Meta Platforms Stock Price Prediction
| META Stock | USD 465.70 12.29 2.71% |
Oversold Vs Overbought
47
Oversold | Overbought |
Quarterly Earnings Growth 1.141 | EPS Estimate Next Quarter 4.76 | EPS Estimate Current Year 20.23 | EPS Estimate Next Year 23.21 | Wall Street Target Price 530.03 |
Using Meta Platforms hype-based prediction, you can estimate the value of Meta Platforms from the perspective of Meta Platforms response to recently generated media hype and the effects of current headlines on its competitors.
Meta Platforms Hype to Price Pattern
Investor biases related to Meta Platforms' public news can be used to forecast risks associated with an investment in Meta. The trend in average sentiment can be used to explain how an investor holding Meta can time the market purely based on public headlines and social activities around Meta Platforms. Please note that most equities that are difficult to arbitrage are affected by market sentiment the most.
Some investors profit by finding stocks that are overvalued or undervalued based on market sentiment. The correlation of Meta Platforms' market sentiment to its price can help taders to make decisions based on the overall investors consensus about Meta Platforms.
The fear of missing out, i.e., FOMO, can cause potential investors in Meta Platforms to buy its stock at a price that has no basis in reality. In that case, they are not buying Meta because the equity is a good investment, but because they need to do something to avoid the feeling of missing out. On the other hand, investors will often sell stocks at prices well below their value during bear markets because they need to stop feeling the pain of losing money.
Meta Platforms after-hype prediction price | USD 465.82 |
There is no one specific way to measure market sentiment using hype analysis or a similar predictive technique. This prediction method should be used in combination with more fundamental and traditional techniques such as stock price forecasting, technical analysis, analysts consensus, earnings estimates, and various momentum models.
Meta |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Meta Platforms' 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.
Meta Platforms After-Hype Price Prediction Density Analysis
As far as predicting the price of Meta Platforms at your current risk attitude, this probability distribution graph shows the chance that the prediction will fall between or within a specific range. We use this chart to confirm that your returns on investing in Meta Platforms or, for that matter, your successful expectations of its future price, cannot be replicated consistently. Please note, a large amount of money has been lost over the years by many investors who confused the symmetrical distributions of Stock prices, such as prices of Meta Platforms, with the unreliable approximations that try to describe financial returns.
Next price density |
| Expected price to next headline |
Meta Platforms Estimiated After-Hype Price Volatility
In the context of predicting Meta Platforms' stock value on the day after the next significant headline, we show statistically significant boundaries of downside and upside scenarios based on Meta Platforms' historical news coverage. Meta Platforms' after-hype downside and upside margins for the prediction period are 463.82 and 467.82, respectively. We have considered Meta Platforms' daily market price in relation to the headlines to evaluate this method's predictive performance. Remember, however, there is no scientific proof or empirical evidence that news-based prediction models outperform traditional linear, nonlinear models or artificial intelligence models to provide accurate predictions consistently.
Current Value
Meta Platforms is very steady at this time. Analysis and calculation of next after-hype price of Meta Platforms is based on 3 months time horizon.
Meta Platforms Stock Price Prediction Analysis
Have you ever been surprised when a price of a Company such as Meta Platforms is soaring high without any particular reason? This is usually happening because many institutional investors are aggressively trading Meta Platforms backward and forwards among themselves. Have you ever observed a lot of a particular company's price movement is driven by press releases or news about the company that has nothing to do with actual earnings? Usually, hype to individual companies acts as price momentum. If not enough favorable publicity is forthcoming, the Stock price eventually runs out of speed. So, the rule of thumb here is that as long as this news hype has nothing to do with immediate earnings, you should pay more attention to it. If you see this tendency with Meta Platforms, there might be something going there, and it might present an excellent short sale opportunity.
| Expected Return | Period Volatility | Hype Elasticity | Related Elasticity | News Density | Related Density | Expected Hype |
0.14 | 2.00 | 0.12 | 0.19 | 9 Events / Month | 2 Events / Month | In about 9 days |
| Latest traded price | Expected after-news price | Potential return on next major news | Average after-hype volatility | ||
465.70 | 465.82 | 0.03 |
|
Meta Platforms Hype Timeline
Meta Platforms is now traded for 465.70. The entity has historical hype elasticity of 0.12, and average elasticity to hype of competition of -0.19. Meta is anticipated to increase in value after the next headline, with the price projected to jump to 465.82 or above. The average volatility of media hype impact on the company the price is over 100%. The price jump on the next news is projected to be 0.03%, whereas the daily expected return is now at 0.14%. The volatility of related hype on Meta Platforms is about 149.25%, with the expected price after the next announcement by competition of 465.51. The company reported the previous year's revenue of 134.9 B. Net Income was 39.1 B with profit before overhead, payroll, taxes, and interest of 92.86 B. Given the investment horizon of 90 days the next anticipated press release will be in about 9 days. Check out Meta Platforms Basic Forecasting Models to cross-verify your projections.Meta Platforms Related Hype Analysis
Having access to credible news sources related to Meta Platforms' direct competition is more important than ever and may enhance your ability to predict Meta Platforms' future price movements. Getting to know how Meta Platforms' peers react to changing market sentiment, related social signals, and mainstream news is a great way to find investing opportunities and time the market. The summary table below summarizes the essential lagging indicators that can help you analyze how Meta Platforms may potentially react to the hype associated with one of its peers.
| HypeElasticity | NewsDensity | SemiDeviation | InformationRatio | PotentialUpside | ValueAt Risk | MaximumDrawdown | |||
| GOOGL | Alphabet Inc Class A | (4.28) | 7 per month | 1.80 | 0.01 | 2.26 | (2.93) | 15.26 | |
| TWLO | Twilio Inc | (0.96) | 10 per month | 0.00 | (0.05) | 2.49 | (2.60) | 9.79 | |
| BIDU | Baidu Inc | (1.76) | 10 per month | 0.00 | (0.09) | 3.67 | (2.61) | 14.45 | |
| PINS | 0.30 | 10 per month | 1.20 | 0.05 | 3.69 | (2.25) | 25.94 | ||
| TCEHY | Tencent Holdings Ltd | 0.00 | 0 per month | 1.35 | 0.05 | 3.48 | (2.53) | 10.31 |
Meta Platforms Additional Predictive Modules
Most predictive techniques to examine Meta price help traders to determine how to time the market. We provide a combination of tools to recognize potential entry and exit points for Meta using various technical indicators. When you analyze Meta charts, please remember that the event formation may indicate an entry point for a short seller, and look at other indicators across different periods to confirm that a breakdown or reversion is likely to occur.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
About Meta Platforms Predictive Indicators
The successful prediction of Meta Platforms stock price could yield a significant profit to investors. But is it possible? The efficient-market hypothesis suggests that all published stock prices of traded companies, such as Meta Platforms, already reflect all publicly available information. This academic statement is a fundamental principle of many financial and investing theories used today. However, the typical investor usually disagrees with a 'textbook' version of this hypothesis and continually tries to find mispriced stocks to increase returns. We use internally-developed statistical techniques to arrive at the intrinsic value of Meta Platforms based on analysis of Meta Platforms hews, social hype, general headline patterns, and widely used predictive technical indicators.
We also calculate exposure to Meta Platforms's market risk, different technical and fundamental indicators, relevant financial multiples and ratios, and then comparing them to Meta Platforms's related companies.
Story Coverage note for Meta Platforms
The number of cover stories for Meta Platforms depends on current market conditions and Meta Platforms' risk-adjusted performance over time. The coverage that generates the most noise at a given time depends on the prevailing investment theme that Meta Platforms is classified under. However, while its typical story may have numerous social followers, the rapid visibility can also attract short-sellers, who usually are skeptical about Meta Platforms' long-term prospects. So, having above-average coverage will typically attract above-average short interest, leading to significant price volatility.
Contributor Headline
Latest Perspective From Macroaxis
Meta Platforms Short Properties
Meta Platforms' future price predictability will typically decrease when Meta Platforms' long traders begin to feel the short-sellers pressure to drive the price lower. The predictive aspect of Meta Platforms often depends not only on the future outlook of the potential Meta Platforms' investors but also on the ongoing dynamics between investors with different trading styles. Because the market risk indicators may have small false signals, it is better to identify suitable times to hedge a portfolio using different long/short signals. Meta Platforms' indicators that are reflective of the short sentiment are summarized in the table below.
| Common Stock Shares Outstanding | 2.6 B | |
| Cash And Short Term Investments | 65.4 B |
Complementary Tools for Meta Stock analysis
When running Meta Platforms' price analysis, check to measure Meta Platforms' 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 Platforms is operating at the current time. Most of Meta Platforms' value examination focuses on studying past and present price action to predict the probability of Meta Platforms' future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move Meta Platforms' price. Additionally, you may evaluate how the addition of Meta Platforms to your portfolios can decrease your overall portfolio volatility.
| Positions Ratings Determine portfolio positions ratings based on digital equity recommendations. Macroaxis instant position ratings are based on combination of fundamental analysis and risk-adjusted market performance | |
| Odds Of Bankruptcy Get analysis of equity chance of financial distress in the next 2 years | |
| Companies Directory Evaluate performance of over 100,000 Stocks, Funds, and ETFs against different fundamentals | |
| Portfolio Comparator Compare the composition, asset allocations and performance of any two portfolios in your account | |
| Equity Valuation Check real value of public entities based on technical and fundamental data |