Oppenheimer Rochester Mutual Fund Forecast - Naive Prediction
| OPCAX Fund | USD 8.12 0.01 0.12% |
The Naive Prediction forecasted value of Oppenheimer Rochester Ca on the next trading day is expected to be 8.21 with a mean absolute deviation of 0.02 and the sum of the absolute errors of 1.47. Oppenheimer Mutual Fund Forecast is based on your current time horizon.
Oppenheimer |
Most investors in Oppenheimer Rochester cannot accurately predict what will happen the next trading day because, historically, fund 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 Oppenheimer Rochester's time series price data and predict how it will affect future prices. One of these methodologies is forecasting, which interprets Oppenheimer Rochester's price structures and extracts relationships that further increase the generated results' accuracy.
A naive forecasting model for Oppenheimer Rochester is a special case of the moving average forecasting where the number of periods used for smoothing is one. Therefore, the forecast of Oppenheimer Rochester Ca 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. Oppenheimer Rochester Naive Prediction Price Forecast For the 21st of June
Given 90 days horizon, the Naive Prediction forecasted value of Oppenheimer Rochester Ca on the next trading day is expected to be 8.21 with a mean absolute deviation of 0.02, mean absolute percentage error of 0.0009, and the sum of the absolute errors of 1.47.Please note that although there have been many attempts to predict Oppenheimer 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 Oppenheimer Rochester's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Oppenheimer Rochester Mutual Fund Forecast Pattern
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Oppenheimer Rochester Forecasted Value
In the context of forecasting Oppenheimer Rochester's Mutual Fund 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. Oppenheimer Rochester's downside and upside margins for the forecasting period are 7.98 and 8.45, respectively. We have considered Oppenheimer Rochester'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 Naive Prediction forecasting method's relative quality and the estimations of the prediction error of Oppenheimer Rochester mutual fund data series using in forecasting. Note that when a statistical model is used to represent Oppenheimer Rochester 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.| AIC | Akaike Information Criteria | 111.0862 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 0.0242 |
| MAPE | Mean absolute percentage error | 0.003 |
| SAE | Sum of the absolute errors | 1.4735 |
Predictive Modules for Oppenheimer Rochester
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Oppenheimer Rochester. 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.Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Oppenheimer Rochester'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.
Other Forecasting Options for Oppenheimer Rochester
For every potential investor in Oppenheimer, whether a beginner or expert, Oppenheimer Rochester's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Oppenheimer Mutual Fund price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Oppenheimer. Basic forecasting techniques help filter out the noise by identifying Oppenheimer Rochester's price trends.Oppenheimer Rochester 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 Oppenheimer Rochester mutual fund to make a market-neutral strategy. Peer analysis of Oppenheimer Rochester could also be used in its relative valuation, which is a method of valuing Oppenheimer Rochester by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
Oppenheimer Rochester Technical and Predictive Analytics
The mutual fund market is financially volatile. Despite the volatility, there exist limitless possibilities of gaining profits and building passive income portfolios. With the complexity of Oppenheimer Rochester'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 Oppenheimer Rochester's current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
Oppenheimer Rochester Market Strength Events
Market strength indicators help investors to evaluate how Oppenheimer Rochester 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 Oppenheimer Rochester shares will generate the highest return on investment. By undertsting and applying Oppenheimer Rochester mutual fund market strength indicators, traders can identify Oppenheimer Rochester Ca entry and exit signals to maximize returns.
| Daily Balance Of Power | 9.2 T | |||
| Rate Of Daily Change | 1.0 | |||
| Day Median Price | 8.12 | |||
| Day Typical Price | 8.12 | |||
| Price Action Indicator | 0.005 | |||
| Period Momentum Indicator | 0.01 | |||
| Relative Strength Index | 61.6 |
Oppenheimer Rochester Risk Indicators
The analysis of Oppenheimer Rochester'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 Oppenheimer Rochester's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting oppenheimer mutual fund 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.
| Mean Deviation | 0.1682 | |||
| Semi Deviation | 0.1529 | |||
| Standard Deviation | 0.2327 | |||
| Variance | 0.0541 | |||
| Downside Variance | 0.0661 | |||
| Semi Variance | 0.0234 | |||
| Expected Short fall | (0.24) |
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.
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.| IMX | Immutable X | |
| PYTH | Pyth Network | |
| JTO | Jito |
Other Information on Investing in Oppenheimer Mutual Fund
Oppenheimer Rochester financial ratios help investors to determine whether Oppenheimer Mutual Fund is cheap or expensive when compared to a particular measure, such as profits or enterprise value. In other words, they help investors to determine the cost of investment in Oppenheimer with respect to the benefits of owning Oppenheimer Rochester security.
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