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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.

Energy Select Sector SPDR Fund Liquidity Chart

Components
Historical price and liquidity 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 Liquidity Heat Map

Left-hand side y-axis orders liquidity rank of individuals chart components.
Right-hand side y-axis coordinates measure the price of XLE.

Component Liquidity

Component Last Value
XOM 70.00000
CVX 54.00000
COP 54.00000
EOG 46.00000
PXD 46.00000
SLB 46.00000
MPC 44.00000
OXY 40.00000
VLO 34.00000
DVN 26.00000
PSX 24.00000
WMB 24.00000
KMI 24.00000
HES 22.00000
OKE 20.00000
HAL 18.00000
BKR 14.00000
FANG 14.00000
CTRA 8.00000
MRO 4.00000
APA 2.00000
Component 1 Month Ago 1 Week Ago Last Value
XOM 70.00000 84.00000 74.00000
CVX 54.00000 82.00000 72.00000
COP 54.00000 74.00000 68.00000
EOG 46.00000 66.00000 58.00000
PXD 46.00000 64.00000 56.00000
SLB 46.00000 62.00000 56.00000
MPC 44.00000 58.00000 56.00000
OXY 40.00000 56.00000 54.00000
VLO 34.00000 54.00000 52.00000
DVN 26.00000 54.00000 48.00000
PSX 24.00000 44.00000 48.00000
WMB 24.00000 40.00000 46.00000
KMI 24.00000 36.00000 44.00000
HES 22.00000 30.00000 44.00000
OKE 20.00000 28.00000 44.00000
HAL 18.00000 28.00000 42.00000
BKR 14.00000 24.00000 34.00000
FANG 14.00000 22.00000 32.00000
CTRA 8.00000 14.00000 18.00000
MRO 4.00000 8.00000 18.00000
APA 2.00000 6.00000 12.00000
All Liquidity Visualizations

About Transactional Liquidity

To paraphrase trader Larry Harris, transactional liquidity in the markets is the ability to trade large order sizes quickly, at low cost and time that is convenient to the trader. Many traders consider liquidity the most important characteristic of well-functioning markets.

As a general observation, low liquidity is accompanied by high volatility in price movements, and vice-versa.

Note that traders who need to perform a more comprehensive analysis can backtest the impact of liquidity on a particular security using the Tradewell platform.

How Liquidity is Calculated

This measure of liquidity displayed on the heat-maps is adapted from Amihud’s illiquidity measure, which is the ratio of absolute close to-close returns to dollar volume (Price * Volume).

This metric is calculated as a the current value ranked over the prior 50 days. A low value indicates high liquidity, while a high value indicates lower liquidity (at the transactional level). In other words, a value that is high implies a large absolute close to close return, but relatively low volume comparatively.

We measure liquidity for each component of a particular index or ETF’s in order to help understand liquidity dynamics underneath the surface.

About Liquidity Heat Map Colors

This liquidity heat map uses a color scale to display the internal liquidity dynamics of Energy Select Sector SPDR Fund. The Y-axis is composed of each individual Energy Select Sector SPDR Fund component, which is sorted each and every day by their liquidity rank on that day compared to the other components. This allows us to see visually whether component liquidity was high, normal, or poor. For example, if the liquidity heatmap shows more red, and darker red on a given day, it is more likely that the majority of components are highly illiquid, and vice-versa.

About Liquidity Heat Map Coordinates

The X-axis displays trading days by date, while the Y-axis contains the liquidity rank of each individual security component comprising Energy Select Sector SPDR Fund.

About Liquidity Heat Map Coordinates

A price chart of Energy Select Sector SPDR Fund is overlaid on top of the heat map so you can observe the impact that different liquidity regimes may be having on the price of the asset.

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 Liquidity Visualizations

Liquidity

This chart shows the liquidity of individual components of XLE over the last year.
Components
Historical price and seasonality data
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Guest Commentary
Robson Chow is a hedge fund manager
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