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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.

SPDR Gold Trust x Precious Metals 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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Precious Metals 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 GLD.

Commodity Pearson Correlations

Correlation Interval Previous Year Previous Month Last Value
20-day 0.60616 0.56699 0.67014
40-day 0.59481 0.52439 0.64265
60-day 0.50697 0.58407 0.59822
80-day 0.52427 0.56006 0.62257
100-day 0.48714 0.56424 0.61694
120-day 0.48014 0.60287 0.59356
140-day 0.51335 0.60387 0.59460
160-day 0.54048 0.59047 0.61856
180-day 0.59349 0.59049 0.60626
All Correlation Visualizations
Correlation Interval Last Value
20-day 0.67014
40-day 0.64265
60-day 0.59822
80-day 0.62257
100-day 0.61694
120-day 0.59356
140-day 0.59460
160-day 0.61856
180-day 0.60626

About Heat Map Colors

This average correlation heat map uses a color scheme to represent how strongly the GLD, SLV, PALL and PPLT 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 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 Gold 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 Gold 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 Gold 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 Gold Trust

The investment objective for GLD is for shares of the trust to mirror the performance of the price of gold bullion. minus the expenses of the Trust’s operations. The Trust mainly hold gold bars, but from time to time, issues Baskets in exchange for deposits of gold. distributing gold in connection with redemptions of Baskets.
All Correlation Visualizations

Correlations

This heat map shows the average Pearson correlation values between GLD, SLV, PALL and PPLT over the past year of trading.
Components
Historical price and correlation data
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Robson Chow is a hedge fund manager
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