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SPDR S&P 500 ETF Trust x Balanced Portfolio Correlations

SPDR S&P 500 ETF Trust x US Equities Correlations

Vanguard Total Stock Market Index Fund ETF x Global Equities Correlations

SPDR S&P 500 ETF Trust x Sector Correlations

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.

Grayscale Bitcoin Trust x Crypto 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 GBTC.

Cryptocurrency Pearson Correlations

All Correlation Visualizations

About Heat Map Colors

This heat map uses a color scheme to represent how strongly BTC-USD.CC, ETH-USD.CC, BNB-USD.CC, SOL-USD.CC, XRP-USD.CC, LUNA-USD.CC, DOT-USD.CC, AVAX-USD.CC, DOGE-USD.CC, MATIC-USD.CC exhibit a positive (red) or somewhat positive (white) correlation, or none at all (green). Different shades of these three colors represent the extent of correlation.

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 Grayscale Bitcoin Trust 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 Grayscale Bitcoin 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 Grayscale Bitcoin 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.

Grayscale Bitcoin Trust

Grayscale Bitcoin Trust is an investment vehicle that lets investors gain access to Bitcoin in the form of an exchange-listed security. The investment objective of the Trust is for the NAV per Share to track Bitcoin's market price, less its liabilities and expenses.

All Correlation Visualizations

Correlations

This heat map shows the average Pearson correlation values between BTC-USD.CC, ETH-USD.CC, BNB-USD.CC, SOL-USD.CC, XRP-USD.CC, LUNA-USD.CC, DOT-USD.CC, AVAX-USD.CC, DOGE-USD.CC, MATIC-USD.CC over the past year of trading.

Components

Historical price and correlation 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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SPDR S&P 500 ETF Trust

Vanguard Total Stock Market Index Fund ETF

SPDR S&P 500 ETF Trust

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