europe	Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Italy, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Republic of Ireland, Romania, Slovak Republic, Slovenia, Spain, Sweden, Switzerland and the United Kingdom.
Latin America	Antigua and Barbuda, Argentina, Aruba, Bahamas, Barbados, Belize, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominica, Dominican Republic, Ecuador, El Salvador, Grenada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Suriname, Trinidad and Tobago, Uruguay and Venezuela.
Rest of the world	All other world jurisdictions excluding: Asia excl. China & Japan, People's Republic of China, Europe, Japan, Latin America and United States.
Individual jurisdictions	People's Republic of China, Japan and United States.

Table A A.2. Ad-hoc regional classifications used in the report

Region	Jurisdictions
Asia	Bangladesh, Cambodia, People's Republic of China, Hong Kong (China), India, Indonesia, Japan, Korea, Lao PDR, Malaysia, Mongolia, Pakistan, the Philippines, Singapore, Sri Lanka, Chinese Taipei, Thailand and Viet Nam.
ASEAN	Cambodia, Indonesia, Lao PDR, Malaysia, the Philippines, Singapore, Thailand and Viet Nam.
Europe (used in Chapter 4)	Albania, Andorra, Austria, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Italy, Latvia, Liechtenstein, Lithuania, Luxembourg, North Macedonia, Malta, Moldova, Monaco, Montenegro, Netherlands, Norway, Poland, Portugal, Republic of Ireland, Romania, San Marino, Serbia, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Ukraine and the United Kingdom.
Other Developed Asia	Chinese Taipei, Hong Kong (China) and Korea.
South Asia	Bangladesh, India, Pakistan and Sri Lanka.

Chapter 1

Public equity

The information on initial public offerings (IPOs) and secondary equity offerings (SEOs) presented in this report is based on transaction and/or firm level data from several commercial databases (LSEG, FactSet and Bloomberg). Considerable resources have been committed to ensuring the consistency and quality of the dataset. Different data sources are checked against each other and, whenever necessary, the information is also controlled against original sources, including regulator, stock exchange and company websites and financial statements.

Country coverage and classification

The dataset includes information on all initial public offerings (IPOs) and secondary equity offerings (SEOs or follow-on offerings) by financial and non-financial companies. All public equity listings following an IPO, including the first-time listings on an exchange other than the primary exchange, are classified as an SEO. If a company is listed in more than one exchange within 180 days, those transactions are consolidated under one IPO. The country breakdown is carried out based on the domicile country of the issuer. In the dataset, an offering made by an issuer is attributed to the country where the issuer has its primary listing. It is possible that a company becomes listed in more than one country when going public. The financial databases record a dual listing as multiple transactions for each country where the company is listed. However, there is also a significant number of cases where dual listings are reported as one transaction only based on the primary market of the listing. For this reason, the country breakdown based on the stock exchange is currently carried out based on the primary listing of the issuer. The IPO and SEO data are collected on a deal basis via commercial databases in current USD values. Issuance amounts initially collected in USD were adjusted by 2025 US Consumer Price Index (CPI).

Exclusion criteria

Table A A.3. Exclusion criteria for IPOs and SEOs

Exclusion criteria for IPOs and SEOs
Financial companies that conduct trust, fiduciary and custody activities	Asset management companies that deal with trusts, estates and agency accounts
Asset management companies such as health and welfare funds, pension funds and their third‑party administration, as well as other financial vehicles	Special Purpose Acquisition Companies (SPACs)
Open-ended investment funds	Closed-ended investment funds
Other financial vehicles	Listings on an over-the-counter (OTC) market
Grant making foundations	Security types classified as “units” and “trust”
Real Estate Investment Trusts (REITs)	Transactions with missing or zero proceeds

Industry classification

LSEG uses the Reference data Business Classification (TRBC) Industry Description. The economic sectors used in the analysis are the following:

Table A A.4. Public offerings industry classification

TRBC Economic Sector
Basic materials	Industrials
Cyclical consumer goods & services	Non-cyclical consumer goods & services
Energy	Real estate
Financials	Technology
Healthcare	Utilities

The following economic sectors and activity categories are used to identify tradable sectors in the chapter. Based on activity-level codes from the Refinitiv Business Classification (TRBC), the classification includes goods-producing industries with significant exposure to international trade and therefore greater potential exposure to tariff measures. It serves as a simplified proxy for tariff exposure, as not all firms within these sectors are exporters and indirect effects may also affect firms operating in other industries.

Table A A.5. Sector and activity classification for tradable sectors

TRBC Economic Sector	TRBC Activity	TRBC Economic Sector	TRBC Activity
Basic materials	Commodity Chemicals	Consumer cyclicals	Appliances, Tools & Housewares
Basic materials	Agricultural Chemicals	Consumer cyclicals	Home Furnishings
Basic materials	Specialty Chemicals	Consumer cyclicals	Recreational Products
Basic materials	Diversified Chemicals	Energy	Integrated Oil & Gas
Basic materials	Non-Gold Precious Metals & Minerals	Energy	Oil & Gas Exploration and Production
Basic materials	Iron & Steel	Energy	Oil & Gas Refining and Marketing
Basic materials	Aluminium	Energy	Oil & Gas Drilling
Basic materials	Specialty Mining & Metals	Energy	Oil Related Services and Equipment
Basic materials	Gold	Energy	Renewable Energy Equipment & Services
Basic materials	Mining Support Services & Equipment	Energy	Renewable Fuels
Basic materials	Diversified Mining	Energy	Uranium
Basic materials	Construction Materials	Healthcare	Advanced Medical Equipment & Technology
Basic materials	Forest & Wood Products	Healthcare	Medical Equipment, Supplies & Distribution
Basic materials	Paper Products	Healthcare	Pharmaceuticals
Basic materials	Non-Paper Containers & Packaging	Healthcare	Biotechnology & Medical Research
Basic materials	Paper Packaging	Industrials	Aerospace & Defense
Consumer non-cyclicals	Brewers	Industrials	Industrial Machinery & Equipment
Consumer non-cyclicals	Distillers & Wineries	Industrials	Heavy Machinery & Vehicles
Consumer non-cyclicals	Non-Alcoholic Beverages	Industrials	Electrical Components & Equipment
Consumer non-cyclicals	Food Processing	Industrials	Heavy Electrical Equipment
Consumer non-cyclicals	Tobacco	Industrials	Shipbuilding
Consumer non-cyclicals	Household Products	Technology	Semiconductors
Consumer non-cyclicals	Personal Products	Technology	Semiconductor Equipment & Testing
Consumer cyclicals	Auto & Truck Manufacturers	Technology	Communications & Networking
Consumer cyclicals	Auto, Truck & Motorcycle Parts	Technology	Electronic Equipment & Parts
Consumer cyclicals	Tires & Rubber Products	Technology	Computer Hardware
Consumer cyclicals	Textiles & Leather Goods	Technology	Phones & Handheld Devices
Consumer cyclicals	Apparel & Accessories	Technology	Household Electronics
Consumer cyclicals	Footwear

Listing information

The information on the number of listed companies and their market capitalisation is based on LSEG. The information refers to end-year data. The 2025 dataset contains information for 50 795 companies listed across 104 jurisdictions.

Table A A.6. Cleaning criteria for listed company information

Cleaning criteria for listed company information
Securities with instrument type classified as “trust”, “fund” and “ETF” are excluded.
For firms with multiple listings, only primary listings are kept.
For firms with multiple observations but different countries of domicile, their true country of domicile is manually checked to remove the duplicates.
Firms trading on over-the-counter (OTC) markets are excluded.
Securities with industry classification of Real Estate Investment Trusts (REITs), Special Purpose Acquisition Companies (SPACs) and Collective Investments are excluded.

Private equity

The private equity and venture capital figures are based on OECD calculations using deal and fund-level data from Preqin. The information contains data on investors, funds, fund managers and deals. Data on fundraising and deals only include funds and deals classified by Preqin as private equity and venture capital. Private equity-style deals in infrastructure or real estate are excluded.

Fundraising data is based on funds in one of the following categories: close ended; a first, second, third, fourth, fifth or sixth close; liquidated; evergreen, open ended, semi-open ended. The latest close date is used to classify the fundraising year. Fundraising is classified through the geographic focus of funds. A significant part of this fundraising is done with a regional rather than national focus, and the regional classifications used is that provided by Preqin.

Table A A.7. Regional classifications used for private equity and venture capital

Region	Jurisdictions
North America	Bermuda, Canada, the Cayman Islands and United States.
Europe	EU countries, Albania, Andorra, Armenia, Azerbaijan, Belarus, Bosnia & Herzegovina, Georgia, Guernsey, Iceland, Jersey, Kosovo, Liechtenstein, Moldova, Montenegro, Monaco, North Macedonia, Norway, Russia, Switzerland, Turkey, the United Kingdom and Ukraine.
Asia	Afghanistan, Bangladesh, Bhutan, Brunei, Cambodia, People's Republic of China, Hong Kong (China), Fiji, India, Indonesia, Japan, Kazakhstan, Korea, Kyrgyzstan, Lao PDR, Malaysia, Maldives, Mongolia, Myanmar, Nepal, Pakistan, Philippines, Singapore, Sri Lanka, Chinese Taipei, Tajikistan, Thailand, Timor-Leste, Turkmenistan, Uzbekistan and Viet Nam.

Investment and exit deals data is based on completed deals. The regional breakdown of investment data is done by the OECD based on the country of headquarter of the target company, following OECD regional classification (Table A A.1). The following deal types have been excluded: general partner stakes, private debt, venture debt and joint venture. Investments and exits are reported according to the location of the portfolio company. Private equity data initially collected in USD were adjusted by 2025 USD Consumer Price Index.

Corporate bonds

Corporate bond analyses are based on a dataset built by the OECD using deal-level information obtained from LSEG on corporate bond issues that are underwritten by an investment bank. The database provides detailed information for each bond starting in 1980, including e.g. the identity, nationality and sector of the issuer, the type, interest rate structure, maturity date and rating category of the bond, as well as the amount of proceeds obtained from the issue and intended uses thereof.

Convertible bonds, deals that were registered but not consummated, preferred shares, sukuk bonds, bonds with an original maturity less than or equal to one year or with an issue size of less than USD 1 million are excluded from the dataset. Industry classifications are based on The Reference data Business Classification (TRBC) from LSEG. Annual issuance amounts initially collected in USD were adjusted by 2025 US Consumer Price Index (CPI).

Given that a significant portion of bonds are issued internationally, it is not possible to systematically assign issues to a certain country of issue. For this reason, the country breakdown is carried out based on the issuer’s country of domicile. The advanced/emerging market categories are based on IMF classifications.

Rating data

Credit rating analyses are based on OECD calculations using data obtained from LSEG. The calculations consider ratings from three leading agencies: S&P, Moody’s and Fitch. For each bond with an available rating in the dataset, the alphanumeric rating is transformed into 21-point numeric scale with 1 being the lowest rating (C) and 21 the highest (AAA for S&P and Fitch and Aaa for Moody’s). There are eleven non‑investment grade categories: five within C (C to CCC+) and six within B (B- to BB+); there are ten investment grade categories: three within B (BBB- to BBB+); and seven within A (A- to AAA).

For bonds with multiple ratings, the average of the available ratings is used. Some bonds do not have rating information available; these are assigned the average rating of all bonds issued by the same company in the same year (t). If the issuer has no rated bonds in year t, year t-1 and year t-2 are also considered, respectively. This procedure increases the number of rated bonds in the dataset and hence improves the representativeness of the analyses. When differentiating between investment and non-investment grade bonds, the final rating is rounded to the closest integer and bonds with a rounded rating less than or equal to 11 are classified as non‑investment grade.

Early redemption data

Bonds that are no longer outstanding due to having been redeemed before their maturity date are deducted in the annual outstanding corporate bond debt calculations. The early redemption data are obtained from LSEG and cover bonds that have been redeemed early due to being repaid via final default distribution, called, liquidated, put or repurchased. The early redemption data are merged with the primary corporate bond market data via International Securities Identification Numbers (ISINs).

Cost of outstanding debt

The weighted average cost of debt is approximated for fixed-rate bond debt by calculating the total outstanding amount of debt across 26 coupon buckets between 0 and 1 250 basis points (increasing in increments of 50 basis points, i.e. 0 to 50, > 50 to 100, etc.). The resulting amounts are then multiplied by the midpoint cost of the corresponding bucket (e.g. 25 basis points for the 0 to 50 bucket, 975 basis points for the 950 to 1 000 bucket) and divided by the total outstanding amount of all fixed-rate bond debt. The sum of those products yields an estimated value-weighted interest rate for all outstanding debt. All debt with coupons above 1 250 basis points are given a midpoint value of 1 275. Where coupon data are not available, the yield to maturity at issuance is used. For bonds that are not issued at par, this may differ from the coupon rate.

Syndicated lending

The syndicated loan analyses are based on OECD calculations using deal-level data from LSEG. This database provides detailed information on each loan, including the borrower’s identity, nationality and sector, as well as the interest rate structure, maturity date and loan amount. The loan credit rating category is defined based on the following criteria:

• Investment grade: Initial pricing up to 299 basis points above the base rate

• Leveraged: Initial pricing between 300 and 399 basis points above the base rate

• Highly leveraged: Initial pricing 400 basis points or more above the base rate.

Only loans classified as "syndicated" or "club syndicate" are included in the analysis. Deals with maturities of less than 90 days are excluded. Annual data are based on the closing date, which is when the syndication on all levels/tiers has been signed and completed. Industry-level analyses follow LSEG’s The Reference data Business Classification (TRBC), while country breakdowns are based on the borrower’s domicile. To account for inflation, issuance amounts originally recorded in USD were adjusted using the 2025 US Consumer Price Index (CPI).

Private credit

The private credit figures are based on OECD calculations using deal and fund-level data from Preqin. This database contains detailed information on private credit deals including the year, the lenders’ identity, the volume and the intended use of proceeds. It also includes information on private credit fund, including the year of establishment and size.

Assets under management are calculated at the end of a calendar year, with the exception of the datapoint for 2025 which is calculated at the end of June. The figures only consider closed-ended, unlisted private debt funds, which includes unlisted Business Development Companies (BDCs). The figures include both debt provided directly to a company without an acquirer involved (i.e. non-sponsored deals) and debt provided by an investment company in support of an investment or acquisition of a private equity sponsor (i.e. sponsored deals). Collateralised debt obligations (CDOs), infrastructure and real estate debt transactions are excluded. The calculation of AUM is based on Preqin’s internal methodology, and the regional AUM breakdown follows Preqin’s internal taxonomy. The regional breakdown of investment data is done by the OECD based on the borrowers’ country of headquarter following OECD regional classification (Table A A.1).

Sovereign bonds

For countries that are not members of the OECD, primary sovereign bond market data are based on original OECD calculations using data obtained from LSEG that provides international security-level data on new issues of sovereign bonds. The data set covers bonds issued by sovereigns in the period from 1 January 2007 to 31 December 2025 and includes both short-term and long-term debt. Short-term debt (“bills”) is defined as any security with a maturity less than or equal to 365 days but no less than 33 days, as bill issuances with a maturity less than 33 days are considered to be done for cash management purposes and excluded from calculations. Bonds issued by central banks that have no government budget financing purposes were excluded.

A number of bonds have been subject to reopening. For these bonds, the initial data only provide the total amount (original issuance plus reopening). To retrieve the issuance amount for such reopened bonds, specific data on the outstanding amount on each reopening date for the concerned bonds have been downloaded separately from LSEG. As the reopening data only provide amounts outstanding, the outstanding amount on the previous date is subtracted from the outstanding amount on that given date, in order to obtain the issuance amount on each relevant date. These calculated issuance amounts are converted on the transaction date using USD foreign exchange data from LSEG. To ensure consistency and comparability, the same method is used for all bonds, including those which have not been subject to reopening.

Outstanding debts in local currency are converted to USD using end-of-year foreign exchange rates. Exchange offers and certain bonds in the dataset have been manually excluded when they did not have any identifier (ISIN, RIC or CUSIP) and when they have not been able to be manually confirmed by comparing with official government data.

For countries that are members of the OECD, figures are based on the 2025 OECD Survey on Central Government Marketable Debt and Borrowing, which collects gross borrowing requirements, redemption and outstanding debt amounts with a breakdown of these items by maturity, currency, interest rate types and ESG-labelling (i.e. sustainable bonds). The OECD Secretariat inserted its own estimates/projections in cases of missing information for 2025 and 2026, using publicly available official information on redemptions and central government budget balances. Where government plans have been announced but not incorporated into financing plans as of the end of December 2025, they are not included in the projections presented in this publication. Also, estimates of government net lending in the OECD Economic Outlook database are used in estimating some missing data. For countries not answering to the Borrowing Survey, these figures are computed by sourcing data from LSEG.

Coverage of institutions and types of debt

In terms of institutions, unless otherwise specified, data cover debt issued by the central government. The latter covers all departments, offices, establishments and other bodies classified under general government, which are agencies or an instrument of the central authority of a country, except for separately organised social security funds or extra-budgetary funds.

In terms of instruments, unless otherwise specified, data cover marketable debt. The latter is defined as financial securities and instruments that can be bought and sold in the secondary market, non-marketable debt is not transferable. For example, bonds and bills issued in capital markets are marketable debt; multilateral and bilateral loans from the official sector are non-marketable debt.

Terminology

Outstanding amount refers to gross debt, or debt stock. This measure corresponds to the outstanding debt at the end of calendar years. This measure does not take the valuation effects from inflation and exchange rate movements; thus, it is equal to the total nominal amount that needs to be redeemed.

Issuance refers to gross borrowing. For a year, this measure is equal to the net borrowing requirement during that year plus the redemptions of long-term instruments in the same year and the redemptions of short-term instruments issued in the previous year. Therefore, this indicator captures the issuances of all securities excluding those that were issued and redeemed in the same calendar year. In other words, the size of gross borrowing in the calendar year amounts to how much the debt management office needs to issue in nominal terms to fully pay back maturing debt issued in previous years, plus the net cash borrowing requirement through any issuance mechanism.

Average-time-to-maturity (ATM)

For Japan, the 2025 ATM figure is an estimation based on fiscal year (as of the end of March) and excludes T-bills for cash management purposes and includes saving bonds for retail.

Credit rating data

LSEG provides rating information from three leading rating agencies: Fitch, Moody’s and S&P. For each country with rating information in the dataset, a value of 1 is assigned to the lowest credit quality rating (C or below) and 21 to the highest credit quality rating (AAA for Fitch and S&P, and Aaa for Moody’s). If ratings are available from several agencies, their average is used. There are 11 non‑investment grade categories: five from C (C to CCC+); and six from B (B- to BB+). There are ten investment grade categories: three from B (BBB- to BBB+); and seven from A (A- to AAA).

Chapter 2

Ownership information

The ownership figures for publicly listed companies are based on OECD calculations using firm-level information from the FactSet Ownership database. The data are complemented and verified using LSEG and Bloomberg. Data are collected at the end of 2025 in current USD, thus no inflation adjustment is needed. Market information for each company is collected from LSEG. The dataset includes the records of owners for 43 852 companies listed across 104 countries covering 99% of the world market capitalisation. For each of the countries/regions presented, the information corresponds to all listed companies in those countries/regions with available information.

The records of owners are collected for each company. Some companies have up to 5 000 records in their list of owners. Each record contains the name of the institution, the percentage of outstanding shares owned, the investor type classification, the origin country of the investor, the ultimate parent name, among other things.

Table A A.8 presents the five categories of owners defined and used in this report following De La Cruz, Medina and Tang (2019[1]). Different types of investors are grouped into these five categories of owners. In many cases, when the ultimate owner is identified as a government, a province or a city and the direct owner was not identified as such, ownership records are reclassified as public sector. For example, public pension funds that are regulated under public sector law are classified as public sector, and sovereign wealth funds (SWFs) are also included in that same category.

Table A A.8. Categories of owners defined and used in the report

Investor category	Categories of owners
	Investor type
Private corporations and holding companies	Business association	Operating division
	Employee stock ownership plan	Private company
	Holding company	Public company
	Joint venture	Subsidiary
	Non-profit organisation
Public sector	Government	Regional governments
	Sovereign wealth manager	Public pension funds
Strategic individuals and family members	Individual (Strategic owners)	Family office
Institutional investors	Bank investment division	Mutual fund manager
	Broker	Other
	College/University	Pension fund
	Foundation/Endowment manager	Pension fund manager
	Fund of funds manager	Private banking/Wealth management
	Fund of hedge funds manager	Private equity fund/Alternative investments
	Hedge fund	Real estate manager
	Hedge fund manager	Research firm
	Insurance company	Stock borrowing/Lending
	Investment adviser	Trust/Trustee
	Market maker	Umbrella fund
	Mutual fund-closed end	Venture capital/Private equity
Other free‑float including retail investors	Shares in the hands of investors that are not required to disclose their holdings. It includes the direct holdings of retail investors who are not required to disclose their ownership and institutional investors that did not exceed the required thresholds for public disclosure of their holdings.

Balance sheet information for listed firms

The information presented in Chapter 2 is based on LSEG Datastream. The unbalanced global panel dataset contains financial statement information for listed companies between 2000 and 2024. The universe covers 79 418 companies registered in 157 countries. The raw financial dataset contains several firm-year observations when a company reports for different purposes. To construct a panel with a unique firm-year observation, the following steps are applied.

Table A A.9. Balance sheet information cleaning criteria

Data cleaning criteria
Financial companies are excluded	Observations missing common equity or total liabilities are excluded
Security types classified as “trust” are excluded	Year of financial information is adjusted based on the fiscal year of the company
Companies classified as “REITs”, “Collective Investments”, “SPACs” and “Acquisition Corporations” are excluded	Financial information is adjusted by annual US Consumer Price Index changes and information is reported in 2025 USD
Financial statements covering a 12-month period are used	For companies missing up to 3 years of information, the data is imputed using linear interpolation

Industry classification

LSEG Datastream uses the Reference data Business Classification (TRBC) see Table A A.4.

Price-to-book (P/B) ratio

The P/B information for non-financial listed companies presented in Chapter 2 comes from LSEG and covers 35 492 companies. To classify companies as those with a P/B ratio greater than or lower than 1, the average of 12-month P/B ratios for each company in 2025 are used. Monthly P/B ratios are calculated as the closing price at the end of each month divided by the book value per share at the end of 2025. When 2025 book value is not available, previous year’s data is used. P/B ratios are winsorised at the 1% tails at the jurisdiction level.

Stock market returns shown in the chapter use the following the indices.

Table A A.10. Equity indices

Jurisdiction	Equity index	Jurisdiction	Equity index
Bangladesh	MSCI Bangladesh	Pakistan	KSE100
China	SSE Composite	Philippines	PSEi
Hong Kong (China)	HANG SENG	Singapore	STI
India	NIFTY500	Sri Lanka	MSCI Sri Lanka
Indonesia	IDX Composite	Thailand	SET Index
Japan	TOPIX	Chinese Taipei	TAIEX
Korea	KOSPI	Viet Nam	MSCI Viet Nam
Malaysia	FBM KLCI

Table A A.11. Secondary stock market liquidity information

Jurisdiction	Source
China	WFE (2026[2]), World Federation of Exchanges, https://www.world-exchanges.org/
Japan	JPX (2026[3]), Market Statistics, https://www.jpx.co.jp/english/markets/statistics-equities/monthly/vk0khi000000tz3r-att/01_sokatu2601.pdf
Korea	KRX (2026[4]), Data market place, https://data.krx.co.kr/contents/MDC/MDI/mdiLoader/index.cmd?menuId=MDC0201
Malaysia	WFE (2026[2]), World Federation of Exchanges, https://www.world-exchanges.org/
Singapore	SGX Group (2026[5]), Market Statistics, https://www.sgx.com/research-education/historical-data/market-statistics
Chinese Taipei	TWSE (2026[6]), Summary Data of Stock Market (by Year), Annual Statistical Data - Taiwan Stock Exchange Corporation
Thailand	SET (2026[7]),Key Market Statistics, https://www.set.or.th/en/market/statistics/market-statistics/main

Value-up initiatives

Table A A.12. Sources of information for value-up initiatives

Jurisdiction	Source
China	CSRC (2024[8]), Announcement No. 14: Regulatory Guidelines No. 10 for Listed Companies - Market Value Management, https://www.csrc.gov.cn/csrc/c101954/c7519046/content.shtml
Japan	JPX (2023[9]), Action to Implement Management that is Conscious of Cost of Capital and Stock Price, https://www.jpx.co.jp/english/equities/follow-up/uorii50000004sse-att/uorii50000004tcv.pdf
Korea	FSC (2024[10]), Press Releases - Active Support to be Provided to Promote Voluntary Efforts of Listed Companies in Enhancing Their Value, https://www.fsc.go.kr/eng/pr010101/81778
Malaysia	Brusa Malaysia (2026[11]), Public Listed Companies Transformation Programme, https://www.bursamalaysia.com/reference/public_listed_companies_transformation/overview; SC Malaysia (2026[12]), SC and Bursa Malaysia Introduce MY Value Up Programme, https://www.sc.com.my/resources/media/media-release/sc-and-bursa-malaysia-introduce-my-value-up-programme
Singapore	MAS (2025[13]), Review Group Completes Equities Market Review, Unveils SGX-Nasdaq Dual Listing Bridge, S$30 million “Value Unlock” Package, and Second Batch of EQDP Asset Managers, https://www.mas.gov.sg/news/media-releases/2025/review-group-completes-equities-market-review
Chinese Taipei	TWSE (2024[14]), Strengthening the Value of Taiwan’s Capital Market to Cultivate and Enhance International Competitiveness, https://www.twse.com.tw/market_insights/en/detail/8a8216d6913637e401919bb98c1901f4
Thailand	SET (2025[15]), SET News :Thai Capital Market Sector Launches Growth Initiatives to Boost Confidence and Sustainability, https://www.set.or.th/en/market/news-and-alert/newsdetails?id=97489200&symbol=SET

Chapter 3

In addition to the information described below, Chapter 3 uses ownership and listing data for which the methodological explanations are provided in the annexes to Chapters 1 and 2.

OECD Survey on Institutional Investor Engagement in Asia

The OECD conducted the “OECD Survey on Institutional Investor Engagement in Asia” to examine engagement practices and related challenges among Asian institutional investors. The survey was disseminated with the support of authorities in 11 jurisdictions: Bangladesh, People’s Republic of China, Hong Kong (China), Japan, Malaysia, Mongolia, Pakistan, Singapore, Sri Lanka, Chinese Taipei, and Thailand.

Assets under management data

The main source of information is the Morningstar Direct data. The data covers open-ended funds and exchange-traded funds, with the USD value of their total assets under management by the end of 2025. For China and India, the data is collected in domestic currencies and converted to USD using end of year exchange rates. The passive and active classification is made based on Morningstar categories, “Index” funds are categorised as passive, and “Non-index” funds are identified as active.

Investor activism campaigns data

The main source of information is LSEG data. The data covers all investor activism campaigns between 2010-2026 period, including more than 7 800 observations globally. Activist investors are hand classified into 4 main investor categories following the criteria used in De La Cruz, Medina and Tang (2019[16]) (see Table A A.2 above).

Stewardship codes

Table A A.13. Source of information for stewardship codes

Economies	Source
Asian economies (overall), Korea, Hong Kong (China), India	CSRC (2024[8]), Announcement No. 14: Regulatory Guidelines No. 10 for Listed Companies - Market Value Management, https://www.csrc.gov.cn/csrc/c101954/c7519046/content.shtmlCFA Institute, (2020[17]), Stewardship 2.0 - Awareness, Effectiveness, and Progression of Stewardship Codes in Asia Pacific, https://rpc.cfainstitute.org/-/media/documents/article/position-paper/stewardship.pdf ; OECD (2025[18]), Institutional Investor Engagement and Stewardship
Japan	FSA (2025[19]), The Expert Panel on the Stewardship Code, https://www.fsa.go.jp/en/refer/councils/stewardship/index.html.
Malaysia	IICM (2022[20]) https://www.iicm.org.my/malaysian-code-for-institutional-investors-2022/;
Pakistan	SECP (2026[11]), Public Listed Companies Transformation Programme, https://www.bursamalaysia.com/reference/public_listed_companies_transformation/overview; SC Malaysia (2026[12]), SC and Bursa Malaysia Introduce MY Value Up Programme, https://www.sc.com.my/resources/media/media-release/sc-and-bursa-malaysia-introduce-my-value-up-programme (2022[21]), Stewardship Guidelines for Institutional Investors, https://www.secp.gov.pk/document/stewardship-guidelines-for-institutional-investors
Singapore	Stewardship Asia (2022[22]), Singapore Stewardship Principles For Responsible Investors 2.0, https://stewardshi pasia.com.sg/docs/saclibraries/default-document-library/ssp_for-20responsible-20investor-202-0-1-.pdf

Chapter 4

Dataset and methodology

The dataset builds on the universe of listed companies at the end of 2024. It is complemented with company information and items from the financial statements using Worldscope database. 32 human capital variables are obtained from LEG for the universe of listed companies at the end of 2024. The global dataset includes human capital metrics for around 10 000 listed companies. An observation reporting more than 3 500 injuries per million hours worked is considered as missing information.

For the analysis of financial performance, companies are grouped by industry (following the Reference data Business Classification (TRBC) Industry Description) and region to construct quartiles based on each financial performance variable. The data are then aggregated at the region–quartile level by computing weighted means of the outcome variables. Market capitalisation is used as the weighting scheme, except the tables mentioned otherwise in this annex, where unweighted means are reported. Negative price-to-earnings (P/E) ratios are set to null. In addition, the top 0.5% and bottom 0.5% of observations for return on equity (ROE), return on assets (ROA), and P/E are treated as outliers and set to null. This reduces the skew provided by large outliers.

Table A A.14. Human capital variables analysed

Category	Variable	Category	Variable
Workforce investment	Average training hours	Health and safety	Injuries to million hours
	Management training		Employee health & safety training hours
	Training costs per employee		Health & safety training
	Training and development policy		Risk assessment health and safety
	Skills training policy		Accidents total
	Career development policy	Compliance and labour practices	Policy working hours
Workforce mobility	Employee turnover		Commitment to UN guiding principles or OECD guidelines
	Employee voluntary turnover rate		Communication of labour policy
	Employee involuntary turnover rate		Human rights policy
	Average employee length of service		Policy child labour
	Announced layoffs to total employees		Policy forced labour
	Employment creation	Employee engagement	Trade union representation
Diversity and inclusion	Women employees		Employee satisfaction
	Women managers	Diversity and inclusion	Ethnic minorities salary gap
	Gender pay gap percentage		Salary gap
	Ethnic minorities employee percentage		Policy diversity and opportunity

Additional details

Table A A.15. Human capital metrics disclosed and women managers by region, 2024

	Human capital average number of metrics disclosed (out of 32)		Women managers (in per cent)
Region	By number of companies	By market capitalisation	By number of companies	By market capitalisation
Global	20	21	30	30
Asia	21	22	27	24
ASEAN	21	23	36	40
China	21	21	25	24
Japan	20	21	15	20
South Asia	21	23	17	17
Other Developed Asia	23	24	29	25
Europe	21	23	32	35
United States	18	20	36	31

Note: The global dataset used contains 10 308 listed companies with human capital information available. Of these, 4 157 are in Asia, 1 032 are in ASEAN, 1 347 are in China, 585 are in Japan, 703 are in South Asia, and 489 are in Other Developed Asia. There are 1 910 companies in Europe and 2 636 companies in the United States with human capital information available in the dataset used. For women managers; the global dataset used contains 5 123 listed companies with data on the percentage of women managers. Of these, 2 099 are in Asia.

Source: OECD Corporate Sustainability dataset, LSEG.

Table A A.16. Reporting rate by region and industry, 2024

In per cent	By number of companies		By market capitalisation
	Global	Asia	Global	Asia
Basic materials	17	16	80	71
Consumer cyclicals	20	12	88	73
Consumer non-cyclicals	21	16	88	82
Energy	27	22	90	88
Financials	30	23	90	92
Healthcare	28	15	90	73
Industrials	19	13	84	70
Real estate	17	17	72	74
Technology	21	14	92	82
Utilities	30	26	82	86

Note: The global dataset used contains 47 542 listed companies. Of these, 27 629 are in Asia.

Source: OECD Corporate Sustainability dataset; LSEG.

Table A A.17. Training hours, union representation, and turnover rate by region and industry, 2024

	Average training hours				Union representation (in per cent)				Turnover rate
	By number of companies		By market capitalisation		By number of companies		By market capitalisation		By number of companies		By market capitalisation
	Global	Asia	Global	Asia	Global	Asia	Global	Asia	Global	Asia	Global	Asia
Basic materials	41	43	48	61	50	52	51	58	13	12	11	10
Consumer cyclicals	25	28	25	33	33	44	37	66	22	21	21	16
Consumer non-cyclicals	26	29	27	37	45	50	45	63	18	17	15	12
Energy	44	52	87	93	44	78	42	92	14	16	8	13
Financials	42	53	46	67	31	47	44	55	13	13	11	9
Healthcare	41	49	34	49	15	41	30	48	16	17	11	14
Industrials	31	35	32	52	41	49	45	58	16	15	12	11
Real estate	27	28	31	39	35	30	28	31	22	24	20	21
Technology	31	35	32	52	26	38	27	53	16	18	9	12
Utilities	49	57	54	76	55	67	52	68	9	8	7	6

Note: The global dataset used contains 4 735 listed companies with data on average training hours. Of these, 2 678 are in Asia. The dataset also contains 4 586 listed companies with data on union membership, 1 523 of which are in Asia. Additionally, there is employee turnover data available for 5 506 listed companies, 2 811 of which are in Asia.

Source: OECD Corporate Sustainability dataset; LSEG.

Table A A.18. Women employees and women managers by region and industry, 2024

	Women employees				Women managers
In per cent	By number of companies		By market capitalisation		By number of companies		By market capitalisation
	Global	Asia	Global	Asia	Global	Asia	Global	Asia
Basic materials	19	17	20	16	22	19	24	15
Consumer cyclicals	41	36	42	34	35	29	34	22
Consumer non-cyclicals	37	34	37	31	33	30	36	24
Energy	22	19	19	19	24	22	18	19
Financials	48	45	49	48	38	33	37	33
Healthcare	50	44	50	41	40	36	43	32
Industrials	27	25	27	25	24	21	25	19
Real estate	43	42	39	39	36	35	33	32
Technology	35	35	32	34	27	23	25	22
Utilities	24	21	23	18	25	21	25	17

Note: The global dataset used contains 7 551 listed companies with data on the percentage of women employees, 3 496 of which are in Asia. The global dataset also contains 5 123 listed companies with data on the percentage of women managers, 2 099 of which are in Asia.

Source: OECD Corporate Sustainability dataset; LSEG.

Table A A.19. Reported injury rate and working hours policy by region and industry, 2024

	Reported injuries per million hours				Working hours policy (in per cent)
	By number of companies		By market capitalisation		By number of companies		By market capitalisation
	Global	Asia	Global	Asia	Global	Asia	Global	Asia
Basic materials	4.9	2.7	2.8	1.5	17	19	29	19
Consumer cyclicals	6.8	2.5	11.8	2.2	20	22	38	30
Consumer non-cyclicals	7.5	3.0	4.7	1.9	22	21	29	25
Energy	2.6	1.4	1.3	0.5	11	18	4	5
Financials	1.9	0.8	1.7	1.1	9	19	9	16
Healthcare	4.2	1.6	2.6	1.5	9	17	21	20
Industrials	5.9	2.4	5.4	1.6	19	23	24	33
Real estate	4.7	3.0	3.8	2.4	13	18	16	19
Technology	1.7	0.8	0.8	0.5	19	23	23	38
Utilities	4.4	1.0	3.3	0.6	11	11	17	9

Note: The global dataset used contains 4 909 listed companies with data on the injury rate. Of these, 2 233 are in Asia. Additionally, the global dataset used contains 10 308 listed companies, 4 157 of which are in Asia, with data on their working hours policy.

Source: OECD Corporate Sustainability dataset; LSEG.

Table A A.20. Average training hours by region, ROA P/E AND ROE quartile, 2024

	ROA (weighted by market cap.)				P/E (weighted by market cap.)				ROE (unweighted average)
Region	25%	50%	75%	100%	25%	50%	75%	100%	25%	50%	75%	100%
Global	32.0	46.2	38.3	45.1	57.9	41.1	43.1	25.7	28.5	32.9	38.1	34.3
Asia	44.7	64.0	53.7	55.8	72.3	57.1	57.8	36.6	31.1	38.3	42.3	39.5
ASEAN	31.0	41.1	40.7	39.6	48.0	52.9	36.2	25.9	23.0	24.4	27.4	26.8
China	56.3	89.5	75.0	61.3	64.8	77.2	83.7	57.0	41.1	48.3	68.4	57.1
Japan	23.7	28.5	25.0	21.0	19.5	33.2	27.7	19.7	17.6	25.9	32.0	23.4
South Asia	30.1	56.5	49.2	48.3	66.0	64.8	51.4	28.4	20.0	29.4	36.2	37.4
Other Developed Asia	42.2	42.6	57.6	70.2	54.9	61.9	75.1	47.2	32.7	36.3	43.9	42.5
Europe	25.6	32.1	28.5	26.7	29.9	33.1	28.0	24.1	20.2	21.0	24.3	25.6
United States	34.1	29.4	20.2	28.0	29.5	36.3	20.2	14.2	22.8	20.9	23.2	27.9

Note: ROA and P/E values are weighted by market capitalisation. The sample used to create this table contains 5 431 listed companies, of which, 2 799 are in Asia, 765 are in ASEAN, 758 are in China, 260 are in Japan, 596 are in South Asia, 419 are in Other Developed Asia, 1 265 are in Europe, and 528 are in the United States. The sample of valid P/E observations is instead from 4 627 listed companies. ROE average training hours correspond to average and not weighted.

Source: OECD Corporate Sustainability dataset; LSEG.

Table A A.21. Employee turnover by region, ROA, P/E and ROE quartile, 2024

	ROA (weighted by market cap.)				P/E (weighted by market cap.)				ROE (unweighted average)
Region	25%	50%	75%	100%	25%	50%	75%	100%	25%	50%	75%	100%
Global	13.1	11.6	12.1	11.7	9.2	12.4	11.1	11.6	21.5	15.9	14.6	15.7
Asia	16.1	8.8	12.8	11.2	6.5	10.5	13.0	14.2	21.3	15.6	14.4	16.3
ASEAN	11.7	12.0	12.9	10.9	10.2	12.3	12.6	11.4	22.4	18.0	17.9	16.3
China	16.5	6.8	7.6	11.8	11.2	6.5	11.1	11.5	20.1	14.6	10.4	14.6
Japan	4.8	6.9	7.0	6.2	5.6	6.4	5.8	7.9	7.0	5.6	5.8	6.6
South Asia	20.6	19.2	19.1	14.8	8.3	13.7	21.2	17.5	23.2	22.1	19.9	17.3
Other Developed Asia	14.1	15.4	13.6	9.1	8.7	13.8	10.2	12.4	18.1	18.2	15.7	18.1
Europe	14.9	13.3	12.6	14.3	12.2	12.8	12.7	15.9	20.6	14.9	15.1	14.5
United States	6.7	12.5	12.0	10.6	13.9	11.4	8.7	9.3	17.6	17.5	16.4	14.6

Note: ROA and P/E values are weighted by market capitalisation. The sample used to create this table contains 5 431 listed companies, of which, 2 799 are in Asia, 765 are in ASEAN, 758 are in China, 260 are in Japan, 596 are in South Asia, 419 are in Other Developed Asia, 1 265 are in Europe, and 528 are in the United States. The sample of valid P/E observations is instead from 4 627 listed companies. ROE turnover values correspond to average and not weighted.

Source: OECD Corporate Sustainability dataset; LSEG.

Chapter 5

Cybersecurity challenges

The table provides a non-exhaustive list of notable security incidents affecting crypto-asset exchanges across Asia. It provides insights into the common exchange-related vulnerabilities that are continually exploited, highlighting that a regulation-based approach alone does not address hot-wallet risks.

Table A A.22. Notable crypto-asset exchange related security incidents

Incident	Description
Mt. Gox	This incident was disclosed in February 2014 and took place in Japan. The magnitude of losses amounted to approximately 850 000 BTC; the fraud took advantage of accounting failures and hot wallet key compromises. No perpetrators have been arrested to date (Trakx, 2025[23]; Gemini, 2025[24]).
Coincheck	The Coincheck incident (also in Japan) was perpetrated in 2018 using phishing techniques, with losses in the range of USD 530 to 534 million. Suspects were not apprehended while funds were traced but not recovered. Vulnerabilities exploited included absence of cold storage and the use of hot wallets (BBC, 2018[25]; Bitdefender, 2018[26]; Euromoney, 2018[27]).
KuCoin	This was a cross-exchange coordinated incident in 2020, with losses amounting to USD 281 million, enabled by the compromise of private keys related to hot wallets. Perpetrators targeted Kucoin Singapore linked operations, and some recoveries were made possible through freezing of assets (US Department of Justice, 2024[28]; Le Monde, 2024[29]).
Upbit	These incidents took place in Korea in 2019 and again in 2025, with losses of USD 50 million and USD 36 million respectively. Hot wallets were targeted and exploited and perpetrators have not been publicly identified to date (Binance, 2025[30]), although Lazarus Group has been strongly suspected as being behind the incident (Decrypt, 2025[31]).

Note: This table provides a non-exhaustive list of exchange-related security incidents focused on exchanges based in Asia.

Source: Publicly available information, as referenced in the table.

Future impacts of quantum technology for crypto-assets

Quantum computing is an emerging field, leveraging quantum mechanics to process information and solve challenges beyond the capabilities of current “classical” computers (OECD, 2026[32]). While quantum computing is at an early stage of development, the technology is advancing rapidly from theory to proof-of-concept. Policymakers and firms are increasingly focusing on the potential applications, including in the financial sector, as well as the future risk profile.

Quantum computing has the potential to significantly enhance tasks that relate to optimisation, stochastic modelling and machine learning (ESMA, 2026[33]). Such applications would be relevant for core financial sector activities such as portfolio management, trade settlement, asset pricing and risk management credit rating or fraud detection.

Quantum computing may however also pose risks for the financial sector, including in various aspects of blockchain-based crypto-assets, with different blockchain ecosystems presenting different vulnerabilities. However, research on such vulnerabilities is still at an early stage, building on the existing knowledge of cyber-risks from classical computing, and drawing on researchers’ evolving understanding of future quantum computing. The scope and level of risk posed by quantum computing will also depend on the speed of development of underlying technologies, as well as the nature of the quantum architecture deployed (Babbush et al., 2026[34]).

Countermeasures are being developed, including the deployment of post-quantum cryptography (PQC) to replace at-risk encryption techniques (Yang et al., 2024[35]). Practical challenges remain, though, namely the complexity of transitioning to PQC across consensus-based blockchains, and the potentially higher resourcing costs of PQC compared to existing encryption techniques (Khodaiemehr, Bagheri and Feng, 2026[36]).

The incorporation of advanced cryptographic techniques that build on quantum information theory, could help to strengthen the cyber-resilience of blockchain technologies, and could also help reduce the energy-intensity of current proof-of-work based architecture (ESMA, 2026[33]; Amin et al., 2026[37]). These two factors may contribute to greater sustainability and scalability of blockchain technology, although this remains in the realm of theory.

Overview of policy initiatives and regulatory measures across Asia

Table A A.23. Crypto-assets: Key policy and regulatory developments across Asia

Jurisdiction	Description
China	In 2026 the People’s Bank of China (PBoC), the National Development and Reform Commission, the National Financial Regulatory Administration, the China Securities Regulatory Commission, and the State Administration for Foreign Exchange issued a notice extending the scope of existing restrictions on crypto-asset activity in place since 2021. The notice expands restrictions to include foreign entities providing crypto-asset services to entities based in China (Hankun Law, 2026[38]).
Hong Kong (China)	In 2025, the Hong Kong Monetary Authority (HKMA) issued the Stablecoin Ordinance (HKMA, 2026[39]). The Stablecoin Ordinance requires stablecoin issuers to apply for a licence before issuing or trading in stablecoins in Hong Kong (China). It also sets various conditions for receiving a licence and for ongoing operation of stablecoins. Notably, issuers are required to ensure that the market value of reserve assets backing a stablecoin are at all times at least equal to the par value of the outstanding stablecoins (HKMA, 2025[40]). Issuers are encouraged to over-collateralise with adequate buffers based on the risk profile of each reserve asset. They must also ensure timely and adequate public disclosure of the reserve assets and how risks are managed. In April 2026, HKMA issued the first two licences under the Stablecoin Ordinance, granted to two large established financial firms (Elliptic, 2026[41]). These two firms are expected to issue stablecoins for differing use cases, including retail peer-to-peer and merchant payments, institutional settlement and cross-border payments. The HKMA has also established a Stablecoin Issuer Sandbox, supporting experimentation and analysis of productive use cases (HKMA, 2024[42]). In 2024, the sandbox was opened to three participating companies (HKMA, 2024[43]).
Indonesia	In January 2025, Indonesia transferred responsibility for crypto-asset regulation and supervision from the Commodity Futures Trading Supervisory Agency to the Financial Services Authority (OJK) (ABNR Law, 2025[44]). In December 2025, OJK issued a new regulation (No.23/2025), to strengthen the regulatory framework with a particular focus on crypto-asset based derivatives. The regulation tightens requirements for traders and exchanges seeking to carry out (or facilitate) trading of such derivatives, for example through compulsory notification to OJK, and requirements to screen customers to ensure they understand risks.
Japan	The Japanese government has also announced plans to regulate crypto-assets under the Financial Instruments and Exchange Act (Tatsuno and Suga, 2026[45]). Currently, crypto-assets fall under the Payment Services Act. The new framework is expected to introduce disclosure requirements, restrictions on unfair trading practices, internal control requirements and strengthened supervision and enforcement provisions – largely in line with requirements for other securities firms. In addition, the Government has signaled that subsidiaries of banks and insurance companies will be allowed to issue, trade and intermediate crypto-assets, while the parent companies will be permitted to hold crypto-assets as part of their investment portfolios. The planned reforms come in the context of growing adoption of crypto-assets by retail and commercial investors, with approximately 8.5 million active crypto-asset accounts as of January 2026 (JVCEA, 2026[46]). In February 2026, the Financial Services Agency (JFSA) issued Draft Guidelines for Strengthening Cybersecurity in Cryptocurrency Exchange Businesses (JFSA, 2026[47]). The Draft Guidelines set out the JFSA’s roadmap for partnering with exchanges and other crypto-asset service providers to strengthen cyber resilience. It encourages industry bodies to develop industry-wide cybersecurity measures and to improve information-sharing. The Draft Guidelines have been shared for public consultation. In November 2025, the JFSA launched the “Payment Innovation Project (PIP)”, within the FinTech Proof-of-Concept Hub. The PIP aims to support proof of concept experimentation in the payments sector. The first selected project, focused on the issuance of a stablecoin by multiple banks (JFSA, 2025[48]). The project is ongoing, with results to be published once concluded. In October 2025, the first yen-backed stablecoin was issued in Japan (Kihara, 2025[49]).
Korea	In 2024, Korea implemented the Protection of Virtual Asset User Protection Act (VAUPA) (FSC Korea, 2024[50]). The Act builds on updates to a separate law that requires virtual asset service providers to be registered and to comply with AML/CFT requirements. It aims to protect deposits and virtual assets, deter unfair trading practices, and empower financial authorities to supervise, inspect and sanction virtual asset service providers who fail to comply. The Act requires virtual assets owned by customers to be kept separate from those owned by a virtual asset service provider, to ensure transparency and avoid misappropriation of assets. To protect against fraud, cyber-attacks and operational risks, virtual asset service providers are also required to hold insurance against hacking and technical malfunctions, to maintain a reserve fund for such incidents, and to maintain a surveillance system for all suspicious transactions. The Act has been complemented by industry-led best practice guidelines for virtual asset exchanges, covering aspects such as disclosures, security standards, minimum requirements for listing a virtual asset, and preparation of a “virtual asset analysis report” ahead of each new listing (DAXA, 2025[51]). In June 2025, a Digital Asset Basic Act (DABA) proposal was introduced into Parliament (Kwon, 2026[52]). DABA aims to be a comprehensive law governing digital asset activity. The DABA will complement the Virtual Asset User Protection Act, introducing a more comprehensive authorisation regime for virtual asset service providers, new regulatory measures specific to stablecoins, and legalising initial coin offerings under close supervision. The DABA remains in the consultation phase as of April 2026. Certain elements of the law remain under consideration, namely the eligibility criteria and governance requirements for firms seeking to issue stablecoins.
Malaysia	In March 2026, the Securities Commission Malaysia (SCM) revised its Guidelines on Exchange-Traded Funds (ETFs). The revisions provide guidance and new requirements to facilitate the offering of digital currency ETFs (SCM, 2026[53]). This follows updates to the Guidelines on Digital Assets and the Guidelines on Technology Risk Management, aiming to strengthen capital market participants’ practices for operational and cyber resilience (SCM, 2024[54]).
Pakistan	In July 2025, Pakistan introduced the Virtual Assets Act, which for the first time permits issuance and trading of crypto-assets and establishes the Pakistan Virtual Assets Regulatory Authority (PVARA, 2025[55]). The Act and PVARA aim to enforce compliance with global AML/CFT, protect consumers and promote safe innovation. To that end, PVARA has established a regulatory sandbox, to enable safe testing of innovative crypto-asset products and services. In parallel, the Pakistan Crypto Council was established, including the Minister of Finance, Governor of the State Bank of Pakistan, the Chair of the Securities and Exchange Commission, and federal secretaries for Information Technology and Law (Dawn News, 2025[56]). The PCC aims to facilitate coordination of policy development to promote crypto-asset activity in Pakistan, and to position Pakistan as a leading jurisdiction in the global digital economy.
Philippines	In May 2025, the Philippines Securities and Exchange Commission issued new rules applying to Crypto-Asset Service Providers (CASPS) (Philippines SEC, 2025[57]). The rules provide definitions for CASPs, set rules for registration and public disclosures, apply AML/CFT rules, and set principles for consumer protection. These rules build on existing regulations by the Central Bank, which require crypto-asset exchanges to register as remittance and transfer companies.
Singapore	Since 2022, the Monetary Authority of Singapore (MAS) has been operating Project Guardian, a public-private partnership initiative bringing together financial institutions, industry associations, central banks and regulators in Singapore and peer countries, to support trials of tokenised funds, bonds, stablecoins and bank liabilities (MAS, 2025[58]). In 2025, two reports were published as part of Project Guardian, the first focused on the expansion of tokenised funds such as tokenised money market funds (Guardian, 2025[59]), and the second providing guidance on operational, commercial, and regulatory considerations for the design and launch of crypto-assets (MAS et al., 2025[60]). The second report closes with an operational readiness checklist for issuers and investors, aiming to increase investor participation, improve operational efficiency in issuance, minimise risk and help maintain trust and legal compliance.
Thailand	In January 2025, Thailand implemented personal income tax exemptions for capital gains from the sale of crypto-assets, for a period of 5 years. The policy aims to align with the tax treatment of public securities, and to encourage uptake. In March 2025, the Thai SEC also approved the issuance and trading of two leading stablecoins in Thailand (Global Legal Insights, 2025[61]).
Viet Nam	In June 2025, Viet Nam’s National Assembly passed the Law on Digital Technology Industry (DTI Law). The DTI law introduces a framework covering AI, semiconductors and crypto-assets, aiming to increase Viet Nam’s competitiveness with a goal of establishing 150 000 digital technology firms by 2035 (Singh, 2025[62]). Notably, the law introduces regulations covering issuance, trading, ownership, cybersecurity, and AML/CFT for crypto-assets, as well as a licensing regime for crypto-asset service providers (Ministry of Finance of Viet Nam, 2025[63]). The law also introduces a regulatory sandbox regime along with measures to promote investment and talent acquisition. The DTI law took effect from 1 January 2026.

Note: This table provides a non-exhaustive list of key policy and regulatory developments in 2024-2026, updated as of early-May 2026.

Source: Publicly available information, as referenced in the table.

Table A A.24. Overview of ongoing CBDC initiatives across Asia

Jurisdiction/ Institution	Description
BIS innovation hub	At the multilateral level, the Bank for International Settlements Innovation Hubs concentrate the exploration of CBDC projects that involve several jurisdictions in Asia and beyond: Project mBridge, which explored a shared multi-CBDC platform for real-time and cross-border wholesale payments, reached minimum viable product stage in 2024 and was subsequently handed over by the BIS to participating central banks (Thailand, China and Hong Kong (China) among them) for further development (BIS, 2024[64]). Project Mariana, completed in September 2023, successfully tested cross-border trading and settlement of wholesale CBDCs between financial institutions in simulated euros, Singapore dollars and Swiss francs, using public-blockchain technology and decentralised finance tools such as automated market makers (BIS, 2023[65]). Project Aurum2.0 (with HKMA) focuses on improving privacy for retail CBDC payments by testing privacy-by-design approaches and technologies such as pseudonymisation and zero-knowledge proofs (BIS, 2024[66]).
China	In December 2025, People’s Bank of China further detailed its action plan to develop the digital yuan, launching a next-generation management and infrastructure framework from January 2026 (Lu Lei, 2025[67]). The focus is on transitioning the e-CNY pilot toward a deposit-based, two-tier system that supports cross-border use while avoiding monetary disintermediation and preserving financial stability. With an accumulated total of 3.5 billion transactions at the end of November 2025, the Chinese CBDC pilot is the world’s largest.
Hong Kong (China)	The Hong Kong Monetary Authority published in October 2025 the Phase 2 report of its retail e-HKD pilot, concluding testing of use cases including programmability and tokenised asset settlement. HKMA stated it would prioritise wholesale CBDC work next while preparing foundations for possible future retail use (HKMA, 2025[68]).
India	In January 2026, India announced the launch of a pilot using its digital rupee (which is also in pilot phase) for payments under the public distribution system (Das, 2026[69]). The initiative uses the Indian retail CBDC in targeted government welfare disbursement. As of March 2025, the total value of e-Rupee in circulation reached 10 billion (approximately USD 100 million), a ten-fold increase since end-2023.
Japan	As of May 2025, Japan reported progress in its CBDC pilot programme testing system functionality, performance, and interoperability with private payment systems. BoJ reaffirmed that any issuance decision depends on future public policy discussions (Bank of Japan, 2025[70]).
Korea	Korea formally entered Phase II of Project Hangang in 2026, building on a large-scale real-transaction pilot conducted between April and June 2025 involving digital currency and deposit tokens issued by the Bank of Korea and commercial banks (Bank of Korea, 2026[71]). Phase II focuses on preparing for formal system adoption and commercialisation of deposit tokens, expanding participating banks and merchants, enabling peer-to-peer transfers, and testing use as a payment rail for AI agents and tokenised securities. In April 2026, the Bank of Korea emphasised that digital money research must support price and financial stability rather than be pursued for speed or symbolism (ChosunBiz, 2026[72]).
Pakistan	The State Bank of Pakistan, aided by Japan’s Soramitsu and the Japanese government, is preparing to launch a retail CBDC pilot for a digital Pakistani rupee in 2026 while building internal capacity for issuance (Nikkei Asia, 2025[73]). The pilot will test offline payment functionality via smartphones to support cash-heavy, low-bank-access regions and reduce cash distribution costs.
Philippines	In early 2026, the Philippines indicated it is exploring a wholesale CBDC for settling tokenised government bond transactions. The initiative is framed as a specialised financial market use case rather than a retail CBDC rollout (Fintech News Philippines, 2026[74]).

Note: This table provides a non-exhaustive list of key policy and regulatory developments in 2024-2026, updated as of early-May 2026.

Source: Publicly available information, as referenced in the table.

References

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