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.

S&P 500: A Bull or a 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

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.

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 aim of this investigation is, to describe the development of the German Stock Market during the inter-war period. Causes for the so called change of the stock exchange functions are analysed. The author wants to make a contribution on special aspects of the economic history of the Weimar Republic and the following NS-regime. In his investigation the researcher analyses the activities of the involved players in a historical-institutional framework.

The Study’s subject In the year 1890 the constitution of security exchange markets and stock markets has been the object of political debate and there has been discussed similar questions according to this topic in public and in policy as today. A current question is about the possibilities to boost the functionality of the security exchange and stock markets, not least in the face of Germany’s position in the global economy. In 1896 as a result of massive political conflicts a stock exchange act has arisen that disappointed the representatives of liberal trading interests because of the restriction of the stock market system’s autonomy and the prohibition of certain forms of trade. In 1908 an amendment to the stock exchange act has been adopted by the parliament. The stock market act in this new form has had validity until today. After the years of the hyperinflation deep changes of the stock market processes has been taken place. This changes can be described as a change of function. The economic-historical study at hand deals with the description of the development of the German security exchange markets during the interwar period. Reasons of the functional changes, which means mainly the decrease in importance, are analysed. In this context the primary investigator’s analysis contributes also to specific aspects of the economic history of the Weimar Republic and the Nazi empire. Due to a lack of date the needed statistical information concerning the period of interest is not available and therefore a statistical analysis cannot meet cliometric requirements. Therefore, the study’s concept is primary a desciptive one. On the basis of the quantitative information an identification of the functional change and the definition of stages of this process is made. The researcher tries to carve out the factors which have led to the functional change particularly during the period between 1924 and 1939. In this context the annual reports of banks, reports of the Chamber of Commerce and Industry, contributions of professional journals, and documents of authorities charged with the stock exchange market, are the empirical basis for the investigation. The researcher analyzed the effects of the banking sector’s concentration-process on the stock exchange market and assessed quantitatively the functional change. On the basis of the collected time series for the period of the late 19th century until 1939 the investigator analyzed the activities at the stock markets. First, the focus on interest is on the development of investments and securities issues. Then information on the securities turnover of German capital market before 1940 are given on the basis of an estimation procedure, developed by the researcher. The sepcial conditions during the inflation between 1914 and 1923 are discussed separately and the long term effects of this hyper-inflation on the stock exchange are identified. The effects of the taxation of stock exchange market visits and the high transaction costs are discussed, too.

Used sources for the investigation have been: Archives of German Public Authorities: - finance ministry of the German Reich, - imperial chancellery - Reich´s ministry of economics - reference files of the German Reichsbank - Imperial commissioner of the stock market in Berlin

Official Statistics, statistics of trade associations, chambers of commerce, enterprises, the press, and scientific publications.

Finally, the author made estimates and calculations.

The Study’s data: Data tables are accessible via the search- and download-system HISTAT unter the Topic ‘State: Finances and Taxes’ (= Staat: Finanzen und Steuern).

The Study’s data are diveded into the following parts:

A. Quantitative Indicators on the Change of Functions (Quantitative Indikatoren des Funktionswandels)

A.1 Structure of floatation (Struktur der Wertpapieremission ausgewählter Zeitspannen (1901-1939).) A.2 Tax revenues of exchange turnover (Börsenumsatzsteueraufkommen (1885-1939).) A.3 Vergleich ...

Long term historical dataset of the NASDAQ Composite stock market index since 1971. 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.

To investigate the issue of inflation-hedging to find appropriate hedging assets against inflation by using the VAR or VECM model. We have collected data encompassing housing price indices, stock indices, price indexes, and money supply from five countries: the United States, Hong Kong, South Korea, Singapore, and Taiwan. The housing price index focuses on the transaction prices of listed residential houses in the metropolitan area as the benchmark, the stock price index is the ordinary stock market index of various countries, the price index is the consumer price index (CPI), and the money supply is M2 aggregate. The time period for obtaining data on the housing price index and stock price index is not the same.

Graph and download economic data for Producer Price Index by Industry: Investment Banking and Securities Intermediation: Brokerage Services, Equities and ETFs (PCU523120523120101) from Dec 1999 to Feb 2025 about brokers, ETF, stocks, equity, stock market, securities, services, PPI, industry, inflation, price index, indexes, price, and USA.

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 4,766.18 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 4,769.83, 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.

The main stock market index in China (SHANGHAI) increased 22 points or 0.66% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on March of 2025.

The Consumer Price Index in the United States increased 0.20 percent in February of 2025 over the previous month. This dataset provides - United States Inflation Rate MoM - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The main stock market index In the Euro Area (EU50) increased 521 points or 10.65% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks this benchmark index from Euro Area. Euro Area Stock Market Index (EU50) - values, historical data, forecasts and news - updated on March of 2025.

Inflation is generally defined as the continued increase in the average prices of goods and services in a given region. Following the extremely high global inflation experienced in the 1980s and 1990s, global inflation has been relatively stable since the turn of the millennium, usually hovering between three and five percent per year. There was a sharp increase in 2008 due to the global financial crisis now known as the Great Recession, but inflation was fairly stable throughout the 2010s, before the current inflation crisis began in 2021. Recent years Despite the economic impact of the coronavirus pandemic, the global inflation rate fell to 3.26 percent in the pandemic's first year, before rising to 4.66 percent in 2021. This increase came as the impact of supply chain delays began to take more of an effect on consumer prices, before the Russia-Ukraine war exacerbated this further. A series of compounding issues such as rising energy and food prices, fiscal instability in the wake of the pandemic, and consumer insecurity have created a new global recession, and global inflation in 2024 is estimated to have reached 5.76 percent. This is the highest annual increase in inflation since 1996. Venezuela Venezuela is the country with the highest individual inflation rate in the world, forecast at around 200 percent in 2022. While this is figure is over 100 times larger than the global average in most years, it actually marks a decrease in Venezuela's inflation rate, which had peaked at over 65,000 percent in 2018. Between 2016 and 2021, Venezuela experienced hyperinflation due to the government's excessive spending and printing of money in an attempt to curve its already-high inflation rate, and the wave of migrants that left the country resulted in one of the largest refugee crises in recent years. In addition to its economic problems, political instability and foreign sanctions pose further long-term problems for Venezuela. While hyperinflation may be coming to an end, it remains to be seen how much of an impact this will have on the economy, how living standards will change, and how many refugees may return in the coming years.

Relative purchasing power parity (PPP) holds for pure price inflations, which affect prices of all goods and services by the same proportion, while leaving relative prices unchanged. Pure price inflations also affect nominal returns of all traded financial assets by exactly the same amount. Recognizing that relative PPP may not hold for the official inflation data constructed from commodity price indices because of relative price changes and other frictions that cause prices to be "sticky," we provide a novel method for extracting a proxy for realized pure price inflation from stock returns. We find strong support for relative PPP in the short run using the extracted inflation measures.

Until the 90s information on risk premiums based on empirical studies for the German capital market was only available sporadically and for short time horizons. Therefore a long term comparison of risk and return was not possible. Markus Morawietz investigates profitability and risk of German stock and bond investments since 1870. He takes inflation and tax issues into account. His work contains a comprehensive collection of primary data since 1870 on key figures on a monthly basis which describe the German capital market. The goal of the study is to identify empirical statements on parameters of the German capital market. Therefore the exposition of theoretical economic models is not of primary importance in this study. A special focus is on the potential applicability of existing Germen index numbers as base data on the empirical investigation. The first chapter “methodological bases of performance measurement” concludes with the definition of the term “performance”. The following hypothesis is tested within this study: “There is a risk premium on securities taking inflation and influences of taxes into account.” The test of this hypothesis is run over the longest time period possible. Therefore monthly data on stock and bond investment are subject of the investigation because they are the most actively traded assets. Furthermore a substitute for the risk-free investment was developed in order to determine the risk premium. Before the explicit performance measurement of the different assets takes place, empirical starting points for performance measurement will be defined. These starting points contain a relevant demarcation of the investigation period and a description of the historical events during the investigation periods for all periods. Hereby special consideration is given to the specific problems of long term German value series (interruption trough the First World War with the following Hyperinflation and the Second World War). The analysis of the basics of performance measurement concludes the empirical starting points for performance measurement. The starting points contain the definition of a substitute for the certain segment, the description and preparation of the underlying data material and the calculation method used to determine performance. The third chapter contains a concrete empirical evaluation of the available data. This evaluation is subdivided into two parts: (a) performance measurement with unadjusted original data and (b) performance measurement with adjusted primary data (adjusted for inflation and tax influences). Both parts are structured in the same way. First the performance measurement of the specific asset (stocks, bonds and risk-free instruments) will be undertaken each by itself subdivided by partial periods. Afterwards the results of the performance measurement over the entire investigation period will be analyzed. The collection of derived partial results in the then following chapter shows return risk differences between the different assets. To calculate the net performance the nominal primary data is adjusted by inflation and tax influences. Therefore measured values for the changes in price level and for tax influences will be determined in the beginning of the third chapter. Following the performance measurement will be undertaken with the adjusted primary data. A comparison of the most important results of the different analysis in the last chapter concludes.

Data tables in histat (topic: money and currencies):

A. Discount and Lombard rate A.1 Discount rate: monthly average values, yearly average values (1870-1992) A.2 Lombard rate: monthly average values, yearly average values (1870-1992)

B. Stock price index, dividends and bond market und B.1a Stock price index: monthly average values, yearly average values (1870-1992) B.2 Dividends: monthly average values (1870-1992) B.3 Bond market: monthly average values, yearly average values (1870-1992)

C. Risk free instrument C.1 Private discount rate: monthly average values, yearly average values (1870-1991) C.2 Overnight rate: monthly average values, yearly average values (1924-1992)

D. Inflation rate D.1 Price index for costs of living (base1913/14 = 100), monthly average values, yearly average values (1870-1992) D.2 Inflation rate (base 1913 = 100), M monthly average values, yearly average values (1870-1992)

Inflation Rate in India decreased to 3.61 percent in February from 4.31 percent in January of 2025. This dataset provides - India Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Dow Jones Industrial Average is (DJIA) is possibly the most well-known and commonly used stock index in the United States. It is a price-weighted index that assesses the stock prices of 30 prominent companies, whose combined prices are then divided by a regularly-updated divisor (0.15199 in February 2021), which gives the index value. The companies included are rotated in and out on a regular basis; as of mid-2022, the longest mainstay on the list is Procter & Gamble, which was added in 1932; whereas Amgen, Salesforce, and Honeywell were all added in 2020. As one of the oldest indices for stock market analysis, the impact of major events, recessions, and economic shocks or booms can be tracked and contextualized over longer periods of time.

Due to inflation, unadjusted figures appear to be more sporadic in recent years, however the greatest fluctuations came in the earliest years of the index. In the given period, the greatest decline came in the wake of the Wall Street Crash in 1929; by 1932 average values had fallen to just one fifth of their 1929 average, from roughly 314 to 65.

Graph and download economic data for Producer Price Index by Commodity: Lumber and Wood Products: Hardwood Cut Stock and Dimension (WPU08120311) from Jun 1984 to Feb 2025 about floor coverings, stocks, wood, commodities, PPI, inflation, price index, indexes, price, and USA.

Graph and download economic data for Producer Price Index by Commodity: Pulp, Paper, and Allied Products: Sanitary Paper Products, Including Stock (WPU091501) from Jan 1947 to Feb 2025 about stocks, paper, commodities, PPI, inflation, price index, indexes, price, and USA.

Graph and download economic data for Producer Price Index by Industry: Railroad Rolling Stock Manufacturing (PCU33653365) from Jun 1984 to Feb 2025 about railroad, stocks, manufacturing, PPI, industry, inflation, price index, indexes, price, and USA.