I have three months worth of stock prices before and after of certain events (bio-clinical success, dividends, m&a, etc.).

I want to analyze the trend after a specific event using these data, and based on this, I want to analyze the trend of new stocks waiting for a specific event.

But I'm not sure which algorithm to use.

Which algorithm should I use, LSTm or ARIMA or etc?

CodePudding user response：

I would recommend starting with something simple like linear regression. Linear regression is used to find trends in data also it is a very simple algorithm that requires little understanding of advanced math compared to other algorithms. In linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Such models are called linear models. Most commonly, the conditional mean of the response given the values of the explanatory variables (or predictors) is assumed to be an affine function of those values; less commonly, the conditional median or some other quantile is used. Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of the response given the values of the predictors, rather than on the joint probability distribution of all of these variables, which is the domain of multivariate analysis. But you can choose what algorithm you want to use