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.

In 2024, ** percent of adults in the United States invested in the stock market. This figure has remained steady over the last few years, and is still below the levels before the Great Recession, when it peaked in 2007 at ** percent. What is the stock market? The stock market can be defined as a group of stock exchanges, where investors can buy shares in a publicly traded company. In more recent years, it is estimated an increasing number of Americans are using neobrokers, making stock trading more accessible to investors. Other investments A significant number of people think stocks and bonds are the safest investments, while others point to real estate, gold, bonds, or a savings account. Since witnessing the significant one-day losses in the stock market during the Financial Crisis, many investors were turning towards these alternatives in hopes for more stability, particularly for investments with longer maturities. This could explain the decrease in this statistic since 2007. Nevertheless, some speculators enjoy chasing the short-run fluctuations, and others see value in choosing particular stocks.

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).

Context

The algorithmic trading space is buzzing with new strategies. Companies have spent billions in infrastructures and R&D to be able to jump ahead of the competition and beat the market. Still, it is well acknowledged that the buy & hold strategy is able to outperform many of the algorithmic strategies, especially in the long-run. However, finding value in stocks is an art that very few mastered, can a computer do that?

Content

This Data repo contains two datasets:

1. Example_2019_price_var.csv. I built this dataset thanks to Financial Modeling Prep API and to pandas_datareader. Each row is a stock from the technology sector of the US stock market (that is available from the aforementioned API, which is free and highly recommended). The column contains the percent price variation of each stock for the year 2019. In other words, it collects the percent price variation of each stock from the first trading day on Jan 2019 to the last trading day of Dec 2019. To compute this price variation I decided to consider the Adjusted Close Price.

2. Example_DATASET.csv. I built this dataset thanks to Financial Modeling Prep API. Each row is a stock from the technology sector of the US stock market (that is available from the aforementioned API). Each column is a financial indicator that can be found in the 2018 10-K filings of each company. There are no Nans or empty cells. Furthermore, the last column is the CLASS of each stock, where:

   1. class = 1 if the price of the stock increases during 2019
   2. class = 0 if the price of the stock decreases during 2019

In other words, the last column is used to classify each stock in buy-worthy or not, and this relationship is what should allow a machine learning model to learn to recognize stocks that will increase their value from those that won't.

NOTE: the number of stocks does not match between the two datasets because the API did not have all the required financial indicators for some stocks. It is possible to remove from Example_2019_price_var.csv those rows that do not appear in Example_DATASET.csv.

Inspiration

I built this dataset during the 2019 winter holidays period, because I wanted to answer a simple question: is it possible to have a machine learning model learn the differences between stocks that perform well and those that don't, and then leverage this knowledge in order to predict which stock will be worth buying? Moreover, is it possible to achieve this simply by looking at financial indicators found in the 10-K filings?

This paper investigates whether gold and silver can be considered safe havens by examining their long-run linkages with 13 stock price indices. More specifically, the stochastic properties of the differential between gold/silver prices and 13 stock indices are analysed applying fractional integration/cointegration methods to daily data, first for a sample from January 2010 until December 2019, then for one from January 2020 until June 2022 which includes the Covid-19 pandemic. The results can be summarised as follows. In the case of the pre-Covid-19 sample ending in December 2019, mean reversion is found for the gold price differential only vis-à-vis a single stock index (SP500). whilst in seven other cases, although the estimated value of d is below 1, the value 1 is inside the confidence interval and thus the unit root null hypothesis cannot be rejected. In the remaining cases the estimated values of d are significantly higher than 1. As for the silver differential, the upper bound is 1 only in two cases, whilst in the others mean reversion does not occur. Thus, the evidence is mixed on whether these precious metals can be seen as safe havens, though it appears that this property characterises gold in a slightly higher number of cases. By contrast, when using the sample starting in January 2020, the evidence in favour of gold and silver as possible safe havens is pretty conclusive since mean reversion is only found in a single case, namely that of the gold differential vis-à-vis the New Zealand stock index.

This paper investigates whether gold and silver can be considered safe havens by examining their long-run linkages with 13 stock price indices. More specifically, the stochastic properties of the differential between gold/silver prices and 13 stock indices are analysed applying fractional integration/cointegration methods to daily data, first for a sample from January 2010 until December 2019, then for one from January 2020 until June 2022 which includes the Covid-19 pandemic. The results can be summarised as follows. In the case of the pre-Covid-19 sample ending in December 2019, mean reversion is found for the gold price differential only vis-à-vis a single stock index (SP500). whilst in seven other cases, although the estimated value of d is below 1, the value 1 is inside the confidence interval and thus the unit root null hypothesis cannot be rejected. In the remaining cases the estimated values of d are significantly higher than 1. As for the silver differential, the upper bound is 1 only in two cases, whilst in the others mean reversion does not occur. Thus, the evidence is mixed on whether these precious metals can be seen as safe havens, though it appears that this property characterises gold in a slightly higher number of cases. By contrast, when using the sample starting in January 2020, the evidence in favour of gold and silver as possible safe havens is pretty conclusive since mean reversion is only found in a single case, namely that of the gold differential vis-à-vis the New Zealand stock index.

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)

This paper examines proxies of money market, capital market, and banks in Nigeria using annual data from 1961 to 2018. We employ autoregressive distributed lag (ARDL) bounds testing approach, Wald test, and vector error correction model (VECM) Granger causality technique to analyse the data. Our findings show that total subscriptions of treasury bills has a positive and negative statistically significant relationship with real gross domestic product (GDP) on the long-run and short-run, respectively. Hence, we argue that markets and banks exhibit competitive interaction in favour of markets in Nigeria. Additionally, our findings show a unidirectional short-run causality from real GDP to value of transactions on the Nigerian Stock Exchange (NSE). Furthermore, our results support the existence of growth-led finance view or demand-following hypothesis in Nigeria, as we observe a unidirectional long-run causality from real GDP to both value of money market instruments outstanding as at end-period and total subscriptions of treasury bills. This study investigates finance-growth nexus in Nigeria with a particular focus on banks and markets. The findings of this research reveal that the role of markets on economic growth is superior to banks in Nigeria. Hence, banks and markets are competitive. Additionally, our empirical findings provide evidence to support the existence of growth-led finance view in Nigeria. This research explains the relevance of the financial system on economic growth in Nigeria and provides corresponding insights to policy makers.

Pre/post RMSPE ratios and P-values for oil & gas firms.

As of May 23, 2025, Microsoft was the leading tech company by market capitalization globally at 3.38 trillion U.S. dollars. Nvidia ranked second at 3.24 trillion U.S. dollars. Tech company stocks were impacted through 2025 as a result of various global tariff threats by the United States government. Apple among the leaders Since its foundation in a Californian garage in 1976, Apple has expanded massively, becoming one of the most valuable companies in the world. The company started its origins in the PC industry with the Macintosh, but soon entered other segments of the consumer electronics market. Today, the iPhone is the most popular Apple product, although Mac, iPad, wearables, and services also contribute to its high revenues. Aiming at innovation, Apple invests every year in research and development, spanning a wide array of technologies from AI through to extended reality. Nvidia's immense growth With a focus that began with origins in gaming, Nvidia's business strategy has been transformed by demand from data centers that sit at the heart of the AI boom. The company's chips have been favored to support in the training and running of a range of large language models, most notably in the development of OpenAI's ChatGPT.