MongoDB Piotroski F Score and MongoDB Altman Z Score analysis. For information on how to trade MongoDB Stock refer to our How to Trade MongoDB Stock guide. MongoDB Long Term Debt to Equity is projected to increase slightly based on the last few years of reporting. The past year's Long Term Debt to Equity was at 1.54. The current year's Debt to Equity Ratio is expected to grow to 2.70, whereas Total Debt is forecasted to decline to about 1.1 B. MongoDB Long Term Debt to Equity is projected to increase slightly based on the last few years of reporting. The past year's Long Term Debt to Equity was at 1.54. The current year's PPandE Turnover is expected to grow to 23.00, whereas Receivables Turnover is forecasted to decline to 4.82.This module uses fundamental data of MongoDB to approximate the value of its Beneish M Score. MongoDB M Score tells investors if the company management is likely to be manipulating earnings. The score is calculated using eight financial indicators that are adjusted by a specific multiplier. Please note, the M Score is a probabilistic model and cannot detect companies that manipulate their earnings with 100% accuracy. Check out
At this time, it appears that MongoDB is an unlikely manipulator. The earnings manipulation may begin if MongoDB's top management creates an artificial sense of financial success, forcing the stock price to be traded at a high price-earnings multiple than it should be. In general, excessive earnings management by MongoDB executives may lead to removing some of the operating profits from subsequent periods to inflate earnings in the following periods. This way, the manipulation of MongoDB's earnings can lead to misrepresentations of actual financial condition, taking the otherwise loyal stakeholders on to the path of questionable ethical practices and plain fraud.
MongoDB Beneish M-Score Indicator Trends
The cure to earnings manipulation is the transparency of financial reporting. It will typically remove the temptation of the top executives to inflate earnings (i.e., to promote the idea of 'winning at any cost'). Because a healthy internal audit department can enhance transparency, the board should promote the auditors' access to all the record-keeping systems across the enterprise. For example, if MongoDB's auditors report directly to the board (not management), the managers will be reluctant to manipulate simply due to the fear of punishment. On the other hand, the auditors will be free to investigate the ledgers properly because they know that the board has their back.
MongoDB Beneish M-Score Driver Matrix
One of the toughest challenges investors face today is learning how to quickly synthesize historical financial statements and information provided by the company, SEC reporting, and various external parties in order to detect the potential manipulation of earnings. Understanding the correlation between MongoDB's different financial indicators related to revenue, expenses, operating profit, and net earnings helps investors identify and prioritize their investing strategies towards MongoDB in a much-optimized way. Analyzing correlations between earnings drivers directly associated with dollar figures is the most effective way to find MongoDB's degree of accounting gimmicks and manipulations.
About MongoDB Beneish M Score
M-Score is one of many grading techniques for value stocks. It was developed by Professor M. Daniel Beneish of the Kelley School of Business at Indiana University and published in 1999 under the paper titled The Detection of Earnings Manipulation. The Beneish score is a multi-factor model that utilizes financial identifiers to compile eight variables used to classify whether a company has manipulated its reported earnings. The variables are built from the officially filed financial statements to create a final score call 'M Score.' The score helps to identify companies that are likely to manipulate their profits if they show deteriorating gross margins, operating expenses, and leverage against growing revenue.
MongoDB Earnings Manipulation Drivers
Although earnings manipulation is typically not the result of intentional misconduct by the c-level executives, it is still a widespread practice by the senior management of public companies such as MongoDB. It is usually done by a series of misrepresentations of various accounting rules and operating activities across multiple financial cycles. The best way to spot the manipulation is to examine the historical financial statement to find inconsistencies in earning reports to find trends in assets or liabilities that are not sustainable in the future.
About MongoDB Fundamental Analysis
The Macroaxis Fundamental Analysis modules help investors analyze MongoDB's financials across various querterly and yearly statements, indicators and fundamental ratios. We help investors to determine the real value of MongoDB using virtually all public information available. We use both quantitative as well as qualitative analysis to arrive at the intrinsic value of MongoDB based on its fundamental data. In general, a quantitative approach, as applied to this company, focuses on analyzing financial statements comparatively, whereas a qaualitative method uses data that is important to a company's growth but cannot be measured and presented in a numerical way.Please read more on our fundamental analysis page.MongoDB, Inc. provides general purpose database platform worldwide. MongoDB, Inc. was incorporated in 2007 and is headquartered in New York, New York. Mongodb Inc operates under SoftwareInfrastructure classification in the United States and is traded on NASDAQ Exchange. It employs 4240 people.
Be your own money managerOur tools can tell you how much better you can do entering a position in MongoDB 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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Check out MongoDB Piotroski F Score and MongoDB Altman Z Score analysis. 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 Risk-Return Analysis module to view associations between returns expected from investment and the risk you assume.
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.
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.
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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.