Throughout the 1920s, prices on the U.S. stock exchange rose exponentially, however, by the end of the decade, uncontrolled growth and a stock market propped up by speculation and borrowed money proved unsustainable, resulting in the Wall Street Crash of October 1929. This set a chain of events in motion that led to economic collapse - banks demanded repayment of debts, the property market crashed, and people stopped spending as unemployment rose. Within a year the country was in the midst of an economic depression, and the economy continued on a downward trend until late-1932.

It was during this time where Franklin D. Roosevelt (FDR) was elected president, and he assumed office in March 1933 - through a series of economic reforms and New Deal policies, the economy began to recover. Stock prices fluctuated at more sustainable levels over the next decades, and developments were in line with overall economic development, rather than the uncontrolled growth seen in the 1920s. Overall, it took over 25 years for the Dow Jones value to reach its pre-Crash peak.

Graph and download economic data for Stocks, Value of Shares Sold on the New York Stock Exchange for United States (M11003USM144NNBR) from Jan 1885 to Dec 1920 about stock market and USA.

The Dow Jones Industrial Average (DJIA) is a stock market index used to analyze trends in the stock market. While many economists prefer to use other, market-weighted indices (the DJIA is price-weighted) as they are perceived to be more representative of the overall market, the Dow Jones remains one of the most commonly-used indices today, and its longevity allows for historical events and long-term trends to be analyzed over extended periods of time. Average changes in yearly closing prices, for example, shows how markets developed year on year. Figures were more sporadic in early years, but the impact of major events can be observed throughout. For example, the occasions where a decrease of more than 25 percent was observed each coincided with a major recession; these include the Post-WWI Recession in 1920, the Great Depression in 1929, the Recession of 1937-38, the 1973-75 Recession, and the Great Recession in 2008.

Graph and download economic data for Dow-Jones Industrial Stock Price Index for United States (M1109BUSM293NNBR) from Dec 1914 to Dec 1968 about stock market, industry, price index, indexes, price, and USA.

This article examines the stock market's changing valuation of corporate patentable assets between 1910 and 1939. It shows that the value of knowledge capital increased significantly during the 1920s compared to the 1910s as investors responded to the quality of technological inventions. Innovation was an important driver of the late 1920s stock market runup, and the Great Crash did not reflect a significant revaluation of knowledge capital relative to physical capital. Although substantial quantities of influential patents were accumulated during the post-crash recovery, high technology firms did not earn significant excess returns over low technology firms for most of the 1930s. (JEL G14, N12, N22, O30)

NYSE - Value of Shares Sold - Historical chart and current data through 1920.

This is the replication file for Hilt, Eric and Rahn, Wendy, "Financial Asset Ownership and Political Partisanship: Liberty Bonds and Republican Electoral Success in the 1920s," Journal of Economic History, 2020. The file contains data and code necessary to replicate all the results presented in that paper and in the Online Appendix.

This dataset is about stocks per day. It has 947 rows and is filtered where the stock is 7039.T. It features 3 columns: stock, and opening price.

The Long Depression was, by a large margin, the longest-lasting recession in U.S. history. It began in the U.S. with the Panic of 1873, and lasted for over five years. This depression was the largest in a series of recessions at the turn of the 20th century, which proved to be a period of overall stagnation as the U.S. financial markets failed to keep pace with industrialization and changes in monetary policy. Great Depression The Great Depression, however, is widely considered to have been the most severe recession in U.S. history. Following the Wall Street Crash in 1929, the country's economy collapsed, wages fell and a quarter of the workforce was unemployed. It would take almost four years for recovery to begin. Additionally, U.S. expansion and integration in international markets allowed the depression to become a global event, which became a major catalyst in the build up to the Second World War. Decreasing severity When comparing recessions before and after the Great Depression, they have generally become shorter and less frequent over time. Only three recessions in the latter period have lasted more than one year. Additionally, while there were 12 recessions between 1880 and 1920, there were only six recessions between 1980 and 2020. The most severe recession in recent years was the financial crisis of 2007 (known as the Great Recession), where irresponsible lending policies and lack of government regulation allowed for a property bubble to develop and become detached from the economy over time, this eventually became untenable and the bubble burst. Although the causes of both the Great Depression and Great Recession were similar in many aspects, economists have been able to use historical evidence to try and predict, prevent, or limit the impact of future recessions.

This dataset is about stocks per day. It has 1,920 rows and is filtered where the stock is 1741.HK. It features 3 columns: stock, and highest price.

Graph and download economic data for Index of Stock Prices (General) for Germany (M1123BDEM334NNBR) from Jan 1924 to Dec 1935 about stock market, Germany, and indexes.

The study on hand is an investigation on seasonal fluctuation ranges of the German Reichsbank’s metal stocks between 1876 and 1920. The test of seasonal unity roots enables the identification of the character of deterministic and random seasonal fluctuations of timeseries. This specific method of timeseries analysis is applied to the monetary time series on monthly basis of the German Reichsbank. The timeseries are collected from weakly surveyed data with 2160 cases. The analysis shows, that a deterministic seasonal fluctuation of cases with particularly strong seasonality at the beginning and at the end of the year could be identified. Data Tables (see Online-Database HISTAT): - Cash flow, Gold- and foreign exchange balance in Million Reichsmark (1876-1920) - Metal stocks of the Reichsbank, weekly data (1876-1920) Variables: Weekly data: - Metal stocks are stocks of gold, taler, and secondary coins.. Yearly data: - Cash flow - Gold- and foreign exchange balance - Cash flow, total (Angaben der Dt. Bundesbank) - Cash flow, gold coins - Gold- and foreign exchange balance of the central bank, total - Gold- and foreign exchange balance of the central bank, gold coins - Metalstocks, yearly averages - average of gold stocks - average stock of german coins. Analysis of the following sources: 1) Official Statistics: Jahrbuch für die amtliche Statistik des preussischen Staates, Statistisches Handbuch für den preussischen Staat, Statistisches Jahrbuch für das Deutsche Reich, Vierteljahreshefte zur Statistik des Deutschen Reichs. 2) Archival Material: Preussische Gesetz-Sammlung (GR 3600 MF, HA10 Bo 100, Microfiches, Staatsbibliothek zu Berlin – Preussischer Kulturbesitz). 3) Reports of the German Reichsbank Die Reichsbank, 1876 – 1900, Berlin 1900, Die Reichsbank, 1901 – 1925, Berlin 1925. Territory of investigation: German Empire. Untersuchung der saisonalen Schwankungsbreite der Metallvorräte der Reichsbank von 1876 bis 1920. Der saisonale Einheitswurzel-Test ermöglicht es, den Charakter von deterministischen und zufälligen saisonalen Schwankungen in Zeitreihen zu bestimmen. Diese spezifische Methode der Zeitreihenanalyse wird auf monetäre Zeitreihen auf Monatsbasis der deutschen Reichsbank angewendet. Die Zeitreihen sind aus wöchentlich erhobenen Daten mit 2160 Beobachtungen gebildet worden. Die Analyse zeigte, dass eine deterministische saisonale Schwankung der Werte mit besonders starker Saisonalität zum Beginn und zum Ende des Jahres festgestellt werden kann. Untergliederung der Studie (Tabellen ZA-Datenbank HISTAT): - Bargeldumlauf, Gold- und Devisenbestand in Millionen Reichsmark (1876-1920) - Die Metallvorräte der Reichsbank, Wochendaten (1876-1920) Variablen: Wochendaten: - Metallvorräte sind Goldbestände, Taler und Scheidemünzen. Jahresdaten: - Bargeldumlauf - Gold- und Devisenbestand - Bargeldumlauf, insgesamt (Angaben der Dt. Bundesbank) - Bargeldumlauf, Goldmünzen - Gold- und Devisenbestand der Notenbank insgesamt - Gold- und Devisenbestand der Notenbank, Goldbestand - Metallvorrat, Jahresdurchschnitt - Durchschnittlicher Goldbestand - Durchschnittlicher Bestand an deutschen Münzen

Average years of education, population, physical capital stock in Central Europe, 1920-2006. Please cite as: Van Leeuwen, B. and Foldvari, P. (2013). Capital Accumulation and Growth in Central Europe, 1920-2006. Eastern European Economics, 51 (5), 69 - 93.

Using taxation and household survey data, this paper estimates top income shares for Indonesia during 1920-2004. Our results suggest that top income shares grew during the 1920s and 1930s, but fell in the post-war era. We observe a sharp rise in top income shares during the late-1990s, coinciding with the 1997-98 economic crisis. Where comparable data are available, top income shares in Indonesia are generally higher than in other countries, a finding that is at odds with the view that Indonesia is a relatively egalitarian society. This suggests that top income shares may provide a more complete picture of developing country inequality in comparative perspective.

The study on hand is an investigation on seasonal fluctuation ranges of the German Reichsbank’s metal stocks between 1876 and 1920.

The test of seasonal unity roots enables the identification of the character of deterministic and random seasonal fluctuations of timeseries. This specific method of timeseries analysis is applied to the monetary time series on monthly basis of the German Reichsbank. The timeseries are collected from weakly surveyed data with 2160 cases. The analysis shows, that a deterministic seasonal fluctuation of cases with particularly strong seasonality at the beginning and at the end of the year could be identified.

Data Tables (see Online-Database HISTAT): - Cash flow, Gold- and foreign exchange balance in Million Reichsmark (1876-1920) - Metal stocks of the Reichsbank, weekly data (1876-1920)

Variables: Weekly data: - Metal stocks are stocks of gold, taler, and secondary coins.. Yearly data: - Cash flow - Gold- and foreign exchange balance - Cash flow, total (Angaben der Dt. Bundesbank) - Cash flow, gold coins - Gold- and foreign exchange balance of the central bank, total - Gold- and foreign exchange balance of the central bank, gold coins - Metalstocks, yearly averages - average of gold stocks - average stock of german coins.

Analysis of the following sources:

1) Official Statistics: Jahrbuch für die amtliche Statistik des preussischen Staates, Statistisches Handbuch für den preussischen Staat, Statistisches Jahrbuch für das Deutsche Reich, Vierteljahreshefte zur Statistik des Deutschen Reichs.

2) Archival Material: Preussische Gesetz-Sammlung (GR 3600 MF, HA10 Bo 100, Microfiches, Staatsbibliothek zu Berlin – Preussischer Kulturbesitz).

3) Reports of the German Reichsbank Die Reichsbank, 1876 – 1900, Berlin 1900, Die Reichsbank, 1901 – 1925, Berlin 1925.

Territory of investigation: German Empire.

AI-powered price forecasts for 1920.HK stock across different timeframes including weekly, monthly, yearly, and multi-year predictions.

Domestic Production, Price Index, Domestic Production, Domestic Production of Fishing Boats, Net International Balance, Domestic Supply, Gross Capital Formation, Gross Capital Formation

Abstract (en): This collection contains an array of economic time series data pertaining to the United States, the United Kingdom, Germany, and France, primarily between the 1920s and the 1960s, and including some time series from the 18th and 19th centuries. These data were collected by the National Bureau of Economic Research (NBER), and they constitute a research resource of importance to economists as well as to political scientists, sociologists, and historians. Under a grant from the National Science Foundation, ICPSR and the National Bureau of Economic Research converted this collection (which existed heretofore only on handwritten sheets stored in New York) into fully accessible, readily usable, and completely documented machine-readable form. The NBER collection -- containing an estimated 1.6 million entries -- is divided into 16 major categories: (1) construction, (2) prices, (3) security markets, (4) foreign trade, (5) income and employment, (6) financial status of business, (7) volume of transactions, (8) government finance, (9) distribution of commodities, (10) savings and investments, (11) transportation and public utilities, (12) stocks of commodities, (13) interest rates, and (14) indices of leading, coincident, and lagging indicators, (15) money and banking, and (16) production of commodities. Data from all categories are available in Parts 1-22. The economic variables are usually observations on the entire nation or large subsets of the nation. Frequently, however, and especially in the United States, separate regional and metropolitan data are included in other variables. This makes cross-sectional analysis possible in many cases. The time span of variables in these files may be as short as one year or as long as 160 years. Most data pertain to the first half of the 20th century. Many series, however, extend into the 19th century, and a few reach into the 18th. The oldest series, covering brick production in England and Wales, begins in 1785, and the most recent United States data extend to 1968. The unit of analysis is an interval of time -- a year, a quarter, or a month. The bulk of observations are monthly, and most series of monthly data contain annual values or totals. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.; Checked for undocumented or out-of-range codes.. Time series of economic statistics pertaining to France, Germany, the United Kingdom, and the United States between 1785 and 1968. 2007-03-26 This study, updated from OSIRIS, now includes SAS, SPSS, and Stata setup files, SAS transport (XPORT) files, SPSS portable files, a Stata system files, and an updated codebook. Funding insitution(s): National Science Foundation. The data were collected between the 1920s and the 1970s, but it is unclear from the documentation as to the exact start and end dates.

PERIOD: Exchanges organized as joint stock companies, FY 1920-1929. For membership exchange, FY 1925-1929. By region or exchange for FY 1929. SOURCE: [Statistical Tables of the Ministry of Commerce and Industry].

Between the Wall Street Crash of 1929 and the end of the Great Depression in the late 1930s, the Soviet Union saw the largest growth in its gross domestic product, growing by more than 70 percent between 1929 and 1937/8. The Great Depression began in 1929 in the United States, following the stock market crash in late October. The inter-connectedness of the global economy, particularly between North America and Europe, then came to the fore as the collapse of the U.S. economy exposed the instabilities of other industrialized countries. In contrast, the economic isolation of the Soviet Union and its detachment from the capitalist system meant that it was relatively shielded from these events. 1929-1932 The Soviet Union was one of just three countries listed that experienced GDP growth during the first three years of the Great Depression, with Bulgaria and Denmark being the other two. Bulgaria experienced the largest GDP growth over these three years, increasing by 27 percent, although it was also the only country to experience a decline in growth over the second period. The majority of other European countries saw their GDP growth fall in the depression's early years. However, none experienced the same level of decline as the United States, which dropped by 28 percent. 1932-1938 In the remaining years before the Second World War, all of the listed countries saw their GDP grow significantly, particularly Germany, the Soviet Union, and the United States. Coincidentally, these were the three most powerful nations during the Second World War. This recovery was primarily driven by industrialization, and, again, the U.S., USSR, and Germany all experienced the highest level of industrial growth between 1932 and 1938.