Stock market price prediction model trained using historical data of 12 stocks, using correlation factor and advanced feature generation.

• Banking sector
  • SBIN
  • HDFCBANK
  • AXISBANK
• Pharmaceutical sector
  • SUNPHARMA
  • DRREDDY
  • CIPLA
• Information technology sector
  • INFY
  • WIPRO
  • TCS
• Energy sector
  • BPCL
  • COALINDIA
  • POWERGRID
• Target stock for price prediction : SBIN

Our model can be broken down into following major parts -

• Input feature generation
• Preprocessing of the new found features
• Finding correlation between various stocks
• Building custom optimizer taking into consideration correlation values
• Designing structure of RNN
• Training of RNN

• Using feature engineering, we are looking for several methods that can act as individual prediction models for predicting the stock price. Furthermore using these predictions as the custom built features into our main RNN model.
• First we break down the data into chunks of 30 days, to convert it into time series data.
  • These individual chunks of data are passed onto various polynomial regression models with variable degrees, which returns a vector of predicted price of the next day by all the models. Polynomial degrees are chosen in a manner to diversify the possible predicted values by taking degrees that lead to some under-fitted, balanced and overfitted models.
  • These individual chunks of data are passed onto a curve similarity model that tries to find the similarity of the current curve, with curves obtained from the historical data of all the stocks used for training. To keep this model balanced, curve lengths are taken in a range starting from 5 days going upto 30 days to take in account the effect of long term and short term movements in the stock.
• These 2 techniques generate nearly 10-15 new features that will be passed further onto the RNN model.
• Other commonly used features like Moving average, Exponential moving average, etc of various ranges are also used for the RNN model.

Features need to be normalized, and scaled in order to generate meaningful results from the models.

This idea is based on the effect of one stock on another. To train a specialized model for prediction of price for a particular stock, data of only that stock can be used. But we have taken into consideration that every stock depends on other stocks and hence the same correlation can be seen in their stock prices. If this correlation can be taken into consideration, then we can use data of other stocks as well, weighted with their correlation values to train a partial generalized model that has the possibility of generating better results. This generalization can be balanced by taking appropriate normalized weight for the correlation values of all the stocks, and hence keeping a variable ratio in the range of 40-90% between the effect of the selected stock, and all other stocks.

• Custom gradient descent function is being created to bring in the effect of correlation values of all the stocks while modifying the weights of RNN in backpropagation.
• During backpropagation, instead of taking a simple average of all the weight gradients, weighted average using correlation values of weight gradients is taken to update the weights.

We are experimenting with various RNN structures and hyperparameters to generate the best results of the program, taking RMSE as loss function.