Indian stock market prediction using artificial neural networks on tick data
Predicting stock market trends using artificial neural networks is a popular approach in finance and investing. The idea behind using neural networks for stock market prediction is to model the non-linear relationships between various market variables, such as stock prices, trading volumes, economic indicators, and news events, to generate more accurate predictions.
Tick data, also known as time and sales data, refers to the individual transactions that take place in the stock market, including the time of the transaction, the price, and the volume. This data is considered to be more granular and can provide a more accurate representation of market activity compared to daily or weekly closing prices.
There are several steps involved in building a stock market prediction model using artificial neural networks and tick data. First, the data needs to be preprocessed and transformed into a suitable format for training the neural network. This can involve normalizing the data, filling in any missing values, and extracting relevant features.
Next, the neural network architecture needs to be designed and optimized. This can involve selecting the appropriate number of layers, the activation functions, and the optimizer. The neural network is then trained on the preprocessed data, using a suitable training algorithm, such as backpropagation.
Once the neural network is trained, it can be used to generate predictions for future stock prices based on new tick data. The accuracy of the predictions can be evaluated using various metrics, such as mean squared error or mean absolute error.
Simulation of Stock Prediction System using Artificial Neural Networks
Stock trading, used to predict the direction of future stock prices, is a dynamic business primarily based on human intuition. This involves analyzing some non-linear fundamental and technical stock variables which are recorded periodically. This study presents the development of an ANN-based prediction model for forecasting closing price in the stock markets. The major steps taken are identification of technical variables used for prediction of stock prices, collection and pre-processing of stock data, and formulation of the ANN-based predictive model. Stock data of periods between 2010 and 2014 were collected from the Nigerian Stock Exchange (NSE) and stored in a database. The data collected were classified into training and test data, where the training data was used to learn non-linear patterns that exist in the dataset; and test data was used to validate the prediction accuracy of the model. Evaluation results obtained from WEKA shows that discrepancies between actual and predicted values are insignificant.
It’s important to note that stock market prediction is a challenging task, and no single model or approach can guarantee accurate predictions. Neural networks are just one of many tools that can be used for stock market prediction, and the success of the model will depend on several factors, including the quality and relevance of the data, the choice of model architecture, and the skill of the modeler in optimizing the model parameters.
