Consumer Confidence in China increased to 88 points in May from 87.80 points in April of 2025. This dataset provides - China Consumer Confidence - actual values, historical data, forecast, chart, statistics, economic calendar and news.

In May 2025, the index for consumer confidence in China ranged at ** points, up from **** points in the previous month. The index dropped considerably in the first half of 2022 and performed a sideways movement during 2023 and 2024. Consumer confidence Index The consumer confidence index (CCI), also called Index of Consumer Sentiment (ICS) is a commonly used indicator to measure the degree of economic optimism among consumers. Based on information about saving and spending activities of consumers, changes in business climate and future spending behavior are being projected. The CCI plays an important role for investors, retailers, and manufacturers in their decision-making processes. However, measurement of consumer confidence varies strongly from country to country. As consumers need time to react to economic changes, the CCI tends to lag behind other indicators like the consumer price index (CPI) and the producer price index (PPI). Development in China As shown by the graph at hand, confidence among Chinese consumers picked up since mid of 2016. In October 2017, the CCI hit a record value of 127.6 index points and entered into a sideward movement. Owing to a relative stability in GDP growth, a low unemployment rate, and a steady development of disposable household income, Chinese consumers gained more confidence in the state of the national economy. Those factors also contribute to the consumers’ spending power, which was reflected by a larger share of consumption in China’s GDP. After the outbreak of the coronavirus pandemic, consumer confidence dropped quickly in the beginning of 2020, but started to recover in the second half of the year, leading to a v-shaped movement of the index in 2020.

Graph and download economic data for Consumer Opinion Surveys: Composite Consumer Confidence for China (CSCICP02CNM460S) from Jan 1990 to Apr 2025 about consumer sentiment, composite, China, and consumer.

Key information about China Consumer Confidence Growth

• China Consumer Confidence dropped by 0.7 % Point in Feb 2025, compared with a decrease of 1.4 % Point in the previous month.
• China Consumer Confidence: YoY Change is updated monthly, available from Jan 1991 to Feb 2025, averaged at 0.0 % Point.
• The data reached an all-time high of 16.7 % Point in Oct 2017 and a record low of -35.0 % Point in May 2022.
• In the latest reports, Retail Sales of China grew 12.7 % YoY in May 2023.

Graph and download economic data for Composite Leading Indicators: Composite Consumer Confidence Amplitude Adjusted for China (CSCICP03CNM665S) from Jan 1990 to Dec 2023 about consumer sentiment, composite, China, and consumer.

China Consumer Confidence: Current Index data was reported at 73.178 Index in Jan 2023. This records an increase from the previous number of 72.959 Index for Dec 2022. China Consumer Confidence: Current Index data is updated monthly, averaging 62.801 Index from Mar 2010 (Median) to Jan 2023, with 155 observations. The data reached an all-time high of 78.438 Index in Nov 2018 and a record low of 45.646 Index in Jun 2012. China Consumer Confidence: Current Index data remains active status in CEIC and is reported by Ipsos Group S.A.. The data is categorized under Global Database’s China – Table CN.IPSOS: Consumer Confidence Survey.

According to Ipsos Consolidated Economic Indicators based on monthly surveys conducted by Ipsos, the Consumer Confidence Index for China ranged at **** points in March 2023, up from **** points in the previous month. In comparison, the Consumer Confidence Index for the United States stood at **** points in March 2023.

China Consumer Confidence: Jobs Index data was reported at 71.101 Index in Jan 2023. This records an increase from the previous number of 67.104 Index for Dec 2022. China Consumer Confidence: Jobs Index data is updated monthly, averaging 67.676 Index from Mar 2010 (Median) to Jan 2023, with 155 observations. The data reached an all-time high of 74.426 Index in Jul 2019 and a record low of 56.440 Index in Oct 2011. China Consumer Confidence: Jobs Index data remains active status in CEIC and is reported by Ipsos Group S.A.. The data is categorized under Global Database’s China – Table CN.IPSOS: Consumer Confidence Survey.

China Consumer Confidence: Expectations Index data was reported at 75.927 Index in Jan 2023. This records an increase from the previous number of 72.545 Index for Dec 2022. China Consumer Confidence: Expectations Index data is updated monthly, averaging 69.642 Index from Mar 2010 (Median) to Jan 2023, with 155 observations. The data reached an all-time high of 77.833 Index in Jan 2021 and a record low of 61.265 Index in Dec 2011. China Consumer Confidence: Expectations Index data remains active status in CEIC and is reported by Ipsos Group S.A.. The data is categorized under Global Database’s China – Table CN.IPSOS: Consumer Confidence Survey.

Bankcard Consumer Confidence Index data was reported at 91.990 % in Jan 2020. This records an increase from the previous number of 91.980 % for Dec 2019. Bankcard Consumer Confidence Index data is updated monthly, averaging 86.460 % from Jan 2010 (Median) to Jan 2020, with 121 observations. The data reached an all-time high of 91.990 % in Jan 2020 and a record low of 78.820 % in Jul 2016. Bankcard Consumer Confidence Index data remains active status in CEIC and is reported by China UnionPay. The data is categorized under China Premium Database’s Household Survey – Table CN.HB: Bankcard Consumer Confidence Index: China UnionPay.

According to a survey conducted at the end of 2024, the Generation * was the most optimistic generation among Chinese consumers, with around ** percent of respondents within this age group saying they were economically confident. By comparison, only ** percent of respondents aged between 26 and 41 (the Millennials) voiced their confidence in the economy.

China Consumer Confidence Indicator: sa: Normalised data was reported at 97.312 Normal=100 in Dec 2023. This records an increase from the previous number of 97.240 Normal=100 for Nov 2023. China Consumer Confidence Indicator: sa: Normalised data is updated monthly, averaging 100.016 Normal=100 from Jan 1990 (Median) to Dec 2023, with 408 observations. The data reached an all-time high of 102.062 Normal=100 in Feb 2021 and a record low of 97.059 Normal=100 in Nov 2022. China Consumer Confidence Indicator: sa: Normalised data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s China – Table CN.OECD.MEI: Consumer Opinion Surveys: Seasonally Adjusted: Non OECD Member. The Consumer Confidence Survey is conducted by China Economic Monitoring and Analysis Center (CEMAC) of the National Bureau of Statistics. Data for Consumer Confidence Indicator are available from June 1996 onwards. Starting from Q4 2009, CEMAC extended the sample size and coverage (including all tiers of urban cities in the East, Central, West and Northwest as well as rural areas).

Consumer Confidence in Taiwan decreased to 63.70 points in June from 64.93 points in May of 2025. This dataset provides - Taiwan Consumer Confidence - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Key information about China Consumer Confidence: Net Balance

• China Consumer Confidence: Net Balance recorded a drop of 5.1 % Point in Mar 2023, compared with a drop of 5.3 % points in the previous month.
• China Consumer Confidence: Net Balance data is updated monthly, available from Jan 1990 to Mar 2023, with an averaged 10.1 % points.
• The data reached an all-time high of 27.0 % in Feb 2021 and a record low of -14.5 % in Nov 2022.

• In the latest reports, Retail Sales of China grew 12.7 % YoY in May 2023.
• China Consumer Confidence dropped by 18.3 % Point in Mar 2023.
• China Annual Household Income per Capita reached 4,805.9 USD in Dec 2013.
• China Annual Household Expenditure per Capita reached 4,515.3 USD in Dec 2022.
• Car Sales of China recorded 2,381,614.0 units in May 2023, representing a growth of 27.9 %.

Hong Kong Index of Consumer Sentiment data was reported at 91.200 Jan2000=100 in Sep 2017. This records an increase from the previous number of 89.800 Jan2000=100 for Jun 2017. Hong Kong Index of Consumer Sentiment data is updated quarterly, averaging 86.400 Jan2000=100 from Mar 2000 (Median) to Sep 2017, with 71 observations. The data reached an all-time high of 116.900 Jan2000=100 in Jun 2007 and a record low of 55.900 Jan2000=100 in Sep 2001. Hong Kong Index of Consumer Sentiment data remains active status in CEIC and is reported by Chinese University of Hong Kong. The data is categorized under Global Database’s Hong Kong SAR – Table HK.H023: Survey of Consumer Confidence and Sentiment (Discontinued). Figure for January 2018 is 98.9.

In this paper, we introduce the mixed-frequency data model (MIDAS) to China’s insurance demand forecasting. We select the monthly indicators Consumer Confidence Index (CCI), China Economic Policy Uncertainty Index (EPU), Consumer Price Index (PPI), and quarterly indicator Depth of Insurance (TID) to construct a Mixed Data Sampling (MIDAS) regression model, which is used to study the impact and forecasting effect of CCI, EPU, and PPI on China’s insurance demand. To ensure forecasting accuracy, we investigate the forecasting effects of the MIDAS models with different weighting functions, forecasting windows, and a combination of forecasting methods, and use the selected optimal MIDAS models to forecast the short-term insurance demand in China. The experimental results show that the MIDAS model has good forecasting performance, especially in short-term forecasting. Rolling window and recursive identification prediction can improve the prediction accuracy, and the combination prediction makes the results more robust. Consumer confidence is the main factor influencing the demand for insurance during the COVID-19 period, and the demand for insurance is most sensitive to changes in consumer confidence. Shortly, China’s insurance demand is expected to return to the pre-COVID-19 level by 2023Q2, showing positive development. The findings of the study provide new ideas for China’s insurance policymaking.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Shanghai Index: A Barometer of China's Economic Health?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

CWT plots comparison of the COVID-19 and the GFC.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

China A50 index poised for moderate gains amid stimulus hopes.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

China A50 Index Poised for Moderate Gains Amidst Policy Support

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data