Meta Price Book Value Ratio from 2010 to 2024
| AIU Stock | USD 0.49 0.03 5.77% |
Price Book Value Ratio | First Reported 2010-12-31 | Previous Quarter 8.7 K | Current Value 9.1 K | Quarterly Volatility 3.9 K |
Check Meta Data financial statements over time to gain insight into future company performance. You can evaluate financial statements to find patterns among Meta 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 exotic indicators such as Price To Sales Ratio of 1.3 K, Dividend Yield of 0.1 or PTB Ratio of 9.1 K. Meta financial statements analysis is a perfect complement when working with Meta Data Valuation or Volatility modules.
Meta | Price Book Value Ratio |
Latest Meta Data's Price Book Value Ratio Growth Pattern
Below is the plot of the Price Book Value Ratio of Meta Data over the last few years. It is Meta Data's Price Book Value Ratio 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.
| Price Book Value Ratio | 10 Years Trend |
|
Price Book Value Ratio |
| Timeline |
Meta Price Book Value Ratio Regression Statistics
| Arithmetic Mean | 2,698 | |
| Geometric Mean | 143.60 | |
| Coefficient Of Variation | 144.87 | |
| Mean Deviation | 3,410 | |
| Median | (0.01) | |
| Standard Deviation | 3,909 | |
| Sample Variance | 15.3M | |
| Range | 10.8K | |
| R-Value | 0.75 | |
| Mean Square Error | 7.3M | |
| R-Squared | 0.56 | |
| Significance | 0 | |
| Slope | 653.30 | |
| Total Sum of Squares | 213.9M |
Meta Price Book Value Ratio History
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 Price Book Value Ratio, 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 Reported | Projected for Next Year | ||
| Price Book Value Ratio | 8.7 K | 9.1 K |
Some investors attempt to determine whether the market's mood is bullish or bearish by monitoring changes in market sentiment. Unlike more traditional methods such as technical analysis, investor sentiment usually refers to the aggregate attitude towards Meta Data in the overall investment community. So, suppose investors can accurately measure the market's sentiment. In that case, they can use it for their benefit. For example, some tools to gauge market sentiment could be utilized using contrarian indexes, Meta Data's short interest history, or implied volatility extrapolated from Meta Data options trading.
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
| 0.81 | IH | IhumanInc | PairCorr |
Moving against Meta Stock
| 0.43 | LRN | Stride Inc Financial Report 20th of August 2024 | PairCorr |
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.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.You can also try the Pair Correlation module to compare performance and examine fundamental relationship between any two equity instruments.
Complementary 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.
| Idea Breakdown Analyze constituents of all Macroaxis ideas. Macroaxis investment ideas are predefined, sector-focused investing themes | |
| Price Ceiling Movement Calculate and plot Price Ceiling Movement for different equity instruments | |
| Price Transformation Use Price Transformation models to analyze the depth of different equity instruments across global markets | |
| Portfolio Optimization Compute new portfolio that will generate highest expected return given your specified tolerance for risk | |
| Portfolio Suggestion Get suggestions outside of your existing asset allocation including your own model portfolios | |
| Commodity Directory Find actively traded commodities issued by global exchanges |
Is Meta Data's industry expected to grow? Or is there an opportunity to expand the business' product line in the future? Factors like these will boost the valuation of Meta Data. If investors know Meta will grow in the future, the company's valuation will be higher. The financial industry is built on trying to define current growth potential and future valuation accurately. All the valuation information about Meta Data listed above have to be considered, but the key to understanding future value is determining which factors weigh more heavily than others.
Earnings Share 0.24 | Revenue Per Share 0.743 | Quarterly Revenue Growth 8.155 | Return On Assets 0.0996 |
The market value of Meta Data is measured differently than its book value, which is the value of Meta that is recorded on the company's balance sheet. Investors also form their own opinion of Meta Data's value that differs from its market value or its book value, called intrinsic value, which is Meta Data's true underlying value. Investors use various methods to calculate intrinsic value and buy a stock when its market value falls below its intrinsic value. Because Meta Data's market value can be influenced by many factors that don't directly affect Meta Data's underlying business (such as a pandemic or basic market pessimism), market value can vary widely from intrinsic value.
Please note, there is a significant difference between Meta Data's value and its price as these two are different measures arrived at by different means. Investors typically determine if Meta Data is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, Meta Data's price is the amount at which it trades on the open market and represents the number that a seller and buyer find agreeable to each party.