Ipsos Global @dvisor wave 30 was conducted on February 7 and February 21, 2012. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Reuters Battery, EH: Retail Confidence, EI: Political Heat Map, EJ: Health and Wellness, EK: Tech Tracker.

Ipsos Global @dvisor wave 34 was conducted on June 5 and June 19, 2012. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Reuters Battery, FA: Olympics, BE: Threat Index.

This statistic displays the Primary Consumer Sentiment Index (PCSI) as measured by Thomson Reuters / Ipsos in Canada from August 2018 to August 2019. The monthly PCSI result is driven by the aggregation of the four, weighted, sub-indices:
Perceived current personal financial situations, perceived economic expectations, perceived investment climate, and job security. In August 2019, the Consumer Confidence Index in Canada was at **** percentage points.

Ipsos Global @dvisor wave 52 was conducted on December 3 and December 17, 2013. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Small Business/Executive Decision Makers Demo, FV: Christmas Questions.

This statistic displays the Primary Consumer Sentiment Index (PCSI) as measured by Thomson Reuters / Ipsos in the United States from *********** to ***********. The monthly PCSI result is driven by the aggregation of the four, weighted, sub-indices:
Perceived current personal financial situations, perceived economic expectations, perceived investment climate, and job security. In ***********, the PCSI in the United States stood at **** percent.

Ipsos Global @dvisor wave 28 was conducted on December 6 and December 19, 2011. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Reuters Battery, DU: Happiness, DV: Workplace.

This statistic displays the Primary Consumer Sentiment Index as measured by Thomson Reuters /Ipsos PCSI in Germany from March 2017 to August 2019. The monthly PCSI result is driven by the aggregation of the four, weighted, sub-Indices:
Perceived current personal financial situations, perceived economic expectations, perceived investment climate, and job security. In August 2019, the PCSI in Germany stood at **** percent, which was the lowest level during the reporting period.

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.

TRI Thomson Reuters Corp Ordinary Shares

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.

Thorn in Reuters' (TRI) Side?

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

Ipsos Global @dvisor wave 32 was conducted on April 3 and April 17, 2012. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Reuters Battery, EQ: Global Retail Intended Purchase Assessment, ET: Languages Used in Business, EU: Online Dating, X: Corporate/Business Risks, C: Corporate Social Responsibility.

This statistic displays the Primary Consumer Sentiment Index as measured by Thomson Reuters /Ipsos PCSI in Hungary from March 2017 to August 2019. The monthly PCSI result is driven by the aggregation of the four, weighted, sub-Indices:
Perceived current personal financial situations, perceived economic expectations, perceived investment climate, and job security. In August 2019, the PCSI in Hungary stood at **** percent which was a decrease from the previous month.

Ipsos Global @dvisor wave 16 was conducted on December 10 and December 20, 2010. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Reuters Battery, BV: Air Travel/New Year.

Ipsos Global @dvisor wave 11 was conducted on July 6 and July 20, 2010. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Reuters Battery, G: Country Image Rating, H: Economy/Spending/Purchasing

Use LSEG Ipsos Primary Consumer Sentiment (PCSI) to integrate primary consumer sentiment in to your investment decisions.

This statistic displays the Primary Consumer Sentiment Index (PCSI) as measured by Thomson Reuters / Ipsos in Mexico from August 2018 to August 2019. The monthly PCSI result is an aggregation of four weighted sub-indices: perceived current personal financial situation, perceived economic expectations, perceived investment climate, and job security. In August 2019, the PCSI in Mexico stood at **** percent.

Im Juni 2025 hat der Indexwert für das Verbrauchervertrauen in den USA bei **** Punkten gelegen. Diese Statistik zeigt die Indexwerte für das Verbrauchervertrauen (Primary Consumer Sentiment Index - PCSI, erhoben von Thomson Reuters und Ipsos) für das Verbrauchervertrauen in den USA im Zeitraum von April 2024 bis Juni 2025. Verbraucherstimmung und Konsumklima in den USA Weitere Indizes, die das Konsumklima bzw. die Verbraucherstimmung in den USA untersuchen, sind:

Consumer Confidence Index (CCI) University of Michigan Consumer Sentiment Index (MCSI) University of Michigan Consumer Sentiment Index (MCSI) nach Parteizugehörigkeit

Primary Consumer Sentiment Index (PCSI) Der Primary Consumer Sentiment Index - PCSI wird durch die Aggregation der folgenden vier gewichteten Subindizes bestimmt:

PCSI Subindex Employment Confidence ("Jobs") PCSI Subindex Economic Expectations ("Erwartungen") PCSI Subindex Investment Climate ("Investitionsklima") PCSI Subindex Current Personal Financial Conditions ("Aktuelle finanzielle Situation")

Der Thomson Reuters / Ipsos Primary Consumer Sentiment Index (PCSI), der seit 2010 erhoben wird, ist eine monatliche nationale Umfrage über die Einstellung der Verbraucher zum aktuellen und zukünftigen Zustand der lokalen Wirtschaft. Der PCSI-Index wird bei seiner Einführung im Januar 2010 mit einer Basislinie von ** (dem historischen Median wirtschaftlicher Bedingungen) bewertet. Die Indexnummer wird unter Verwendung von Daten aus den Umfrageergebnissen berechnet. Bei den Angaben handelt sich um gewichtete gleitende Monatswerte, siehe Hinweise und Anmerkungen. Die für die PCSI verwendeten Fragen lauten wie folgt:

Denken Sie nun über unsere wirtschaftliche Situation nach, wie würden Sie die derzeitige wirtschaftliche Situation in ihrem Land beschreiben? Ist sie... sehr gut, eher gut, eher schlecht oder sehr schlecht Bewerten Sie den aktuellen Stand der Wirtschaft in Ihrer Region anhand einer Skala von * bis *, wobei * für eine sehr starke Wirtschaft steht und * für eine sehr schwache Wirtschaft. Wenn Sie * Monate vorausblicken, erwarten Sie, dass die Wirtschaft in Ihrer Region viel stärker, etwas stärker, ungefähr gleich, etwas schwächer oder viel schwächer als jetzt ist? Bewerten Sie Ihre aktuelle finanzielle Situation anhand einer Skala von * bis *, wobei * bedeutet, dass Ihre persönliche finanzielle Situation heute sehr stark ist und * bedeutet, dass sie sehr schwach ist Wenn Sie * Monate nach vorn schauen, erwarten Sie, dass Ihre persönliche finanzielle Situation viel stärker, etwas stärker, ungefähr gleich, etwas schwächer oder viel schwächer als jetzt ist? Verglichen mit vor * Monaten, sind Sie JETZT mehr oder weniger entspannt bei einem größeren Kauf, wie ein Eigenheim oder ein Auto? Verglichen mit vor * Monaten, sind Sie JETZT mehr oder weniger entspannt, andere Haushaltskäufe zu tätigen? Sind Sie im Vergleich zu vor * Monaten JETZT mehr oder weniger zuversichtlich in Bezug auf die Arbeitsplatzsicherheit für sich selbst, Ihrer Familie und anderer Personen, die Sie persönlich kennen? Sind Sie vor * Monaten mehr oder weniger zuversichtlich gewesen, dass Sie in die Zukunft investieren können, einschließlich Ihrer Möglichkeiten, Geld für Ihren Ruhestand oder die Bildung Ihrer Kinder zu sparen? Wenn Sie an die letzten * Monate denken, haben Sie, jemand in Ihrer Familie oder jemand anders, den Sie persönlich kennen, aufgrund der wirtschaftlichen Bedingungen seinen Arbeitsplatz verloren? Jetzt schauen Sie auf die nächsten * Monate voraus. Wie wahrscheinlich ist es, dass Sie, jemand in Ihrer Familie oder jemand anderes, den Sie persönlich kennen, seinen Arbeitsplatz in den nächsten * Monaten aufgrund der wirtschaftlichen Bedingungen verlieren wird?

Ipsos Global @dvisor wave 17 was conducted on January 14 and January 24, 2011. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Reuters Battery, BY: Consumer Goods Questions.

Ipsos Global @dvisor wave 24 was conducted on August 5 and August 18, 2011. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Reuters Battery, BD: Retail Confidence.

Ipsos Global @dvisor wave 36 was conducted on August 7 and August 21, 2012. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Reuters Battery, FL: Employee Mobility, FM: Financial Literacy.

Ipsos Global @dvisor wave 23 was conducted on July 5 and July 18, 2011. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Reuters Battery, H: Economy/Spending/Purchasing, CW: Media Questions