T Rowe Mutual Fund Forecast - Naive Prediction

TRPNXDelisted Fund  USD 16.52  0.00  0.00%   
The Naive Prediction forecasted value of T Rowe Price on the next trading day is expected to be 16.73 with a mean absolute deviation of 0.10 and the sum of the absolute errors of 6.38. TRPNX Mutual Fund Forecast is based on your current time horizon.
  
A naive forecasting model for T Rowe is a special case of the moving average forecasting where the number of periods used for smoothing is one. Therefore, the forecast of T Rowe Price 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.

T Rowe Naive Prediction Price Forecast For the 29th of November

Given 90 days horizon, the Naive Prediction forecasted value of T Rowe Price on the next trading day is expected to be 16.73 with a mean absolute deviation of 0.10, mean absolute percentage error of 0.02, and the sum of the absolute errors of 6.38.
Please note that although there have been many attempts to predict TRPNX Mutual Fund 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 T Rowe's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

T Rowe Mutual Fund Forecast Pattern

Backtest T RoweT Rowe Price PredictionBuy or Sell Advice 

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 T Rowe mutual fund data series using in forecasting. Note that when a statistical model is used to represent T Rowe mutual fund, 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 Criteria113.9651
BiasArithmetic mean of the errors None
MADMean absolute deviation0.1047
MAPEMean absolute percentage error0.0066
SAESum of the absolute errors6.3844
This model is not at all useful as a medium-long range forecasting tool of T Rowe Price. 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 T Rowe. 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 T Rowe

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as T Rowe Price. Regardless of method or technology, however, to accurately forecast the mutual fund market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the mutual fund 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.
Hype
Prediction
LowEstimatedHigh
16.5216.5216.52
Details
Intrinsic
Valuation
LowRealHigh
15.1415.1418.17
Details

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

T Rowe Market Strength Events

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

Also Currently Popular

Analyzing currently trending equities could be an opportunity to develop a better portfolio based on different market momentums that they can trigger. Utilizing the top trending stocks is also useful when creating a market-neutral strategy or pair trading technique involving a short or a long position in a currently trending equity.
Check out World Market Map to better understand how to build diversified portfolios. Also, note that the market value of any mutual fund could be closely tied with the direction of predictive economic indicators such as signals in board of governors.
You can also try the Stock Tickers module to use high-impact, comprehensive, and customizable stock tickers that can be easily integrated to any websites.

Other Consideration for investing in TRPNX Mutual Fund

If you are still planning to invest in T Rowe Price check if it may still be traded through OTC markets such as Pink Sheets or OTC Bulletin Board. You may also purchase it directly from the company, but this is not always possible and may require contacting the company directly. Please note that delisted stocks are often considered to be more risky investments, as they are no longer subject to the same regulatory and reporting requirements as listed stocks. Therefore, it is essential to carefully research the T Rowe's history and understand the potential risks before investing.
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