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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.

XLY Components Realized Volatility Streamgraph

Components
Historical price and realized volatility data
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Seasonality

This multi-factor forecast for Paypal Holdings (PYPL) is based on a weighted average of five factor-dervied forecasts.
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XLY Components Realized Volatility Streamgraph

Left-hand side y-axis orders liquidity rank of individuals chart components.
Right-hand side y-axis coordinates measure the price of XLY.

Components Realized Volatility

All Realized Volatility Visualizations
Component 1 Month Ago 1 Week Ago Last Value
AMZN 61.14909 60.53642 30.63105
APTV 61.63432 62.25658 43.48509
AZO 38.17511 38.17546 31.54744
BKNG 49.60055 45.13136 42.35230
CMG 59.80028 61.90879 43.12653
DG 39.95512 43.28021 33.06597
DHI 61.70291 63.87551 48.11894
DLTR 43.83570 47.67806 32.71291
EBAY 47.24810 51.31464 41.47051
EXPE 71.11021 59.11882 57.77572
F 73.87277 75.51528 58.19534
GM 60.01407 59.28124 49.77635
HD 39.51305 40.38440 29.83867
HLT 52.08951 50.24046 37.07816
LOW 46.83824 47.83998 27.56463
MAR 50.00157 48.07953 38.93091
MCD 25.11142 25.22212 16.15428
NKE 37.07040 35.60426 31.10476
ORLY 43.60977 38.92439 29.48587
ROST 45.17335 47.79110 38.06805
SBUX 35.59499 36.49219 32.76251
TGT 35.16069 35.66953 31.18217
TJX 38.68933 42.13255 32.62307
TSLA 91.42543 94.89381 81.06650
YUM 34.81986 31.84008 22.47139

About Realized Volatility

Realized volatility (as derived from the square root of variance) is a measurement of the standard deviation of returns of an asset over a given time period, typically annualized.

How Realized Volatility is Measured

Realized volatility can be measured many ways. The classical way of calculating realized volatility is by taking the log returns of close to close prices.

Per Euan Sinclair, “there is no uncertainty due to measurement. But there is uncertainty over whether the measure is truly representative of the underlying reality.”

The streamgraph visualization above displays realized volatility over the previous 21 days by applying the Yang-Zhang method of calculating realized volatility. This measurement utilizes more data points than the typical close-to-close estimator, which results in a measurement that is considered more accurate.

We measure realized volatility for each component of a particular index or ETFs in order to help understand volatility dynamics, and anomalies underneath the surface.

How to Read the Streamgraph

The streamgraph is a data visualization that enables the representation of many timeseries in an efficient manner. The Tradewell realized volatility streamgraph shows the change in realized volatility through time across multiple datasets, displaced around a central axis (the 0-line).

The streamgraph highlights three main attributes of realized volatility:

1. The overall level of realized volatility at the index or etf level relative to history.
If you notice the streamgraph expanding and then contracting, that behavior is representative of individual component volatility expanding and contracting. The widest part of the streamgraph represents the period with the most volatility across components, while the narrowest part of the streamgraph represents the period with the least volatility across components.

2. Anomalies in individual components realized through time.
When companies have large moves, ie volatility increases significantly due to earnings, unexpected events or otherwise, the streamgraph will immediately highlight those anomalies visually — the width of an individual securities contribution to the streamgraph will widen considerably and the individual component ticker will be displayed on the streamgraph on the Date where realized volatility was highest for Consumer Discretionary Select Sector SPDR Fund. As volatility clusters, it is common to see the width persist after an anomalous move in a particular security.

3. The level of realized volatility of individual components relative to other components that comprise Consumer Discretionary Select Sector SPDR Fund.

About the Streamgraph Coordinates

The X-axis displays trading days by date, and the Y-axis contains the component realized volatility. The absolute distance between each line on the chart is the 21-day realized volatility for Consumer Discretionary Select Sector SPDR Fund.

Consumer Discretionary Select Sector SPDR Fund

XLY is an exchange traded fund whose portfolio is comprised of US consumer-discretionary stocks. This investment seeks to provide the same performance as stocks in the Consumer Discretionary Select Sector Index. The index includes securities of companies from the following industries: retail, hotels, restaurants and leisure; textiles, apparel and luxury goods; household durables; automobiles.
All Realized Volatility Visualizations

Realized Volatility

This chart shows the component realized volatility of XLYover the past year of trading.
Components
Historical price and realized volatility data
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Robson Chow is a hedge fund manager
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