Can stock prices be predicted? Comparative study of LSTM and SVR for financial market forecast

Prediction of financial markets is a challenging task due to its volatility and the presence of noise. This work comparatively analyses the implementations of LSTM (Long Short Term Memory) and three kernels of SVR (Support Vector Regressor), namely linear kernel, RBF (Radial Basis Function) kernel and polynomial kernel. A series of experiments are conducted, and results and accuracy are compared. The results indicate that a single kernel of SVR is not sufficient to predict the stocks for all days, although if all the kernels are considered, one of them gets a close result but the LSTM with linear activation function and 4 layers give better result.