South African monthly The FTSE/JSE All Share Index data was procured from Bloomberg and the nominal effective exchange rate (NEER) from South African Reserve Bank (SARB) database, where the data has been seasonally adjusted specifying 2015 as the base year. Volatility measures in these markets are generated through a multivaraite EGARCH model in the WinRATS software. South African monthly consumer price index (CPI) data was procured from the International Monetary Fund’s International Financial Statistics (IFS) database, where the data has been seasonally adjusted, specifying 2010 as the base year. The inflation rate is constructed by taking the year-on-year changes in the monthly CPI figures. Inflation uncertainty was generated through the GARCH model in Eviews software. The following South African macroeconomic variables were procured from the SARB: real industrial production (IP), which is used as a proxy for real GDP, real investment (I), real consumption (C), inflation (CPI), broad money (M3), the 3-month treasury bill rate (TB3) and the policy rate (R), a measure of U.S. EPU developed by Baker et al. (2016) to account for global developments available at http://www.policyuncertainty.com/us_monthly.html.

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.

This is not going to be an article or Op-Ed about Michael Jordan. Since 2009 we've been in the longest bull-market in history, that's 11 years and counting. However a few metrics like the stock market P/E, the call to put ratio and of course the Shiller P/E suggest a great crash is coming in-between the levels of 1929 and the dot.com bubble. Mean reversion historically is inevitable and the Fed's printing money experiment could end in disaster for the stock market in late 2021 or 2022. You can read Jeremy Grantham's Last Dance article here. You are likely well aware of Michael Burry's predicament as well. It's easier for you just to skim through two related videos on this topic of a stock market crash. Michael Burry's Warning see this YouTube. Jeremy Grantham's Warning See this YouTube. Typically when there is a major event in the world, there is a crash and then a bear market and a recovery that takes many many months. In March, 2020 that's not what we saw since the Fed did some astonishing things that means a liquidity sloth and the risk of a major inflation event. The pandemic represented the quickest decline of at least 30% in the history of the benchmark S&P 500, but the recovery was not correlated to anything but Fed intervention. Since the pandemic clearly isn't disappearing and many sectors such as travel, business travel, tourism and supply chain disruptions appear significantly disrupted - the so-called economic recovery isn't so great. And there's this little problem at the heart of global capitalism today, the stock market just keeps going up. Crashes and corrections typically occur frequently in a normal market. But the Fed liquidity and irresponsible printing of money is creating a scenario where normal behavior isn't occurring on the markets. According to data provided by market analytics firm Yardeni Research, the benchmark index has undergone 38 declines of at least 10% since the beginning of 1950. Since March, 2020 we've barely seen a down month. September, 2020 was flat-ish. The S&P 500 has more than doubled since those lows. Look at the angle of the curve: The S&P 500 was 735 at the low in 2009, so in this bull market alone it has gone up 6x in valuation. That's not a normal cycle and it could mean we are due for an epic correction. I have to agree with the analysts who claim that the long, long bull market since 2009 has finally matured into a fully-fledged epic bubble. There is a complacency, buy-the dip frenzy and general meme environment to what BigTech can do in such an environment. The weight of Apple, Amazon, Alphabet, Microsoft, Facebook, Nvidia and Tesla together in the S&P and Nasdaq is approach a ridiculous weighting. When these stocks are seen both as growth, value and companies with unbeatable moats the entire dynamics of the stock market begin to break down. Check out FANG during the pandemic. BigTech is Seen as Bullet-Proof me valuations and a hysterical speculative behavior leads to even higher highs, even as 2020 offered many younger people an on-ramp into investing for the first time. Some analysts at JP Morgan are even saying that until retail investors stop charging into stocks, markets probably don’t have too much to worry about. Hedge funds with payment for order flows can predict exactly how these retail investors are behaving and monetize them. PFOF might even have to be banned by the SEC. The risk-on market theoretically just keeps going up until the Fed raises interest rates, which could be in 2023! For some context, we're more than 1.4 years removed from the bear-market bottom of the coronavirus crash and haven't had even a 5% correction in nine months. This is the most over-priced the market has likely ever been. At the night of the dot-com bubble the S&P 500 was only 1,400. Today it is 4,500, not so many years after. Clearly something is not quite right if you look at history and the P/E ratios. A market pumped with liquidity produces higher earnings with historically low interest rates, it's an environment where dangerous things can occur. In late 1997, as the S&P 500 passed its previous 1929 peak of 21x earnings, that seemed like a lot, but nothing compared to today. For some context, the S&P 500 Shiller P/E closed last week at 38.58, which is nearly a two-decade high. It's also well over double the average Shiller P/E of 16.84, dating back 151 years. So the stock market is likely around 2x over-valued. Try to think rationally about what this means for valuations today and your favorite stock prices, what should they be in historical terms? The S&P 500 is up 31% in the past year. It will likely hit 5,000 before a correction given the amount of added liquidity to the system and the QE the Fed is using that's like a huge abuse of MMT, or Modern Monetary Theory. This has also lent to bubbles in the housing market, crypto and even commodities like Gold with long-term global GDP meeting many headwinds in the years ahead due to a...

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.

[Video] S&P 500: Bull or Bear?

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

Interactive chart of the S&P 500 stock market index since 1927. Historical data is inflation-adjusted using the headline CPI and each data point represents the month-end closing value. The current month is updated on an hourly basis with today's latest value.

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.

Inflation: Friend or Foe to the Stock Market?

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

Inflation Rate in China increased to 0.10 percent in June from -0.10 percent in May of 2025. This dataset provides - China Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Interactive chart of the Dow Jones Industrial Average (DJIA) stock market index for the last 100 years. Historical data is inflation-adjusted using the headline CPI and each data point represents the month-end closing value. The current month is updated on an hourly basis with today's latest value.

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.

During the period beginning roughly in the mid-1980s until the Global Financial Crisis (2007-2008), the U.S. economy experienced a time of relative economic calm, with low inflation and consistent GDP growth. Compared with the turbulent economic era which had preceded it in the 1970s and the early 1980s, the lack of extreme fluctuations in the business cycle led some commentators to suggest that macroeconomic issues such as high inflation, long-term unemployment and financial crises were a thing of the past. Indeed, the President of the American Economic Association, Professor Robert Lucas, famously proclaimed in 2003 that "central problem of depression prevention has been solved, for all practical purposes". Ben Bernanke, the future chairman of the Federal Reserve during the Global Financial Crisis (GFC) and 2022 Nobel Prize in Economics recipient, coined the term 'the Great Moderation' to describe this era of newfound economic confidence. The era came to an abrupt end with the outbreak of the GFC in the Summer of 2007, as the U.S. financial system began to crash due to a downturn in the real estate market.

Causes of the Great Moderation, and its downfall

A number of factors have been cited as contributing to the Great Moderation including central bank monetary policies, the shift from manufacturing to services in the economy, improvements in information technology and management practices, as well as reduced energy prices. The period coincided with the term of Fed chairman Alan Greenspan (1987-2006), famous for the 'Greenspan put', a policy which meant that the Fed would proactively address downturns in the stock market using its monetary policy tools. These economic factors came to prominence at the same time as the end of the Cold War (1947-1991), with the U.S. attaining a new level of hegemony in global politics, as its main geopolitical rival, the Soviet Union, no longer existed. During the Great Moderation, the U.S. experienced a recession twice, between July 1990 and March 1991, and again from March 2001 tom November 2001, however, these relatively short recessions did not knock the U.S. off its growth path. The build up of household and corporate debt over the early 2000s eventually led to the Global Financial Crisis, as the bursting of the U.S. housing bubble in 2007 reverberated across the financial system, with a subsequent credit freeze and mass defaults.

Inflation Rate in Turkey decreased to 35.05 percent in June from 35.41 percent in May of 2025. This dataset provides the latest reported value for - Turkey Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Sources:

German Central Bank (ed.), 1975: Deutsches Geld- und Bankwesen in Zahlen 1876 – 1975. (German monetary system and banking system in numbers 1876 – 1975) German Central Bank (ed.), different years: monthly reports of the German Central Bank, statistical part, interest rates German Central Bank (ed.), different years: Supplementary statistical booklets for the monthly reports of the German Central Bank 1959 – 1992, security statistics Reich Statistical Office (ed.), different years: Statistical yearbook of the German empire Statistical Office (ed.), 1985: Geld und Kredit. Index der Aktienkurse (Money and Credit. Index of share prices) – Lange Reihe; Fachserie 9, Reihe 2. Statistical Office (ed.), 1987: Entwicklung der Nahrungsmittelpreise von 1800 – 1880 in Deutschland. (Development of food prices in Germany 1800 – 1880) Statistical Office (ed.), 1987: Entwicklung der Verbraucherpreise (Development of consumer prices) seit 1881 in Deutschland. (Development of consumer prices since 1881 in Germany) Statistical Office (ed.), different years: Fachserie 17, Reihe 7, Preisindex für die Lebenshaltung (price index for costs of living) Donner, 1934: Kursbildung am Aktienmarkt; Grundlagen zur Konjunkturbeobachtung an den Effektenmärkten. (Prices on the stock market; groundwork for observation of economic cycles on the stock market) Homburger, 1905: Die Entwicklung des Zinsfusses in Deutschland von 1870 – 1903. (Development of the interest flow in Germany, 1870 – 1903) Voye, 1902: Über die Höhe der verschiedenen Zinsarten und ihre wechselseitige Abhängigkeit.(On the values of different types of interests and their interdependence).

The Standard & Poor’s (S&P) 500 Index is an index of 500 leading publicly traded companies in the United States. In 2021, the index value closed at ******** points, which was the second highest value on record despite the economic effects of the global coronavirus (COVID-19) pandemic. In 2023, the index values closed at ********, the highest value ever recorded. What is the S&P 500? The S&P 500 was established in 1860 and expanded to its present form of 500 stocks in 1957. It tracks the price of stocks on the major stock exchanges in the United States, distilling their performance down to a single number that investors can use as a snapshot of the economy’s performance at a given moment. This snapshot can be explored further. For example, the index can be examined by industry sector, which gives a more detailed illustration of the economy. Other measures Being a stock market index, the S&P 500 only measures equities performance. In addition to other stock market indices, analysts will look to other indicators such as GDP growth, unemployment rates, and projected inflation. Similarly, since these indicators say something about the economic future, stock market investors will use these indicators to speculate on the stocks in the S&P 500.

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.

LON:STG Stock: The Stock Market Bubble Is About to Burst

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 annual returns of the Nasdaq 100 Index from 1986 to 2024. fluctuated significantly throughout the period considered. The Nasdaq 100 index saw its lowest performance in 2008, with a return rate of ****** percent, while the largest returns were registered in 1999, at ****** percent. As of June 11, 2024, the rate of return of Nasdaq 100 Index stood at ** percent. The Nasdaq 100 is a stock market index comprised of the 100 largest and most actively traded non-financial companies listed on the Nasdaq stock exchange. How has the Nasdaq 100 evolved over years? The Nasdaq 100, which was previously heavily influenced by tech companies during the dot-com boom, has undergone significant diversification. Today, it represents a broader range of high-growth, non-financial companies across sectors like consumer services and healthcare, reflecting the evolving landscape of the global economy. The annual development of the Nasdaq 100 recently has generally been positive, except for 2022, when the NASDAQ experienced a decline due to worries about escalating inflation, interest rates, and regulatory challenges. What are the leading companies on Nasdaq 100? In August 2023, ***** was the largest company on the Nasdaq 100, with a market capitalization of **** trillion euros. Also, ****************************************** were among the five leading companies included in the index. Market capitalization is one of the most common ways of measuring how big a company is in the financial markets. It is calculated by multiplying the total number of outstanding shares by the current market price.

A monthly and quarterly data set spanning July 1995 to December 2016 of the following macro-economic variables 1. South African stock market 2. South African GDP3. United States GDP 4. South African interest rate 5. US interest rate 6. South African inflation rate 7. US inflation rate 8. South African Money Supply 9. Rand/Dollar Exchange 10. FTSE

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.

LON:EMIS Stock Forecast: A Hold For The Next 3 Month

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

Graph and download economic data for Producer Price Index by Commodity: Lumber and Wood Products: Softwood Cut Stock and Dimension (WPU08110503) from Jan 1987 to May 2025 about stocks, wood, commodities, PPI, inflation, price index, indexes, price, and USA.

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.

Do Wars Fuel Inflation? A Statistical Exploration

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