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Robson Chow is a hedge fund manager
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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.

iShares 20 Plus Year Treasury Bond ETF x Global Debt Correlations

Components
Historical price and correlation 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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Global Debt Pearson Correlations Heat Map

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 TLT.

Global Debt Pearson Correlations

Correlation Interval Previous Year Previous Month Last Value
20-day 0.55838 0.61958 0.69508
40-day 0.55324 0.50513 0.64097
60-day 0.49419 0.60137 0.62532
80-day 0.46873 0.59120 0.62944
100-day 0.44266 0.61068 0.60880
120-day 0.41403 0.57393 0.62378
140-day 0.38112 0.55919 0.62822
160-day 0.37614 0.55345 0.58167
180-day 0.38212 0.54094 0.58265
All Correlation Visualizations
Correlation Interval Last Value
20-day 0.69508
40-day 0.64097
60-day 0.62532
80-day 0.62944
100-day 0.60880
120-day 0.62378
140-day 0.62822
160-day 0.58167
180-day 0.58265

About Heat Map Colors

This average correlation heat map uses a color scheme to represent how strongly the TLT, EMB, IGOV and HYG components 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 timeframes. For example, The 180-day average of Pearson correlation value calculated on November 21 appears on the heat map at the intersection of Y-coordinate "180" and X-coordinate "November 21".

About Chart Overlay

A price chart of iShares 20 Plus Year Treasury Bond ETF is overlaid on top of the heat map so you can observe the impact that different correlation regimes may have on the forward price of the asset.

For example, you may notice that prices tend to move in a direction when near term correlation register extremes; or when large differences take place between short term and long term correlation values.

Note that traders who need a perform a more comprehensive analysis can backtest the impact of each of these n-day 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 iShares 20 Plus Year Treasury Bond ETF, 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 iShares 20 Plus Year Treasury Bond ETF, 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.
iShares 20 Plus Year Treasury Bond ETF

iShares 20 Plus Year Treasury Bond ETF

TLT is an exchange traded fund that invests in U.S. government bonds, seeking to track the index ICE® US Treasury 20+ Year Bond Index. The underlying index measures the performance of long-term debt obligations, issued by the U.S. Treasury, that have a remaining maturity greater than or equal to twenty years.
All Correlation Visualizations

Correlations

This heat map shows the average Pearson correlation values between TLT, EMB, IGOV and HYG over the past year of trading.
Components
Historical price and correlation data
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