China Company Transformation Indicator

Measuring the resilience and future readiness of companies in the Chinese market sector by sector

China Company Transformation Indicator

Measuring the resilience and future readiness of companies in the Chinese market sector by sector

Business in China is complex and dynamic. China has changed significantly in the last decade following Chinese President Xi Jinping’s announcement of a “new normal” of slower but more stable growth to focus on maturing the foundations of the economy. While the first generation of companies in China’s reform period, which started in the 1990s, could rely on double digit growth numbers and massive demand, this is no longer the case. Today, companies in China have to think about the business fundamentals; building up resilience and leveraging emerging opportunities while also innovating for the future to create new growth engines. While foreign multinationals have typically experienced several significant transformations in their history, most Chinese companies are just experiencing their first significant transformation curve.

To understand how well companies are faring in China’s new economic realities, we went out to do some research. Going sector by sector, we identified the leading companies and assessed their readiness to transform and thrive in the long term. We call this the China Company Transformation Indicator (CCTI). Research for the CCTI is done by the IMD research center in Shenzhen (China) led by Wei Wei and Kunjian Li under the supervision of the research director Mark Greeven.

Our rule-based methodology

We utilize and enhance the methodology developed by Howard Yu at IMD’s Center for Future Readiness for their Future Readiness Indicator, but localize it to the country level (China) and customize it to different sectors. This includes careful consideration of data sources, verification and triangulation, variable selection, outlier management, and appropriate normalization to ensure the model’s strength and reliability. Additionally, we employ advanced data science tools such as text mining algorithms, factor analysis, and random forest to broaden our data sources and enhance the accuracy of our indicators.

Our China Company Transformation Indicator measures the transformative momentum of leading players by revenue and market capitalization in each industry. The Indicator amalgamates information and data including financial statement analysis, company strategy, innovation initiatives, market circumstances, supply chain links, government associations, media impact, customer relations, social responsibility, environmental protection, and more.

The indicator consists of two pillars, Core Resilience, and Future Readiness, and eight main factors. These factors vary from sector to sector. Take the Food & Beverage industry as an example:

Core Resilience

Business success

Business robustness

Business diversity

Customer engagement

Future readiness

Investors’ expectations of future growth

R&D efforts

Early innovation results

ESG

The main factors vary by sector. In the pharmaceutical industry, for example, the “customer engagement” factor is replaced by the “pharmaceutics pipeline” factor. In the technology industry, we assess the integration of innovative technologies such as edge computing, artificial intelligence, and cloud services.

How we calculate the rankings

1. Our data resources

Our variables are hard data publicly available on company websites, announcements, annual reports, press releases, news stories, white papers, academic journal articles, and equity research reports.

We consult trustworthy databases like Refinitiv, Pitchbook, Factiva, and more, and utilize local data vendors East Money Choice. Specifically, Refinitiv provides access to company financial data and economic indicators as well as news, analytics, and productivity tools. Pitchbook offers a detailed view into companies, investments, buy-side deals, investors, funds, financials, valuations, and more. Factiva is an archive of over 32,000 major global newspapers, newswires, industry publications, magazines, reports, and other sources.

These sources are also supplemented by administrations including the National IP Administration and National Medical Products Administration. Data extracted from the above databases are cross-checked and calibrated with the data from Statista, Yahoo Finance, Wallmine, Stock.us, Google Finance, HKEXnews, Wind, and so on.

2. Our statistical method

Upon the collection of company data, we perform pre-processing methods including identifying and removing errors and duplicates, filling the blanks, and processing the outliers. Then we model the distribution of each variable based on their nature and characteristics and perform appropriate standardization. We also utilize the truncated regression model to improve the normalization process.

The standardized data is passed to the ranking calculation operator. Equal weight is assigned to each factor and allocated evenly to inner variables. We compute the ranking based on the sum of weighted standardized data. The company’s overall ranking, together with eight factor-specific rankings is calculated. Then we plug the ranking results into algorithms designed to analyze their sensitivity, robustness, and validation.