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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.

Utilities Select Sector SPDR Fund Liquidity Chart

Historical price and liquidity 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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XLU 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 XLU.

Component Liquidity

Component Last Value
NEE 84.00000
SO 60.00000
DUK 58.00000
D 50.00000
SRE 44.00000
AEP 38.00000
EXC 36.00000
XEL 36.00000
ED 34.00000
PEG 34.00000
CEG 32.00000
WEC 28.00000
ES 26.00000
AWK 24.00000
PCG 22.00000
EIX 16.00000
DTE 16.00000
ETR 14.00000
FE 12.00000
AEE 12.00000
PPL 12.00000
CNP 12.00000
AES 4.00000
CMS 4.00000
ATO 2.00000
Component 1 Month Ago 1 Week Ago Last Value
NEE 84.00000 98.00000 98.00000
SO 60.00000 96.00000 96.00000
DUK 58.00000 96.00000 96.00000
D 50.00000 92.00000 96.00000
SRE 44.00000 90.00000 96.00000
AEP 38.00000 90.00000 94.00000
EXC 36.00000 88.00000 94.00000
XEL 36.00000 88.00000 92.00000
ED 34.00000 84.00000 92.00000
PEG 34.00000 82.00000 88.00000
CEG 32.00000 80.00000 84.00000
WEC 28.00000 80.00000 82.00000
ES 26.00000 78.00000 80.00000
AWK 24.00000 76.00000 78.00000
PCG 22.00000 76.00000 78.00000
EIX 16.00000 72.00000 72.00000
DTE 16.00000 66.00000 70.00000
ETR 14.00000 66.00000 70.00000
FE 12.00000 66.00000 68.00000
AEE 12.00000 56.00000 68.00000
PPL 12.00000 50.00000 66.00000
CNP 12.00000 44.00000 64.00000
AES 4.00000 32.00000 36.00000
CMS 4.00000 30.00000 14.00000
ATO 2.00000 24.00000 4.00000
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 Utilities Select Sector SPDR Fund. The Y-axis is composed of each individual Utilities 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 Utilities Select Sector SPDR Fund.

About Liquidity Heat Map Coordinates

A price chart of Utilities 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.

Utilities Select Sector SPDR Fund

XLU is an exchange traded fund whose portfolio is comprised of US utilities sector stocks. This investment seeks to provide the same performance as stocks in the Utilities Select Sector Index. The index includes securities of companies from the following industries: electric utilities, water utilities, multi-utilities, independent power and renewable electricity producers and gas utilities.
All Liquidity Visualizations


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