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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.

XLE 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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XLE 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 XLE.

Components Realized Volatility

Component Last Value
APA 76.97592
BKR 61.83007
COP 51.10456
CTRA 60.86656
CVX 36.52307
DVN 77.42601
EOG 53.62489
FANG 71.31763
HAL 65.04724
HES 61.36952
KMI 33.52440
MPC 44.25003
MRO 67.01910
OKE 41.77761
OXY 73.65094
PSX 51.99428
PXD 51.37607
SLB 65.04958
VLO 53.09718
WMB 35.65425
XOM 43.87446
All Realized Volatility Visualizations
Component 1 Month Ago 1 Week Ago Last Value
APA 76.97592 80.92710 70.79116
BKR 61.83007 61.06602 43.66561
COP 51.10456 52.26280 40.39192
CTRA 60.86656 62.12524 59.82910
CVX 36.52307 36.14953 24.60941
DVN 77.42601 74.56492 62.83271
EOG 53.62489 54.98381 48.15896
FANG 71.31763 70.96441 58.04006
HAL 65.04724 62.79271 45.15352
HES 61.36952 62.50193 50.11448
KMI 33.52440 33.29327 28.95623
MPC 44.25003 43.75212 34.69348
MRO 67.01910 67.50274 55.58857
OKE 41.77761 42.88288 34.27060
OXY 73.65094 73.64924 63.97147
PSX 51.99428 50.19104 39.48870
PXD 51.37607 49.47625 39.46748
SLB 65.04958 62.57526 46.81648
VLO 53.09718 52.08252 37.93556
WMB 35.65425 35.96300 29.29614
XOM 43.87446 44.42603 32.63606

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 Energy 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 Energy 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 Energy Select Sector SPDR Fund.

Energy Select Sector SPDR Fund

XLE is an exchange traded fund whose portfolio is comprised of US energy sector stocks. This investment seeks to provide the same performance as stocks in the Energy Select Sector Index. The index includes companies from the following industries: Oil, Gas, and Consumable Fuels; and Energy Equipment and Services.
All Realized Volatility Visualizations

Realized Volatility

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