Discussing the article: "The case for using Hospital-Performance Data with Perceptrons, this Q4, in weighing SPDR XLV's next Performance"

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Check out the new article: The case for using Hospital-Performance Data with Perceptrons, this Q4, in weighing SPDR XLV's next Performance.

XLV is SPDR healthcare ETF and in an age where it is common to be bombarded by a wide array of traditional news items plus social media feeds, it can be pressing to select a data set for use with a model. We try to tackle this problem for this ETF by sizing up some of its critical data sets in MQL5.

This article aims to look at a variety of datasets related to the ETF XLV, that could be key in driving momentum and or setting direction for the ETF. And the approach to be adopted here will be to select one of these data sets as a feed to a multi-layer perceptron. Perceptron’s can work with any data set in making a forecast so we attempt to answer the question which of the available datasets, pertinent to XLV, is better suited in light of the recent quarter performance & news to make the projection for the next quarter’s momentum. This is a very critical step for which am sure traders who use perceptrons have to consider from time to time. Our methodology will primarily dwell on using feature importance for data analysis to make this selection.

In the recent trailing quarter for XLV we have witnessed some selling off which markets are attributing to the waning off of the covid-19 pandemic, whose vaccines were a key driver for performance 2021-2022. But also, at a macro level a hawkish Fed is setting up bearish undertones in the market which are putting a lot of selling pressure on many ETFs right across the sectors. Medical Breakthroughs, Clinical trial results, FDA Drug approvals, and FDA Safety alerts, etc. have all played a part in the performance of XLV this past quarter which is why it would be helpful to start by listing the possible datasets we should consider for our model.

Author: Stephen Njuki