The datasets from which these PYPL forecasts are drawn originate from FactSet. They represent the aggregated estimates made available to academics or practitioners via the Institutional Brokers’ Estimate System (IBES). Although this seems like a fair way of predicting future profits given that they have some level expertise in investment banking, studies show there's still an optimism bias present among these professionals.
Regression-based models suffer from the use of past earnings in a linear or exponential framework. This can lead to bias because these models assume that future performance will mirror historical trends exactly, whereas business cycle dynamics and seasonality may introduce randomness over time periods.
While there is a clear consensus that a factor-based approach to investment is rewarded over time, it goes without saying that the implementation of factor investing strategies, especially in the world of long-only money-management, is rarely subject to the same consensus. Index providers who offer funds that generally contain a small number of stocks in relation to the size and risk level they are designed for, often do so by selecting certain conditions or factors within each company.
For example, some commercial indexes aim at proportionality between price movements and dividends paid out over time while others look exclusively on liquidity considerations alone; yet still more restrict their selection criteria based around corporate governance issues like transparency reports rating various aspects such as soundness levels among others relevant metrics available about any given firm when deciding whether it should be included into an investor’s portfolio.
SPDR S&P 500 ETF Trust x Component CorrelationsSPY
Historical price and seasonality data
This multi-factor forecast for Paypal Holdings (PYPL) is based on a weighted average of five factor-dervied forecasts.Backtest PYPL
Apple, Inc. engages in the design, manufacture, and sale of smartphones, personal computers, tablets, wearables and accessories, and other variety of related services. It operates through the following geographical segments: Americas, Europe, Greater China, Japan, and Rest of Asia Pacific. The Americas segment includes North and South America. The Europe segment consists of European countries, as well as India, the Middle East, and Africa. The Greater China segment comprises of China, Hong Kong, and Taiwan. The Rest of Asia Pacific segment includes Australia and Asian countries. Its products and services include iPhone, Mac, iPad, AirPods, Apple TV, Apple Watch, Beats products, Apple Care, iCloud, digital content stores, streaming, and licensing services.
What does this SPY heat map mean?
Why do correlations matter?
What components are included in the calculation?
The Pearson correlation coefficient
is a measure of linear correlation between two sets of data. It is used to determine the strength and direction of a relationship between two variables.
In the example of this heat map
analysis of SPY, the coefficient is measuring the strength of the relationship between average changes in the value of index components over distinct time periods.
A coefficient value of -1 represents a maximally inverse relationship between the variables, whereas a value of 1 represents a maximally positive relationship. A value of 0 indicates no relationship between the variables.
For example, a 20-day average correlation value of 1 would indicate that the index components have been moving together in concert over the past 20 days. A value of -1 would indicate the components have been moving in opposite directions.
The Pearson correlation coefficient is a measure of linear correlation between two variables, and does not take into account other types of relationships or correlations. As a result, it should only be used when there is a linear relationship between the variables.