Indian Stock Market Dataset Download

Nowadays, the most significant challenges in the stock market is to predict the stock prices. The stock price data represents a financial time series data which becomes more difficult to predict due to its characteristics and dynamic nature.

Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are widely used for prediction of stock prices and its movements. Every algorithm has its way of learning patterns and then predicting. Artificial Neural Network (ANN) is a popular method which also incorporate technical analysis for making predictions in financial markets.

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Most common techniques used in the forecasting of financial time series are Support Vector Machine (SVM), Support Vector Regression (SVR) and Back Propagation Neural Network (BPNN). In this article, we use neural networks based on three different learning algorithms, i.e., Levenberg-Marquardt, Scaled Conjugate Gradient and Bayesian Regularization for stock market prediction based on tick data as well as 15-min data of an Indian company and their results compared.

As the technology is increasing, stock traders are moving towards to use Intelligent Trading Systems rather than fundamental analysis for predicting prices of stocks, which helps them to take immediate investment decisions. One of the main aims of a trader is to predict the stock price such that he can sell it before its value decline, or buy the stock before the price rises. The efficient market hypothesis states that it is not possible to predict stock prices and that stock behaves in the random walk. It seems to be very difficult to replace the professionalism of an experienced trader for predicting the stock price. But because of the availability of a remarkable amount of data and technological advancements we can now formulate an appropriate algorithm for prediction whose results can increase the profits for traders or investment firms. Thus, the accuracy of an algorithm is directly proportional to gains made by using the algorithm.

There are three conventional approaches for stock price prediction: technical analysis, traditional time series forecasting, and machine learning method. Earlier classical regression methods such as linear regression, polynomial regression, etc. were used to predict stock trends. Also, traditional statistical models which include exponential smoothing, moving average, and ARIMA makes their prediction linearly. Nowadays, Support Vector Machines (Cortes & Vapnik, 1995) (SVM) and Artificial Neural Networks (ANN) are widely used for the prediction of stock price movements. Every algorithm has its way of learning patterns and then predicting. Artificial Neural Network (ANN) is a popular and more recent method which also incorporate technical analysis for making predictions in financial markets. ANN includes a set of threshold functions. These functions trained on historical data after connecting each other with adaptive weights and they are used to make future predictions. (Trippi & Turban, 1992; Walczak, 2001; Shadbolt & Taylor, 2002) (Kuan & Liu, 1995) investigated the out-of-sample forecasting ability of recurrent and feedforward neural networks based on empirical foreign exchange rate data (Kuan & Liu, 1995). In 2017, Mehdi Khashei and Zahra Haji Rahimi evaluated the performance of series and parallel strategies to determine a more accurate one using ARIMA and MLP (Multilayer Perceptron) (Mehdi & Zahra, 2017).

Artificial neural networks have been used widely to solve many problems due to its versatile nature. (Samek & Varachha, 2013) (Yodele et al., 2012), presented a hybridized approach, i.e., a combination of the variables of fundamental and technical analysis of stock market indicators to predict future stock prices to improve the existing methods, (Yodele et al., 2012) (Y Kara & A Boyacioglu, 2011) discussed stock price index movement using two models based on Artificial Neural Network (ANN) and Support Vector Machine (SVM). They compared the performances of both the models and concluded that the average performance of the ANN model was significantly better than the SVM model. (Y Kara & A Boyacioglu, 2011) (Qi & Zhang, 2008) investigated the best modeling of trend time series using Neural Network. They used four different approaches, i.e., raw data, raw data with a time index, de-trending and differencing for modeling various trend patterns and concluded Neural Network gives better results (Qi & Zhang, 2008). H.K. Cigizoglu, (2003) discussed the application of ANN forecasting, estimation and extrapolation of the daily flow data belonging to the rivers in the East Mediterranean region of Turkey. In their study, they found that ANN provides a better fit to the data than conventional methods (Cigizoglu, 2003). ANN can consider as a computation or a mathematical model which is inspired by the functional or structural characteristics of biological neural networks. These neural networks are developed in such a way that it can extract patterns from noisy data. ANN first train a system using a large sample of data known as training phase then it introduces the network to the data which was not included in the training phase, this phase known as validation or prediction phase. The sole motive of this procedure is to predict new outcomes. (Bishop, 1995) This idea of learning from training and then predicting outcomes in ANN comes from the human brain which can learn and respond. Thus ANN has been used in many applications and is proven successful in executing complex functions in a variety of fields (Fausett, 1994).

In this case study, the data of past 30 business days used. A more extensive dataset can be used to bring in seasonal and annual factors that affect the stock price movement. Also predicting the minute by minute data can reduce dataset size by 70% and may be able to give comparable results while allowing us to use historical data of a more significant period. Recurrent Neural Networks may provide better predictions than the neural networks used in this study, e.g., LSTM (Long Short-Term Memory). Since statements and opinions of renowned personalities are known to affect stock prices, some Sentiment Analysis can help in getting an extra edge in stock price prediction.

Among all the application areas of the time-series prediction, stock market prediction is the most challenging task due to its dynamic nature, and dependency on many volatile factors. The unpredictable fatal events called Black Swan events also highly influence the stock market. If the successful stock trends prediction is achieved, then the investors can adopt a more appropriate trading strategy, and that can significantly reduce the risk of investment. In this work, a time-efficient hybrid stock trends prediction framework(HSTPF) is proposed to successfully predict the future trends of the stock market even during the periods of Black Swan events. Here, to improve the prediction accuracy of HSTPF, the Black Swan events analysis and features selection operations are performed, and also the performance of various machine learning classifiers are analyzed. A vast number of experiments are conducted on the two real-world stock market datasets S&P BSE SENSEX and Nifty 50, to analyze the performance of the proposed framework. The framework is applied for the single-step and multi-step ahead predictions. The experimental results show that the proposed framework produces over 86% of accuracy, and during the Black Swan events, its accuracy is almost 80% for single-step ahead predictions. For the multi-step ahead of predictions, the HSTPF is produced satisfactory results. The framework also outperforms other existing similar works even during the Black Swan events in terms of prediction accuracy, and its computational time is also very low.

Prediction is one of the biggest challenges for any research area. If a successful prediction is achieved, then that can smoothen the livelihood of the people. One of the most challenging time-series prediction research areas is the stock trend prediction because of its nonlinearity and highly dynamic behavior. The stock market is also suffered from unpredictable events that have a serious negative impact on it and is called the Black Swan events. If the successful stock trend is predicted, then the stock market regulators and the investors can develop better trading strategies, which helps them to reduce the risk of the investment and also increase profitability.

In recent times, numerous research work has been done to develop the most appropriate model for the stock market prediction. These stock market predictions have been done through two types of analysis, viz. fundamental analysis and technical analysis. The fundamental analysis [1, 2] uses various types of economic and financial information, such as macroeconomic factors and foreign exchange rates. On the other hand, technical analysis [3, 4] is done based on the stock price information. Nowadays, machine learning and deep learning are two popular artificial intelligence tools that are widely used for stock prediction. Gaussian Nave Bayes (GNB), Autoregressive Integrated Moving Average (ARIMA), Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbor (KNN), Classification and Regression Tree (CART), Linear Discriminant Analysis (LDA), Random Forest Classifier (RF) are the popular machine learning algorithms that are extensively used in various stock market prediction research works [5,6,7]. On the other hand, Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Networks (CNN), Deep Belief Network (DBN), Restricted Boltzmann Machine ( RBM), Autoencoders (AE) are widely used deep learning algorithms, and they are successfully applied to predict the stock market [8,9,10,11]. Recently, the deep learning methods have become more popular compared to machine learning methods due to their automatic spatiotemporal feature extraction capabilities, whereas to deploy the machine learning method, domain expert knowledge is required for feature extraction. Recent research work [12, 13] compares the performance of machine learning approaches with deep learning approaches for time-series forecasting and shows that the deep learning methods outperform machine learning methods for large volume input datasets, whereas the performance of machine learning methods is better when the dataset volume is small. Another major drawback of deep learning methods is their high computational time. e24fc04721