Simplify Volatility Correlations

A perfect positive correlation (i.e., a correlation coefficient of +1) implies that as Simplify Volatility moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if Simplify Volatility Premium moves in either direction, the perfectly negatively correlated security will move in the opposite direction.

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Related Correlations Analysis

	META	MSFT	UBER	F	T	A	CRM	JPM	MRK	XOM
META		0.69	0.68	0.59	0.39	0.88	-0.39	0.73	0.25	0.55	META
MSFT	0.69		0.53	0.82	0.44	0.52	-0.05	0.94	0.04	0.51	MSFT
UBER	0.68	0.53		0.73	0.2	0.66	0.09	0.66	0.19	0.5	UBER
F	0.59	0.82	0.73		0.32	0.54	0.0	0.88	0.28	0.69	F
T	0.39	0.44	0.2	0.32		0.49	-0.05	0.42	0.26	0.17	T
A	0.88	0.52	0.66	0.54	0.49		-0.33	0.61	0.43	0.58	A
CRM	-0.39	-0.05	0.09	0.0	-0.05	-0.33		-0.1	-0.6	-0.31	CRM
JPM	0.73	0.94	0.66	0.88	0.42	0.61	-0.1		0.21	0.6	JPM
MRK	0.25	0.04	0.19	0.28	0.26	0.43	-0.6	0.21		0.49	MRK
XOM	0.55	0.51	0.5	0.69	0.17	0.58	-0.31	0.6	0.49		XOM
	META	MSFT	UBER	F	T	A	CRM	JPM	MRK	XOM

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Correlation Matchups

Over a given time period, the two securities move together when the Correlation Coefficient is positive. Conversely, the two assets move in opposite directions when the Correlation Coefficient is negative. Determining your positions' relationship to each other is valuable for analyzing and projecting your portfolio's future expected return and risk.

High positive correlations

JPM	+ 0.94	MSFT
A	+ 0.88	META
JPM	+ 0.88	F
F	+ 0.82	MSFT
JPM	+ 0.73	META
F	+ 0.73	UBER

High negative correlations

MRK	- 0.6	CRM
CRM	- 0.39	META
CRM	- 0.33	A
XOM	- 0.31	CRM
JPM	- 0.1	CRM
CRM	- 0.05	T

Simplify Volatility Competition Risk-Adjusted Indicators

There is a big difference between Simplify Etf performing well and Simplify Volatility ETF doing well as a business compared to the competition. There are so many exceptions to the norm that investors cannot definitively determine what's good or bad unless they analyze Simplify Volatility's multiple risk-adjusted performance indicators across the competitive landscape. These indicators are quantitative in nature and help investors forecast volatility and risk-adjusted expected returns across various positions.

	Mean Deviation	Jensen Alpha	Sortino Ratio	Treynor Ratio	Semi Deviation	Expected Shortfall	Potential Upside	Value @Risk	Maximum Drawdown
META	1.31	0.46	0.19	(2.12)	0.73	(1.62)	3.62	(1.93)	10.48
MSFT	0.79	0.37	0.52	0.73	0.00	(1.01)	2.33	(0.94)	8.85
UBER	1.48	0.12	0.03	0.39	1.39	(1.81)	4.11	(2.02)	10.87
F	1.28	0.10	0.05	0.31	1.38	(1.40)	2.69	(2.33)	7.46
T	0.94	0.04	(0.10)	0.47	1.19	(0.99)	2.03	(2.23)	5.71
A	1.47	0.25	0.00	(0.88)	1.77	(1.56)	2.82	(2.89)	14.01
CRM	1.20	(0.12)	(0.03)	0.11	1.40	(1.20)	2.91	(2.57)	9.31
JPM	0.83	0.17	0.16	0.37	0.53	(0.96)	2.04	(1.31)	5.90
MRK	1.36	0.16	(0.07)	(0.10)	1.97	(1.29)	2.90	(3.31)	10.58
XOM	1.05	0.01	(0.11)	0.28	1.42	(1.02)	2.14	(2.58)	6.26

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Simplify Volatility Correlations

Related Correlations Analysis

Correlation Matchups

Simplify Volatility Competition Risk-Adjusted Indicators

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