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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.

XLF 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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XLF 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 XLF.

Components Realized Volatility

Component Last Value
AIG 50.25145
AON 42.20420
AXP 47.53624
BAC 39.98614
BK 50.72343
BLK 42.22525
C 35.70296
CB 35.41000
CME 37.38192
COF 48.72807
GS 38.99302
ICE 31.42350
JPM 35.73350
MCO 49.03261
MET 40.17614
MMC 32.92309
MS 41.68894
PGR 30.96192
PNC 39.46480
SCHW 43.60664
SPGI 40.46286
TFC 43.65110
USB 40.63004
WFC 38.61422
All Realized Volatility Visualizations
Component 1 Month Ago 1 Week Ago Last Value
AIG 50.25145 46.80416 34.01012
AON 42.20420 43.86754 30.17080
AXP 47.53624 47.42119 31.76497
BAC 39.98614 40.16533 34.75392
BK 50.72343 49.81825 35.90706
BLK 42.22525 41.52089 28.48538
C 35.70296 37.01313 30.98507
CB 35.41000 34.38486 25.38860
CME 37.38192 37.30048 23.63870
COF 48.72807 49.70987 37.00165
GS 38.99302 42.67405 36.01640
ICE 31.42350 34.21501 25.40340
JPM 35.73350 37.87825 28.98112
MCO 49.03261 45.43346 31.30452
MET 40.17614 38.54274 28.30826
MMC 32.92309 33.60178 27.25529
MS 41.68894 43.46435 35.13911
PGR 30.96192 30.88821 26.74025
PNC 39.46480 39.25997 36.28717
SCHW 43.60664 44.51081 37.42249
SPGI 40.46286 39.84225 27.57884
TFC 43.65110 43.45544 35.12571
USB 40.63004 40.48395 28.93348
WFC 38.61422 39.26806 34.56479

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 Financial 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 Financial 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 Financial Select Sector SPDR Fund.

Financial Select Sector SPDR Fund

XLF is an exchange traded fund whose portfolio is comprised of US financial services stocks. This investment seeks to provide the same performance as stocks in the Financial Select Sector Index. This index includes securities of companies from the following industries: diversified financial services; insurance; banks; capital markets; mortgage real estate investment trusts ("REITs"); consumer finance, thrifts and mortgage finance.
All Realized Volatility Visualizations

Realized Volatility

This chart shows the component realized volatility of XLFover 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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