New York Stock Forecast - Simple Regression

NYMT Stock  USD 6.27  0.06  0.95%   
The Simple Regression forecasted value of New York Mortgage on the next trading day is expected to be 6.45 with a mean absolute deviation of  0.24  and the sum of the absolute errors of 14.52. New Stock Forecast is based on your current time horizon. Investors can use this forecasting interface to forecast New York stock prices and determine the direction of New York Mortgage's future trends based on various well-known forecasting models. We recommend always using this module together with an analysis of New York's historical fundamentals, such as revenue growth or operating cash flow patterns. Although New York's naive historical forecasting may sometimes provide an important future outlook for the firm, we recommend always cross-verifying it against solid analysis of New York's systematic risk associated with finding meaningful patterns of New York fundamentals over time.
Check out Historical Fundamental Analysis of New York to cross-verify your projections.
  
At this time, New York's Receivables Turnover is comparatively stable compared to the past year. Fixed Asset Turnover is likely to gain to 62.32 in 2024, despite the fact that Inventory Turnover is likely to grow to (33.13). . Common Stock Shares Outstanding is likely to gain to about 95.6 M in 2024, despite the fact that Net Loss is likely to grow to (291.2 M).

Open Interest Against 2024-06-21 New Option Contracts

Although open interest is a measure utilized in the options markets, it could be used to forecast New York'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 in New York's options reflects these daily shifts, investors could use the patterns of these changes to develop long and short-term trading strategies for New York stock based on available contracts left at the end of a trading day.
Please note that to derive more accurate forecasting about market movement from the current New York's open interest, investors have to compare it to New York'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 New York 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 New. 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 New York 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 New York's time series price data and predict how it will affect future prices. One of these methodologies is forecasting, which interprets New York's price structures and extracts relationships that further increase the generated results' accuracy.
Simple Regression model is a single variable regression model that attempts to put a straight line through New York price points. This line is defined by its gradient or slope, and the point at which it intercepts the x-axis. Mathematically, assuming the independent variable is X and the dependent variable is Y, then this line can be represented as: Y = intercept + slope * X.

New York Simple Regression Price Forecast For the 21st of May

Given 90 days horizon, the Simple Regression forecasted value of New York Mortgage on the next trading day is expected to be 6.45 with a mean absolute deviation of 0.24, mean absolute percentage error of 0.08, and the sum of the absolute errors of 14.52.
Please note that although there have been many attempts to predict New 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 New York's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

New York Stock Forecast Pattern

Backtest New YorkNew York Price PredictionBuy or Sell Advice 

New York Forecasted Value

In the context of forecasting New York'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. New York's downside and upside margins for the forecasting period are 4.32 and 8.58, respectively. We have considered New York'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
6.27
6.45
Expected Value
8.58
Upside

Model Predictive Factors

The below table displays some essential indicators generated by the model showing the Simple Regression forecasting method's relative quality and the estimations of the prediction error of New York stock data series using in forecasting. Note that when a statistical model is used to represent New York 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 Criteria115.5448
BiasArithmetic mean of the errors None
MADMean absolute deviation0.2381
MAPEMean absolute percentage error0.0352
SAESum of the absolute errors14.5216
In general, regression methods applied to historical equity returns or prices series is an area of active research. In recent decades, new methods have been developed for robust regression of price series such as New York Mortgage historical returns. These new methods are regression involving correlated responses such as growth curves and different regression methods accommodating various types of missing data.

Predictive Modules for New York

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as New York Mortgage. 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 New York'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
4.186.318.44
Details
Intrinsic
Valuation
LowRealHigh
5.737.869.99
Details
8 Analysts
Consensus
LowTargetHigh
11.3012.4213.79
Details
Please note, it is not enough to conduct a financial or market analysis of a single entity such as New York. Your research has to be compared to or analyzed against New York's peers to derive any actionable benefits. When done correctly, New York's competitive analysis will give you plenty of quantitative and qualitative data to validate your investment decisions or develop an entirely new strategy toward taking a position in New York Mortgage.

Other Forecasting Options for New York

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

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

New York Mortgage 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 New York'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 New York's current price.

New York Market Strength Events

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

New York Risk Indicators

The analysis of New York'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 New York's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting new 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.

Pair Trading with New York

One of the main advantages of trading using pair correlations is that every trade hedges away some risk. Because there are two separate transactions required, even if New York position performs unexpectedly, the other equity can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in New York will appreciate offsetting losses from the drop in the long position's value.

Moving against New Stock

  0.78TW Tradeweb MarketsPairCorr
  0.73GS Goldman Sachs Group Financial Report 17th of July 2024 PairCorr
  0.72VCXB 10X Capital VenturePairCorr
  0.62MS Morgan Stanley Financial Report 16th of July 2024 PairCorr
  0.62DPCS DP Cap AcquisitionPairCorr
The ability to find closely correlated positions to New York could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace New York when you sell it. If you don't do this, your portfolio allocation will be skewed against your target asset allocation. So, investors can't just sell and buy back New York - that would be a violation of the tax code under the "wash sale" rule, and this is why you need to find a similar enough asset and use the proceeds from selling New York Mortgage to buy it.
The correlation of New York is a statistical measure of how it moves in relation to other instruments. This measure is expressed in what is known as the correlation coefficient, which ranges between -1 and +1. A perfect positive correlation (i.e., a correlation coefficient of +1) implies that as New York moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if New York Mortgage moves in either direction, the perfectly negatively correlated security will move in the opposite direction. If the correlation is 0, the equities are not correlated; they are entirely random. A correlation greater than 0.8 is generally described as strong, whereas a correlation less than 0.5 is generally considered weak.
Correlation analysis and pair trading evaluation for New York can also be used as hedging techniques within a particular sector or industry or even over random equities to generate a better risk-adjusted return on your portfolios.
Pair CorrelationCorrelation Matching
When determining whether New York Mortgage is a good investment, qualitative aspects like company management, corporate governance, and ethical practices play a significant role. A comparison with peer companies also provides context and helps to understand if New Stock is undervalued or overvalued. This multi-faceted approach, blending both quantitative and qualitative analysis, forms a solid foundation for making an informed investment decision about New York Mortgage Stock. Highlighted below are key reports to facilitate an investment decision about New York Mortgage Stock:
Check out Historical Fundamental Analysis of New York to cross-verify your projections.
You can also try the Stock Screener module to find equities using a custom stock filter or screen asymmetry in trading patterns, price, volume, or investment outlook..

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Is New York'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 New York. If investors know New 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 New York listed above have to be considered, but the key to understanding future value is determining which factors weigh more heavily than others.
Quarterly Earnings Growth
(0.65)
Dividend Share
1
Earnings Share
(1.86)
Revenue Per Share
1.846
Quarterly Revenue Growth
(0.59)
The market value of New York Mortgage is measured differently than its book value, which is the value of New that is recorded on the company's balance sheet. Investors also form their own opinion of New York's value that differs from its market value or its book value, called intrinsic value, which is New York'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 New York's market value can be influenced by many factors that don't directly affect New York'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 New York's value and its price as these two are different measures arrived at by different means. Investors typically determine if New York is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, New York'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.