Meta Total Operating Expenses from 2010 to 2024

AIU Stock  USD 0.41  0.03  7.89%   
Meta Data Total Operating Expenses yearly trend continues to be comparatively stable with very little volatility. Total Operating Expenses will likely drop to about 2 M in 2024. Total Operating Expenses is the total costs associated with the day-to-day operations of a business, excluding the cost of goods sold but including selling, general, and administrative expenses. View All Fundamentals
 
Total Operating Expenses  
First Reported
2016-11-30
Previous Quarter
919 K
Current Value
906.1 K
Quarterly Volatility
622.2 M
 
Covid
Check Meta Data financial statements over time to gain insight into future company performance. You can evaluate financial statements to find patterns among Meta Data's main balance sheet or income statement drivers, such as Depreciation And Amortization of 8.6 K, Interest Expense of 4.2 M or Selling General Administrative of 1.9 M, as well as many indicators such as Price To Sales Ratio of 1.3 K, Dividend Yield of 6.0E-4 or PTB Ratio of 9.1 K. Meta financial statements analysis is a perfect complement when working with Meta Data Valuation or Volatility modules.
  
Check out the analysis of Meta Data Correlation against competitors.
For more information on how to buy Meta Stock please use our How to Invest in Meta Data guide.

Latest Meta Data's Total Operating Expenses Growth Pattern

Below is the plot of the Total Operating Expenses of Meta Data over the last few years. It is the total costs associated with the day-to-day operations of a business, excluding the cost of goods sold but including selling, general, and administrative expenses. Meta Data's Total Operating Expenses historical data analysis aims to capture in quantitative terms the overall pattern of either growth or decline in Meta Data's overall financial position and show how it may be relating to other accounts over time.
Total Operating Expenses10 Years Trend
Pretty Stable
   Total Operating Expenses   
       Timeline  

Meta Total Operating Expenses Regression Statistics

Arithmetic Mean1,366,736,239
Geometric Mean287,097,134
Coefficient Of Variation148.45
Mean Deviation1,368,442,806
Median445,907,000
Standard Deviation2,028,881,490
Sample Variance4116360.1T
Range6.2B
R-Value0.28
Mean Square Error4089665.6T
R-Squared0.08
Significance0.32
Slope126,256,382
Total Sum of Squares57629041.4T

Meta Total Operating Expenses History

2024M
20232.1 M
20221.8 M
20206.2 B
20191.7 B
20181.7 B
20171.2 B

About Meta Data Financial Statements

There are typically three primary documents that fall into the category of financial statements. These documents include Meta Data income statement, its balance sheet, and the statement of cash flows. Meta Data investors use historical funamental indicators, such as Meta Data's Total Operating Expenses, to determine how well the company is positioned to perform in the future. Although Meta Data investors may use each financial statement separately, they are all related. The changes in Meta Data's assets and liabilities, for example, are also reflected in the revenues and expenses that we see on Meta Data's income statement, which results in the company's gains or losses. Cash flows can provide more information regarding cash listed on a balance sheet, but not equivalent to net income shown on the income statement. We offer a historical overview of the basic patterns found on Meta Data Financial Statements. Understanding these patterns can help to make the right decision on long term investment in Meta Data. Please read more on our technical analysis and fundamental analysis pages.
Last ReportedProjected for Next Year
Total Operating Expenses2.1 MM

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 against Meta Stock

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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 CorrelationCorrelation 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.