DATA TYPES Flashcards
Definition of DATA
can be defined as a collection of numberpanel data, characters, words, and text—as well as images, audio, and video—in a raw or organized format to represent facts or information.
Three differen
perspectives of classifications
numerical versus categorical data; cross-sectional vs.
time-series versus panel data; and structured versus unstructured data.
Numerical data
are values that represent measured or counted quantities as a number
Numerical data are also called
quantitative data.
Numerical (quantitative) data can be split into
two types:
continuous data and discrete data.
Continuous data
are data that can be measured and can take on any numerical
value in a specified range of values
Examples of Continuous data
future value of a lump-sum, price returns of a stock
Discrete data
are numerical values that result from a counting process (finite number of values)
Categorical Data
also called qualitative data
Examples of categorical data
bankrupt vs. not bankrupt and dividends increased
vs. no dividend action
Nominal Data
are categorical values that are not amenable to being organized in a logical order.
Example of Nominal Data
the classification of publicly
listed stocks into 11 sectors (GICS - Global Industry Classification Standard)
MSCI
Morgal Stanley Capital Internacional - 11 sectors, 24 industry groups, 69 industries,
and 158 sub-industries
Often, financial models, such as regression models, require input data to be numerical
so, nominal data in the input dataset must be coded numerically before applying an
algorithm
Ordinal data
are categorical values that can be logically ordered or ranked
Cash dividends per share are
continuous data
Number of coupon payments is
discrete data.
Credit ratings are
ordinal data (arithmetic operations cannot be done on credit ratings)
Hedge fund classification types
are nominal data
Another data classification standard is based on how data are collected
cross-sectional, time series, and panel.
A variable is
a characteristic or quantity that
can be measured, counted, or categorized and is subject to change. A variable can also
be called a field, an attribute, or a feature.
Examples of data variable
For example, stock price, market capitalization, dividend and dividend yield, earnings per share (EPS), and price-to-earnings
ratio (P/E) are basic data variables for the financial analysis of a public company
Cross-sectional data
are a list of the observations of a specific variable from multiple observational units at a given point in time. For example, January inflation rates (i.e., the variable) for each of the euro-area countries (i.e., the observational units) in the European Union for a given year constitute cross-sectional data.
Time-series data
are a sequence of observations for a single observational unit
of a specific variable collected over time and at discrete and typically equally spaced
intervals of time, such as daily, weekly, monthly, annually, or quarterly. For example,
the daily closing prices (i.e., the variable) of a particular stock recorded for a given
month constitute time-series data.
