PURPOSE (possible uses)

Non-professional investors often try to find an interesting stock among those in an index (such as the Standard and Poor's 500, Nasdaq, etc.). They need only one company, the best, and they don't want to fail (perform poorly). So, the metric to optimize is accuracy, described as:

Accuracy = True Positives / (True Positives + False Positives)

And the predictive model can be a binary classifier.

The data covers the price and volume of shares of 31 NASDAQ companies in the year 2022.

Context

Every data set I found to predict a stock price (investing) aims to find the price for the next day, and only for that stock. But in practical terms, people like to find the best stocks to buy from an index and wait a few days hoping to get an increase in the price of this investment.

Content

Rows are grouped by companies and their age (newest to oldest) on a common date. The first column is the company. The following are the age, market, date (separated by year, month, day, hour, minute), share volume, various traditional prices of that share (close, open, high...), some price and volume statistics and target. The target is mainly defined as 1 when the closing price increases by at least 5% in 5 days (open market days). The target is 0 in any other case.

Complex features and target were made by executing: https://www.kaggle.com/code/luisandresgarcia/202307

Thanks

Many thanks to everyone who participates in scientific papers and Kaggle notebooks related to financial investment.

This dataset graphically tracks Foreign Direct Investment in the United States. The dataset covers many types of investment, including manufacturing, trade, and financial aspects. This data covers 2006 figures, and shows which markets are heavily invested in by foreign nations. This data was collected from the Bureau of Economic Analysis : http://www.bea.gov/scb/pdf/2007/07%20July/0707_dip_article.pdf and credit is given to Marilyn Ibarra and Jennifer Koncz. The authors of : Direct Investment Positions for 2006 Country and Industry Detail The data was accessed on October 1, 2007. Statistics are quoted in the Millions.

Household Saving Rate in the United States remained unchanged at 4.40 percent in July from 4.40 percent in June of 2025. This dataset provides - United States Personal Savings Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

SharkTank dataset of USA/American business reality television series. Currently, the data set has information from SharkTank season 1 to Shark Tank US season 16. The dataset has 53 fields/columns and 1440+ records.

Below are the features/fields in the dataset:

• Season Number - Season number
• Startup Name - Company name or product name
• Episode Number - Episode number within the season
• Pitch Number - Overall pitch number
• Season Start - Season first aired date
• Season End - Season last aired date
• Original Air Date - Episode original/first aired date, on OTT/TV
• Industry - Industry name or type
• Business Description - Business Description
• Company Website - Website of startup/company
• Pitchers Gender - Gender of pitchers
• Pitchers City - US city of pitchers
• Pitchers State - US state or country of pitchers, two letter shortcut
• Pitchers Average Age - Average age of all pitchers, <30 young, 30-50 middle, >50 old
• Entrepreneur Names - Pitcher names
• Multiple Entrepreneurs - Multiple entrepreneurs are present ? 1-yes, 0-no
• US Viewership - Viewership in US, TRP rating, in millions
• Original Ask Amount - Original Ask Amount, in USD
• Original Offered Equity - Original Offered Equity, in percentages
• Valuation Requested - Valuation Requested, in USD
• Got Deal - Got the deal or not, 1-yes, 0-no
• Total Deal Amount - Total Deal Amount, in USD, including debt/loan amount
• Total Deal Equity - Total Deal Equity, in percentages
• Deal Valuation - Deal Valuation, in USD
• Number of sharks in deal - Number of sharks in deal
• Investment Amount Per Shark - Investment Amount Per Shark
• Equity Per Shark - Equity received by each Shark
• Royalty Deal - Is it royalty deal or not (1-yes)
• Advisory Shares Equity - Deal with Advisory shares or equity, in percentages
• Loan - Loan/debt (line of credit) amount given by sharks, in USD
• Deal has conditions - Deal has conditions or not? (yes or no)
• Barbara Corcoran Investment Amount - Amount Invested by Barbara Corcoran
• Barbara Corcoran Investment Equity - Equity received by Barbara Corcoran
• Mark Cuban Investment Amount - Amount Invested by Mark Cuban
• Mark Cuban Investment Equity - Equity received by Mark Cuban
• Lori Greiner Investment Amount - Amount Invested by Lori Greiner
• Lori Greiner Investment Equity - Equity received by Lori Greiner
• Robert Herjavec Investment Amount - Amount Invested by Robert Herjavec
• Robert Herjavec Investment Equity - Equity received by Robert Herjavec
• Daymond John Investment Amount - Amount Invested by Daymond John
• Daymond John Investment Equity - Equity received by Daymond John
• Kevin O Leary Investment Amount - Amount Invested by Kevin O'Leary
• Kevin O Leary Investment Equity - Equity received by Kevin O'Leary
• Guest Investment Amount - Amount Invested by Guests
• Guest Investment Equity - Equity received by Guests
• Guest Name - Name of Guest shark, if invested in deal
• Barbara Corcoran Present - Whether Barbara Corcoran present in episode or not
• Mark Cuban Present - Whether Mark Cuban present in episode or not
• Lori Greiner Present - Whether Lori Greiner present in episode or not
• Robert Herjavec Present - Whether Robert Herjavec present in episode or not
• Daymond John Present - Whether Daymond John present in episode or not
• Kevin O Leary Present - Whether Kevin O Leary present in episode or not
• Guest Present - Whether Guest present in episode or not

The values of any financial assets held including both formal investments, such as bank or building society current or saving accounts, investment vehicles such as Individual Savings Accounts, endowments, stocks and shares, and informal savings.

• Name: Characterization of investments profiles on the energy transition for european citizens
• Summary: The dataset contains: (1) surveyee consent form for the study, (2) different scenarios about the energy transition, (3) determinant factors about those scenarios, (4) socioeconomic description of the surveyee, (5) investment decisions, (6) and household characterization/description.
• License: cc-BY-SA
• Acknowledge: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.
• Disclaimer: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European commission (Ec). EASME or the Ec are not responsible for any use that may be made of the information contained therein.
• Collection Date: 22/07/2022
• Publication Date: 15/10/2023
• DOI: 10.5281/zenodo.4455198
• Other repositories:
• Author: University of Deusto
• Objective of collection: This data was originally collected to analyze quantitatively the decisions of everyday people in relation to their energy consumption and their reactions to specific political interventions.
• Description: The dataset contains a CSV file file containing data collected from a survey about energy consumption investments. The fields that can be found for each entry are (1) Different scenarios about the energy transition and reactions to those scenarios, (money spent on energy investments, decisions about scenarios, actions taken under a blackout, etc.) (2) Determinant factors about the chosen scenarios in the previous question, which include different choices that could affect your decision about a scenario (3) socioeconomic information about the user (age, country of residence, studies), (4) estimation of the prices of various technologies related to the energy transition and (5) descriptive statistics about the household living situation (gender of user, people living in household, yearly rent, average savings per month, type of house, size of house) and also includes questions about climate change expertise. Next you can found a description of each field in the dataset
  • Section 1 - Scenarios for energy transition.
    • ID90. Rank in order of priority, from top to bottom, in which scenario you will be willing to live or to contribute/invest to make it possible.
    • ID36, ID38, ID43, ID44, ID72. Percentage of money people are willing to spend/save out of their income per scenario
    • ID191, ID192.. Amount of money people would spend based on an assumed case.
    • ID191, ID192. Priority service provision in case of Intermittent energy service. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority for you and 10 stars means it is absolutely necessary for you.
    • [ID325, ID326, ID327, ID328, ID329, ID330, ID331, ID332, ID333, ID334, ID335, ID336, ID337, ID338, ID339, ID340, ID341, ID133, ID242]. Priority service provision in case of Intermittent energy service. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority and 10 stars means it is absolutely necessary.
    • [ID251, ID256, ID257, ID292, ID293, ID294, ID295, ID296, ID297, ID298, ID299, ID301, ID302, ID303, ID304, ID305, ID306, ID250, ID251]. Priority service provision in case of full black-outs. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority and 10 stars means it is absolutely necessary.
    • [ID141, ID5, ID147]. Used for statements that best represent survey responder

      </li>
      <li><strong>Section 2 - Determinants (factors).</strong> Questions used to rate (from 0 to 100) factors that may influence the decision-making process contributing to make an ideal scenario possible.
      <ul>
        <li><strong>ID100</strong> Risk profile</li>
        <li><strong>ID101</strong> Added value</li>
        <li><strong>ID102</strong> Self-Satisfaction</li>
        <li><strong>ID103</strong> Technical Fit</li>
        <li><strong>ID104</strong> Own competence</li>
        <li><strong>ID105</strong> Knowledge</li>
        <li><strong>ID106</strong> Cost-Efficiency</li>
        <li><strong>ID107</strong> Safety</li>
        <li><strong>ID108</strong> Trust</li>
        <li><strong>ID109</strong> Autarky</li>
        <li><strong>ID110</strong> Legal</li>
        <li><strong>ID111</strong> Climate Protection</li>
        <li><strong>ID112</strong> Wellbeing</li>
        <li><strong>ID113</strong> Coziness</li>
        <li><strong>ID114</strong> Rights and Duties</li>
        <li><strong>ID115</strong> Peer-Pressure</li>
        <li><strong>ID116</strong> Socialising</li>
        <li><strong>ID117</strong> Support</li>
        <li><strong>ID118</strong> Agreement</li>
        <li><strong>ID119</strong> Brag</li>
        <li><strong>ID120</strong> Fun</li>
        <li><strong>ID121</strong> Novelty</li>
        <li><strong>ID122</strong> Trends</li>
        <li><strong>ID123</strong> Authority</li>
        <li><strong>ID124</strong> Own Significance</li>
        <li><strong>ID125</strong> Poseur</li>
        <li><strong>ID2</strong> Frugality</li>
        <li><strong>ID3</strong> Environmental concerns</li>
        <li><strong>ID31</strong> Adherence</li>
        <li><strong>ID52</strong> Commitment</li>
        <li><strong>ID97</strong> Profits</li>
        <li><strong>ID99</strong> Credit Score</li>
      </ul>
      </li>
      <li><strong>Section 3 - “Socio-economic” description. </strong>Questions about the socio-economic information of the survey respondents for data stratification. The indentation represents the dependency of questions and whether this data was asked
      <ul>
        <li><strong>ID164</strong> Understanding of questions</li>
        <li><strong>ID300</strong> Country of residence</li>
        <li><strong>ID137</strong> Age</li>
        <li><strong>ID178</strong> Highest level of education</li>
        <li><strong>ID136</strong> Willingness to provide data on the investment decision (respond apply for -Investment decision section)</li>
      </ul>
      </li>
      <li><strong>Section 4 - Investment decision</strong>. Questions about specific prices of potential purchases-decisions related to four scenarios (respondent's lifestyle)
      <ul>
        <li>Appliances
        <ul>
          <li><strong>ID42</strong> Affordable cost of a Regular refrigerator</li>
          <li><strong>ID45</strong> Energy efficient refrigerator costs</li>
          <li><strong>ID50</strong> Willingness to purchase an energy efficient refrigerator
          <ul>
            <li><strong>ID65</strong> Why no</li>
            <li><strong>ID66</strong> affordable cost of an energy efficient option</li>
            <li><strong>ID67</strong> Years to amortize an efficient option</li>
          </ul>
          </li>
        </ul>
        </li>
        <li>Insulation
        <ul>
          <li><strong>ID47</strong> Affordable cost of updating to a state of the art insulation on the facade</li>
          <li><strong>ID56</strong> Willingness for paying/invest
          <ul>
            <li><strong>ID74</strong> Why no?</li>
            <li><strong>ID20</strong> affordable cost of an energy efficient option</li>
            <li><strong>ID34</strong> Years to amortize an energy efficient option</li>
          </ul>
          </li>
        </ul>
        </li>
        <li>Energy Generation
        <ul>
          <li><strong>ID68</strong> Affordable cost of a solar photovoltaic system</li>
          <li><strong>ID76</strong> Willingness for paying/invest
          <ul>
            <li><strong>ID84</strong> Why no?</li>
            <li><strong>ID132</strong> Affordable cost of a photovoltaic system</li>
            <li><strong>ID138</strong> Years that amortize a photovoltaic system</li>
          </ul>
          </li>
        </ul>
        </li>
        <li>Energy Storage
        <ul>
          <li><strong>ID142</strong> Affordable cost of an energy storage system</li>
          <li><strong>ID146</strong> Willingness for paying/invest
          <ul>
            <li><strong>ID181</strong> Why no? </li>
            <li><strong>ID182</strong> Affordable cost of an energy storage system </li>
            <li><strong>ID183</strong> Years that amortize an energy storage systems</li>
          </ul>
          </li>
        </ul>
        </li>
        <li>Heating
        <ul>
          <li><strong>ID140</strong> Affordable cost of a gas boiler</li>
          <li><strong>ID209</strong> Affordable cost of an energy efficient heating system</li>
          <li><strong>ID217</strong> Willingness for paying/invest
          <ul>
            <li><strong>ID238</strong> Why no?</li>
            <li><strong>ID239</strong> Affordable cost of a energy efficient option</li>
            <li><strong>ID241</strong> Years that amortize a heat pumps</li>
          </ul>
          </li>
        </ul>
        </li>
        <li>Mobility
        <ul>
          <li><strong>ID41</strong> Average kilometers traveled a typical day</li>
          <li><strong>ID51</strong> Usual travel option</li>
          <li><strong>ID264</strong> Affordable cost of a diesel or gasoline mid-range brand new car</li>
          <li><strong>ID265</strong> Affordable cost of a mid-range brand new electric car</li>
          <li><strong>ID281</strong> Willingness to buy an electric car
          <ul>
            <li><strong>ID289</strong> Why no?</li>
            <li><strong>ID290</strong> Affordable price of an electric car</li>
            <li><strong>ID291</strong> Years that amortize an electric car</li>
          </ul>
          </li>
        </ul>
        </li>
      </ul>
      </li>
      <li><strong>Section 5 - Household characterization</strong>
      <ul>
        <li><strong>ID127</strong> Selecting an asked value</li>
        <li><strong>ID189</strong> Type of living area</li>
        <li><strong>ID202</strong> Gender
    

Census tracts designated as Opportunity Zones. Qualified census tracts are those that have a poverty rate of at least 20 percent of a median income that does not exceed 80 percent of the area median income. The opportunity fund model encourages investors to pool their resources in opportunity zones, increasing the scale of investments going to underserved areas. The program provides a federal tax incentive for investors to re-invest unrealized capital gains into opportunity zones through opportunity funds. Under the terms of the program, the governor of each state must submit a plan to the federal government designating up to 25 percent of the qualified census tracts in their state as opportunity zones, which is then subject to approval of the Secretary of the Treasury.

Graph and download economic data for Personal Saving Rate (PSAVERT) from Jan 1959 to Jul 2025 about savings, personal, rate, and USA.

Dataset

This dataset was created by Arshan Khan

Contents

It contains the following files:

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of obesity, inactivity and inactivity/obesity-related illnesses. Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.The analysis incorporates data relating to the following:Obesity/inactivity-related illnesses (asthma, cancer, chronic kidney disease, coronary heart disease, depression, diabetes mellitus, hypertension, stroke and transient ischaemic attack)Excess weight in children and obesity in adults (combined)Inactivity in children and adults (combined)The analysis was designed with the intention that this dataset could be used to identify locations where investment could encourage greater levels of activity. In particular, it is hoped the dataset will be used to identify locations where the creation or improvement of accessible green/blue spaces and public engagement programmes could encourage greater levels of outdoor activity within the target population, and reduce the health issues associated with obesity and inactivity.ANALYSIS METHODOLOGY1. Obesity/inactivity-related illnessesThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to:- Asthma (in persons of all ages)- Cancer (in persons of all ages)- Chronic kidney disease (in adults aged 18+)- Coronary heart disease (in persons of all ages)- Depression (in adults aged 18+)- Diabetes mellitus (in persons aged 17+)- Hypertension (in persons of all ages)- Stroke and transient ischaemic attack (in persons of all ages)This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.For each of the above illnesses, the percentage of each MSOA’s population with that illness was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of patients registered with each GP that have that illness The estimated percentage of each MSOA’s population with each illness was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with each illness, within the relevant age range.For each illness, each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have that illnessB) the NUMBER of people within that MSOA who are estimated to have that illnessAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA predicted to have that illness, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from an illness, and where those people make up a large percentage of the population, indicating there is a real issue with that illness within the population and the investment of resources to address that issue could have the greatest benefits.The scores for each of the 8 illnesses were added together then converted to a relative score between 1 – 0 (1 = worst, 0 = best), to give an overall score for each MSOA: a score close to 1 would indicate that an area has high predicted levels of all obesity/inactivity-related illnesses, and these are areas where the local population could benefit the most from interventions to address those illnesses. A score close to 0 would indicate very low predicted levels of obesity/inactivity-related illnesses and therefore interventions might not be required.2. Excess weight in children and obesity in adults (combined)For each MSOA, the number and percentage of children in Reception and Year 6 with excess weight was combined with population data (up to age 17) to estimate the total number of children with excess weight.The first part of the analysis detailed in section 1 was used to estimate the number of adults with obesity in each MSOA, based on GP-level statistics.The percentage of each MSOA’s adult population (aged 18+) with obesity was estimated, using GP-level data (see section 1 above). This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of adult patients registered with each GP that are obeseThe estimated percentage of each MSOA’s adult population with obesity was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of adults in each MSOA with obesity.The estimated number of children with excess weight and adults with obesity were combined with population data, to give the total number and percentage of the population with excess weight.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have excess weight/obesityB) the NUMBER of people within that MSOA who are estimated to have excess weight/obesityAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA predicted to have excess weight/obesity, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from excess weight/obesity, and where those people make up a large percentage of the population, indicating there is a real issue with that excess weight/obesity within the population and the investment of resources to address that issue could have the greatest benefits.3. Inactivity in children and adultsFor each administrative district, the number of children and adults who are inactive was combined with population data to estimate the total number and percentage of the population that are inactive.Each district was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that district who are estimated to be inactiveB) the NUMBER of people within that district who are estimated to be inactiveAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the district predicted to be inactive, compared to other districts. In other words, those are areas where a large number of people are predicted to be inactive, and where those people make up a large percentage of the population, indicating there is a real issue with that inactivity within the population and the investment of resources to address that issue could have the greatest benefits.Summary datasetAn average of the scores calculated in sections 1-3 was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer the score to 1, the greater the number and percentage of people suffering from obesity, inactivity and associated illnesses. I.e. these are areas where there are a large number of people (both children and adults) who are obese, inactive and suffer from obesity/inactivity-related illnesses, and where those people make up a large percentage of the local population. These are the locations where interventions could have the greatest health and wellbeing benefits for the local population.LIMITATIONS1. For data recorded at the GP practice level, data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Levels of obesity, inactivity and associated illnesses: Summary (England). Areas with data missing’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children, we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of

The Investment / Foreign Direct Investment (FDI) dataset is collected or analyzed by the Food and Agriculture Organization of the United Nations (FAO) on foreign direct investment flows and stocks in the agriculture, forestry, and fisheries sectors. FDI is an investment which aims to acquire a lasting management influence (10 percent or more of the voting stock) in an enterprise operating in a foreign economy. FDI may be undertaken by individuals, as well as business entities. The foreign direct investor most often is aiming to gain access to natural resources, to markets, to labour supply, to technology, to ensure security of supplies or to control the quality of a certain product. FDI can be decomposed into two types of investments: mergers and acquisitions (MA) and greenfield investments. The latter type results in the creation of new entities and the setting up of offices, buildings, plants or factories from scratch in a foreign economy. FDI is the sum of equity capital, reinvested earnings and other FDI capital. Equity capital comprises equity in branches, all shares in subsidiaries and associates (except non-participating, preferred shares that are treated as debt securities and are included under other FDI capital) and other contributions such as the provision of machinery. Reinvested earnings consist of the direct investor's share (in proportion to equity participation) of earnings not distributed by the direct investment enterprise. Other FDI capital (loans) includes the borrowing and lending of funds, including debt securities and trade credits between direct investors and direct investment enterprises. FDI inflows and outflows are important for tracking the direct investment conditions each year. Outward Foreign Direct Investment (FDI) flows record the value of cross-border direct investment transactions from the reporting economy during a year. It represents transactions affecting the investment in enterprises resident abroad. Whereas, Inward Foreign Direct Investment (FDI) flows record the value of cross-border direct investment transactions received by the reporting economy during a year. It represents transactions affecting the investment in enterprises of a specific industry resident in the reporting economy. The data included in Data360 is a subset of the data available from the source. Please refer to the source for complete data and methodology details. This collection includes only a subset of indicators from the source dataset.

Abstract copyright UK Data Service and data collection copyright owner. The Annual Inquiry into Foreign Direct Investment (AFDI) collects financial data concerning Foreign Direct Investment (FDI). The survey is conducted in two parts: the inward FDI relates to foreign investment into the UK from foreign parent or subsidiary companies, while the outward FDI relates to investment by UK-based companies to their overseas parents or subsidiary companies. In particular, the AFDI surveys UK companies about their relationship with oversees affiliates in terms of flows of investment, earnings from investment and international investment positions. Companies are restricted to those where the foreign investment amounts to at least 10 percent of the ordinary shares or voting power. Two files are available for each year: for inward and outward FDI, respectively. Data on earnings for subsidiaries and branches are available, as well as transactions between UK companies and their foreign subsidiaries or parent companies, including transfers of capital. These data are useful to researchers who wish to analyse the relationship between UK companies and overseas-based parent companies or subsidiaries. The data are also useful for broader research into firm-level activity and foreign direct investment. Surveys are sent to enterprise group 'heads' (i.e. the headquarters of a company, which in turn may own a number of smaller companies) based in the UK. The register from which the firms are sampled comes from sources including HM Customs and Revenue, Dunn and Bradstreet's 'Worldbase' system, and ONS inquiries on Acquisitions and Mergers. Sampling is stratified and based on the value of the net investment position of the foreign affiliates (outward FDI) or the UK group (inward FDI). The sample is heavily weighted towards larger enterprise groups, which account for the vast majority of FDI assets. The largest firms all receive the survey forms, while only a proportion of the smaller firms receive the forms. The sample is rotated each year for the smaller firms. Data are collected on both the UK company and its foreign affiliate(s), which may be the overseas-based parent company or an overseas-based subsidiary. Linking to other business studies These data contain Inter-Departmental Business Register reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research. Latest edition information For the eighth edition (July 2021), the data files for 2017 have been updated, and the provisional data files for 2018 and 2019 have been added to the study.

Dividend-Per-Share Time Series for JM Financial Limited. JM Financial Limited, together with its subsidiaries, provides various integrated and diversified financial services to corporations, financial institutions, government organizations, high net-worth individuals, and retail customers in India and internationally. The company operates through Investment Bank; Mortgage Lending; Alternative & Distressed Credit; and Asset Management, Wealth Management & Securities Business (Platform AWS) segments. The Investment Bank segment manages capital markets transactions, as well as advises on mergers and acquisitions, and private equity syndication. This segment also provides institutional equities business and research, portfolio management, private equity funds, fixed income, syndication, and finance services. The Mortgage Lending segment offers finance against commercial and residential real estate to a range of corporate and non-corporate clients; housing finance; and lending services to educational institutions. The Alternative & Distressed Credit segment provides securitization and reconstruction of financial assets and manages alternative credit funds. The Platform AWS segment offers investment advisory and distribution services, which include equity brokerage, elite and retail wealth management, and margin trade financing; distributes financial products; and manages mutual fund assets through various schemes. The company also provides real estate consulting and capital market lending services. The company was formerly known as J.M. Share and Stock Brokers Private Limited and changed its name to JM Financial Limited in September 2004. JM Financial Limited was founded in 1973 and is headquartered in Mumbai, India.

This dataset displays the total Portfolio Investment Assets by country at year end of 2005. By selecting each attribute country, the map will show the amount in which other countries are investing assets into the selected country. The purpose of the CPIS is to collect information on the stock of cross-border holdings of equities and long- and short-term debt securities valued at market prices prevailing at the time of the CPIS, and broken down by the economy of residence of the issuer. The CPIS calls for data on holdings of securities at end-December of the reference year. In addition to this core (i.e., mandated) set of data, the CPIS also encourages the reporting of supplementary information that is considered to be useful, as indicated below. Data available from the IMF directly at: http://www.imf.org/external/np/sta/pi/geo.htm Access Date: November 27, 2007

The Gross Domestic Product (GDP) in the United States expanded 3.30 percent in the second quarter of 2025 over the previous quarter. This dataset provides the latest reported value for - United States GDP Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Dataset

This dataset was created by Dipangshu Kar

Contents

Context

This data was extracted from the open database of quotations of currencies and precious metals located on the site of the Bank of Russia. The link https://www.cbr.ru/Eng/statistics/?PrtId=finr is available for all internet users, the website is in Russian and in English.

Content

It consists of 1128 observations of 23 variables. Variables that indicating exchange rates are measured in rubles, the prices of precious metals are denoted in rubles per gram, foreign exchange.

The special variable dual currency basket is calculated according to the formula: 0.55 USD + 0.45 EUR.

The variables k_CNY, k_JPY are coefficients for the currencies values.

Foreign exchange reserves and monetary gold reserves consist of official data points for every month about the state reserves in Russia.

Acknowledgments

From publicly available data the files in 'xlsx' and 'csv' formats have been generated and downloaded. They are absolutely free for usage.

Usage

A set of financial indicators is suitable for training in the field of data visualization and learning simple regression algorithms.

Context

This dataset came from Ethereum dataset (kaggle.com/abhimaneukj/ethereum-historical-dataset)

Content

Ethereum (OCEAN-USD) Historical Dataset from 2020 to 2021

• Change %: represent the percentage change between the closing price and the opening price

• Date: Represents the date at which the share is traded in the stock market.

• Open: Represents the opening price of the stock at a particular date. It is the price at which a stock started trading when the opening bell rang.

• Price: Represents the closing price of the stock at a particular date. It is the last buy-sell order executed between two traders. The closing price is the raw price, which is just the cash value of the last transacted price before the market closes.

• High: The high is the highest price at which a stock is traded during a period. Here the period is a day.

• Low: The low is the lowest price at which a stock is traded during a period. Here the period is a day.

• Volume: Volume is the number of shares of security traded during a given period of time. Here the security is stock and the period of time is a day.

  Sources:

  investing.com

Context

The Turkish Lira is losing its value against U.S. Dollar constantly. As of October 22, 2021, 1 USD = 9.61 Turkish Lira (TRY). On the other hand, interest rates are quite high, especially for the Turkish Lira.

I set out to investigate if I had $100000 in 2010 and invested this money in different interest rates in both Turkish Lira (TRY) and US Dollar (USD), which investment would bring more gain in 2021.

Content

The data has been gathered from Türkiye Cumhuriyeti Merkez Bankasi (TCMB), aka the Turkish FED, website. The data shows the historical interest rates as well as USD/TRY conversion rates between July 2010 and July 2021. The original data’s all column names and relative explanations were Turkish, so the columns are renamed and the data is cleaned.

There are ten cleaned columns on the dataset: Date, 1-month TRY interest rates, 3 months TRY interest rates, 6 months TRY interest rates, 1-year TRY interest rates, 1 month USD interest rates, 3 months USD interest rates, 6 months USD interest rates, 1 year USD interest rates, USD/TRY Buying Conversion Rate, USD/TRY Selling Conversion Rate.

** USD Buying means, the customer is selling USD to the bank/ exchange office ** USD Selling means, the customer is buying USD to the bank/ exchange office

Inspiration

Would it be more beneficial if I converted my $100000 in July 2010 to Turkish Lira, which is the equivalent of 153631.36 TRY using July 2010’s rates and invested with Turkish high-interest rates or kept my money as U.S. Dollars and invested with relatively lower U.S. Dollar interest rates until July 2021? $100000 is equivalent to 861294.12 TRY in July 2021.

The release of the first iPhone was On June 29, 2007 so this dataset provide the historical data of APPl stock

This dataset contains historical data for appl stock. The dataset provides information for each trading day, including the date, price, open, high, low, volume, and percentage change.

Here is a description of the columns in the dataset :

1) Date: (datetime64) This column represents the date of the trading day. It indicates when the data was recorded.

2) Price: (float64) The price column represents the index's closing price on each trading day. It shows the value at which the index concluded its trading session.

3) Open: (float64) This column denotes the opening price of the index on each trading day. It represents the value at which the index began trading at the start of the session.

4) High: (float64) The high column indicates the highest price reached by the index during the trading day. It represents the peak value recorded for the index's price.

5) Low: (float64) The low column represents the lowest price reached by the index during the trading day. It indicates the minimum value recorded for the index's price.

6) Vol.: (object) The volume column denotes the trading volume, usually measured in millions, for each trading day. It represents the total number of shares or contracts traded during the session.

7) Change %: (float64) This column provides the percentage change in the index's price from the previous trading day. It indicates the daily price movement of the index.

The dataset contains 4034 rows, including the header row that describes the columns. The actual data starts from the second row and provides information for each trading day in descending order, with the most recent date appearing first.