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S&P 500 Components Liquidity Chart

S&P 500 Sector Liquidity Chart

iShares Core MSCI International Developed Mkt ETF Liquidity Chart

iShares MSCI Emerging Markets ETF Liquidity Chart

Consumer Discretionary Select Sector SPDR Fund Liquidity Chart

Health Care Select Sector SPDR Fund Liquidity Chart

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.

S&P 500 Sector Liquidity Chart

Components

Historical price and liquidity data

Seasonality

This multi-factor forecast for Paypal Holdings (PYPL) is based on a weighted average of five factor-dervied forecasts.

Backtest PYPL

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

Component Liquidity

Component	Last Value
XLY	98.00000
XLI	98.00000
XLP	92.00000
XLU	86.00000
XLV	72.00000
XLK	70.00000
XLB	62.00000
XLF	44.00000
XLE	36.00000
XLC	30.00000

Component	1 Month Ago	1 Week Ago	Last Value
XLY	98.00000	68.00000	62.00000
XLI	98.00000	66.00000	52.00000
XLP	92.00000	64.00000	36.00000
XLU	86.00000	52.00000	36.00000
XLV	72.00000	50.00000	32.00000
XLK	70.00000	42.00000	24.00000
XLB	62.00000	26.00000	10.00000
XLF	44.00000	18.00000	8.00000
XLE	36.00000	8.00000	8.00000
XLC	30.00000	2.00000	6.00000

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.
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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 SPDR S&P 500 ETF Trust. The Y-axis is composed of each individual SPDR S&P 500 ETF Trust 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 SPDR S&P 500 ETF Trust.

About Liquidity Heat Map Coordinates

A price chart of SPDR S&P 500 ETF Trust 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.

SPDR S&P 500 ETF Trust

SPY is an investment trust that seeks to match the price and yield performance of the S&P 500 Index. As such, SPY holds a portfolio of common stocks in proportion to their weighting in the index. The goal of this exchange-traded security is to provide investment returns broadly comparable to those of the S&P 500 Index.

All Liquidity Visualizations

Liquidity

This chart shows the liquidity of individual components of SPY over the last year.

Components

Historical price and seasonality data

AAPL

Guest Commentary

Robson Chow is a hedge fund manager

Thank you for reading... and hope you are enjoying the holidays. The full post can be found here https://t.co/uLKVnqB5ZS
— QuartersPower (@HalfersPower) December 30, 2021

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