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The Ultimate Guide to Fixed Income Data 2021
What is Fixed Income Data?
Fixed income data is information relating to municipal and corporate financial assets where an investor is paid a fixed interest or dividends rate. Fixed income data tells a user about the terms and conditions surrounding bonds and securites which are traded on a fixed income basis. It also proivdes information about the organizations, businesses and individuals involved in fixed income investing, as well as historical yields of fixed income trades.
How is Fixed Income Data collected?
Fixed income data providers typically source their data from the major financial markets and stock exchanges, like the FTSE 100 or Dow Jones. Fixed income data providers deploy automated and ML systems, as well as web scraping tools, to capture annoucements and news from these exchanges in real-time. Other methods of collecting fixed income data include taking information from central bank reports, custodians and depositries. When it comes to fixed income data collection, the more sources the data provider consults, the broader picture of fixed income trading you’ll get, and the likelihood of biased information also decreases.
What are the typical attributes of Fixed Income Data?
A fixed income data feed will provide lots of different attributes. Typically, these include:
** Stock ticker ** - this indicates the price of the fixed income share, including whether this rises or falls during a trading window.
** Industry code** - this describes the industry that the fixed income asset comes from, for example retail or energy.
** Maturity ** - how long the fixed income arrangement has existed, and how long the stock has been openly traded on the stock exchange.
How is Fixed Income Data used?
The global interest rate derivatives market is worth an estimated $436 trillion. So it’s no wonder that investors are turning to fixed income data to give their strategies the edge over competitors and win themselves a share of the market. Fixed income data is used throughout the investment journey, beginning with risk management. Investors use fixed income data to conduct portfolio analysis so they can select the bonds and derivatives where the risk-return balance is optimal. Next, they use real-time fixed income data updates to make truly data-driven investment decsions. Elsewhere, fixed income datasets are used for stock market data enrichment, for example for academic research purposes and economic analytics.
What are examples of Fixed Income investments?
The most common fixed income investments include:
• Bonds – Bonds are an obligation or loan advanced by an investor to an issuer that may be a government or a company. In return, the issuer pledges to repay the principal amount of the bond at a given maturity date in addition to interests payments to be made on a regular basis.
• Treasury bills – Treasury bills are considered the safest short-term fixed income investment models advanced by the federal government. They have a validity period that ranges from one month to twelve months and are very liquid and secure.
• Banker’s acceptance – Banker’s acceptance are short-termed promissory notes that are advanced by a given company while having the guarantee of a Chartered Bank.
What is the difference between Fixed Income and Variable Income?
A fixed income is any given kind of investment that has a regular fixed return in investment. It is a type of investment that gives a return in terms of fixed periodic payments that culminate into the return of principal amount upon the point or date of maturity. In contrast, a variable income security is a type of investment plan whose return in investment varies for every payment period depending on underlying benchmark factors, such as short-term interest rates fluctuations, or depending on the issuer company’s profit margin posted before the issuance of dividends.
What is Fixed Income Data analytics?
The supply of fixed income data continues to grow. With this growth comes a number of benefits, including increased price transparency. However, given that the data available on fixed income investment is increasing, one of the greatest challenges encountered by fixed income investors is the process that goes into identifying and accessing trusted and relevant data. Fixed income data analysis involves sorting through numerous data types and fragmented data sources from varying providers who are controlled by regulatory bodies. Fixed income data analytics tools help traders assess and develop a holistic view of the entire fixed income market, and allows traders to simplify the vast amount of data available by separating the most relevant fixed income data points from excess information.
What is a Fixed Income benchmark index?
In financial trading, a benchmark index refers to a pool of securities that is applied by investors to gauge the performance of other securities in the market. They are considered by investors as critical indicators of performance of mutual funds, stocks, bonds and securities in the securities markets since they provide the standard for performance. Therefore, through a fixed income benchmark index, investors can determine the performance of their investment by using the benchmark as a reference point. Tracking the performance of a benchmark index will give the investor some meaningful insight about the projected interest they are likely to receive from the fixed income investments.
What is the definition of Fixed Income Data science?
Fixed income data science refers to a combination of tools, algorithms, and machine learning techniques that are used to uncover hidden patterns from raw fixed income data about the types of investment security that pay investors fixed interests or dividend payments upon maturity. Providers of fixed income investment data in data marketplaces apply the core principles of data science to compile and analyze their data to reveal trends, patterns, and developments in fixed income trading - these insights are available to buy from fixed income data providers.
Where to get a Fixed Income Data model?
For fixed income investment, portfolio managers frequently need to draw upon external sources for detailed information about a given security’s features, its market valuation data, and to seek models and calculations. There are a vast number of fixed income data vendors that provide database management systems and models for portfolio managers to purchase fixed income investment data. Data marketplaces such as Datarade allow portfolio managers to search for and find the best providers of fixed income data models which suit their use case.
How to get Fixed Income Data in Python?
Bonds, which are seen as the most common fixed income securities, pay a fixed amount of interest to the lender either quarterly, semi-annually or annually. The interests earned are quoted as the fractions of the face value of the bond on a fiscal basis. An investor who intends to invest at a later time might be looking to find out what the future outlook of interest rate might look like as implied by the current structure of interest rates. This is a predictive analytics initiative of a fixed income investment effort that helps investors make informed decisions. As a way to enhance predictive analytics, Python is a very critical tool that continues to help investors to predict forward rates and see if a given fixed-income investment initiative has the potential to advance meaningful coupon yields, hence minimizing the risk of a possible defaulting by the issuer. By Python programming, fixed income data as far as calculating the yield to maturity of a bond, determination of the price of a bond, and data on bond duration can be obtained. However, since Python programming is a skill, online data vendors who are experts in the field analyze fixed income data by Python programming and make it available to investors. Depending on what kind of data is needed, investors shop for fixed income data from these data providers and analytics companies.
How to assess the quality of Fixed Income Data?
As is the case with most financial market data categories, fixed income data is only considered high-quality when it’s instantly usable. It’s no use having access to a highly accurate, up-to-date fixed income database if it requires huge amounts of analysis to be of real-world use. Your fixed income data provider must standardize their data before distributing it to users, otherwise buyers waste time de-crypting the data and integrating it into their current systems. And with billions of commodities traded each day on the fixed income market, this delay means that a golden opportunity for investment could be lost.
Who are the best Fixed Income Data providers?
Finding the right Fixed Income Data provider for you really depends on your unique use case and data requirements, including budget and geographical coverage. Popular Fixed Income Data providers that you might want to buy Fixed Income Data from are Exchange Data International, Bloomberg, Intercontinental Exchange (ICE), xignite, and Refinitiv.
Where can I buy Fixed Income Data?
Data providers and vendors listed on Datarade sell Fixed Income Data products and samples. Popular Fixed Income Data products and datasets available on our platform are EDI Fixed Income Data Global (40 event types, 13 years history) by Exchange Data International, Televisory’s Fixed Income Pricing Data- Global Coverage of 300,000+ Bonds by Televisory, and EDI Fixed Income Evaluated Pricing Data for Canada by Exchange Data International.
How can I get Fixed Income Data?
You can get Fixed Income Data via a range of delivery methods - the right one for you depends on your use case. For example, historical Fixed Income 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 Fixed Income Data APIs, feeds and streams to download the most up-to-date intelligence.
What are similar data types to Fixed Income Data?
Fixed Income Data is similar to Stock Market Data, Alternative Data, ESG Data, Merger & Acquisition Data, and Proprietary Market Data. These data categories are commonly used for Portfolio Valuation and Asset Management.