Dynamic prediction of Indian stock market: an artificial neural network approach

Abstract

Purpose Artificial neural network (ANN) is a powerful technique to forecast the time series data such as the stock market. Therefore, this study aims to predict the Indian stock market closing price using ANNs. Design/methodology/approach The input variables identified from the literature are some macroeconomic variables and a global stock market factor. The study uses an ANN with Scaled Conjugate Gradient Algorithm (SCG) to forecast the Bombay Stock Exchange (BSE) Sensex. Findings The empirical findings reveal that the ANN model is able to achieve 93% accuracy in predicting the BSE Sensex closing prices. Moreover, the results indicate that the Morgan Stanley Capital International world index is the most important variable and the index of industrial production is the least important in predicting Sensex. Research limitations/implications The findings of the study have implications for the investors of all categories such as foreign institutional investors, domestic institutional investors and investment houses. Originality/value The novelty of this study lies in the fact that there are hardly any studies that use ANN to forecast the Indian stock market using macroeconomic indicators.

Review on volatility and return analysis including emerging developments: evidence from stock market empirics

“…It can detect the noise and chaos including the structural breaks, regime-switching, and, most likely the nonlinear behavior of the data series easily (Hinton, 1992; Gupta and Kashyap, 2016; Alaloul et al , 2018; Marcos et al , 2020). Moreover, the results of the studies show that the stock market prophecies are quite achievable applying AI-driven hybrid nonlinear volatility models, and, it produces better results in comparison to the benchmark GARCH model and its counterparts (Gonzalez Miranda and Burgess, 1997; Roh, 2007; Atsalakis and Valavanis, 2009; Chen et al , 2010; Guresen et al , 2011; Lahmiri and Boukadoum, 2015; Gopal and Ramasamy, 2017; Chkili and Hamdi, 2021; Goel and Singh, 2021). Though, the models depicted in the present study are restricted to boundaries such as GARCH model is more effective in forecasting short-term volatility than long-term volatility; IGARCH, FIGARCH, HYGARCH model suitability in capturing long-memory volatility; EGARCH, TGARCH, GJR-GARCH model better account asymmetrical behavior in volatility; multivariate-GARCH model appropriateness in simultaneous computing volatility of multiple assets; high frequency based multivariate heterogeneous autoregressive model suitability for longer forecast horizons, etc.…”

“…It has created the opportunity to rediscover the inter-relationship and patterns among the market variables from huge data sources (Gopal and Ramasamy, 2017). This gap is fulfilled by sophisticated computer-based artificial intelligence (AI), machine learning techniques to forecast volatility in a chaotic and noisy environment (Kaastra and Boyd, 1996; Goel and Singh, 2021).…”

Machine intelligence in dynamical systems: \A state‐of‐art review

“…Using BP NN modeling and Phyton tools, 9900 units of data are employed and divided into 90% training data and 10% testing data. Goel and Singh (2022) forecasted dynamics in an Indian stock market using macroeconomic indicators by ANN. In their research, authors have used MLPNN to forecast the closing price of the BSE Sensex by adding macroeconomic indicators such as IIP, CPI, LTINT, FX, and MSCI.…”

A Hybrid Model to Predict Stock Closing Price Using Novel Features and a Fully Modified Hodrick–Prescott Filter

“…They built a hybrid model with the outputs of the GARCH family of models and various key factors as input variables. Goel and Singh [26] proposed a neural network that uses macroeconomic variables identified from the literature as input variables and a global stock market factor. Chandra and He [12] used innovative Bayesian neural network approaches for multi-step-ahead stock price forecasting.…”