Russia Social Sentiment Index (SSI): Mar2008=100 data was reported at 99.000 Mar2008=100 in Jan 2025. This records an increase from the previous number of 98.000 Mar2008=100 for Nov 2024. Russia Social Sentiment Index (SSI): Mar2008=100 data is updated monthly, averaging 79.500 Mar2008=100 from Jan 1995 (Median) to Jan 2025, with 176 observations. The data reached an all-time high of 106.000 Mar2008=100 in May 2024 and a record low of 45.000 Mar2008=100 in Sep 1998. Russia Social Sentiment Index (SSI): Mar2008=100 data remains active status in CEIC and is reported by Levada Analytical Center. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HE004: Sentiment Index: Levada-Center.

The Consumer Sentiment Index in the United States stood at 64.7 in January 2025, an increase from the previous month. The index is normalized to a value of 100 in December 1964 and based on a monthly survey of consumers, conducted in the continental United States. It consists of about 50 core questions which cover consumers' assessments of their personal financial situation, their buying attitudes and overall economic conditions.

Monthly Actual Data from September 1974. Covers: -Current condition index - Expectations index - Family finances last - 12 months - Family finances next - 12 months - Economic conditions - Next 12 months - Economic conditions - Next 5 years - Time to buy major household items.

  The survey is conduct monthly by telephone and the sample size is typically 1200 households. Each respondent is characterized by: gender, age, occupation, education, political party preference, home ownership, household income, and postcode. The Survey is used to compile the following Reports:
  -Westpac-Melbourne Institute Survey of Consumer Sentiment
  - Westpac-Melbourne Institute Survey of Consumer Sentiment: NSW, Vic., Qld, WA, SA.

Monthly Actual Data from January 1996.Covers: -Consumer Sentiment Index - Age 18-24, Age 25-44, Age over 45. -Consumer Sentiment Index - Live with children < 18, Does not live with child < 18. - Consumer Sentiment Index - Tenant, Mortgagee, Owned. - Consumer Sentiment Index - Coalition, ALP, Democrat, Others. - Consumer Sentiment Index - Manager & Professional, Paraprofessional & Trades, Sales & Clerical, Labourer & Operator, Retiree, Unemployed, Not working. - Consumer Sentiment Index - Male, Female. - Consumer Sentiment Index - Primary, Secondary, Trade, Tertiary. - Consumer Sentiment Index - Up to 20k, 20-40k, 40-60k, over 60k. The survey is conduct monthly by telephone and the sample size is typically 1200 households. Each respondent is characterized by: gender, age, occupation, education, political party preference, home ownership, household income, and postcode. The Survey is used to compile the following Reports. - Westpac-Melbourne Institute Survey of Consumer Sentiment. - Westpac-Melbourne Institute Survey of Consumer Sentiment: NSW, Vic., Qld, WA, SA.

Central bank minutes, corpus of annotated sentences on monetary policy stance, and central bank sentiment indices. Change *.changetozip to *.zip and unzip the archive files.

Ireland Consumer Sentiment Index data was reported at 87.701 4Q1995=100 in Apr 2019. This records a decrease from the previous number of 93.140 4Q1995=100 for Mar 2019. Ireland Consumer Sentiment Index data is updated monthly, averaging 91.732 4Q1995=100 from Feb 1996 (Median) to Apr 2019, with 279 observations. The data reached an all-time high of 130.854 4Q1995=100 in Jan 2000 and a record low of 39.593 4Q1995=100 in Jul 2008. Ireland Consumer Sentiment Index data remains active status in CEIC and is reported by Economic and Social Research Institute. The data is categorized under Global Database’s Ireland – Table IE.H019: Consumer Sentiment Indicator.

Renewable Energy Sentiment Index Market Outlook

As per our latest research, the global Renewable Energy Sentiment Index market size reached USD 1.42 billion in 2024, reflecting robust momentum in the sector. The market is poised to grow at a CAGR of 14.8% from 2025 to 2033, driven by the accelerating transition towards sustainable energy solutions and the increasing need for real-time sentiment analytics. By 2033, the Renewable Energy Sentiment Index market is forecasted to reach USD 4.72 billion, underpinned by technological advancements, regulatory shifts, and a heightened focus on ESG (Environmental, Social, and Governance) metrics across the energy landscape. This growth is strongly influenced by increased investments in digital infrastructure and rising demand from both public and private stakeholders to gauge market sentiment and inform strategic decisions.

The primary growth factor fueling the Renewable Energy Sentiment Index market is the global shift towards decarbonization and the adoption of renewables. Governments and private entities are increasingly prioritizing clean energy investments, leading to a surge in data generation across the energy value chain. As a result, stakeholders require sophisticated tools to analyze public perception, investor confidence, and policy sentiment, all of which are critical for project success and risk mitigation. The integration of artificial intelligence and machine learning into sentiment analysis platforms further enhances the accuracy and relevance of insights, enabling organizations to swiftly respond to market dynamics and regulatory changes. This trend is particularly pronounced in regions with aggressive net-zero targets and ambitious renewable energy mandates.

Another substantial driver is the growing reliance on digital communication channels, which has amplified the volume and velocity of sentiment data. Social media, news outlets, and online surveys now serve as primary sources for gauging public opinion on renewable energy projects, policy developments, and technology adoption. The Renewable Energy Sentiment Index market leverages these diverse data streams to provide actionable intelligence for utilities, investors, and policymakers. The rise of ESG investing and the need for transparent reporting have further intensified the demand for sentiment analysis, allowing organizations to align their strategies with stakeholder expectations and market trends. This digital transformation is fostering a data-driven culture within the renewable energy sector, propelling market expansion.

The proliferation of cloud-based analytics platforms and the increasing sophistication of software solutions are also pivotal to market growth. Cloud deployment offers scalability, real-time processing, and seamless integration with diverse data sources, making it the preferred choice for many organizations. Additionally, the growing emphasis on predictive analytics and scenario modeling is encouraging the adoption of advanced sentiment index tools, which can identify emerging opportunities and potential risks in real time. As the renewable energy sector becomes more competitive and interconnected, the ability to harness sentiment data for strategic decision-making is emerging as a key differentiator. This evolution is expected to continue, supported by ongoing investments in digital infrastructure and a global push for energy sustainability.

From a regional perspective, North America and Europe are leading the Renewable Energy Sentiment Index market, driven by strong policy frameworks, advanced digital ecosystems, and high levels of renewable energy adoption. The Asia Pacific region is rapidly catching up, fueled by large-scale renewable projects, government incentives, and growing investor interest. Latin America and the Middle East & Africa are also witnessing increased activity, albeit at a slower pace due to infrastructural and regulatory challenges. Overall, the market is characterized by a dynamic interplay of regional drivers, with each geography offering unique opportunities and challenges for sentiment analytics providers.

Introduction​ Promoting well-being is one of the key targets of Sustainable Development Goals at the United Nations. Many governments worldwide are incorporating subjective well-being (SWB) indicators to complement traditional objective and economic metrics. Our Twitter Sentiment Geographical Index (TSGI) can provide a high granularity monitor of well-being worldwide. This dataset is a joint effort of the Sustainable Urbanization Lab at MIT and Center for Geographic Analysis at Harvard. ===== ===== ===== ===== ===== ===== ===== ===== ===== ===== ===== ===== Granularity Geographical granularity: We provide a sentiment index on four levels: Globe, Country, State/Province, County/City Temporal granularity: The data covers 2012 to the present. And we update the sentiment data on a monthly basis. ===== ===== ===== ===== ===== ===== ===== ===== ===== ===== ===== ===== Fields DATE---- date ---- the date of the sentiment index NAME_0 ---- string ---- the country name NAME_1 ---- string ---- the state/province name NAME_2 ---- string ---- the county/city name SCORE ---- float ---- a float value between 0 and 1 representing the sentiment index where 1 represents a positive sentiment and 0 represents the negative sentiment. N ---- int ---- the number of posts generated given the specific date ===== ===== ===== ===== ===== ===== ===== ===== ===== ===== ===== ===== Citation rule If you use the TSGI in your research, please cite it as below: "Twitter Sentiment Geographical Index (https://doi.org/10.7910/DVN/3IL00Q)" ===== ===== ===== ===== ===== ===== ===== ===== ===== ===== ===== ===== Additional information For more information regarding the source dataset, please visit: here This dataset is free of usage for academic purposes. Please contact us should you have any questions or other usage cases. Thanks!

This dataset captures a vibrant tapestry of emotions, trends, and interactions across various social media platforms. It provides a snapshot of user-generated content, encompassing text, timestamps, hashtags, countries, likes, and retweets. Each entry unveils unique stories—moments of surprise, excitement, admiration, thrill, and contentment—shared by individuals worldwide. It is designed to offer insights into social media dynamics and user sentiments.

Columns

• Unnamed: 0: An index column, typically for record identification within the dataset.
• Text: User-generated content, presenting the original social media post from which sentiments are derived.
• Sentiment: The categorised emotion or sentiment expressed in the 'Text' column, such as Positive, Negative, or Neutral.
• Timestamp: The exact date and time when the social media content was posted.
• User: A unique identifier for the individual user who contributed the content.
• Platform: The specific social media platform where the content originated (e.g., Twitter, Instagram, Facebook).
• Hashtags: Keywords or phrases prefixed with '#' that identify trending topics and themes within the content.
• Retweets: A numerical value reflecting the popularity of the content, indicating how many times it was shared or re-posted.
• Likes: A numerical value quantifying user engagement, representing the number of 'likes' the post received.
• Country: The geographical origin of the social media post.
• Year: The year the social media post was published.
• Month: The month the social media post was published.
• Day: The day of the month the social media post was published.
• Hour: The hour of the day the social media post was published.

Distribution

The dataset is typically provided as a data file, most often in CSV format. A sample file will be updated separately to the platform. The structure is tabular, organised into the columns described above. Specific numbers for rows or records are not available, but it represents a daily snapshot of social media activity.

Usage

This dataset is a rich source of information that can be leveraged for various analytical purposes: * Sentiment Analysis: Explore the emotional landscape by conducting sentiment analysis on the 'Text' column. Classify user-generated content into categories such as surprise, excitement, admiration, thrill, and contentment. * Temporal Analysis: Investigate trends over time using the 'Timestamp' column. Identify patterns, fluctuations, or recurring themes in social media content. * User Behaviour Insights: Analyse user engagement through the 'Likes' and 'Retweets' columns to discover popular content and user preferences. * Platform-Specific Analysis: Examine variations in content across different social media platforms using the 'Platform' column. Understand how sentiments vary across platforms. * Hashtag Trends: Identify trending topics and themes by analysing the 'Hashtags' column. Uncover popular or recurring hashtags. * Geographical Analysis: Explore content distribution based on the 'Country' column. Understand regional variations in sentiment and topic preferences. * User Identification: Utilise the 'User' column to track specific users and their contributions. Analyse the impact of influential users on sentiment trends. * Cross-Analysis: Combine multiple features for in-depth insights. For example, analyse sentiment trends over time or across different platforms and countries.

Coverage

The dataset offers global geographical coverage, capturing posts from individuals worldwide. Examples in the sample data include content originating from the USA, Canada, the UK, Australia, and India. The data represents a snapshot of user-generated content, with the provided sample covering a few days in January 2023. The demographic scope is tied to general social media users, with no specific demographic breakdowns noted.

License

CCO

Who Can Use It

This dataset is ideal for: * Data scientists and machine learning engineers looking to train and validate models for sentiment analysis, natural language processing, and social media analytics. * Researchers and academics studying social trends, public opinion, digital communication, and user engagement patterns. * Marketing and brand analysts seeking to understand consumer sentiment, track brand mentions, and evaluate the reception of campaigns across different social platforms. * Anyone interested in gaining insights into the emotional landscape and dynamic interactions occurring within social media environments.

Dataset Name Suggestions

• Social Media Sentiments Analysis Dataset
• Global Social Sentiment Tracker
• User-Generated Content Emotion Data
• Digital Conversation Sentiment Snapshot
• Social Media Engagement Sentiment

Attributes

Original

Monthly Actual Data from January 1996. Covers: -Consumer Sentiment Index - Sydney -Consumer Sentiment Index - Melbourne -Consumer Sentiment Index - Other Capital Cities -Consumer Sentiment Index - Metro -Consumer Sentiment Index - Rural The survey is conduct monthly by telephone and the sample size is typically 1200 households. Each respondent is characterized by: gender, age, occupation, education, political party preference, home ownership, household income, and postcode. The Survey is used to compile the following Reports: - Westpac-Melbourne Institute Survey of Consumer Sentiment. - Westpac-Melbourne Institute Survey of Consumer Sentiment: NSW, Vic., Qld, WA, SA.

Australia & States Australia: Monthly Actual Data from September 1974. Covers: - Consumer Sentiment Index - Australia

  States: Monthly Actual Data from January 1996. Covers:
  - Consumer Sentiment Index - NSW
  - Consumer Sentiment Index - Vic
  - Consumer Sentiment Index - Qld
  - Consumer Sentiment Index - WA
  - Consumer Sentiment Index - SA
  - Consumer Sentiment Index - Tas.
  The survey is conduct monthly by telephone and the sample size is typically 1200 households. Each respondent is characterized by: gender, age, occupation, education, political party preference, home ownership, household income, and postcode. The Survey is used to compile the following Reports:
  -Westpac-Melbourne Institute Survey of Consumer Sentiment.
  -Westpac-Melbourne Institute Survey of Consumer Sentiment: NSW, Vic., Qld, WA, SA.

Description

This dataset, titled "Cryptocurrency Market Sentiment & Prediction," is a synthetic collection of real-time crypto market data designed for advanced analysis and predictive modeling. It captures a comprehensive range of features including price movements, social sentiment, news impact, and trading patterns for 10 major cryptocurrencies. Tailored for data scientists and analysts, this dataset is ideal for exploring market volatility, sentiment analysis, and price prediction, particularly in the context of significant events like the Bitcoin halving in 2024 and increasing institutional adoption.

Key Features Overview: - Price Movements: Tracks current prices and 24-hour price change percentages to reflect market dynamics. - Social Sentiment: Measures sentiment scores from social media platforms, ranging from -1 (negative) to 1 (positive), to gauge public perception. - News Sentiment and Impact: Evaluates sentiment from news sources and quantifies their potential impact on market behavior. - Trading Patterns: Includes data on 24-hour trading volumes and market capitalization, crucial for understanding market activity. - Technical Indicators: Features metrics like the Relative Strength Index (RSI), volatility index, and fear/greed index for in-depth technical analysis. - Prediction Confidence: Provides a confidence score for predictive models, aiding in assessing forecast reliability.

Purpose and Applications: - Perfect for machine learning tasks such as price prediction, sentiment-price correlation studies, and volatility classification. - Supports time series analysis for forecasting price movements and identifying volatility clusters. - Valuable for research into the influence of social media and news on cryptocurrency markets, especially during high-impact events.

Dataset Scope: - Covers a simulated 30-day period, offering a snapshot of market behavior under varying conditions. - Focuses on major cryptocurrencies including Bitcoin, Ethereum, Cardano, Solana, and others, ensuring relevance to current market trends.

Dataset Structure Table:

Dataset Statistics Table:

This dataset is a powerful resource for machine learning projects, sentiment analysis, and crypto market research, providing a robust foundation for AI/ML model development and testing.

Social Sentiment Index (SSI): Mar2008=100在2025-01达99.000Mar2008=100，相较于2024-11的98.000Mar2008=100有所增长。Social Sentiment Index (SSI): Mar2008=100数据按月度更新，1995-01至2025-01期间平均值为79.500Mar2008=100，共176份观测结果。该数据的历史最高值出现于2024-05，达106.000Mar2008=100，而历史最低值则出现于1998-09，为45.000Mar2008=100。CEIC提供的Social Sentiment Index (SSI): Mar2008=100数据处于定期更新的状态，数据来源于Levada Analytical Center，数据归类于Russia Premium Database的Household Survey – Table RU.HE004: Sentiment Index: Levada-Center。

The data set comes from our working paper "Tweet Sentiments and Stock Market: New Evidence from China", including the stock prices, number of stock-related tweets with different emotions at different days.It shows the closing price of Shanghai composite index (SHCI), volumes of Tweets with different sentiments and two indices based on the Tweets. The first column shows the time, covering the period of 2014/06/03-2014/12/31. The second column is the SHCI of each trading day. The 3rd-8th columns are the numbers of Tweets with different sentiments, including anger, joyful, disgust, fear and sadness. The 9th column is the number of Tweets with negative sentiments. The last two columns show the indices of Agreement and Bullishness.Please cite the paper: Yingying Xu, Zhixin Liu, Jichang Zhao and Chiwei Su. Weibo sentiments and stock return: A time- frequency view. PLoS ONE 12(7): e0180723, 2017.

The global economy has been hard hit by the COVID-19 pandemic. Many countries are experiencing a severe and destructive recession. A significant number of firms and businesses have gone bankrupt or been scaled down, and many individuals have lost their jobs. The main goal of this study is to support policy- and decision-makers with additional and real-time information about the labor market flow using Twitter data. We leverage the data to trace and nowcast the unemployment rate of South Africa during the COVID-19 pandemic. First, we create a dataset of unemployment-related tweets using certain keywords. Principal Component Regression (PCR) is then applied to nowcast the unemployment rate using the gathered tweets and their sentiment scores. Numerical results indicate that the volume of the tweets has a positive correlation, and the sentiments of the tweets have a negative correlation with the unemployment rate during and before the COVID-19 pandemic. Moreover, the now-casted unemployment rate using PCR has an outstanding evaluation result with a low Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Symmetric MAPE (SMAPE) of 0.921, 0.018, 0.018, respectively and a high R2-score of 0.929.

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.

MSCI World: Reflecting Global Economic Trends or Inflated Valuations?

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 Stock Market Dataset is designed for predictive analysis and machine learning applications in financial markets. It includes 13647 records of simulated stock trading data with features commonly used in stock price forecasting.

🔹 Key Features Date – Trading day timestamps (business days only) Open, High, Low, Close – Simulated stock prices Volume – Trading volume per day RSI (Relative Strength Index) – Measures market momentum MACD (Moving Average Convergence Divergence) – Trend-following momentum indicator Sentiment Score – Simulated market sentiment from financial news & social media Target – Binary label (1: Price goes up, 0: Price goes down) for next-day prediction This dataset is useful for training hybrid deep learning models such as LSTM, CNN, and Attention-based networks for stock market forecasting. It enables financial analysts, traders, and AI researchers to experiment with market trends, technical analysis, and sentiment-based predictions.

This dataset has been compiled to provide insights into the recent #BoycottBollywood trends observed on Twitter. It offers valuable data for analysts and researchers aiming to understand social media sentiment and online movements related to the Indian entertainment industry. The dataset is particularly suited for Natural Language Processing (NLP) projects, enabling the analysis of text-based social media content.

Columns

The dataset is structured with four key columns in each file: * #: An index number for each entry. * Date: The date and time when the tweet was posted. * User: The username of the individual who posted the tweet. * Tweet: The full text content of the tweet itself.

Distribution

The dataset is provided in CSV file format. Currently, it consists of five individual CSV files. The content of these files is scheduled for monthly or quarterly updates to ensure continued relevance and currency. While specific row counts per file are not detailed, the total number of records across the dataset appears to be approximately 41,796.

Usage

This dataset is an ideal resource for various applications, including: * Natural Language Processing (NLP) projects, such as sentiment analysis, topic modelling, and text classification. * Social media trend analysis, particularly for understanding public opinion and protest movements related to the Bollywood industry. * Research into online activism and its impact on cultural sectors in India. * Social listening and monitoring of public discourse surrounding entertainment and media consumption.

Coverage

The dataset primarily covers social media activity, specifically tweets, originating from India. The time range of the tweets included spans from August 2022 to September 2022, with observations from 1st August 2022 to 20th September 2022. The data reflects the collective activity of Twitter users engaging with the #BoycottBollywood trend during this period.

License

CC0

Who Can Use It

This dataset is particularly beneficial for: * Data scientists and NLP engineers seeking real-world text data for model training and research. * Social media strategists and marketing professionals interested in understanding consumer sentiment and brand perception in the Indian entertainment sector. * Academic researchers studying social movements, digital humanities, or media studies in the context of India. * Journalists and media analysts investigating public reactions and online discourse surrounding major cultural industries.

Dataset Name Suggestions

• Bollywood Boycott Twitter Data
• Indian Film Industry Social Media Trends
• #BoycottBollywood Tweets Analysis
• Bollywood Protest Sentiment Dataset

Attributes

Original Data Source: BoycottBollywood tweets dataset

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.

Indonesia to Become a Global EV Battery Hub with $9 Billion Investment

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

China Banks' Wealth Management Product Sentiment Index (BWMPSI) data was reported at 5,856.250 Jan2009=100 in Jul 2017. This records an increase from the previous number of 5,552.841 Jan2009=100 for Jun 2017. China Banks' Wealth Management Product Sentiment Index (BWMPSI) data is updated monthly, averaging 1,716.476 Jan2009=100 from Jan 2009 (Median) to Jul 2017, with 103 observations. The data reached an all-time high of 6,511.364 Jan2009=100 in Mar 2017 and a record low of 100.000 Jan2009=100 in Jan 2009. China Banks' Wealth Management Product Sentiment Index (BWMPSI) data remains active status in CEIC and is reported by Institute of Finance and Banking, Chinese Academy of Social Sciences. The data is categorized under China Premium Database’s Financial Market – Table CN.ZAM: Banks' Wealth Management Product: Index Series. Since July 2017, the data source (Institute of Finance and Banking, Chinese Academy of Social Sciences) ceased to provide data to CEIC, and the data of this series was therefore no longer updated.