# MongoDB Stock Forecast - Double Exponential Smoothing

MDB Stock | USD 224.01 15.78 7.58% |

MongoDB Stock Forecast is based on your current time horizon. Investors can use this forecasting interface to forecast MongoDB historical stock prices and determine the direction of MongoDB's future trends based on various well-known forecasting models. However, solely looking at the historical price movement is usually misleading. Macroaxis recommends to always use this module together with analysis of MongoDB historical fundamentals such as revenue growth or operating cash flow patterns. Although MongoDB naive historical forecasting may sometimes provide an important future outlook for the firm we recommend to always cross-verify it against solid analysis of MongoDB systematic risk associated with finding meaningful patterns of MongoDB fundamentals over time.

Additionally, see Historical Fundamental Analysis of MongoDB to cross-verify your projections. MongoDB |

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### Open Interest Agains t 2023-02-03 MongoDB Option Contracts

Although open interest is a measure utilized in the options markets, it could be used to forecast MongoDB's spot prices because the number of available contracts in the market changes daily, and new contracts can be created or liquidated at will. Since open interest MongoDB's options reflect these daily shifts, investors could use the patterns of these changes to develop long and short trading strategies MongoDB stock based on available contracts left at the end of a trading day.

Please note, to derive more accurate forecasting about market movement from the current MongoDB's open interest, investors have to compare it to MongoDB's spot prices. As Ford's stock price increases, high open interest indicates that money is entering the market, and the market is strongly bullish. Conversely, if the price of MongoDB is decreasing and there is high open interest, that is a sign that the bearish trend will continue, and investors may react by taking short positions in MongoDB. So, decreasing or low open interest during a bull market indicates that investors are becoming uncertain of the depth of the bullish trend, and a reversal in sentiment will likely follow.

Most investors in MongoDB cannot accurately predict what will happen the next trading day because, historically, stock markets tend to be unpredictable and even illogical. Modeling turbulent structures requires applying different statistical methods, techniques, and algorithms to find hidden data structures or patterns within the MongoDB's time series price data and predict how it will affect future prices. One of these methodologies is forecasting, which interprets MongoDB's price structures and extracts relationships that further increase the generated results' accuracy.

Double exponential smoothing - also known as Holt exponential smoothing is a refinement of the popular simple exponential smoothing model with an additional trending component. Double exponential smoothing model for MongoDB works best with periods where there are trends or seasonality. ## MongoDB Double Exponential Smoothing Price Forecast For the 29th of January

Given 90 days horizon, the Double Exponential Smoothing forecasted value of MongoDB on the next trading day is expected to be 222.43 with a mean absolute deviation of 7.34, mean absolute percentage error of 95.80, and the sum of the absolute errors of 440.50.Please note that although there have been many attempts to predict MongoDB Stock prices using its time series forecasting, we generally do not recommend using it to place bets in the real market. The most commonly used models for forecasting predictions are the autoregressive models, which specify that MongoDB's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

## MongoDB Stock Forecast Pattern

Backtest MongoDB | MongoDB Price Prediction | Buy or Sell Advice |

## MongoDB Forecasted Value

In the context of forecasting MongoDB's Stock value on the next trading day, we examine the predictive performance of the model to find good statistically significant boundaries of downside and upside scenarios. MongoDB's downside and upside margins for the forecasting period are 216.63 and 228.23, respectively. We have considered MongoDB's daily market price to evaluate the above model's predictive performance. Remember, however, there is no scientific proof or empirical evidence that traditional linear or nonlinear forecasting models outperform artificial intelligence and frequency domain models to provide accurate forecasts consistently.

## Model Predictive Factors

The below table displays some essential indicators generated by the model showing the Double Exponential Smoothing forecasting method's relative quality and the estimations of the prediction error of MongoDB stock data series using in forecasting. Note that when a statistical model is used to represent MongoDB stock, the representation will rarely be exact; so some information will be lost using the model to explain the process. AIC estimates the relative amount of information lost by a given model: the less information a model loses, the higher its quality.AIC | Akaike Information Criteria | Huge |

Bias | Arithmetic mean of the errors | -2.2893 |

MAD | Mean absolute deviation | 7.3417 |

MAPE | Mean absolute percentage error | 0.0412 |

SAE | Sum of the absolute errors | 440.5 |

## Predictive Modules for MongoDB

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as MongoDB. Regardless of method or technology, however, to accurately forecast the stock or bond market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the stock market accurately is still an essential part of the overall investment decision process. Using different forecasting techniques and comparing the results might improve your chances of accuracy even though unexpected events may often change the market sentiment and impact your forecasting results.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.

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.

## Other Forecasting Options for MongoDB

For every potential investor in MongoDB, whether a beginner or expert, MongoDB's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. MongoDB Stock price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in MongoDB. Basic forecasting techniques help filter out the noise by identifying MongoDB's price trends.## MongoDB Related Equities

One of the popular trading techniques among algorithmic traders is to use market-neutral strategies where every trade hedges away some risk. Because there are two separate transactions required, even if one position performs unexpectedly, the other equity can make up some of the losses. Below are some of the equities that can be combined with MongoDB stock to make a market-neutral strategy. Peer analysis of MongoDB could also be used in its relative valuation, which is a method of valuing MongoDB by comparing valuation metrics with similar companies.

Risk & Return | Correlation |

## MongoDB Technical and Predictive Analytics

The stock market is financially volatile. Despite the volatility, there exist limitless possibilities of gaining profits and building passive income portfolios. With the complexity of MongoDB's price movements, , a comprehensive understanding of forecasting methods that an investor can rely on to make the right move is invaluable. These methods predict trends that assist an investor in predicting the movement of MongoDB's current price.Cycle Indicators | ||

Math Operators | ||

Math Transform | ||

Momentum Indicators | ||

Overlap Studies | ||

Pattern Recognition | ||

Price Transform | ||

Statistic Functions | ||

Volatility Indicators | ||

Volume Indicators |

## MongoDB Risk Indicators

The analysis of MongoDB's basic risk indicators is one of the essential steps in helping accuretelly forecast its future price. The process involves identifying the amount of risk involved in MongoDB's investment and either accepting that risk or mitigating it. Along with some funamental techniques of forecasting MongoDB stock price, 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 | 4.29 | |||

Semi Deviation | 3.95 | |||

Standard Deviation | 5.76 | |||

Variance | 33.17 | |||

Downside Variance | 16.96 | |||

Semi Variance | 15.6 | |||

Expected Short fall | (5.70) |

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

## 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.

## Becoming a Better Investor with Macroaxis

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.## Build Optimal Portfolios

### Align your risk with return expectations

Additionally, see Historical Fundamental Analysis of MongoDB to cross-verify your projections. 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 My Watchlist Analysis module to analyze my current watchlist and to refresh optimization strategy. Macroaxis watchlist is based on self-learning algorithm to remember stocks you like.

## 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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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 Capitalization15.5 B | Quarterly Revenue Growth0.47 | 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.