Meta Data Stock Forecast - Accumulation Distribution

Summary

Meta Stock Forecast is based on your current time horizon.

On May 21, 2024 Meta Data had Accumulation Distribution of 1037.46. The accumulation distribution (A/D) indicator shows the degree to which Meta Data is accumulated by the market over a given period. It uses the quote sensitivity to the highest or lowest daily price of Meta Data to determine if accumulation or reduction is taking place in the market. This value is adjusted by Meta Data trading volume to give more weight to distributions with higher volume over lower volume.

More About Accumulation Distribution All Equity Analysis

Previous Accumulation Distribution	Accumulation Distribution	Trend
809.93	1037.46

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Trend Details

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Meta Data Trading Date Momentum

On May 22 2024 Meta Data was traded for 3.03 at the closing time. Highest Meta Data's price during the trading hours was 3.14 and the lowest price during the day was 2.90 . The net volume was 4.1 K. The overall trading history on the 22nd of May contributed to the next trading period price appreciation. The overall trading delta to the next next day price was 2.71% . The trading delta at closing time to current price is 7.46% .

Accumulation distribution indicator can signal that a trend is either nearing completion, at a continuation, or is about to break-outs. The actual value of this indicator is of no significance. What is significant is the change in value of over time. The formula for A/D of a given trading day can be expressed as follow: ((Close - Low) - (High - Close)) / (High - Low) X Volume

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Risk & Return

Correlation

Meta Data Risk Indicators

The analysis of Meta Data's basic risk indicators is one of the essential steps in accurately forecasting its future price. The process involves identifying the amount of risk involved in Meta Data's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting meta stock prices, we also provide a set of basic risk indicators that can assist in the individual investment decision or help in hedging the risk of your existing portfolios.

	Mean Deviation	7.91
	Standard Deviation	15.38
	Variance	236.5

Please note, the risk measures we provide can be used independently or collectively to perform a risk assessment. When comparing two potential investments, we recommend comparing similar equities with homogenous growth potential and valuation from related markets to determine which investment holds the most risk.

Thematic Opportunities

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Build portfolios using Macroaxis predefined set of investing ideas. Many of Macroaxis investing ideas can easily outperform a given market. Ideas can also be optimized per your risk profile before portfolio origination is invoked. Macroaxis thematic optimization helps investors identify companies most likely to benefit from changes or shifts in various micro-economic or local macro-level trends. Originating optimal thematic portfolios involves aligning investors' personal views, ideas, and beliefs with their actual investments.

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Check out Trending Equities to better understand how to build diversified portfolios. 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.

You can also try the Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.

Other Consideration for investing in Meta Stock

If you are still planning to invest in Meta Data check if it may still be traded through OTC markets such as Pink Sheets or OTC Bulletin Board. You may also purchase it directly from the company, but this is not always possible and may require contacting the company directly. Please note that delisted stocks are often considered to be more risky investments, as they are no longer subject to the same regulatory and reporting requirements as listed stocks. Therefore, it is essential to carefully research the Meta Data's history and understand the potential risks before investing.

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