Exponential regression for trade management?

<p>Regression analysis is a supervised machine learning technique mainly used to forecast and trend projection and dependency between two stocks/assets in financial markets. The goal is to find the regression curve, which is least distant from all the data points.</p>

<p id="isPasted">To find stocks with a smoothly upward moving stock price you want to calculate the average daily percent increase in the stock price. To do this Andreas uses exponential regression.&nbsp;</p><p>For example, using exponential regression, you can calculate that over a 90 day period a stock price increased by 0.05% per day.&nbsp;</p><p>Then, if you assume there are 260 trading days in a year, theoretically the share price may go up 13.88% (0.05% to the power 260) over a year.</p><p>Keep in mind that you do not expect to make this return but it gives you an idea of how much the stock price can increase over a year if you assume the increase over the past 90 days continues.</p>

<p id="isPasted">To get rid of companies with a large jump in share price (bad momentum) you want to make sure that the calculated daily price increase is as close as possible to the real daily increase in the stock price.&nbsp;</p><p>Luckily there is a value that allows us to do exactly that.&nbsp;</p><p>It is called R2 (the coefficient of determination) and it tells you how well the calculated daily stock price increase fits the real daily increase.&nbsp;</p><p>If R2 is equal to 1 the real daily increase is exactly equal to the calculated value and if it is close to zero the calculated value is not at all close to the real daily increase.&nbsp;</p><p>To see how well the daily stock price increase fits the actual share price movements is easy to calculate.</p><p>Simply multiply the calculated increase (exponential regression) by the R2 value.&nbsp;</p><p>If the fit is good (close to 1) the calculated value will remain the same but if it is bad (R2 less than 1) the calculated share price increase will decrease.&nbsp;</p><p>In the screener we call the indicator (exponential regression x R2) the Adjusted Slope of the stock price.</p>

<p>Past performance does not mean future results. The future is uncertain and no matter how much you tweak your model, something can always happen that will break it. Forecasting is difficult. I don't know of any analyst that uses a regression model for stock prices, I think the most common model is some form of discounted cash flow.</p>

<p id="isPasted">There is no one regression model that is best for predicting or forecasting stock prices. It depends on the type of data you are working with and the specific needs of your business.</p><p>Some common regression models used to predict stock prices include:</p><p>1. Linear regression: This model uses a straight line to predict the future value of a variable based on past values.</p><p>2. Logistic regression: This model uses a mathematical equation to predict the probability of an event, such as whether someone will buy or sell a particular security.</p><p>3. Time-series analysis: This model uses historical data to identify patterns and trends that can be used to make predictions about future events.</p>