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Balanced Portfolio Pearson Correlations Heat Map

US Equities Pearson Correlations Heat Map

Global Equities Pearson Correlations Heat Map

S&P 500 Sector Pearson Correlations Heat Map

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.

SPDR S&P 500 ETF Trust x Sector Correlations

Components

Historical price and correlation 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 coordinates represent the 1 to 180-day look back periods for Pearson correlation values.
Right-hand side y=axis coordinates measure the price level of SPY.

Component Pearson Correlations

Correlation Interval	Last Value
20-day	0.52332
40-day	0.40008
60-day	0.36704
80-day	0.36980
100-day	0.42688
120-day	0.39915
140-day	0.38216
160-day	0.39682
180-day	0.40580

Correlation Interval	Previous Year	Previous Month	Last Value
20-day	0.41686	0.27981	0.52332
40-day	0.41904	0.26337	0.40008
60-day	0.47374	0.31811	0.36704
80-day	0.46317	0.39138	0.36980
100-day	0.48774	0.36798	0.42688
120-day	0.49052	0.37932	0.39915
140-day	0.52120	0.38078	0.38216
160-day	0.55543	0.39818	0.39682
180-day	0.58659	0.39249	0.40580

All Correlation Visualizations

About Heat Map Colors

This average correlation heat map uses a color scheme to represent how strongly XLU, XLE, XLP, XLF, XLI, XLRE, XLB, XLV, XLY and XLK exhibit a positive (red), somewhat positive (white), or low / inverse average correlation (green). Different shades of these three colors represent the strength or weakness of the average correlation as shown on the heatmap colorbar legend, to the right of the heatmap itself.

About Heat Map Coordinates

The X-axis displays trading days by date, and the Y-axis contains different n-day average correlation look back periods. For example, The 180-day average of Pearson correlation value calculated on a specific date appears on the heat map at the intersection of Y-coordinate "180" and X-coordinate of the specific date.

About Chart Overlay

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 average correlation regimes may be having on the price of the asset.

As a general observation, high correlation regimes are accompanied by high volatility in price movements, and vice-versa.

Note that traders who need a perform a more comprehensive analysis can backtest the impact of each of these n-day average correlations using the Tradewell platform.

About Pearson Correlations

The Pearson correlation coefficient is a measure of linear strength between two sets of data. In financial markets is used to determine whether the variability of returns between a group of assets is linearly related.

In the example of this heat map analysis of SPDR S&P 500 ETF Trust, we are measuring the average correlations between the components over various look-back periods, spanning 10 days to 180 days. In the example of this heat map analysis of SPDR S&P 500 ETF Trust, we are measuring the average correlations between the components over various look-back periods, spanning 10 days to 180 days.

A coefficient value of -1 represents a maximally inverse relationship between the variables, whereas a value of 1 represents a maximally positive relationship. A value of 0 indicates no linear relationship between the variables.

For example, a 20-day average correlation value of 1 would indicate that variability of the index components returns has been perfectly linearly related over the previous 20 days.

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 Correlation Visualizations

Correlations

This heat map shows the average Pearson correlation values between XLU, XLE, XLP, XLF, XLI, XLRE, XLB, XLV, XLY and XLK components over the past year of trading.

Components

Historical price and correlation data

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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