SOFI TECHNOLOGIES Stock Forecast - Polynomial Regression
| 6B0 Stock | 15.64 0.17 1.10% |
The Polynomial Regression forecasted value of SOFI TECHNOLOGIES on the next trading day is expected to be 16.35 with a mean absolute deviation of 0.36 and the sum of the absolute errors of 21.72. SOFI Stock Forecast is based on your current time horizon. We recommend always using this module together with an analysis of SOFI TECHNOLOGIES's historical fundamentals, such as revenue growth or operating cash flow patterns.
SOFI |
SOFI TECHNOLOGIES Polynomial Regression Price Forecast For the 3rd of December
Given 90 days horizon, the Polynomial Regression forecasted value of SOFI TECHNOLOGIES on the next trading day is expected to be 16.35 with a mean absolute deviation of 0.36, mean absolute percentage error of 0.18, and the sum of the absolute errors of 21.72.Please note that although there have been many attempts to predict SOFI 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 SOFI TECHNOLOGIES's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
SOFI TECHNOLOGIES Stock Forecast Pattern
| Backtest SOFI TECHNOLOGIES | SOFI TECHNOLOGIES Price Prediction | Buy or Sell Advice |
SOFI TECHNOLOGIES Forecasted Value
In the context of forecasting SOFI TECHNOLOGIES'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. SOFI TECHNOLOGIES's downside and upside margins for the forecasting period are 12.58 and 20.13, respectively. We have considered SOFI TECHNOLOGIES'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 Polynomial Regression forecasting method's relative quality and the estimations of the prediction error of SOFI TECHNOLOGIES stock data series using in forecasting. Note that when a statistical model is used to represent SOFI TECHNOLOGIES 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 | 116.3985 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 0.356 |
| MAPE | Mean absolute percentage error | 0.0384 |
| SAE | Sum of the absolute errors | 21.7166 |
Predictive Modules for SOFI TECHNOLOGIES
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as SOFI TECHNOLOGIES. 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.Other Forecasting Options for SOFI TECHNOLOGIES
For every potential investor in SOFI, whether a beginner or expert, SOFI TECHNOLOGIES's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. SOFI Stock price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in SOFI. Basic forecasting techniques help filter out the noise by identifying SOFI TECHNOLOGIES's price trends.SOFI TECHNOLOGIES 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 SOFI TECHNOLOGIES stock to make a market-neutral strategy. Peer analysis of SOFI TECHNOLOGIES could also be used in its relative valuation, which is a method of valuing SOFI TECHNOLOGIES by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
SOFI TECHNOLOGIES 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 SOFI TECHNOLOGIES'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 SOFI TECHNOLOGIES's current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
SOFI TECHNOLOGIES Market Strength Events
Market strength indicators help investors to evaluate how SOFI TECHNOLOGIES stock reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading SOFI TECHNOLOGIES shares will generate the highest return on investment. By undertsting and applying SOFI TECHNOLOGIES stock market strength indicators, traders can identify SOFI TECHNOLOGIES entry and exit signals to maximize returns.
SOFI TECHNOLOGIES Risk Indicators
The analysis of SOFI TECHNOLOGIES'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 SOFI TECHNOLOGIES's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting sofi 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.
| Mean Deviation | 2.63 | |||
| Semi Deviation | 2.05 | |||
| Standard Deviation | 3.75 | |||
| Variance | 14.08 | |||
| Downside Variance | 6.82 | |||
| Semi Variance | 4.21 | |||
| Expected Short fall | (3.44) |
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.
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Additional Information and Resources on Investing in SOFI Stock
When determining whether SOFI TECHNOLOGIES offers a strong return on investment in its stock, a comprehensive analysis is essential. The process typically begins with a thorough review of SOFI TECHNOLOGIES's financial statements, including income statements, balance sheets, and cash flow statements, to assess its financial health. Key financial ratios are used to gauge profitability, efficiency, and growth potential of Sofi Technologies Stock. Outlined below are crucial reports that will aid in making a well-informed decision on Sofi Technologies Stock:Check out Historical Fundamental Analysis of SOFI TECHNOLOGIES to cross-verify your projections. For more detail on how to invest in SOFI Stock please use our How to Invest in SOFI TECHNOLOGIES guide.You can also try the Risk-Return Analysis module to view associations between returns expected from investment and the risk you assume.