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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.

XLP Components Realized Volatility Streamgraph

Historical price and realized volatility data
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This multi-factor forecast for Paypal Holdings (PYPL) is based on a weighted average of five factor-dervied forecasts.
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XLP 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 XLP.

Components Realized Volatility

Component Last Value
ADM 34.91587
CAG 26.30294
CHD 36.39152
CL 25.69468
CLX 45.10831
COST 35.10272
EL 48.23734
GIS 24.15843
HSY 27.11078
KHC 34.62611
KMB 29.99459
KO 27.80037
KR 40.21118
MDLZ 27.41507
MKC 29.47051
MNST 38.15310
MO 24.54260
PEP 28.21999
PG 28.77561
PM 27.97221
STZ 35.13574
SYY 46.77684
TSN 35.93838
WBA 35.77851
WMT 26.42807
All Realized Volatility Visualizations
Component 1 Month Ago 1 Week Ago Last Value
ADM 34.91587 33.84686 20.22974
CAG 26.30294 27.77306 31.86106
CHD 36.39152 36.27940 22.40455
CL 25.69468 24.42160 16.51801
CLX 45.10831 45.36118 25.67038
COST 35.10272 36.12365 30.78648
EL 48.23734 49.16173 37.23068
GIS 24.15843 22.89208 22.45884
HSY 27.11078 27.31564 18.21713
KHC 34.62611 29.75216 22.57869
KMB 29.99459 29.77861 20.76098
KO 27.80037 23.74531 16.24217
KR 40.21118 42.26406 39.79785
MDLZ 27.41507 26.27573 17.02948
MKC 29.47051 28.75174 21.86244
MNST 38.15310 40.39497 32.73779
MO 24.54260 26.23182 23.75029
PEP 28.21999 26.12869 15.10019
PG 28.77561 28.15611 22.77358
PM 27.97221 25.79575 20.41259
STZ 35.13574 26.89821 31.13386
SYY 46.77684 46.16044 31.77451
TSN 35.93838 35.35968 21.68512
WBA 35.77851 32.50012 30.47287
WMT 26.42807 26.15021 20.67129

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 Staples 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 Staples 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 Staples Select Sector SPDR Fund.

Consumer Staples Select Sector SPDR Fund

XLP is an exchange traded fund whose portfolio is comprised of US consumer staples stocks. This investment seeks to provide the same performance as stocks in the Consumer Staples Select Sector Index. This index includes stocks from the following industries: food and staples retailing; household products; food products; beverages, tobacco, personal care.
All Realized Volatility Visualizations

Realized Volatility

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