Hands On: How to use Alphacast to monitor SMEs financing?
Hands On: How to use Alphacast to monitor SME financing?
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The Argentine economy has a significant part of its production, employment, and consumption destined to Small and Medium Enterprises (SMEs). To understand their economic and financial situation, especially considering the scarcity of relevant high frequency official statistics, it is possible to use data on their financing. For this special segment of the economy, ECHEQs are fundamental - and Alphacast has a real-time updated Stock Market dataset of them, discriminating by maturity, asset type, and currency.
The complexity of the dataset, in addition to the amount of data and variables available, makes it crucial to know how to process and analyze them. Therefore, in this hands-on guide, we will show how to filter the data, transform them, and present them graphically for their best use. For this, we will use the Alphacast pipeline engine to transform and plot data from guaranteed ECHEQs. Since the source is available in our database, the information is updated regularly and automatically, as soon as it is made public - both in the original dataset and in all uses.
Step by step
First, we start by creating a pipeline. This is simple: select "create new", then "pipeline", and it is given a name and a location. The name is up to you, but it should be convenient and easy to find - for making changes or troubleshooting, even if it is public. The location simply determines whether the dataset is public (visible to all users) or private (visible to users with access) - this is relevant especially if you want other users to be able to edit it.
The second step is to select the dataset. It is available here, and first no variable is filtered, as it is organized by entities. It is not recommended, if available, to use "raw data" versions of the datasets, as they are not organized or processed.
The third step is the most complex; it involves calculating a variable corresponding to the Plazo (term) of each ECHEQ. For this, a chained conditional formula is used: if the term is greater than, less than, and/or equal to a certain duration, it is named as that duration. For example, the first if statement (if(@Plazo<=30, '<30d',...) means "if the term is less than or equal to 30 (days), the variable is named '<30d'"; what follows is if the term is not of that duration, which is several chained conditionals for each desired duration.
The next step is to filter columns; not all of the original dataset is of interest. In this case, all are used except "vencimiento" (maturity), which is not an analysis dimension you want to use.
Next, we go to regroup entities. The objective in this step is to reclassify the usage variables according to the term variable used; first "maturity" is selected as the filter, and then a form of regrouping is applied to the rest of the variables in the dataset - such that the sorting by entity occurs on the basis of maturity and not on the basis of, for example, currency.
Now, the dataset has the desired format; it is possible, then, to use it for analysis and to plot the relationships between variables. A first option is to calculate the term in days and then verify its relationship with the average rate; the way to do this is as follows:
Finally, a chart of this relationship may be created:
The next step is to work with the entities again, so that the data can be presented in a more appropriate way for the next chart to be worked on. The transformation is analogous to the previous one, but in the opposite direction - so that you "ungroup" by time frame and regroup the bulk of the checks. In this way, the following chart is created:
This procedure allows a simpler visualization of two key variables of interest for the understanding of the financial situation of Argentine SMEs. It can be extended to other types of ECHEQs also available in Alphacast - guaranteed and not guaranteed - with the same format and the same indications, so that creating, for example, a dashboard of SME ECHEQs would be trivial. The MAVSA repository, at the same time, has the three datasets already mentioned, and others for different means of financing, such as Promissory notes.
