Mongodb Stock Market Value
| MDB Stock | USD 353.47 1.22 0.34% |
| Symbol | MongoDB |
MongoDB Price To Book Ratio
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.
Earnings Share (2.47) | Revenue Per Share 23.622 | Quarterly Revenue Growth 0.268 | Return On Assets (0.05) | Return On Equity (0.20) |
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 if MongoDB is a good investment 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.
| 02/12/2024 |
| 05/12/2024 |
If you would invest 0.00 in MongoDB on February 12, 2024 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 90 days. MongoDB is related to or competes with Datasea, Priority Technology, Cemtrex, Hub Cyber, and VirnetX Holding. 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.
| Information Ratio | (0.16) | |||
| Maximum Drawdown | 13.17 | |||
| Value At Risk | (4.29) | |||
| Potential Upside | 5.26 |
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.| Risk Adjusted Performance | (0.07) | |||
| Jensen Alpha | (0.50) | |||
| Total Risk Alpha | (0.69) | |||
| Treynor Ratio | (0.19) |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that 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.
MongoDB Backtested Returns
MongoDB has Sharpe Ratio of -0.18, which conveys that the firm had a -0.18% return per unit of risk over the last 3 months. MongoDB exposes twenty-three different technical indicators, which can help you to evaluate volatility embedded in its price movement. Please verify MongoDB's Standard Deviation of 2.77, risk adjusted performance of (0.07), and Mean Deviation of 2.03 to check out the risk estimate we provide. The company secures a Beta (Market Risk) of 1.88, which conveys a somewhat significant risk relative to the market. As the market goes up, the company is expected to outperform it. However, if the market returns are negative, MongoDB will likely underperform. MongoDB has an expected return of -0.47%. Please make sure to verify MongoDB kurtosis, and the relationship between the value at risk and rate of daily change , to decide if MongoDB performance from the past will be repeated at some point in the near future.
Auto-correlation | -0.45 |
Modest reverse predictability
MongoDB has modest reverse predictability. Overlapping area represents the amount of predictability between MongoDB time series from 12th of February 2024 to 28th of March 2024 and 28th of March 2024 to 12th of May 2024. 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 -0.45 indicates that just about 45.0% of current MongoDB price fluctuation can be explain by its past prices.
| Correlation Coefficient | -0.45 | |
| Spearman Rank Test | -0.27 | |
| Residual Average | 0.0 | |
| Price Variance | 138.59 |
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 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 price 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 market does not have solid backing from leading economists and market statisticians.
Investor biases related to MongoDB's public news can be used to forecast risks associated with an 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 equities 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 | 51.92 |
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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Try AI Portfolio ArchitectCheck out MongoDB Correlation, MongoDB Volatility and MongoDB Alpha and Beta module to complement your research on MongoDB. For information on how to trade MongoDB Stock refer to our How to Trade MongoDB Stock guide.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 the Theme Ratings module to determine theme ratings based on digital equity recommendations. Macroaxis theme ratings are based on combination of fundamental analysis and risk-adjusted market performance.
Complementary Tools for MongoDB Stock analysis
When running MongoDB's 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 the 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.