The aim of this project is to forecast the next day's returns of a tunisian company using daily stock data from Tunisia Stock Exchange market . At the beginning, we performed ordinary tasks such as preprocessing and wrangling where we found these two major points: 1-we can create two new attributes capable of reducing the dimensionality of market data. 2-the data is a time series indexed by the dates of each trading operation, to eliminate the time dependency, we have introduced a new variable which is the 'current profit' which is shifted by a period of the next day return . 3- thanks to the calculation of the kurtosis and skewness coefficients, we found that we do not need to make distribution transformations. Then , we did a dimensionality reduction thanks to the PCA method , which forms the cornerstone of this project . This technique needs to determine the value of its hyperparameter which is the number of principal components that will allow the explanation of the maximum variance of all the variables. To do this, we have recourse to the analysis by factor and precisely the eigenvalues criterion . This criterion allows each time the code is run to determine the optimal number of factors (those with eigenvalues ​​>1) and finally we arrived at a number equal to 6 factors and which explain 95.5% of the variance. Finally, we predicted the value of 'next day return' thanks to a linear regression model, it is true that it is quite basic but the goal was to master the technique of PCA and FA. we can improve the result in future projects by applying, for example, another model such as XGBoost .