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.

Inflation on the Rise: What Does This Mean for You?

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

The main stock market index of United States, the US500, rose to 6397 points on August 11, 2025, gaining 0.12% from the previous session. Over the past month, the index has climbed 2.04% and is up 19.69% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on August of 2025.

In Sweden, a total of ** percent of the population is concerned about the rising inflation's impact on their private economy. Whereas roughly ** percent of the respondents in the survey were very concerned, ** percent were somewhat concerned. In Sweden, as in the rest of the world, prices have been increasing rapidly through 2022.

The Dow Jones U.S. Consumer Services index is expected to experience moderate growth in the near future. Key factors driving this growth include rising consumer spending, increased disposable income, and favorable economic conditions. However, risks associated with the index include rising inflation, geopolitical uncertainty, and supply chain disruptions.

In April 2025, approximately 35 percent of people in the Republic of Ireland thought that inflation / the rising cost of living was one of the two most important issues facing the country. This was down from 65 percent in July 2022, and 55 percent in November 2023.

S&P 500 index is predicted to continue its upward trajectory, driven by strong earnings and economic growth. However, risks to this prediction include geopolitical tensions, rising interest rates, and inflation.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

Explore the impact of AI data centers on energy stocks, highlighting major gains for Constellation Energy and Vistra, driven by federal deals and a focus on sustainable power sources.

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.

Inflation: The Rising Tide That Can Capsize Your Business

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

In November 2024, the inflation rate in Turkey corresponded to **** percent. The monthly inflation rate in Turkey reached ***** percent in October 2022, the highest inflation rate recorded during the provided time interval. In June 2023, the year-on-year change in the Consumer Price Index (CPI) was recorded at ***** percent, the lowest since January 2022. Since the second half of 2019, Turkey’s inflation rate has consistently been in double digits, with inflation accelerating at the fastest rate in 2022. High production costs In Turkey, domestic producer price indices have been continuously rising, which has directly resulted in a price increase in all consumer goods and services. Accordingly, the Consumer Price Index (CPI) in all commodity groups increased extremely since 2022. In the same year, the food and non-alcoholic beverages category had one of the highest inflation rates in the CPI. This particularly affected Turkish consumers, as these products accounted for the highest share of household expenditure in 2023. Soaring food prices Since 2020, food prices have increased significantly around the world, and Turkey is no exception. Although inflation has started to slow down recently, food prices in Turkey continue to go up steadily, increasing by **** percent in November 2024 compared to the same month in the previous year. It is not surprising that food inflation has not simmered down, as the producer price index (PPI) of agricultural products followed a constant increasing trend in the country over the past few years.

In June 2025, the Consumer Price Index including owner occupiers' housing costs (CPIH) inflation rate of the United Kingdom was ****percent, up from **** percent in the previous month. The inflation rate fell noticeably after the COVID-19 pandemic but rose sharply between Spring 2021 and Autumn 2022. After peaking at *** percent in October 2022, CPIH inflation declined throughout 2023 and into 2024, falling to *** percent by September of that year, before increasing again recently. Cost of living problems persist into 2025 Although it is likely that the worst of the recent inflation surge may have passed, the issues caused by it look set to linger into 2025 and beyond. While the share of households experiencing living cost rises has fallen from ** percent in August 2022, to ** percent in July 2024, this share rose towards the end of the year, with more than half of households reporting rising costs in December. Even with lower inflation, overall consumer prices have already increased by around ** percent in the last three years, rising to almost ** percent for food prices, which lower income households typically spend more of their income on. The significant increase in people relying on food banks across the UK, is evidence of the magnitude of this problem, with approximately **** million people using food banks in 2023/24. Other measures of inflation While the CPIH inflation rate displayed here is the preferred index of the UK's Office of National Statistics, the Consumer Price Index (CPI) is often more prominently featured in the media in general. An older index, the Retail Price Index (RPI) is also still used by the government to calculate certain taxes and rail fares. Other metrics include the core inflation rate, which measures price increases without the volatility of food and energy costs, while price increases in goods and services can also be tracked separately. The inflation rate of individual sectors can also be measured, and as of December 2024, prices were rising fastest in the communications sector, at *** percent, with costs falling in the transport and furniture sectors.

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.

What happens to gold if CPI increases?

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

Japan's main stock market index, the JP225, rose to 41820 points on August 8, 2025, gaining 1.85% from the previous session. Over the past month, the index has climbed 5.02% and is up 19.40% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on August of 2025.

This paper studies how a large increase in the price level is transmitted to the real economy through firm balance sheets. Using newly digitized macro- and micro-level data from the German inflation of 1919-1923, we show that inflation led to a large reduction in real debt burdens and bankruptcies. Firms with higher nominal liabilities at the onset of inflation experienced a larger decline in interest expenses, a relative increase in their equity values, and higher employment during the inflation. The results are consistent with real effects of a debt-inflation channel that operates even when prices and wages are flexible.

United States Core PCEPI Inflation Nowcast: sa: Contribution: Real Estate Prices: Listings w/ Price Increases: Share: YoY data was reported at 0.000 % in 12 May 2025. This stayed constant from the previous number of 0.000 % for 05 May 2025. United States Core PCEPI Inflation Nowcast: sa: Contribution: Real Estate Prices: Listings w/ Price Increases: Share: YoY data is updated weekly, averaging 0.000 % from Apr 2019 (Median) to 12 May 2025, with 320 observations. The data reached an all-time high of 0.000 % in 12 May 2025 and a record low of 0.000 % in 12 May 2025. United States Core PCEPI Inflation Nowcast: sa: Contribution: Real Estate Prices: Listings w/ Price Increases: Share: YoY data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s United States – Table US.CEIC.NC: CEIC Nowcast: Personal Consumption Expenditure (PCE) Inflation: Core.

Inflation Rate in India decreased to 2.10 percent in June from 2.82 percent in May of 2025. This dataset provides - India Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

S&P 500 Inflation Risk

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

U.S. stock futures see an uptick as investors evaluate earnings reports and await inflation data. Key stocks like Nvidia and Snowflake show positive activity, while Salesforce experiences a decline.

In 2022, the vast majority of struggling consumers with low purchase power in the United States said that the current wave of inflation has heavily impacted their financial situation. Overall, there were few consumers in the U.S. who said they had not felt the impact of rising prices at all.

The global automatic tire inflation system (ATIS) sales were recorded at USD 1.8 billion in 2020. This figure is estimated to rise to USD 2.4 billion by 2025. Over the forecast period through 2035, the market is projected to reach USD 4.1 billion, exhibiting a compound annual growth rate (CAGR) of 5.6%.

Metric	Value
Industry Size (2025E)	USD 2.4 billion
Industry Value (2035F)	USD 4.1 billion
CAGR (2025 to 2035)	5.6%

Country-Wise Analysis

Country	CAGR (2025 to 2035)
United States	5.4%

Country	CAGR (2025 to 2035)
United Kingdom	5.3%

Country	CAGR (2025 to 2035)
Germany	5.8%

Country	CAGR (2025 to 2035)
Japan	5.7%

Country	CAGR (2025 to 2035)
South Korea	5.6%

Competitive Outlook

Company Name	Estimated Market Share (%)
Dana Incorporated	12-18%
SAF-Holland	10-15%
Michelin	9-13%
The Goodyear Tire & Rubber Company	7-12%
Hendrickson USA	5-9%
Other Companies	40-50%