Meta Interest Expense vs Cost Of Revenue Analysis

AIU Stock

USD 0.41 0.03 7.89%

Meta Data financial indicator trend analysis is infinitely more than just investigating Meta Data recent accounting drivers to predict future trends. We encourage investors to analyze account correlations over time for multiple indicators to determine whether Meta Data is a good investment. Please check the relationship between Meta Data Interest Expense and its Cost Of Revenue accounts. Check out Trending Equities to better understand how to build diversified portfolios, which includes a position in Meta Data. Also, note that the market value of any company could be closely tied with the direction of predictive economic indicators such as signals in gross domestic product.

For more information on how to buy Meta Stock please use our How to Invest in Meta Data guide.

Interest Expense vs Cost Of Revenue

Interest Expense vs Cost Of Revenue Correlation Analysis

The overlapping area represents the amount of trend that can be explained by analyzing historical patterns of Meta Data Interest Expense account and Cost Of Revenue. At this time, the significance of the direction appears to have very strong relationship.

The correlation between Meta Data's Interest Expense and Cost Of Revenue is 0.89. Overlapping area represents the amount of variation of Interest Expense that can explain the historical movement of Cost Of Revenue in the same time period over historical financial statements of Meta Data, assuming nothing else is changed. The correlation between historical values of Meta Data's Interest Expense and Cost Of Revenue is a relative statistical measure of the degree to which these accounts tend to move together. The correlation coefficient measures the extent to which Interest Expense of Meta Data are associated (or correlated) with its Cost Of Revenue. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when Cost Of Revenue has no effect on the direction of Interest Expense i.e., Meta Data's Interest Expense and Cost Of Revenue go up and down completely randomly.

Correlation Coefficient

0.89

Relationship Direction	Positive
Relationship Strength	Strong

View All Relationships

Interest Expense

The cost incurred by an entity for borrowed funds, including loans, bonds, or lines of credit.

Cost Of Revenue

Cost of Revenue is found on Meta Data income statement and represents the costs associated with goods and services Meta Data provides. Indirect cost, such as salaries, is not included. In other words, cost of revenue is the total cost incurred to obtain a sale. It is more than the traditional cost of goods sold, since it includes specific selling and marketing activities.

Most indicators from Meta Data's fundamental ratios are interrelated and interconnected. However, analyzing fundamental ratios indicators one by one will only give a small insight into Meta Data current financial condition. On the other hand, looking into the entire matrix of fundamental ratios indicators, and analyzing their relationships over time can provide a more complete picture of the company financial strength now and in the future. Check out Trending Equities to better understand how to build diversified portfolios, which includes a position in Meta Data. Also, note that the market value of any company could be closely tied with the direction of predictive economic indicators such as signals in gross domestic product.

For more information on how to buy Meta Stock please use our How to Invest in Meta Data guide.Discontinued Operations is likely to gain to about 825.9 M in 2024, whereas Selling General Administrative is likely to drop slightly above 1.9 M in 2024.

	2020	2022	2023	2024 (projected)
Gross Profit	1.3B	15.4M	13.9M	13.2M
Total Revenue	3.4B	32.4M	37.3M	35.4M

Gross Profit Total Revenue

Meta Data fundamental ratios Correlations

	Total Assets	Other Current Liab	Total Current Liabilities	Total Stockholder Equity	Property Plant And Equipment Net	Current Deferred Revenue	Net Debt	Retained Earnings	Cash	Non Current Assets Total	Non Currrent Assets Other	Cash And Short Term Investments	Net Receivables	Common Stock Shares Outstanding	Short Term Investments	Non Current Liabilities Total	Other Current Assets	Total Liab	Total Current Assets	Common Stock	Other Assets	Capital Surpluse	Other Stockholder Equity	Cash And Equivalents	Property Plant Equipment	Short Long Term Debt Total
Total Assets		0.39	0.59	0.4	0.89	0.15	0.39	-0.19	0.92	0.98	0.98	0.85	0.54	-0.32	0.51	0.91	-0.61	0.72	0.9	-0.11	0.98	0.28	0.59	0.89	0.89	0.76
Other Current Liab	0.39		0.09	0.35	0.43	-0.48	0.0	0.08	0.19	0.32	0.32	0.53	-0.11	-0.49	0.78	0.2	-0.72	0.13	0.47	-0.49	0.37	-0.31	0.03	0.33	0.45	0.09
Total Current Liabilities	0.59	0.09		-0.5	0.56	0.39	0.77	-0.89	0.44	0.58	0.56	0.37	0.18	-0.51	0.17	0.72	-0.16	0.98	0.51	-0.17	0.57	0.41	0.86	0.41	0.55	0.85
Total Stockholder Equity	0.4	0.35	-0.5		0.28	-0.33	-0.53	0.8	0.51	0.35	0.39	0.55	0.39	0.17	0.44	0.11	-0.53	-0.34	0.43	0.0	0.38	-0.22	-0.35	0.52	0.3	-0.2
Property Plant And Equipment Net	0.89	0.43	0.56	0.28		0.18	0.58	-0.17	0.71	0.94	0.89	0.61	0.13	-0.22	0.3	0.91	-0.47	0.71	0.67	-0.06	0.92	0.26	0.48	0.65	1.0	0.82
Current Deferred Revenue	0.15	-0.48	0.39	-0.33	0.18		0.54	-0.44	0.11	0.23	0.22	-0.09	-0.06	0.54	-0.3	0.36	0.64	0.4	-0.01	0.78	0.15	0.95	0.64	0.1	0.16	0.5
Net Debt	0.39	0.0	0.77	-0.53	0.58	0.54		-0.73	0.09	0.52	0.46	-0.07	-0.18	-0.1	-0.24	0.71	0.16	0.8	0.08	0.19	0.48	0.56	0.71	0.02	0.57	0.88
Retained Earnings	-0.19	0.08	-0.89	0.8	-0.17	-0.44	-0.73		-0.03	-0.18	-0.18	-0.02	-0.01	0.4	0.01	-0.39	-0.15	-0.79	-0.16	0.06	-0.18	-0.45	-0.81	-0.06	-0.15	-0.62
Cash	0.92	0.19	0.44	0.51	0.71	0.11	0.09	-0.03		0.85	0.88	0.9	0.7	-0.27	0.5	0.74	-0.59	0.55	0.92	-0.12	0.86	0.19	0.45	0.96	0.71	0.54
Non Current Assets Total	0.98	0.32	0.58	0.35	0.94	0.23	0.52	-0.18	0.85		0.99	0.71	0.44	-0.22	0.34	0.96	-0.51	0.73	0.78	-0.01	0.99	0.34	0.58	0.78	0.94	0.83
Non Currrent Assets Other	0.98	0.32	0.56	0.39	0.89	0.22	0.46	-0.18	0.88	0.99		0.76	0.54	-0.22	0.4	0.95	-0.54	0.72	0.83	-0.01	0.99	0.36	0.61	0.83	0.89	0.8
Cash And Short Term Investments	0.85	0.53	0.37	0.55	0.61	-0.09	-0.07	-0.02	0.9	0.71	0.76		0.61	-0.42	0.84	0.56	-0.74	0.45	0.98	-0.29	0.75	0.04	0.4	0.95	0.61	0.36
Net Receivables	0.54	-0.11	0.18	0.39	0.13	-0.06	-0.18	-0.01	0.7	0.44	0.54	0.61		-0.25	0.32	0.37	-0.46	0.25	0.64	-0.15	0.49	0.07	0.29	0.67	0.13	0.18
Common Stock Shares Outstanding	-0.32	-0.49	-0.51	0.17	-0.22	0.54	-0.1	0.4	-0.27	-0.22	-0.22	-0.42	-0.25		-0.48	-0.21	0.73	-0.45	-0.47	0.93	-0.28	0.51	-0.16	-0.28	-0.23	-0.2
Short Term Investments	0.51	0.78	0.17	0.44	0.3	-0.3	-0.24	0.01	0.5	0.34	0.4	0.84	0.32	-0.48		0.18	-0.7	0.19	0.77	-0.41	0.39	-0.15	0.23	0.67	0.3	0.03
Non Current Liabilities Total	0.91	0.2	0.72	0.11	0.91	0.36	0.71	-0.39	0.74	0.96	0.95	0.56	0.37	-0.21	0.18		-0.36	0.85	0.67	0.05	0.95	0.46	0.71	0.66	0.9	0.94
Other Current Assets	-0.61	-0.72	-0.16	-0.53	-0.47	0.64	0.16	-0.15	-0.59	-0.51	-0.54	-0.74	-0.46	0.73	-0.7	-0.36		-0.23	-0.72	0.75	-0.59	0.5	0.01	-0.61	-0.49	-0.14
Total Liab	0.72	0.13	0.98	-0.34	0.71	0.4	0.8	-0.79	0.55	0.73	0.72	0.45	0.25	-0.45	0.19	0.85	-0.23		0.59	-0.11	0.72	0.45	0.87	0.51	0.69	0.93
Total Current Assets	0.9	0.47	0.51	0.43	0.67	-0.01	0.08	-0.16	0.92	0.78	0.83	0.98	0.64	-0.47	0.77	0.67	-0.72	0.59		-0.29	0.81	0.11	0.52	0.96	0.66	0.5
Common Stock	-0.11	-0.49	-0.17	0.0	-0.06	0.78	0.19	0.06	-0.12	-0.01	-0.01	-0.29	-0.15	0.93	-0.41	0.05	0.75	-0.11	-0.29		-0.08	0.78	0.23	-0.11	-0.07	0.1
Other Assets	0.98	0.37	0.57	0.38	0.92	0.15	0.48	-0.18	0.86	0.99	0.99	0.75	0.49	-0.28	0.39	0.95	-0.59	0.72	0.81	-0.08		0.29	0.57	0.8	0.92	0.8
Capital Surpluse	0.28	-0.31	0.41	-0.22	0.26	0.95	0.56	-0.45	0.19	0.34	0.36	0.04	0.07	0.51	-0.15	0.46	0.5	0.45	0.11	0.78	0.29		0.75	0.23	0.23	0.56
Other Stockholder Equity	0.59	0.03	0.86	-0.35	0.48	0.64	0.71	-0.81	0.45	0.58	0.61	0.4	0.29	-0.16	0.23	0.71	0.01	0.87	0.52	0.23	0.57	0.75		0.51	0.46	0.8
Cash And Equivalents	0.89	0.33	0.41	0.52	0.65	0.1	0.02	-0.06	0.96	0.78	0.83	0.95	0.67	-0.28	0.67	0.66	-0.61	0.51	0.96	-0.11	0.8	0.23	0.51		0.65	0.47
Property Plant Equipment	0.89	0.45	0.55	0.3	1.0	0.16	0.57	-0.15	0.71	0.94	0.89	0.61	0.13	-0.23	0.3	0.9	-0.49	0.69	0.66	-0.07	0.92	0.23	0.46	0.65		0.81
Short Long Term Debt Total	0.76	0.09	0.85	-0.2	0.82	0.5	0.88	-0.62	0.54	0.83	0.8	0.36	0.18	-0.2	0.03	0.94	-0.14	0.93	0.5	0.1	0.8	0.56	0.8	0.47	0.81

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Meta Data Account Relationship Matchups

Meta Data fundamental ratios Accounts

	2019	2020	2021	2022	2023	2024 (projected)
Total Assets	6.1B	7.9B	503.2M	121.1M	109.0M	103.6M
Other Current Liab	12.8M	430.1M	35.4M	13.2M	11.9M	11.3M
Total Current Liabilities	3.4B	5.1B	4.7B	115.1M	132.4M	125.8M
Total Stockholder Equity	1.1B	400.6M	(4.6B)	4.8M	4.3M	4.5M
Property Plant And Equipment Net	568.0M	2.1B	37.0M	24K	27.6K	26.2K
Current Deferred Revenue	2.0B	2.2B	2.5B	2.8B	3.2B	2.2B
Net Debt	290.0M	2.2B	1.3B	(17.9M)	(20.6M)	(19.6M)
Retained Earnings	(4.3B)	(5.0B)	(10.1B)	(946.1M)	(851.4M)	(894.0M)
Cash	1.4B	1.2B	29.6M	121.1M	109.0M	103.6M
Non Current Assets Total	3.7B	5.7B	37.0M	24K	27.6K	26.2K
Non Currrent Assets Other	267.6M	529.4M	659.3M	5.7M	6.6M	6.2M
Cash And Short Term Investments	1.8B	1.6B	111.2M	121.1M	109.0M	103.6M
Common Stock Shares Outstanding	3.2B	1.3B	6.6M	43.6B	50.2B	52.7B
Liabilities And Stockholders Equity	6.1B	7.9B	503.2M	121.1M	109.0M	103.6M
Non Current Liabilities Total	1.5B	2.4B	406.8M	1.2M	1.4M	1.3M
Other Current Assets	10.0M	11.7M	8.6M	355.0M	408.3M	428.7M
Total Liab	4.9B	7.4B	5.1B	116.4M	133.8M	127.1M
Total Current Assets	2.4B	2.2B	466.2M	121.1M	109.0M	103.6M
Accumulated Other Comprehensive Income	134.1M	133.5M	111.4M	109.1M	125.5M	86.1M
Long Term Debt Total	1.3B	1.3B	361.1M	241.1M	277.3M	263.4M
Capital Surpluse	5.5B	5.6B	5.3B	5.8B	6.7B	4.4B
Other Stockholder Equity	5.3B	5.3B	5.3B	950.8M	1.1B	1.0B
Cash And Equivalents	1.4B	1.2B	29.6M	202.4M	182.2M	173.1M
Short Term Debt	321.7M	1.1B	1.0B	102.0M	117.3M	111.4M
Property Plant Equipment	2.1B	37.0M	0.0	24K	21.6K	20.5K
Short Long Term Debt Total	1.7B	3.3B	1.4B	103.2M	118.7M	112.7M
Other Liab	41.2M	177.9M	147.7M	45.7M	52.6M	93.6M
Net Tangible Assets	(1.4B)	(4.6B)	(5.4B)	4.8M	4.3M	4.5M
Accounts Payable	74.8M	894.9M	1.0B	875.3M	1.0B	760.5M
Long Term Debt	1.3B	361.1M	241.1M	1.2M	1.1M	1.0M
Net Invested Capital	2.7B	(3.3B)	(4.7B)	107.9M	97.1M	102.0M
Short Long Term Debt	1.1B	1.0B	454.4M	101.9M	117.2M	111.4M

Pair Trading with Meta Data

One of the main advantages of trading using pair correlations is that every trade hedges away some risk. Because there are two separate transactions required, even if Meta Data position performs unexpectedly, the other equity can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in Meta Data will appreciate offsetting losses from the drop in the long position's value.

Moving together with Meta Stock

0.66

VSTA

Vasta Platform

Pair Corr

Moving against Meta Stock

0.66	LRN	Stride Inc Financial Report 20th of August 2024	Pair Corr
0.58	FEDU	Four Seasons Education	Pair Corr
0.39	GV	Visionary Education Symbol Change	Pair Corr

The ability to find closely correlated positions to Meta Data could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace Meta Data when you sell it. If you don't do this, your portfolio allocation will be skewed against your target asset allocation. So, investors can't just sell and buy back Meta Data - that would be a violation of the tax code under the "wash sale" rule, and this is why you need to find a similar enough asset and use the proceeds from selling Meta Data to buy it.

The correlation of Meta Data is a statistical measure of how it moves in relation to other instruments. This measure is expressed in what is known as the correlation coefficient, which ranges between -1 and +1. A perfect positive correlation (i.e., a correlation coefficient of +1) implies that as Meta Data moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if Meta Data moves in either direction, the perfectly negatively correlated security will move in the opposite direction. If the correlation is 0, the equities are not correlated; they are entirely random. A correlation greater than 0.8 is generally described as strong, whereas a correlation less than 0.5 is generally considered weak.

Correlation analysis and pair trading evaluation for Meta Data can also be used as hedging techniques within a particular sector or industry or even over random equities to generate a better risk-adjusted return on your portfolios.

Pair Correlation Correlation Matching

Additional Tools for Meta Stock Analysis

When running Meta Data's price analysis, check to measure Meta Data's market volatility, profitability, liquidity, solvency, efficiency, growth potential, financial leverage, and other vital indicators. We have many different tools that can be utilized to determine how healthy Meta Data is operating at the current time. Most of Meta Data's value examination focuses on studying past and present price action to predict the probability of Meta Data's future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move Meta Data's price. Additionally, you may evaluate how the addition of Meta Data to your portfolios can decrease your overall portfolio volatility.

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