Goldman Sachs Etf Forecast - Polynomial Regression

AAAU Etf

USD 19.60 0.22 1.11%

Goldman Etf Forecast is based on your current time horizon. Investors can use this forecasting interface to forecast Goldman Sachs historical stock prices and determine the direction of Goldman Sachs Physical's future trends based on various well-known forecasting models. However, solely looking at the historical price movement is usually misleading. Macroaxis recommends to always use this module together with analysis of Goldman Sachs historical fundamentals such as revenue growth or operating cash flow patterns.

Check out Historical Fundamental Analysis of Goldman Sachs to cross-verify your projections. For more information on how to buy Goldman Etf please use our How to Invest in Goldman Sachs guide.

Most investors in Goldman Sachs 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 Goldman Sachs' time series price data and predict how it will affect future prices. One of these methodologies is forecasting, which interprets Goldman Sachs' price structures and extracts relationships that further increase the generated results' accuracy.

Goldman Sachs polinomial regression implements a single variable polynomial regression model using the daily prices as the independent variable. The coefficients of the regression for Goldman Sachs Physical as well as the accuracy indicators are determined from the period prices.

More About Polynomial Regression All Equity Analysis

Goldman Sachs Polynomial Regression Price Forecast For the 27th of March

Given 90 days horizon, the Polynomial Regression forecasted value of Goldman Sachs Physical on the next trading day is expected to be 20.47 with a mean absolute deviation of 0.17, mean absolute percentage error of 0.0467, and the sum of the absolute errors of 10.34.

Please note that although there have been many attempts to predict Goldman Etf 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 Goldman Sachs' next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Goldman Sachs Etf Forecast Pattern

Backtest Goldman Sachs

Goldman Sachs Price Prediction

Buy or Sell Advice

Goldman Sachs Forecasted Value

In the context of forecasting Goldman Sachs' Etf 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. Goldman Sachs' downside and upside margins for the forecasting period are 19.45 and 21.48, respectively. We have considered Goldman Sachs' 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 19.60

19.45

Downside

20.47

Expected Value

Target Odds

Above Odds

Below Odds

21.48

Upside

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 Goldman Sachs etf data series using in forecasting. Note that when a statistical model is used to represent Goldman Sachs etf, 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	115.0465
Bias	Arithmetic mean of the errors	None
MAD	Mean absolute deviation	0.1696
MAPE	Mean absolute percentage error	0.009
SAE	Sum of the absolute errors	10.3441

A single variable polynomial regression model attempts to put a curve through the Goldman Sachs historical price points. Mathematically, assuming the independent variable is X and the dependent variable is Y, this line can be indicated as: Y = a₀ + a₁*X + a₂*X² + a₃*X³ + ... + a_m*X^m

Predictive Modules for Goldman Sachs

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Goldman Sachs Physical. Regardless of method or technology, however, to accurately forecast the stock or bond 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, frequently view the market will even out over time. This tendency of Goldman Sachs' 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. Please use the tools below to analyze the current value of Goldman Sachs in the context of predictive analytics.

Hype

Prediction

Low	Estimated Value	High
18.58	19.60	20.62

Details

Intrinsic

Valuation

Low	Real Value	High
17.63	18.65	19.67

Details

Bollinger

Band Projection (param)

Lower	Middle Band	Upper
17.59	18.85	20.10

Details

Please note, it is not enough to conduct a financial or market analysis of a single entity such as Goldman Sachs. Your research has to be compared to or analyzed against Goldman Sachs' peers to derive any actionable benefits. When done correctly, Goldman Sachs' competitive analysis will give you plenty of quantitative and qualitative data to validate your investment decisions or develop an entirely new strategy towards taking a position in Goldman Sachs Physical.

Other Forecasting Options for Goldman Sachs

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

Goldman Sachs 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 Goldman Sachs etf to make a market-neutral strategy. Peer analysis of Goldman Sachs could also be used in its relative valuation, which is a method of valuing Goldman Sachs by comparing valuation metrics with similar companies.

WalmartSPDR SP 500Vanguard Value IndexVanguard Mid-Cap IndexIShares Core SPVanguard Small-Cap IndexVanguard FTSE DevelopedVanguard Total BondVanguard Growth IndexVanguard Total StockAmerican AirlinesAlcoa CorpApple IncBest BuyCitigroup

Risk & Return

Correlation

Goldman Sachs Physical 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 Goldman Sachs' 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 Goldman Sachs' current price.

	Cycle Indicators
	Math Operators
	Math Transform
	Momentum Indicators
	Overlap Studies
	Pattern Recognition
	Price Transform
	Statistic Functions
	Volatility Indicators
	Volume Indicators

Goldman Sachs Risk Indicators

The analysis of Goldman Sachs' basic risk indicators is one of the essential steps in helping accuretelly forecast its future price. The process involves identifying the amount of risk involved in Goldman Sachs' investment and either accepting that risk or mitigating it. Along with some funamental techniques of forecasting Goldman Sachs stock price, 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	0.7596
	Semi Deviation	0.8199
	Standard Deviation	1.02
	Variance	1.05
	Downside Variance	1.07
	Semi Variance	0.6723
	Expected Short fall	(0.83)

Please note, the risk measures we provide can be used independently or collectively to perform a risk assessment. When comparing two potential stock investments, we recommend comparing similar equities with homogenous growth potential and valuation from related markets to determine which investment holds the most risk.

Be your own money manager

Our tools can tell you how much better you can do entering a position in Goldman Sachs without increasing your portfolio risk or giving up the expected return. As an individual investor, you need to find a reliable way to track all your investment portfolios. However, your requirements will often be based on how much of the process you decide to do yourself. In addition to allowing all investors analytical transparency into all their portfolios, our tools can evaluate risk-adjusted returns of your individual positions relative to your overall portfolio.

Did you try this?

Run Transaction History Now

Transaction History

View history of all your transactions and understand their impact on performance

All Next

Launch Module

Pair Trading with Goldman Sachs

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 Goldman Sachs 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 Goldman Sachs will appreciate offsetting losses from the drop in the long position's value.

Moving together with Goldman Sachs

0.85

BND

Vanguard Total Bond

Pair Corr

Moving against Goldman Sachs

	-	0.81	TMV	Direxion Daily 20			Pair Corr
	-	0.41	FXP	ProShares UltraShort FTSE			Pair Corr

The ability to find closely correlated positions to Goldman Sachs could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace Goldman Sachs 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 Goldman Sachs - 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 Goldman Sachs Physical to buy it.

The correlation of Goldman Sachs is a statistical measure of how it moves in relation to other equities. 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 Goldman Sachs moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if Goldman Sachs Physical 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 Goldman Sachs 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 Correlation Correlation Matching

Check out Historical Fundamental Analysis of Goldman Sachs to cross-verify your projections. For more information on how to buy Goldman Etf please use our How to Invest in Goldman Sachs guide. Note that the Goldman Sachs Physical information on this page should be used as a complementary analysis to other Goldman Sachs' statistical models used to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try Piotroski F Score module to get Piotroski F Score based on binary analysis strategy of nine different fundamentals.

Complementary Tools for Goldman Etf analysis

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

	Idea Breakdown Analyze constituents of all Macroaxis ideas. Macroaxis investment ideas are predefined, sector-focused investing themes
	Sync Your Broker Sync your existing holdings, watchlists, positions or portfolios from thousands of online brokerage services, banks, investment account aggregators and robo-advisors.
	Transaction History View history of all your transactions and understand their impact on performance
	Price Exposure Probability Analyze equity upside and downside potential for a given time horizon across multiple markets
	Insider Screener Find insiders across different sectors to evaluate their impact on performance
	My Watchlist Analysis Analyze my current watchlist and to refresh optimization strategy. Macroaxis watchlist is based on self-learning algorithm to remember stocks you like

The market value of Goldman Sachs Physical is measured differently than its book value, which is the value of Goldman that is recorded on the company's balance sheet. Investors also form their own opinion of Goldman Sachs' value that differs from its market value or its book value, called intrinsic value, which is Goldman Sachs' 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 Goldman Sachs' market value can be influenced by many factors that don't directly affect Goldman Sachs' 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 Goldman Sachs' value and its price as these two are different measures arrived at by different means. Investors typically determine Goldman Sachs value by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, Goldman Sachs' 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.

Macroaxis helps investors of all levels and skills to maximize the upside of all their holdings and minimize the risk associated with market volatility, economic swings, and company-specific events. View terms and conditions

Feedback Blog

Made with optimal in San Francisco

2023 Macroaxis LLC

	Naive Prediction
	Simple Exponential Smoothing
	Double Exponential Smoothing
	Triple Exponential Smoothing
	Simple Regression
	Polynomial Regression
	Simple Moving Average
	4 Period Moving Average
	8 Period Moving Average
	20 Period Moving Average

Goldman Sachs Etf Forecast - Polynomial Regression

Goldman Sachs Polynomial Regression Price Forecast For the 27th of March

Goldman Sachs Etf Forecast Pattern

Goldman Sachs Forecasted Value

Model Predictive Factors

Predictive Modules for Goldman Sachs

Other Forecasting Options for Goldman Sachs

Goldman Sachs Related Equities

Goldman Sachs Physical Technical and Predictive Analytics

Goldman Sachs Risk Indicators

Be your own money manager

Did you try this?

Run Transaction History Now

Transaction History

Pair Trading with Goldman Sachs

Moving together with Goldman Sachs

Moving against Goldman Sachs

Complementary Tools for Goldman Etf analysis

COMMON DESTINATIONS

MAIN CATEGORIES

COMPANY