FactSet Research Stock Forecast - Naive Prediction

FDS Stock  USD 477.46  3.58  0.76%   
The Naive Prediction forecasted value of FactSet Research Systems on the next trading day is expected to be 480.40 with a mean absolute deviation of 4.85 and the sum of the absolute errors of 296.09. FactSet Stock Forecast is based on your current time horizon.
  
At this time, FactSet Research's Inventory Turnover is comparatively stable compared to the past year. Payables Turnover is likely to gain to 6.42 in 2024, whereas Receivables Turnover is likely to drop 6.69 in 2024. . Common Stock Shares Outstanding is likely to gain to about 46.9 M in 2024. Net Income Applicable To Common Shares is likely to gain to about 565.3 M in 2024.
Forecasting cash, or other financial indicators, requires analysts to apply different statistical methods, techniques, and algorithms to find hidden patterns within the FactSet Research's financial statements to predict how it will affect future prices.
 
Cash  
First Reported
1996-05-31
Previous Quarter
453.1 M
Current Value
423 M
Quarterly Volatility
172.2 M
 
Dot-com Bubble
 
Housing Crash
 
Credit Downgrade
 
Yuan Drop
 
Covid
A naive forecasting model for FactSet Research is a special case of the moving average forecasting where the number of periods used for smoothing is one. Therefore, the forecast of FactSet Research Systems value for a given trading day is simply the observed value for the previous period. Due to the simplistic nature of the naive forecasting model, it can only be used to forecast up to one period.

FactSet Research Naive Prediction Price Forecast For the 12th of November 2024

Given 90 days horizon, the Naive Prediction forecasted value of FactSet Research Systems on the next trading day is expected to be 480.40 with a mean absolute deviation of 4.85, mean absolute percentage error of 36.18, and the sum of the absolute errors of 296.09.
Please note that although there have been many attempts to predict FactSet 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 FactSet Research's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

FactSet Research Stock Forecast Pattern

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FactSet Research Forecasted Value

In the context of forecasting FactSet Research'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. FactSet Research's downside and upside margins for the forecasting period are 479.27 and 481.52, respectively. We have considered FactSet Research'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.
Market Value
477.46
479.27
Downside
480.40
Expected Value
481.52
Upside

Model Predictive Factors

The below table displays some essential indicators generated by the model showing the Naive Prediction forecasting method's relative quality and the estimations of the prediction error of FactSet Research stock data series using in forecasting. Note that when a statistical model is used to represent FactSet Research 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.
AICAkaike Information Criteria121.6991
BiasArithmetic mean of the errors None
MADMean absolute deviation4.8539
MAPEMean absolute percentage error0.0107
SAESum of the absolute errors296.0908
This model is not at all useful as a medium-long range forecasting tool of FactSet Research Systems. This model is simplistic and is included partly for completeness and partly because of its simplicity. It is unlikely that you'll want to use this model directly to predict FactSet Research. Instead, consider using either the moving average model or the more general weighted moving average model with a higher (i.e., greater than 1) number of periods, and possibly a different set of weights.

Predictive Modules for FactSet Research

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as FactSet Research Systems. Regardless of method or technology, however, to accurately forecast the stock 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, anticipate that the market will even out over time. This tendency of FactSet Research'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.
Hype
Prediction
LowEstimatedHigh
476.42477.54478.66
Details
Intrinsic
Valuation
LowRealHigh
429.71493.96495.08
Details
21 Analysts
Consensus
LowTargetHigh
404.95445.00493.95
Details
Earnings
Estimates (0)
LowProjected EPSHigh
4.074.254.51
Details

Other Forecasting Options for FactSet Research

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

FactSet Research 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 FactSet Research stock to make a market-neutral strategy. Peer analysis of FactSet Research could also be used in its relative valuation, which is a method of valuing FactSet Research by comparing valuation metrics with similar companies.
 Risk & Return  Correlation

FactSet Research Systems 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 FactSet Research'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 FactSet Research's current price.

FactSet Research Market Strength Events

Market strength indicators help investors to evaluate how FactSet Research stock reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading FactSet Research shares will generate the highest return on investment. By undertsting and applying FactSet Research stock market strength indicators, traders can identify FactSet Research Systems entry and exit signals to maximize returns.

FactSet Research Risk Indicators

The analysis of FactSet Research's basic risk indicators is one of the essential steps in accurately forecasting its future price. The process involves identifying the amount of risk involved in FactSet Research's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting factset stock prices, 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.
Please note, the risk measures we provide can be used independently or collectively to perform a risk assessment. When comparing two potential investments, we recommend comparing similar equities with homogenous growth potential and valuation from related markets to determine which investment holds the most risk.

Thematic Opportunities

Explore Investment Opportunities

Build portfolios using Macroaxis predefined set of investing ideas. Many of Macroaxis investing ideas can easily outperform a given market. Ideas can also be optimized per your risk profile before portfolio origination is invoked. Macroaxis thematic optimization helps investors identify companies most likely to benefit from changes or shifts in various micro-economic or local macro-level trends. Originating optimal thematic portfolios involves aligning investors' personal views, ideas, and beliefs with their actual investments.
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Additional Tools for FactSet Stock Analysis

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