By design, MATLAB®software can efficiently perform repeated operations on collections of data stored in vectors and matrices. MATLAB code that is written to operate simultaneously on different arrays is said to be vectorized. Vectorized code is not only clean and concise, but is also efficiently processed by MATLAB.
Because MATLAB is optimized for processing vectorized code, many Financial Toolbox™ functions accept either vector or matrix input arguments, rather than single (scalar) values.
One example of such a function is the
irr function, which computes
the internal rate of return of a cash flow stream. If you input a vector of cash
flows from a single cash flow stream, then
irr returns a scalar rate of
return. If you input a matrix of cash flows from multiple cash flow streams,
where each matrix column represents a different stream, then
irr returns a vector of
internal rates of return, where the columns correspond to the columns of the
input matrix. Many other Financial Toolbox functions work similarly.
As an example, suppose that you make an initial investment of $100, from which you then receive by a series of annual cash receipts of $10, $20, $30, $40, and $50. This cash flow stream is stored in a vector
CashFlows = [-100 10 20 30 40 50]'
CashFlows = -100 10 20 30 40 50
irr function to compute the
internal rate of return of the cash flow stream.
Rate = irr(CashFlows)
Rate = 0.1201
For the single cash flow stream
CashFlows, the function
returns a scalar rate of return of
0.1201, or 12.01%.
Now, use the
irr function to compute
internal rates of return for multiple cash flow streams.
Rate = irr([CashFlows CashFlows CashFlows])
Rate = 0.1201 0.1201 0.1201
MATLAB performs the same computation on all the assets at once. For the
three cash flow streams, the
irr function returns a vector
of three internal rates of return.
In the Financial Toolbox context, vectorized programming is useful in portfolio management. You can organize multiple assets into a single collection by placing data for each asset in a different matrix column or row, then pass the matrix to a Financial Toolbox function.
Enter MATLAB character vectors surrounded by single quotes (
A character vector is stored as a character array, one ASCII character per element. Thus, the date character vector is
DateCharacterVector = '9/16/2017'
This date character vector is actually a
9 vector. If you create a vector
or matrix of character vectors, each character vector must have the same length.
Using a column vector to create a vector of character vectors can allow you to
visually check that all character vectors are the same length. If your character
vectors are not the same length, use spaces or zeros to make them the same
length, as in the following code.
DateFields = ['01/12/2017' '02/14/2017' '03/03/2017' '06/14/2017' '12/01/2017'];
DateFields is a
10 array of character
You cannot mix numbers and character vectors in a vector or matrix. If you input a vector or matrix that contains a mix of numbers and character vectors, MATLAB treats every entry as a character. As an example, input the following code
Item = [83 90 99 '14-Sep-1999']
Item = SZc14-Sep-1999
The software understands the input not as a
4 vector, but as a
14 character array with the value
Some functions return no arguments, some return just one, and some return multiple arguments. Functions that return multiple arguments use the syntax
[A, B, C] = function(input_arguments...)
to return arguments
C. If you omit all but one, the function returns the first
argument. Thus, for this example if you use the syntax
X = function(input_arguments...)
function returns a value for
A, but not
Some functions that return vectors accept only scalars as arguments. Such functions cannot accept vectors as arguments and return matrices, where each column in the output matrix corresponds to an entry in the input. Output vectors can be variable length.
For example, most functions that require asset life as an input, and return values
corresponding to different periods over the asset life, cannot handle vectors or
matrices as input arguments. These functions include
depsoyd. For example, consider a car for which you want to compute
the depreciation schedule. Use the
depfixdb function to compute a
stream of declining-balance depreciation values for the asset. Set the initial value
of the asset and the lifetime of the asset. Note that in the returned vector, the
asset lifetime determines the number of rows. Now consider a collection of cars with
different lifetimes. Because
depfixdb cannot output a matrix
with an unequal number of rows in each column,
depfixdb cannot accept a single
input vector with values for each asset in the collection.