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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.

Emerging Markets 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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Emerging Markets 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 EEM.

Components Realized Volatility

Component Last Value
AGT 21.96147
ECH 39.52756
EIDO 16.08370
EPHE 16.08971
EPOL 28.40218
EPU 35.77770
ERUS 45.20100
EWM 12.38285
EWT 22.25362
EWW 29.00662
EWY 22.55735
EWZ 26.94492
EZA 29.84813
FXI 28.01354
ICOL 18.43018
INDA 18.68762
KSA 14.43165
KWT 10.51393
QAT 18.89931
THD 15.53786
TUR 37.77481
UAE 19.49211
All Realized Volatility Visualizations
Component 1 Month Ago 1 Week Ago Last Value
AGT 21.96147 24.44867 21.72157
ECH 39.52756 39.17884 37.36596
EIDO 16.08370 15.98853 11.70157
EPHE 16.08971 14.82744 14.43249
EPOL 28.40218 25.39397 19.18546
EPU 35.77770 36.20812 27.24215
ERUS 45.20100 41.93408 34.67010
EWM 12.38285 12.73732 10.97713
EWT 22.25362 20.87818 12.12180
EWW 29.00662 26.79824 17.59920
EWY 22.55735 22.95051 16.51427
EWZ 26.94492 29.65864 28.47149
EZA 29.84813 30.68247 24.03887
FXI 28.01354 29.12985 26.99051
ICOL 18.43018 21.82083 21.67249
INDA 18.68762 16.64305 11.62214
KSA 14.43165 13.94615 11.86487
KWT 10.51393 11.12984 8.45529
QAT 18.89931 14.84311 12.10198
THD 15.53786 15.41431 13.39449
TUR 37.77481 41.57841 131.64655
UAE 19.49211 18.26369 21.59998

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 iShares MSCI Emerging Markets ETF. 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 iShares MSCI Emerging Markets ETF.

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 iShares MSCI Emerging Markets ETF.

iShares MSCI Emerging Markets ETF

iShares MSCI Emerging Markets ETF captures large and mid cap representation across 25 emerging market countries. The index covers 85% of the free float-adjusted market capitalization in each country.
All Realized Volatility Visualizations

Realized Volatility

This chart shows the component realized volatility of EEMover 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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