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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.

XLU 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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XLU 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 XLU.

Components Realized Volatility

Component Last Value
AEE 26.75892
AEP 27.02070
AES 42.44039
AWK 34.14915
CMS 27.78035
CNP 29.40721
D 27.44679
DTE 27.59860
DUK 28.66239
ED 27.79435
EIX 26.29273
ES 32.12702
ETR 29.87942
EVRG 29.82704
EXC 38.35831
FE 30.32291
LNT 30.40356
NEE 51.67279
PEG 27.58044
PPL 21.71586
SO 28.59980
SRE 26.73876
WEC 28.44287
XEL 33.86823
All Realized Volatility Visualizations
Component 1 Month Ago 1 Week Ago Last Value
AEE 26.75892 25.55100 19.43670
AEP 27.02070 24.82951 20.27990
AES 42.44039 38.01257 32.46363
AWK 34.14915 34.90670 32.29324
CEG 0.00000 0.00000 0.00000
CMS 27.78035 26.31455 22.12670
CNP 29.40721 27.69997 22.07188
D 27.44679 26.39318 22.80288
DTE 27.59860 24.31386 19.47724
DUK 28.66239 23.69080 20.08349
ED 27.79435 27.07889 21.21661
EIX 26.29273 26.47366 24.21434
ES 32.12702 33.42066 26.70202
ETR 29.87942 28.87722 26.53046
EVRG 29.82704 27.58563 21.58484
EXC 38.35831 37.86535 24.16003
FE 30.32291 24.30317 18.69053
LNT 30.40356 28.88632 22.86672
NEE 51.67279 51.66053 30.20013
PEG 27.58044 25.34454 21.54874
PPL 21.71586 19.04603 16.86269
SO 28.59980 24.81797 19.83377
SRE 26.73876 26.71733 22.21452
WEC 28.44287 27.96973 23.39715
XEL 33.86823 32.85037 22.75528

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 Utilities 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 Utilities 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 Utilities Select Sector SPDR Fund.

Utilities Select Sector SPDR Fund

XLU is an exchange traded fund whose portfolio is comprised of US utilities sector stocks. This investment seeks to provide the same performance as stocks in the Utilities Select Sector Index. The index includes securities of companies from the following industries: electric utilities, water utilities, multi-utilities, independent power and renewable electricity producers and gas utilities.
All Realized Volatility Visualizations

Realized Volatility

This chart shows the component realized volatility of XLUover 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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