MongoDB Stock Market Value

MDB Stock  USD 224.01  15.78  7.58%   
MongoDB's market value is the price at which a share of MongoDB stock trades on a public exchange. It measures the collective expectations of MongoDB investors about the entity's future performance. With this module, you can estimate the performance of a buy and hold strategy of MongoDB and determine expected loss or profit from investing in MongoDB over a given investment horizon. Additionally, see MongoDB Correlation, MongoDB Volatility and MongoDB Alpha and Beta module to complement your research on MongoDB.
Symbol


Is MongoDB'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 MongoDB. If investors know MongoDB 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 MongoDB listed above have to be considered, but the key to understanding future value is determining which factors weigh more heavily than others.
Market Capitalization
15.5 B
Quarterly Revenue Growth
0.47
Return On Assets
(0.09) 
Return On Equity
(0.54) 
The market value of MongoDB is measured differently than its book value, which is the value of MongoDB that is recorded on the company's balance sheet. Investors also form their own opinion of MongoDB's value that differs from its market value or its book value, called intrinsic value, which is MongoDB'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 MongoDB's market value can be influenced by many factors that don't directly affect MongoDB'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 MongoDB's value and its price as these two are different measures arrived at by different means. Investors typically determine MongoDB value by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, MongoDB'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.

MongoDB '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 MongoDB'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 MongoDB.
0.00
12/29/2022
No Change 0.00  0.0 
In 31 days
01/28/2023
0.00
If you would invest  0.00  in MongoDB on December 29, 2022 and sell it all today you would earn a total of 0.00 from holding MongoDB or generate 0.0% return on investment in MongoDB over 30 days. MongoDB is related to or competes with ACI Worldwide, Adobe Systems, Allot Communications, Altair Engineering, Appian Corp, A10 Network, and BlackBerry. MongoDB, Inc. provides general purpose database platform worldwide More

MongoDB 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 MongoDB'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 MongoDB upside and downside potential and time the market with a certain degree of confidence.

MongoDB Market Risk Indicators

Today, many novice investors tend to focus exclusively on investment returns with little concern for MongoDB's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as MongoDB's standard deviation. In reality, there are many statistical measures that can use MongoDB historical prices to predict the future MongoDB's volatility.
Sophisticated investors, who have witnessed many market ups and downs, frequently view the market will even out over time. This tendency of MongoDB'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 MongoDB in the context of predictive analytics.
Hype
Prediction
LowEstimated ValueHigh
220.38226.18231.98
Details
Intrinsic
Valuation
LowReal ValueHigh
201.61295.96301.76
Details
12 Analysts
Consensus
LowTarget PriceHigh
450.00565.40660.00
Details
Please note, it is not enough to conduct a financial or market analysis of a single entity such as MongoDB. Your research has to be compared to or analyzed against MongoDB's peers to derive any actionable benefits. When done correctly, MongoDB'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 MongoDB.

MongoDB Backtested Returns

MongoDB appears to be very steady, given 3 months investment horizon. MongoDB has Sharpe Ratio of 0.0844, which conveys that the firm had 0.0844% 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-one technical indicators for MongoDB, which you can use to evaluate the future volatility of the firm. Please exercise MongoDB's Mean Deviation of 4.29, risk adjusted performance of 0.0994, and Downside Deviation of 4.12 to check out if our risk estimates are consistent with your expectations.
On a scale of 0 to 100, MongoDB holds a performance score of 6. The company secures a Beta (Market Risk) of 2.5734, which conveys a somewhat significant risk relative to the market. Let's try to break down what MongoDB's beta means in this case. As the market goes up, the company is expected to outperform it. However, if the market returns are negative, MongoDB will likely underperform. Although it is vital to follow MongoDB price patterns, it is good to be conservative about what you can do with the information regarding equity 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 MongoDB technical indicators, you can presently evaluate if the expected return of 0.49% will be sustainable into the future. Please exercises MongoDB value at risk, and the relationship between the jensen alpha and semi variance to make a quick decision on whether MongoDB current price movements will revert.

Auto-correlation

    
  0.08  

Virtually no predictability

MongoDB has virtually no predictability. Overlapping area represents the amount of predictability between MongoDB time series from 29th of December 2022 to 13th of January 2023 and 13th of January 2023 to 28th of January 2023. The more autocorrelation exist between current time interval and its lagged values, the more accurately you can make projection about the future pattern of MongoDB price movement. The serial correlation of 0.08 indicates that barely 8.0% of current MongoDB price fluctuation can be explain by its past prices.
Correlation Coefficient0.08
Spearman Rank Test-0.85
Residual Average0.0
Price Variance79.98

MongoDB lagged returns against current returns

Autocorrelation, which is MongoDB 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 MongoDB's stock expected returns. We can calculate the autocorrelation of MongoDB returns to help us make a trade decision. For example, suppose you find that MongoDB 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   
       Timeline  

MongoDB 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 MongoDB stock is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if MongoDB stock is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in MongoDB stock over time.
   Current vs Lagged Prices   
       Timeline  

MongoDB Lagged Returns

When evaluating MongoDB's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of MongoDB stock have on its future price. MongoDB 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, MongoDB autocorrelation shows the relationship between MongoDB stock current value and its past values and can show if there is a momentum factor associated with investing in MongoDB.
   Regressed Prices   
       Timeline  

MongoDB Investors Sentiment

The influence of MongoDB's investor sentiment on the probability of its price appreciation or decline could be a good factor in your decision-making process regarding taking a position in MongoDB. The overall investor sentiment generally increases the direction of a stock movement in a one-year investment horizon. However, the impact of investor sentiment on the entire stock markets does not have a solid backing from leading economists and market statisticians.
Investor biases related to MongoDB's public news can be used to forecast risks associated with investment in MongoDB. The trend in average sentiment can be used to explain how an investor holding MongoDB can time the market purely based on public headlines and social activities around MongoDB. Please note that most equiteis that are difficult to arbitrage are affected by market sentiment the most.
MongoDB's market sentiment shows the aggregated news analyzed to detect positive and negative mentions from the text and comments. The data is normalized to provide daily scores for MongoDB's and other traded tickers. The bigger the bubble, the more accurate is the estimated score. Higher bars for a given day show more participation in the average MongoDB's news discussions. The higher the estimated score, the more favorable is the investor's outlook on MongoDB.

MongoDB Implied Volatility

    
  73.2  
MongoDB's implied volatility exposes the market's sentiment of MongoDB stock's possible movements over time. However, it does not forecast the overall direction of its price. In a nutshell, if MongoDB's implied volatility is high, the market thinks the stock has potential for high price swings in either direction. On the other hand, the low implied volatility suggests that MongoDB stock will not fluctuate a lot when MongoDB's options are near their expiration.
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 MongoDB 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, MongoDB's short interest history, or implied volatility extrapolated from MongoDB options trading.

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Macroaxis puts the power of mathematics on your side. We analyze your portfolios and positions such as MongoDB using complex mathematical models and algorithms, but make them easy to understand. There is no real person involved in your portfolio analysis. We perform a number of calculations to compute absolute and relative portfolio volatility, correlation between your assets, value at risk, expected return as well as over 100 different fundamental and technical indicators.

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Additionally, see MongoDB Correlation, MongoDB Volatility and MongoDB Alpha and Beta module to complement your research on MongoDB. Note that the MongoDB information on this page should be used as a complementary analysis to other MongoDB's statistical models used to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try Correlation Analysis module to reduce portfolio risk simply by holding instruments which are not perfectly correlated.

Complementary Tools for MongoDB Stock analysis

When running MongoDB price analysis, check to measure MongoDB'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 MongoDB is operating at the current time. Most of MongoDB's value examination focuses on studying past and present price action to predict the probability of MongoDB's future price movements. You can analyze the entity against its peers and financial market as a whole to determine factors that move MongoDB's price. Additionally, you may evaluate how the addition of MongoDB to your portfolios can decrease your overall portfolio volatility.
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MongoDB 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 MongoDB technical analysis is to determine if market prices reflect all relevant information impacting that market. A technical analyst looks at the history of MongoDB 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...