Heartland Financial Preferred Stock Forecast - Simple Regression

Simple Regression model is a single variable regression model that attempts to put a straight line through Heartland Financial 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.

Heartland Financial Simple Regression Price Forecast For the 17th of February 2025

Given 90 days horizon, the Simple Regression forecasted value of Heartland Financial USA on the next trading day is expected to be 25.30 with a mean absolute deviation of 0.09, mean absolute percentage error of 0.02, and the sum of the absolute errors of 5.20.

Heartland Financial Preferred Stock Forecast Pattern

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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 Heartland Financial preferred stock data series using in forecasting. Note that when a statistical model is used to represent Heartland Financial preferred 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. 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 Heartland Financial USA 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 Heartland Financial

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Heartland Financial USA. Regardless of method or technology, however, to accurately forecast the preferred stock market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the preferred 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.

Hype Prediction Low Estimated High 24.61 25.05 25.49 Details

Intrinsic Valuation Low Real High 20.87 21.31 27.56 Details

Bollinger Band Projection (param) Low Middle High 25.01 25.24 25.47 Details

Please note, it is not enough to conduct a financial or market analysis of a single entity such as Heartland Financial. Your research has to be compared to or analyzed against Heartland Financial's peers to derive any actionable benefits. When done correctly, Heartland Financial'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 Heartland Financial USA.

Heartland Financial Related Equities

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Heartland Financial Market Strength Events

Heartland Financial Risk Indicators

Pair Trading with Heartland Financial

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 Heartland Financial 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 Heartland Financial will appreciate offsetting losses from the drop in the long position's value.

Other Consideration for investing in Heartland Preferred Stock