US Commodity Correlations

ALUM Etf

33.21 2.43 6.82%

The current 90-days correlation between US Commodity Funds and FT Vest Equity is 0.15 (i.e., Average diversification). A perfect positive correlation (i.e., a correlation coefficient of +1) implies that as US Commodity moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if US Commodity Funds moves in either direction, the perfectly negatively correlated security will move in the opposite direction.

US Commodity Correlation With Market

Significant diversification

The correlation between US Commodity Funds and DJI is 0.06 (i.e., Significant diversification) for selected investment horizon. Overlapping area represents the amount of risk that can be diversified away by holding US Commodity Funds and DJI in the same portfolio, assuming nothing else is changed.

Check out Trending Equities to better understand how to build diversified portfolios. Also, note that the market value of any etf could be closely tied with the direction of predictive economic indicators such as signals in employment.

Moving together with ALUM Etf

0.72	DSJA	DSJA	Pair Corr
0.75	RSPY	Tuttle Capital Management	Pair Corr
0.81	MEME	Roundhill Investments	Pair Corr
0.79	WGMI	Valkyrie Bitcoin Miners	Pair Corr
0.71	BAC	Bank of America Aggressive Push	Pair Corr
0.66	HPQ	HP Inc	Pair Corr
0.7	AXP	American Express Fiscal Year End 24th of January 2025	Pair Corr
0.74	INTC	Intel Fiscal Year End 23rd of January 2025	Pair Corr
0.74	AA	Alcoa Corp Fiscal Year End 15th of January 2025	Pair Corr
0.66	DIS	Walt Disney Aggressive Push	Pair Corr
0.76	CSCO	Cisco Systems	Pair Corr

Moving against ALUM Etf

0.36	VZ	Verizon Communications Aggressive Push	Pair Corr
0.81	KO	Coca Cola Aggressive Push	Pair Corr
0.51	BA	Boeing Fiscal Year End 29th of January 2025	Pair Corr

Related Correlations Analysis

	META	MSFT	UBER	F	T	A	CRM	JPM	MRK	XOM
META		0.27	0.58	-0.01	0.64	0.02	0.73	0.31	-0.58	0.74	META
MSFT	0.27		0.22	0.12	0.18	-0.03	0.04	-0.16	0.1	-0.18	MSFT
UBER	0.58	0.22		0.0	0.09	0.23	0.12	-0.03	-0.01	0.42	UBER
F	-0.01	0.12	0.0		-0.03	-0.36	0.33	0.61	-0.22	0.27	F
T	0.64	0.18	0.09	-0.03		-0.44	0.7	0.43	-0.73	0.38	T
A	0.02	-0.03	0.23	-0.36	-0.44		-0.45	-0.64	0.62	0.02	A
CRM	0.73	0.04	0.12	0.33	0.7	-0.45		0.8	-0.92	0.67	CRM
JPM	0.31	-0.16	-0.03	0.61	0.43	-0.64	0.8		-0.82	0.48	JPM
MRK	-0.58	0.1	-0.01	-0.22	-0.73	0.62	-0.92	-0.82		-0.55	MRK
XOM	0.74	-0.18	0.42	0.27	0.38	0.02	0.67	0.48	-0.55		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	+ 0.8	CRM
XOM	+ 0.74	META
CRM	+ 0.73	META
CRM	+ 0.7	T
XOM	+ 0.67	CRM
T	+ 0.64	META

High negative correlations

MRK	- 0.92	CRM
MRK	- 0.82	JPM
MRK	- 0.73	T
JPM	- 0.64	A
MRK	- 0.58	META
XOM	- 0.55	MRK

US Commodity Competition Risk-Adjusted Indicators

There is a big difference between ALUM Etf performing well and US Commodity 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 US Commodity'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	1.07	0.05	0.02	0.16	1.41	(1.15)	2.62	(3.15)	8.02
MSFT	0.89	(0.10)	0.00	(0.05)	0.00	0.00	2.08	(2.03)	8.19
UBER	1.56	(0.15)	0.00	(0.06)	0.00	0.00	2.53	(2.44)	20.10
F	1.40	(0.09)	(0.02)	0.02	2.20	(1.37)	2.53	(2.90)	11.72
T	0.91	0.26	0.16	(47.59)	0.84	(1.03)	2.56	(1.72)	6.47
A	1.09	(0.16)	0.00	(0.27)	0.00	0.00	2.11	(2.42)	9.02
CRM	1.25	0.24	0.20	0.30	0.94	(1.47)	3.18	(1.91)	9.09
JPM	1.09	0.03	0.06	0.10	1.43	(1.11)	2.05	(2.00)	15.87
MRK	0.83	(0.26)	0.00	(1.64)	0.00	0.00	1.68	(2.13)	4.89
XOM	1.03	0.04	0.00	0.15	1.22	(1.09)	2.14	(2.09)	5.78

US Commodity Related Equities

One of the popular trading techniques among algorithmic traders is to use market-neutral strategies where every trade hedges away some risk. Because there are two separate transactions required, even if one position performs unexpectedly, the other equity can make up some of the losses. Below are some of the equities that can be combined with US Commodity etf to make a market-neutral strategy. Peer analysis of US Commodity could also be used in its relative valuation, which is a method of valuing US Commodity by comparing valuation metrics with similar companies.

FT Vest Equity Zillow Group Class Northern Lights VanEck Vectors Moodys BZDYF Freedom Day Dividend SSGA Active Trust SPDR Nuveen Municipal American Airlines Group Alcoa Corp Apple Inc Best Buy Co Citigroup SentinelOne CVS Health Corp

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