Im Februar 2025 liegt der Indexwert für das Verbrauchervertrauen in China bei rund 88,4 Punkten. Die Statistik zeigt den Index für das Verbrauchervertrauen in China von Februar 2021 bis Februar 2025. Index für Verbrauchervertrauen (Consumer Confidence Index) Indizes für das nationale Verbrauchervertrauen zählen zu den wichtigsten wirtschaftlichen Frühindikatoren. Der Index misst, mittels Befragung, die Konsumlaune von Privathaushalten, als Teil der Binnennachfrage. Lassen die befragten Privathaushalte die Absicht steigender Konsumausgaben erkennen, ist der Index positiv und steigt. In der Folge lässt sich ein erhöhter Absatz von Konsumgütern verzeichnen. Umgekehrt gibt ein sinkender Verbrauchervertrauensindex erste Hinweise für einen Rückgang in der Binnennachfrage und schließlich auch der Wirtschaftsleistung. Der Index für Verbrauchervertrauen in China (Consumer Confidence Index) basiert auf einer Befragung von rund 700 Personen über 15 Jahren aus 20 Städten in ganz China. Ein Indexwert über 100 bedeutet, dass die Verbraucher im Hinblick auf ihre wirtschaftliche Situation optimistisch sind; ein Wert unter 100, dass die Situation pessimistisch bewertet wird.

Consumer confidence survey in Chine, janvier, 2025 Pour cet indicateur, National Bureau of Statistics of China fournit des données pour la Chine de janvier 2006 à janvier 2025. La valeur moyenne pour Chine pendant cette période était de 106.95 points avec un minimum de 85.5 points en novembre 2022 et un maximum de 127 points en février 2021. | TheGlobalEconomy.com

Consumer Confidence Score: Current Local Economy: Positive Response data was reported at 35.937 Score in Jan 2023. This records a decrease from the previous number of 55.274 Score for Dec 2022. Consumer Confidence Score: Current Local Economy: Positive Response data is updated monthly, averaging 23.354 Score from Mar 2010 (Median) to Jan 2023, with 155 observations. The data reached an all-time high of 57.176 Score in Nov 2021 and a record low of 12.172 Score in Sep 2013. Consumer Confidence Score: Current Local Economy: Positive Response 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.

Consumer Confidence Score: Household Purchase data was reported at 51.000 Score in Jan 2023. This stayed constant from the previous number of 51.000 Score for Dec 2022. Consumer Confidence Score: Household Purchase data is updated monthly, averaging 40.000 Score from Mar 2010 (Median) to Jan 2023, with 155 observations. The data reached an all-time high of 71.900 Score in Oct 2018 and a record low of -4.800 Score in Jan 2012. Consumer Confidence Score: Household Purchase 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.

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

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