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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.

XLI 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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XLI 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 XLI.

Components Realized Volatility

Component Last Value
BA 58.52730
CARR 44.83642
CAT 38.89910
CSX 37.07067
DE 41.88990
EMR 35.25931
ETN 40.44186
FDX 32.29798
GD 30.02797
GE 44.96301
HON 37.49433
INFO 39.84368
ITW 37.29769
JCI 42.11219
LHX 38.60038
LMT 30.28290
MMM 35.95577
NOC 40.02086
NSC 36.64797
PH 53.14573
ROP 36.60775
RTX 39.76168
UNP 32.24636
UPS 42.94538
WM 28.76727
All Realized Volatility Visualizations
Component 1 Month Ago 1 Week Ago Last Value
BA 58.52730 57.20713 43.00801
CARR 44.83642 42.47251 34.90990
CAT 38.89910 39.64391 33.32550
CSX 37.07067 36.38901 26.06008
DE 41.88990 41.28325 36.17855
EMR 35.25931 33.02115 25.54655
ETN 40.44186 38.87132 26.86316
FDX 32.29798 28.34447 25.86737
GD 30.02797 28.86015 21.05279
GE 44.96301 42.00127 29.01445
HON 37.49433 35.39457 23.60873
INFO 39.84368 39.02264 27.37327
ITW 37.29769 36.30069 20.32582
JCI 42.11219 42.20688 29.94290
LHX 38.60038 35.03572 30.48709
LMT 30.28290 27.23464 22.86408
MMM 35.95577 34.38066 20.47314
NOC 40.02086 34.31539 24.02009
NSC 36.64797 38.11019 29.81445
PH 53.14573 52.40607 28.73840
ROP 36.60775 38.41401 31.92695
RTX 39.76168 38.80982 24.28336
UNP 32.24636 31.74807 24.42639
UPS 42.94538 40.98302 25.27855
WM 28.76727 28.43329 22.96117

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 Industrial 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 Industrial 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 Industrial Select Sector SPDR Fund.

Industrial Select Sector SPDR Fund

XLI is an exchange traded fund whose portfolio is comprised of US industrial sector stocks. This investment seeks to provide the same performance as stocks in the Industrial Select Sector Index. This index includes stocks from the following industries: aerospace and defense; industrial conglomerates; marine; transportation infrastructure, machinery, road and rail, air freight and logistics.
All Realized Volatility Visualizations

Realized Volatility

This chart shows the component realized volatility of XLIover 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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