Meta Data Stock Market Value

AIU
 Stock
  

USD 1.03  0.05  4.63%   

Meta Data's market value is the price at which a share of Meta Data stock trades on a public exchange. It measures the collective expectations of Meta Data investors about the entity's future performance. With this module, you can estimate the performance of a buy and hold strategy of Meta Data and determine expected loss or profit from investing in Meta Data over a given investment horizon. Please continue to Meta Data Correlation, Meta Data Volatility and Meta Data Alpha and Beta module to complement your research on Meta Data.
Symbol


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 Data 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.
Market Capitalization
17.4 M
Quarterly Revenue Growth YOY
(0.11) 
Return On Assets
(0.48) 
The market value of Meta Data is measured differently than its book value, which is the value of Meta Data 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 Meta Data value 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.

Meta Data 'What if' Analysis

In the world of financial modeling, what-if analysis is part of sensitivity analysis performed to test how changes in assumptions impact individual outputs in a model. When applied to Meta Data's stock what-if analysis refers to the analyzing how the change in your past investing horizon will affect the profitability against the current market value of Meta Data.
0.00
11/05/2022
No Change 0.00  0.0 
In 31 days
12/05/2022
0.00
If you would invest  0.00  in Meta Data on November 5, 2022 and sell it all today you would earn a total of 0.00 from holding Meta Data or generate 0.0% return on investment in Meta Data over 30 days. Meta Data is related to or competes with American Public, Adtalem Global, China Online, New Oriental, Graham Holdings, Laureate Education, and Grand Canyon. Meta Data Limited provides tutoring services for the students of kindergarten and primary, middle, and high schools in t... More

Meta Data Upside/Downside Indicators

Understanding different market momentum indicators often help investors to time their next move. Potential upside and downside technical ratios enable traders to measure Meta Data's stock current market value against overall market sentiment and can be a good tool during both bulling and bearish trends. Here we outline some of the essential indicators to assess Meta Data upside and downside potential and time the market with a certain degree of confidence.

Meta Data Market Risk Indicators

Today, many novice investors tend to focus exclusively on investment returns with little concern for Meta Data's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Meta Data's standard deviation. In reality, there are many statistical measures that can use Meta Data historical prices to predict the future Meta Data's volatility.
Sophisticated investors, who have witnessed many market ups and downs, frequently view the market will even out over time. This tendency of Meta Data's price to converge to an average value over time is called mean reversion. However, historically, high market prices usually discourage investors that believe in mean reversion to invest, while low prices are viewed as an opportunity to buy. Please use the tools below to analyze the current value of Meta Data in the context of predictive analytics.
Hype
Prediction
LowEstimated ValueHigh
0.051.033.89
Details
Intrinsic
Valuation
LowReal ValueHigh
0.050.913.77
Details
Please note, it is not enough to conduct a financial or market analysis of a single entity such as Meta Data. Your research has to be compared to or analyzed against Meta Data's peers to derive any actionable benefits. When done correctly, Meta Data's competitive analysis will give you plenty of quantitative and qualitative data to validate your investment decisions or develop an entirely new strategy towards taking a position in Meta Data.

Meta Data Backtested Returns

We consider Meta Data dangerous. Meta Data has Sharpe Ratio of 0.0193, which conveys that the firm had 0.0193% of return per unit of risk over the last 3 months. Our standpoint towards estimating the volatility of a stock is to use all available market data together with stock-specific technical indicators that cannot be diversified away. We have found twenty-eight technical indicators for Meta Data, which you can use to evaluate the future volatility of the firm. Please verify Meta Data Mean Deviation of 1.91, downside deviation of 3.14, and Risk Adjusted Performance of 0.064 to check out if the risk estimate we provide is consistent with the expected return of 0.0551%.
Meta Data has a performance score of 1 on a scale of 0 to 100. The company secures a Beta (Market Risk) of 0.0863, which conveys not very significant fluctuations relative to the market. Let's try to break down what Meta Data's beta means in this case. As returns on the market increase, Meta Data returns are expected to increase less than the market. However, during the bear market, the loss on holding Meta Data will be expected to be smaller as well. Although it is important to respect Meta Data price patterns, it is better to be realistic regarding the information on the equity's historical price patterns. The philosophy towards estimating future performance of any stock is to evaluate the business as a whole together with its past performance, including all available fundamental and technical indicators. By analyzing Meta Data technical indicators, you can presently evaluate if the expected return of 0.0551% will be sustainable into the future. Meta Data right now secures a risk of 2.86%. Please verify Meta Data value at risk, as well as the relationship between the skewness and day median price to decide if Meta Data will be following its current price movements.

Auto-correlation

    
  -0.93  

Near perfect reversele predictability

Meta Data has near perfect reversele predictability. Overlapping area represents the amount of predictability between Meta Data time series from 5th of November 2022 to 20th of November 2022 and 20th of November 2022 to 5th of December 2022. The more autocorrelation exist between current time interval and its lagged values, the more accurately you can make projection about the future pattern of Meta Data price movement. The serial correlation of -0.93 indicates that approximately 93.0% of current Meta Data price fluctuation can be explain by its past prices.
Correlation Coefficient-0.93
Spearman Rank Test-0.9
Residual Average0.0
Price Variance0.0

Meta Data lagged returns against current returns

Autocorrelation, which is Meta Data stock's lagged correlation, explains the relationship between observations of its time series of returns over different periods of time. The observations are said to be independent if autocorrelation is zero. Autocorrelation is calculated as a function of mean and variance and can have practical application in predicting Meta Data's stock expected returns. We can calculate the autocorrelation of Meta Data returns to help us make a trade decision. For example, suppose you find that Meta Data stock has exhibited high autocorrelation historically, and you observe that the stock is moving up for the past few days. In that case, you can expect the stock movement to match the lagging time series.
   Current and Lagged Values   
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       Timeline  

Meta Data regressed lagged prices vs. current prices

Serial correlation can be approximated by using the Durbin-Watson (DW) test. The correlation can be either positive or negative. If Meta Data stock is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Meta Data stock is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Meta Data stock over time.
   Current vs Lagged Prices   
Share
       Timeline  

Meta Data Lagged Returns

When evaluating Meta Data's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Meta Data stock have on its future price. Meta Data autocorrelation represents the degree of similarity between a given time horizon and a lagged version of the same horizon over the previous time interval. In other words, Meta Data autocorrelation shows the relationship between Meta Data stock current value and its past values and can show if there is a momentum factor associated with investing in Meta Data.
   Regressed Prices   
Share
       Timeline  

Be your own money manager

Our tools can tell you how much better you can do entering a position in Meta Data without increasing your portfolio risk or giving up the expected return. As an individual investor, you need to find a reliable way to track all your investment portfolios. However, your requirements will often be based on how much of the process you decide to do yourself. In addition to allowing all investors analytical transparency into all their portfolios, our tools can evaluate risk-adjusted returns of your individual positions relative to your overall portfolio.

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Technical Analysis

Check basic technical indicators and analysis based on most latest market data
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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 Data

-0.53BGSBG Foods Normal TradingPairCorr
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 equities. 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
Please continue to Meta Data Correlation, Meta Data Volatility and Meta Data Alpha and Beta module to complement your research on Meta Data. You can also try Fund Screener module to find actively-traded funds from around the world traded on over 30 global exchanges.

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When running Meta Data 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 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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Meta Data technical stock analysis exercises models and trading practices based on price and volume transformations, such as the moving averages, relative strength index, regressions, price and return correlations, business cycles, stock market cycles, or different charting patterns.
A focus of Meta Data technical analysis is to determine if market prices reflect all relevant information impacting that market. A technical analyst looks at the history of Meta Data trading pattern rather than external drivers such as economic, fundamental, or social events. It is believed that price action tends to repeat itself due to investors' collective, patterned behavior. Hence technical analysis focuses on identifiable price trends and conditions. More Info...