Form 13F was adopted pursuant to statutory directive designed to provide the Commission with data from larger managers about their investment activities and holdings.

Introduction In the course of researching the common ownership hypothesis, we found a number of issues with the Thomson Reuters (TR) "S34" dataset used by many researchers and frequently accessed via Wharton Research Data Services (WRDS). WRDS has done extensive work to improve the database, working with other researchers that have uncovered problems, specifically fixing a lack of records of BlackRock holdings. However, even with the updated dataset posted in the summer of 2018, we discovered a number of discrepancies when accessing data for constituent firms of the S&P 500 Index. We therefore set out to separately create a dataset of 13(f) holdings from the source documents, which are all public and available electronically from the Securities and Exchange Commission (SEC) website. Coverage is good starting in 1999, when electronic filing became mandatory. However, the SEC's Inspector General issued a critical report in 2010 about the information contained in 13(f) filings. The process: We gathered all 13(f) filings from 1999-2017 here. The corpus is over 318,000 filings and occupies ~25GB of space if unzipped. (We do not include the raw filings here as they can be downloaded from EDGAR). We wrote code to parse the filings to extract holding information using regular expressions in Perl. Our target list of holdings was all public firms with a market capitalization of at least $10M. From the header of the file, we first extract the filing date, reporting date, and reporting entity (Central Index Key, or CIK, and CIKNAME). Beginning with the September 30 2013 filing date, all filings were in XML format, which made parsing fairly straightforward, as all values are contained in tags. Prior to that date, the filings are remarkable for the heterogeneity in formatting. Several examples are linked to below. Our approach was to look for any lines containing a CUSIP code that we were interested in, and then attempting to determine the "number of shares" field and the "value" field. To help validate the values we extracted, we downloaded stock price data from CRSP for the filing date, as that allows for a logic check of (price * shares) = value. We do not claim that this will exhaustively extract all holding information. We can provide examples of filings that are formatted in such a way that we are not able to extract the relevant information. In both XML and non-XML filings, we attempt to remove any derivative holdings by looking for phrases such as OPT, CALL, PUT, WARR, etc. We then perform some final data cleaning: in the case of amended filings, we keep an amended level of holdings if the amended report a) occurred within 90 days of the reporting date and b) the initial filing fails our logic check described above. The resulting dataset has around 48M reported holdings (CIK-CUSIP) for all 76 quarters and between 4,000 and 7,000 CUSIPs and between 1,000 and 4,000 investors per quarter. We do not claim that our dataset is perfect; there are undoubtedly errors. As documented elsewhere, there are often errors in the actual source documents as well. However, our method seemed to produce more reliable data in several cases than the TR dataset, as shown in Online Appendix B of the related paper linked above. Included Files Perl Parsing Code (find_holdings_snp.pl). For reference, only needed if you wish to re-parse original filings. Investor holdings for 1999-2017: lightly cleaned. Each CIK-CUSIP-rdate is unique. Over 47M records. The fields are CIK: the central index key assigned by the SEC for this investor. Mapping to names is available below. CUSIP: the identity of the holdings. Consult the SEC's 13(f) listings to identify your CUSIPs of interest. shares: the number of shares reportedly held. Merging in CRSP data on shares outstanding at the CUSIP-Month level allows one to construct \beta. We make no distinction for the sole/shared/none voting discretion fields. If a researcher is interested, we did collect that starting in mid-2013, when filings are in XML format. rdate: reporting date (end of quarter). 8 digit, YYYYMMDD. fdate: filing date. 8 digit, YYYYMMDD. ftype: the form name. Notes: we did not consolidate separate BlackRock entities (or any other possibly related entities). If one wants to do so, use the CIK-CIKname mapping file below. We drop any CUSIP-rdate observation where any investor in that CUSIP reports owning greater than 50% of shares outstanding (even though legitimate cases exist - see, for example, Diamond Offshore and Loews Corporation). We also drop any CUSIP-rdate observation where greater than 120% of shares outstanding are reported to be held by 13(f) investors. Cases where the shares held are listed as zero likely mean the investor filing lists a holding for the firm but that our code could not find the number of shares due to the formatting of the file. We leave these in the data so that any researchers that find a zero know to go back to that source filing to manually gather the holdings for the securities they are interested in. Processed 13f holdings (airlines.parquet, cereal.parquet, out_scrape.parquet). These are used in our related AEJ:Microeconomics paper. The files contain all firms within the airline industry, RTE cereal industry, and all large cap firms (a superset of the S&P 500) respectively. These are a merged version of the scrape_parsed.csv file described above, that include the shares outstanding and percent ownership used to calculate measures of common ownership. These are distributed as brotli compressed Apache Parquet (binary) files. This preserves date information correctly. mgrno: manager number (which is actually CIK in the scraped data) rdate: reporting date ncusip: cusip rrdate: reportaing date in stata format mgrname: manager name shares: shares sole: shares with sole authority shared: shares with shared authority none: shares with no authority isbr/isfi/iss/isba/isvg: is this blackrock, statestreet, vanguard, barclay, fidelity numowners: how many owners prc: price at reporting date shares_out: shares outstanding at reporting date value: reported value in 13(f) beta: shares/shares_out permno: permno Profit weight values (i.e. \kappa) for all firms in the sample. (public_scrape_kappas_XXXX.parquet). Each file represents one year of data and is around 200MB and distributed as a compressed (brotli) parquet file. Fields are simply CUSIP_FROM, CUSIP_TO, KAPPA, QUARTER. Note that these have not been adjusted for multi-class share firms, insider holdings, etc. If looking at a particular market, some additional data cleaning on the investor holdings (above) followed by recomputing profit weights is recommended. For this, we did merge the separate BlackRock entities prior to computing \kappa. CIKmap.csv (~250K observations) Mapping is from CIK-rdate to CIKname. Use this if you want to consolidate holdings across reporting entities or explore the identities of reporting firms. In the case of amended filings that use different names than original ones, we keep the earliest name. Example of Parsing Challenge Prior to the XML era, filings were far from uniform, which creates a notable challenge for parsing them for holdings. In the examples directory we include several example text files of raw 13f filings. Example 1 is a "well behaved" filing, with CUSIP, followed by value, followed by number of shares, as recommended by the SEC. Example 2 shows a case where the ordering is changed: CUSIP, then shares, then value. The column headers show "item 5" coming before "item 4". Example 3 shows a case of a fixed width table, which in principle could be parsed very easily using the tags at the top, although not all filings consistently use these tags. Example 4 shows a case with a fixed width table, with no tag for the CUSIP column. Also, notice that if the firm holds more than 10M shares of a firm, that number occupies the entire width of the column and there is no longer a column separator (i.e. Cisco Systems on line 374). Example 5 shows a comma-separated table format. Example 6 shows a case of changing the column ordering, but also adding an (unrequired) column for share price. Example 7 shows a case where the table is split across subsequent pages, and so the CUSIP appears on a different line than the number of shares.

This dataset captures the quarterly investment holdings of institutional investment managers and maps the ownership structure of public firms. These Schedule 13F reports are submitted to the Securities and Exchange Commission quarterly by all institutional investment managers with at least $100 million in assets under management. Most academic research examining the common ownership of corporations and the portfolio holdings of large investment managers is based on proprietary commercial databases. This hinders the replication of prior work due to unequal access to these subscriptions and because the data manipulation steps in commercial databases are often opaque. To overcome these limitations, the presented dataset is created from the original regulatory filings; it is updated daily and includes all information reported by investment managers without alteration.

By using this dataset or accompanying code, you agree to cite both the data source and the related publication.

Balogh, A. Layline Institutional Holding Reports. Harvard Dataverse https://doi.org/10.7910/DVN/TZM1QT (2023).

Balogh, A. Mapping the common ownership of public firms. Working paper (2023).

Context

The dataset consists of quarterly filings of institutions scraped from the SEC EDGAR database.

Content

The Insititutions.csv contains the names of institutions and their unique ids. The stock_names.csv contains the names of companies and their unique ids.

13Fdata.csv Includes three years of quarterly filings from 2015 - 2017 for all the institutions in the SEC database.

Acknowledgements

Data courtesy: SEC

Inspiration

Interesting questions to ask: Can you pick the most promising securities to invest in based on how institutions are trading it? How about some cool dataviz?

This data set represents BlackRocks first quarter 2022 holdings, according to their form 13f filings. It includes the following information about: - Stock Ticker - Company Name - Shares Held - ESG Risk - Market Value - Percent of Portfolio

A list of the top 50 Scion Asset Management holdings showing which stocks are owned by Michael Burry's hedge fund.

Context

These are the publicly traded U.S. stocks owned by Warren Buffett’s holding company Berkshire Hathaway, as reported to the SEC.

Warren Edward Buffett is an American investor, business tycoon, philanthropist, and the chairman and CEO of Berkshire Hathaway. He is considered one of the most successful investors in the world and has a net worth of US$78.9 billion as of August 2020, making him the world's seventh-wealthiest person.

The information can be found in the SEC 13F File. The Securities and Exchange Commission's (SEC) Form 13F is a quarterly report that is required to be filed by all institutional investment managers with at least $100 million in assets under management. It discloses their equity holdings and can provide some insights into what the smart money is doing in the market.

After obtaining the list of companies in which Berkshire Hathaway invests, we proceeded to search the historical data of each company on NASDAQ.

Content

In total there are 49 files in csv format. They are composed as follows: - 45 files contain the U.S. stocks owned by Berkshire Hathaway. - 2 files contain the historical data from Berkshire Hathaway. Class A stock (BRK-A) and Class B stock (BRK-B). - 1 file contain the list of all the companies with additional information. - 1 file contain the SEC Form 13F.

Column Description

Every company file has the same structure with the same columns: - Date: It is the date on which the prices were recorded. - Close/Last: Is the last price at which a stock trades during a regular trading session. - Volume: Is the number of shares that changed hands during a given day. - Open: Is the price at which a stock started trading when the opening bell rang. - High: Is the highest price at which a stock traded during the course of the trading day. - Low: Is the lowest price at which a stock traded during the course of the trading day.

The two other files have different columns names:

Company List - Name: Name of the company. - Symbol: Ticker symbol of the company.
- Holdings: Number of shares. - Market Price: Current price at which a stock can be purchased or sold. (10/18/20) - Value: (Holdings * Market Price).
- Stake: The amount of stocks an investor owns from a company.

SEC Form 13F

Name of Issuer, Title of Class, CUSIP Number, Market Value, Amount and Type of Security, Investment Discretion (Sole, Shared-Defined, Shared-Other), Other Managers, Voting Authority.

You can find detail information of each column in the SEC General Instructions Form 13F in page 5.

Acknowledgements

SEC EDGAR | Company Filings NASDAQ | Historical Quotes

Inspiration

Possible questions which could be answered are:

• Of the companies that make up the Berkshire Hathaway portfolio, which are the most profitable companies?
• On February 21, 2020, Berkshire Hathaway suffered a large drop in its share price, what was the reason for this large drop?
• The presidential elections in the United States will be soon, do you think this will affect a particular company in the Warren Buffett portfolio and if so, which would affect them more?

More Datasets

This table reports summary statistics of the key variables in multivariate OLS analysis. The sample comes from multiple sources. Firm-level financial data come from COMPUSTAT database. Corporate social responsibility data come from MSCI ESG KLD database. Institutional investor holdings data come from Thomson Reuters Institutional (13F) Holdings database (adjusted by Factset Institutional Holdings database after June 2013). Analyst coverage data come from Institutional Brokers Estimate System (I\B\E\S). We require observations to satisfy the following criteria: (1) Book equity is positive; (2) Each firm should at least have 2-year consecutive observations; (3) Variables are available in all observations; (4) Firms are not in financial (SIC code 6000–6999) or utility (SIC codes 4900–4999) industries. Finally, the sample consists of 13,112 observations that meet these criteria during 1995–2014 when both Thomson Reuters Institutional (13F) Holdings and KLD are available. All continuous variables are winsorized at 1st and 99th percentiles to alleviate the potential disturbance from outliers. The variable definitions are provided in Table A1 of S1 Appendix.

The asset management industry's growth has led to firms often having multiple institutional blockholders, which has a significant impact on corporate governance through exit strategies. A model has been proposed to illuminate this dynamic, emphasizing the role of open-ended institutional investors like mutual funds in enhancing corporate governance when informed blockholders exit. This model introduces a novel perspective on the influence of mutual funds in shaping corporate governance. Empirical evidence consistent with this framework is presented by examining mutual fund trading around exits by activist hedge funds.

The empirical analysis centers on a dataset of 399 firms that underwent activist campaigns ending in exit between 1994 and 2011. Findings support the model's predictions, even after adjusting for unobservable firm-level variations and broader economic conditions. Following exits by activist hedge funds, mutual funds driven by fund flows significantly reduce their holdings in the target company compared to other institutional investors. This divergence in trading behavior is more pronounced when activists exit at a financial loss, campaigns show no clear success, or market reactions suggest liquidity concerns weren't the main driver.

To conduct this analysis, data on activist campaigns is merged with institutional holdings information from the Thomson Reuters 13F database and the Morningstar Open-end Mutual Fund portfolio holdings dataset. The activist campaign data is based on Schedule 13D filings and aligns with the data collection methods of previous studies. Trading behavior of other blockholders is tracked using quarterly 13F filings, a requirement for institutional investors managing $100 million or more. The S34 dataset (13F filings) from Thomson Reuters is combined with the Morningstar Open End Mutual Funds database to classify mutual funds throughout the 1994–2013 period.

In the research programme outlined in this grant proposal, we shall study corporate governance in economies with intermediated equity ownership.

When public corporations are characterised by dispersed equity ownership, blockholders - holders of non-trivial percentages of a company's shares - are key to good governance. In contrast to small shareholders, who have neither the incentive nor the capacity to effectively monitor management, blockholders are able to govern firms to the benefit of all.

The governance role of blockholders must be viewed in the backdrop of the large-scale intermediation of equity ownership in recent decades. Fifty years ago households directly owned around 70% (55%) of US (UK) equity. Today direct ownership accounts for only around 20% (10%) of US (UK) equity. The rest is indirectly owned via institutional investors such as pension funds, mutual funds, and hedge funds. Thus, a majority of blockholders in public corporations in the US and the UK today are money managers. In other words, modern-day corporate governance is intermediated: Funds that manage other people's money must monitor company executives who make business choices funded by external investors, a case of agents watching agents.

Several questions arise immediately. What are the consequences of myopia induced by asset management contracts on the nature of governance? Intermediation of equity ownership increases the distance between ultimate owners and ultimate decision makers while ownership chains also fragment equity holdings: how does this affect coordinated shareholder engagement and corporate decision making? Are there systemic stability concerns that arise from the predominance of asset managers as monitors of firms? In the aftermath of the financial crisis, prominent policy reviews have highlighted several such questions. Yet, the academic literature is yet to engage comprehensively with these issues.

We propose to fill this gap. By a combination of theoretical and empirical methods, we shall develop a comprehensive analysis of several key facets of intermediated corporate governance that are of interest to policy makers. The work will be carried out via seven, interrelated, projects. The emphasis throughout will be on the incentives of asset managers and how these impact the nature of corporate governance in firms in which they hold blocks. We shall document the landscape and legal environment of intermediated ownership around the world, delineating how incentives vary along the ownership chain (Project 1). We shall examine theoretically and empirically how such incentives affect the nature of coordinated engagement in settings with multiple small blockholders (Projects 2 and 3), engender long ownership chains (Project 4), and provide commitment mechanisms for monitoring (Project 5). We shall also examine whether the ways in which such incentives induce asset managers to govern firms foster new channels for systemic risk (Projects 6 and 7).

Our research approach will be positive: in other words, the projects will develop conceptual frameworks backed up by empirical analysis to provide a basis for understanding intermediated governance as it exists today. It is only by gaining such understanding that we can carefully evaluate the normative proposals in the policy reviews for what intermediated governance should be.

We shall facilitate knowledge exchange between the academic and policy communities. By engaging carefully with issues highlighted by policy reviews, we shall enhance awareness of policy-relevant questions amongst academic researchers. By providing thorough theoretical and empirical analyses of issues of direct interest to policy makers, we shall facilitate the development of informed and effective policy to regulate the role of institutional investors in corporate governance.

This table reports the summary statistics of the key variables in the quasi-natural experiment based on mergers between financial institutional blockholders during 1995–2012. The sample comes from multiple sources. Firm-level financial data come from COMPUSTAT database. Corporate social responsibility data come from MSCI ESG KLD database. Institutional investor holdings data come from Thomson Reuters Institutional (13F) Holdings database. Analyst coverage data come from Institutional Brokers Estimate System (I\B\E\S). We require observations to satisfy the following criteria: (1) Book equity is positive; (2) Each firm should at least have 2-year consecutive observations; (3) Variables are available in all observations; (4) Firms are not in financial (SIC code 6000–6999) or utility (SIC codes 4900–4999) industries. Our sample includes 3,778 firm-years that meet these criteria during 1995–2012 when Thomson Reuters Institutional (13F) Holdings and KLD are available and firms can be matched to blockholder mergers. All continuous variables are winsorized at 1st and 99th percentiles to alleviate the potential disturbance from outliers.

We empirically assess the implications of the common ownership hypothesis from a historical perspective using the set of S&P 500 firms from 1980–2017. We show that the dramatic rise in common ownership in the time series is driven primarily by the rise of indexing and diversification and, in the cross–section, by investor concentration, which the theory presumes to drive a wedge between cash flow rights and control. We also show that the theory predicts incentives for expropriation of undiversified shareholders via tunneling, even in the Berle and Means (1932) world of the widely held firm.We include additional data on scraped SEC 13f forms for 2000-2017. The rest of the data is provided or downloaded from WRDS and requires a WRDS account.

For nanoSIMS measurements of R. mucilaginosa cells, we analyzed three sample sets: (i) the original inoculum (Control), (ii) after incubation with 13C-labelled polyethylene without and (iii) with UV-treatment. We measured a total of 1144 regions of interest (ROIs, i.e., corresponding to 1144 individual cells). For IRMS and GCMS measurements we measured 3 replicates for the development of ?13C-CO2 values and CO2 concentration in incubations with R. mucilaginosa (RM) and with 13C-polyethylene (PE), with prior UV-treatment (+UV) and without (-UV) as the sole carbon source as well as in incubations with UV-treated 13C-polyethylene (PE) without R. mucilaginosa.

Observed two-dimensional likelihood scans for $g^{(2)}_{ZZ}$ vs. $g^{(4)}_{ZZ}$ while other coefficients are fixed at their SM values.

If I were to boil the thesis down to a few bullets, I’d say: Uranium is an essential input for nuclear reactors with no substitute. Following the Fukushima disaster, there was a massive supply glut as reactors were taken offline due to safety concerns Now a supply crunch is looming, with a current market deficit of ~40m lbs Nuclear power plants usually contract uranium supplies several years out before their inventory gets run down. Due to the oversupply coming out of the previous cycle, however, they have been purchasing additional supply needs in the spot market instead of contracting years in advance. 13f filings indicate that the power plants’ coverage rates (contracted lbs of uranium supply / lbs of uranium required) are beginning to trend below 100%, indicating utilities have less locked-in supply than they need to keep running their reactors, at a time when market supply is tightening (note utilities typically look to maintain coverage ratios well above 100% to ensure no unforeseen shortfalls) Global demand for uranium is increasing, with ~56 new reactors under construction an a further 99 in planning currently. Nuclear power currently generates ~10% of the world’s electricity but with the closure of coal and fossil fuel power plants due to ESG considerations, nuclear energy is increasingly being seen as the only viable way to make up up the lost energy capacity. Putting all of this together, a fundamental supply/demand imbalance for an essential commodity with price insensitive buyers and ESG tailwinds makes the bull case extremely compelling. But a picture is worth a thousand words, so some historic charts probably best provide a sense of the future upside expected in the next cycle. Using the data of form 8k, at the peak of the previous uranium bull market in 2007 (when there was no supply deficit) the uranium spot price reached ~$136/lb after a run up from ~$15/share at the start of 2004 (~9x increase). Today the current price is ~$42/lb with the view that the price will reach new highs in this coming cycle: Many uranium investors, based on the majority of form 10q, focus on the miners rather than the commodity as being the way to play the new uranium bull market, as these are more levered to price increases in the underlying commodity. The share price for Canadian-based Cameco Corporation (CCO / CCJ, the second largest uranium producer in the world) increased from USD $3/share to $55/share ( ~18x bagger) during the previous bull market from ~2004 – 2007: While Cameco’s performance was impressive, it was not the biggest winner during the previous uranium bull market. Australian miner Paladin Energy ($PALAF) went from AUD $0.01 to AUD $10.70 (~1000x! ) between late 2003 and the market peak in Q1 2007, according to their stock price in Google Sheets: Similar multibagger returns for uranium stocks will be seen again if a new bull market in uranium materializes in the coming 2-3 years when utilities’ uranium supply falls to inoperable levels & they begin contracting again for new supplies. Based on SEC form 4, Paladin in particular is expected to be big winner in any new bull market, as it operates one of the lowest cost uranium mines in the world, the Langer Heinrich mine in Namibia, which was a fully producing mine before being idled in the last bear market. As such, it is a ready-to-go miner rather than a speculative prospect, and so is in a position to immediately capitalise on an uptick in uranium prices and a new contracting cycle with utilities. Given the extent of the structural supply/demand imbalance (which again wasn’t present during the previous bull market) combined with utilities likely becoming forced purchasers of uranium at almost any price, market commentators are forecasting the uranium spot price to reach highs of up to $150/lb, thus enabling the producers to contract at price levels 3x+ the current spot price, driving a massive increase in profitability and cash flows. With some very interesting dynamics and the sprott uranium trust acting as a catalyst, I think the uranium market has the potential to offer a really unique and asymmetric return over the next 2 years. To reproduce this analysis, use this guide on how to get stock price in Excel. You will also need high-quality stock data, I recommend you check out Finnhub Stock Api Cheers!

Wenige Themen sind in der Wirtschaftsgeschichte derart kontrovers diskutiert worden wie der deutsche Kapitalexport vor dem Ersten Weltkrieg. Die Beurteilungen reichen von der Ausbeutungsthese des Lenin’schen Finanzimperialismus bis hin zur Interpretation als einer frühen Form der Entwicklungshilfe. Die vorliegende Arbeit will diese Diskussion durch die Aufarbeitung einer breiten Faktenbasis auf eine solide Grundlage stellen. Dabei wird deutlich, dass es sich beim Kapitalexport um einen komplexen Vorgang handelt, bei dem neben der Rolle der Banken und der heimischen und auswärtigen Politik die Bedeutung des kapitalanlegenden Publikums und der Kreditnehmer bislang zu wenig betrachtet wurde. Außerdem wird gezeigt, wie die Bildung internationaler Syndikate mit einer starken Konkurrenz der deutschen Banken auf nationaler Ebene einherging, und so die bisher herrschende Auffassung widerlegt, nach der sich am internationalen Kapitalmarkt nationale Bankengruppen in einer Weise gegenübergestanden hätten, die die Konfrontation des Ersten Weltkrieges bereits vorweg genommen hätte. Zum Schluss wird auf breiter Datenbasis die Rentabilität der Auslandsanlagen analysiert. „Kapitalanlage meint im Zusammenhang dieser Arbeit immer eine Investition in verbriefte Forderungsrechte, die den Schuldner verpflichten, dem Gläubiger für die Übereignung von Geld eine Gegenleistung zu gewähren. Die Wirtschaft kennt auch andere Formen der Kapitalanlage (die Investition in Sachkapital und die Direktinvestition im Ausland). In dieser Arbeit soll jedoch nur die erstgenannte Form der Kapitalanlage, die Portfolioinvestition, behandelt werden. Durch die Betrachtung der Auslandsinvestition wird das Blickfeld auf die Interdependenz von Volkswirtschaften gelenkt. Die Betonung des Wertpapieraspekts hat zur Folge, dass sich ständig wiederholende Vorgänge betrachtet und die Veränderung der Umweltbedingungen auf die einzelnen Handlungen beobachtet werden können“ (Schaefer, K. C., a. a. O., S. 13f). Zunächst wir in den ersten drei Kapiteln der Rahmen für den Kapitalexport dargestellt: die Institutionen, das Recht, die volkswirtschaftliche Ausgangssituation. Den Hauptteil der Arbeit bildet eine regional gegliederte Darstellung des deutschen Kapitalexports. Dabei zeigt sich, dass die regionalen Teilmärkte vielfach ganz indi-viduelle Charakteristika aufwiesen, vor allem hinsichtlich des Einflusses der Politik auf die Kapitalanlage und hinsichtlich der Konsortialbildung der Banken. Dem musste der Aufbau der einzelnen Regionalstudien Rechnung tragen, die darum in unterschiedlicher Weise gegliedert sind. Im abschließenden fünften Kapitel der Arbeit steht eine Analyse der Wirkungen des Kapitalexports für die Privatanleger, die Banken und die Kreditnehmer.Die Studie enthält einen umfangreichen Datenanhang mit Tabellen zur deutschen Zahlungsbilanz und zum Nettosozialprodukt, die Emissionsstatistik aus „Der Ökonomist“ (Nominalwerte, Kurswerte) sowie Zeitreihen zu Effektivzinsen, Renditen, Risikomaß und Markthomogenität. Datentabellen in HISTAT:A. Tabellen aus dem AnhangA.01 Deutsche Zahlungsbilanz und Nettosozialprodukt (1880-1913A.02 Emissionsstatistik: Nominalwerte (1883-1914)A.03 Emissionsstatistik: Kurswerte (1883-1914)A.04 Effektivzinsen der Staatsobligationen nach Regionen (1871-1914)A.05 Effektivzinsen der variablen Staatswerte (1871-1914)A.06 Effektivzinsen der fest verzinslichen Eisenbahnwerte (1871-1914)A.07 Effektivzinsen der variabel verzinslichen Eisenbahnwerte (1871-1914)A.08 Effektivzinsen nach Wertpapierarten (1870-1914)A.09 Renditen der festverzinslichen Staatswerte (1870-1914)A.10 Rendite der variabel verzinslichen Staatswerte (1870-1914)A.11 Festverzinsliche Eisenbahnwerte (1870-1914)A.12 Rendite der variabel verzinslichen Eisenbahnwerte (1871-1914)A.13 Renditen nach Wertpapierarten (1871-1914)A.14 Homogenität der Staatsobligationen nach Regionen (1871-1914)A.15 Homogenität der Eisenbahnobligationen (1872-1914)A.16 Homogenität der Eisenbahnaktien (1872-1914)A.17 Homogenität nach Wertpapierarten (1871-1914)A.18 Risiko der Staatsobligationen (1875-1914)A.19 Risiko der Eisenbahnobligationen (1875-1914)A.20 Risiko der Eisenbahnaktien (1875-1914)A.21 Risiko nach Wertpapierarten (1875-1914) T. Tabellen aus dem TextT.01a Aktienkapital deutscher Großbanken (1870-1910)T.01b Deutscher Besitz an ausländischen Wertpapieren (1914-1918)T.02a Effektenkurse in Berlin, Jahresultimowerte nach Salings (1870-1893)T.02b Effektivzinsen der österreichischen und der ungarischen Goldrenten (1875-1914)T.03a An deutschen Börsen zugelassene österreichisch-ungarischen Eisenbahnwerte (1840-1914)T.03b Gesamtkapital der garantierten österreich-ungarischen Eisenbahngesellschaften (1880-1890)T.04a Auslandsbesitz an österreich-ungarischen Wertpapieren: Deutschland (1901-1912)T.04b Auslandsbesitz an österreich-ungarischen Wertpapieren: Frankreich (1901-1912)T.04c Auslandsbesitz an österreich-ungarischen Wertpapieren: England (1901-1912)T.04d Auslandsbesitz an österreich-un...

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