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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.

XLC 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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XLC 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 XLC.

Components Realized Volatility

All Realized Volatility Visualizations
Component 1 Month Ago 1 Week Ago Last Value
ATVI 43.33446 49.09793 53.56285
CHTR 37.54314 39.27546 34.35043
CMCSA 45.96566 44.91892 24.47684
DIS 45.20359 41.70019 27.46067
DISCK 92.57649 96.38790 85.16434
DISH 57.98006 58.52329 47.58022
EA 51.85786 52.72718 38.67817
FB 74.13471 72.16161 40.05213
FOX 48.85130 43.26331 31.60814
FOXA 51.60039 45.35583 33.04298
GOOG 45.60288 45.86100 31.83633
GOOGL 45.88067 46.39835 32.91925
IPG 62.11271 40.68247 33.07638
LUMN 54.64883 41.85022 43.65459
LYV 57.65552 58.51965 54.64907
MTCH 68.61250 70.81711 57.42052
NFLX 84.54667 82.97433 38.69769
NWSA 46.87693 45.58604 36.71185
OMC 45.27504 35.28893 26.85982
T 53.81150 54.26257 29.94120
TMUS 41.81601 47.86593 38.10776
TTWO 62.86615 72.30326 56.63879
TWTR 74.13822 70.70208 53.88692
VIAC 69.12662 69.70864 63.93742
VZ 27.76722 27.94008 19.61988

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 Communication Services 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 Communication Services 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 Communication Services Select Sector SPDR Fund.

Communication Services Select Sector SPDR Fund

XLC is an exchange traded fund whose portfolio is comprised of US communications services stocks. This investment seeks to provide the same performance as stocks in the Communications Services Select Sector Index. The index includes companies that have been identified as Communication Services companies by the GICS®, including securities from industries such as telecommunication services, wireless providers, entertainment and media conglomerates.
All Realized Volatility Visualizations

Realized Volatility

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