The Ultimate Guide to ETF Data 2021
What is ETF Data?
ETF data (exchange traded funds data) is a sub-category of financial market data. ETF data refers to information on shares sold on an exchange. It is financial market data on securities with the combined features or characteristics and benefits of mutual funds, bonds and stocks. Exchange traded funds shares are traded like shares on the basis of demand and supply, and also represent an assembled portfolio, like mutual funds, so an ETF dataset represents all information on these securities.
How is ETF Data collected?
Since ETF data is related directly to financial market data, the means of data collection lie in the regular collection methods and processes used for finanical market data. An array of sources provide the information used to compile ETF data feeds. These sources include market research firms, securities exchanges, news aggregators, brokers, traders, online services and investors. These sources monitor the listings of securities and their performances, analyzing details such as trading indexes and prices. This is then compiled on daily basis and used to make portfolio decisions in choosing what securities to buy and invest in.
What are the attributes of ETF Data?
Exchange traded funds are traded like both day to day stocks and mutual funds. Because of these characteristics, exchange traded funds data must be made up of information on equity portfolios as well as bonds portfolios. ETF data must show an index tracking of a market commodity or pool of assets.
ETF data attributes can also refer to how popular market items are traded, for example the ease and speed of trade that stocks are associated with.
What is ETF Data used for?
ETF data is essential in the creation and optimization of portfolios. Portfolio optimization here involves the selection of exchange traded securities or products and combining them in such a way that they mature and yield profitable returns to investors. The data allows investors to understand and predict the performance of the exchange traded funds. Proper analysis of ETF data gives investors a range of market options consisting of bonds, stocks, currencies or commodities and affords them the opportunity to pick out the best options that are not just profitable, but also come with as low risks as possible.
How can a user assess the quality of ETF Data?
The basic process for assessing the quality of any sub-cateogry of financial market data applies here. Exchange traded funds data must possess the following characteristics to be deemed high quality:
Authenticity - ETF data must come from a trustworthy source. Check for ETF data provider reviews to verify that the information it contains is accurate.
Reliability - any intelligience provided by ETF data must be impartial and unbiased.
Accessibility - all information in ETF data must be straightforward, easy to access and primed for analytics.
Timeliness - ETF data must match the time of its usage, as market conditions change every second.
Relevance - ETF data must be fit for purpose and match its user’s intended use case.
How to find ETFs?
For investors, buying Exchange Traded Funds is considered a small and low-cost strategy to build an optimal portfolio. When finding ETFs, it is critical that investors follow the factors below:
• Level of assets - When looking to find a more viable selection of investment choice, an ETF is expected to have a bottom line level of assets, a common threshold being $10 million as the bare minimum.
• Trading activity - Trading activities is an important indicator of liquidity so much so that higher trading volume of an ETF, can be interpreted to mean higher liquidity - Underlying index or asset upon which the ETF is founded should be taken into account. As far as diversification is concerned, finding ETFs that are based on a wider scope rather than an obscure index.
Can you analyse ETF Data by holdings?
ETF data analysis by the depth of holdings is an important initiative that helps investors to have access to a list of securities with a mere purchase of a single share. They provide avenues for small time investors to gain access to a large, and well-diversified pool of assets. The example below shows that it is possible to analyse ETF data by holdings:
> ‘The FTSE/Xinhua China 25 Index Fund (FXI A-) has 54 individual holdings, while the SPDR S&P China ETF (GXC A) has about 356.’
From this data, an analysis can be drawn to show that more isn’t particularly better, but at times, it is advantageous to concentrate holdings in a small group of securities but the overall depth of exposure given can show a meaningful impact on an ETF’s risk/return profile.
How to carry out ETF analytics?
In the analysis of ETFs, it is critical that investors take into account the underlying indexes and classification benchmarks that are meant to determine the types of stocks that are more likely to be part of the ETF. Additionally, tracking down of the fund’s trading history in the given wide-ranging markets is important initiatives that can help investors assess how the fund performed. Carrying out ETFs analytics involves looking at charts for the ETFs of concern to determine their historical performance. For a well-seasoned investor, understanding ETFs charts is simple because they trade just the same way as stocks in most major exchange markets. While looking at ETFs historical data performance may not necessarily give full insight about the future changes, it still gives investors the opportunity to determine and decide the kind of ETFs they want to use.
What is an ETF screener?
An ETF screener is basically an internet-based or software program that assists its users to find and determine ETFs, a process that is accomplished upon setting of certain conditions in order to narrow down or churn out the search from all the ETFs currently available on the market. The process of using an ETF screener usually entails the investor inputting search queries that narrows down on the type of ETF. From this point, the investor then uses the ETF screener to display all the publicly traded ETFs that are further grouped either as large-cap stock, or a sub-category such as large-cap growth or large-cap value, further setting the screener to sort the funds data to smaller subsets.
What is an ETF holdings database?
An ETF is a type of security that involves a collection of securities, that usually tracks an underlying index, but they can also invest in any given industry sectors. ETFs can encompass various types of investments that range from stocks, commodities, bonds, or a combination of various investment types. The key feature of ETFs is that they are marketable securities, a factor that identifies them to have an associated price for them to be easily bought and sold. Therefore, an ETF holdings database refers to a system of information that stores data about ETFs trading activities on the security exchange markets. This database captures the ETF share price fluctuations, the types of investments that include stocks, commodities or bonds and the types of expense ratios associated with them. The ETF holding databases are made available by data commercial data providers where they are listed in such a way that consumers can buy ETF data, either through over-the-counter method or they can buy ETF data online while in a remote location.
How do ETFs make money?
ETFs consists of various types which are made available for people looking to invest for the purpose of generating income, speculation, price increases, and to hedge or partly offset risks in an investor’s portfolio. Types of ETFs used by investors for making money include:
• Bond ETFs that include government binds, corporate bonds, and state and local bonds.
• Industry ETFs that are designed to keep track of a certain industry such as technology and banking.
• Commodity ETFs that entails investing in commodities such as crude oil or gold.
• Currency ETFs that involve investing in foreign currencies.
What is ETF performance data?
Looking at historical ETF data may not tell an investor how the same ETFs are bound to perform in the future. However, looking at this historical data, while combined together with real-time ETFs data can give an investor some meaningful insights of how the ETF has performed in different market settings and conditions. Therefore, an ETF performance data refers to historical and real-time information that shows how a selected ETF has been performing in the market place. Information about the performance of ETFs is available as a commercial ETF dataset in the data marketplace which can be acquired by interested parties, investors, through purchase of ETF data, by means of ETF data subscription that can range from monthly to yearly. The cost of ETF data as provided for by data vendors is dependent on the quality attributes of the data such as completeness and accuracy as well as volume of the data that an investor is looking to buy.
Who are the best ETF Data providers?
Finding the right ETF Data provider for you really depends on your unique use case and data requirements, including budget and geographical coverage. Popular ETF Data providers that you might want to buy ETF Data from are Exchange Data International, RIMES Technologies, Goldbaum, Risklio, and Pynk.
Where can I buy ETF Data?
Data providers and vendors listed on Datarade sell ETF Data products and samples. Popular ETF Data products and datasets available on our platform are RIMES Global ETF Data Management by RIMES Technologies, Goldbaum EOD and Historical Data Analytics for ETF/ETP/ETCs - Global Coverage by Goldbaum, and EDI Short Interest Data - market sentiment indicator with global coverage by Exchange Data International.
How can I get ETF Data?
You can get ETF Data via a range of delivery methods - the right one for you depends on your use case. For example, historical ETF Data is usually available to download in bulk and delivered using an S3 bucket. On the other hand, if your use case is time-critical, you can buy real-time ETF Data APIs, feeds and streams to download the most up-to-date intelligence.
What are similar data types to ETF Data?
ETF Data is similar to Short Interest Data, Stock Fundamental Data, Intraday Stock Data, Stock Price Data, and Corporate Actions Data. These data categories are commonly used for Portfolio Optimization and ETF Data analytics.
What are the most common use cases for ETF Data?
The top use cases for ETF Data are Portfolio Optimization.