Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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Knowing about the future returns attract every investor. Investors can take appropriate decisions once they know what would be the future returns based on their investments. To know the returns, the earlier studies have proposed basic models like Efficient Marker Hypothesis and Random Walk Model, whereas these theories have their own limitations in predicting the direction of the stock and the next day value of the stock. Later with the evolution of Machine learning and Deep Learning Techniques, there were many experiments which were made to study the stock markets. The present research paper aims at applying the Deep Learning technique of Artificial Neural Network to predict the direction of the stock index. The data consist of daily open price, close price, high price, low price and volume of NIFTY 50, S&P 500, New York Stock Index, Korean Stock Index, Dow Jones Index and Shanghai Stock Index from Jan 2015 to May 2020. The open price of the index is fed as input to the Artificial Neural Network. The model is evaluated on different performance metrics of Accuracy, Precision, Recall and F1-Score.

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Predicting stock market movements using artificial neural networks

The stock market index forecasting is quite a popular topic in the present economy. There are many micro and macro-economic factors which influence the stock prices. With the emergence of machine learning techniques, algorithmic trading became most popular in forecasting the stock prices. There are many traditional machine learning techniques like ARMA, ARIMA which are used to forecast the stock market index. However these techniques consider trends and patterns involved in the data. Since the stock market data is a time series and irregular in nature there will be some noise involved. The present research paper studies the forecasting of stock market index by applying Hidden Markov models which considers the hidden states within the stock market index and traditional ARIMA model. HMM considers the posterior probabilities on different hidden states using Expectation Maximization algorithm. The data considered for the study includes 5 different stock market index Dow Jones, NIFTY 50, S P 500, New York Stock Index (NYSE) and KOSPI. The data includes daily prices of Low, High, Close, Open, Volume for a period of 5 years that is 2014-2019 which accounts to approximately 1380 data points. In HMM model the closing price of the index is considered for determining the transition states and posterior probabilities at 2, 3, 4 and 5 hidden states. Akaike Information Criteria (AIC) and Bayesian information Criterion (BIC) are used to determine the states from HMM. The research paper studies the direction of the market the closing price of the stock index considering HMM. The paper is divided into 5 parts which include Part 1: Introduction, Part 2: Past study, Part 3: Machine Learning Algorithms, Part4: Data Analysis using HMM and Part 5: Results and Conclusion. © 2020 IEEE.

2020 6th International Conference on Advanced Computing and Communication Systems, ICACCS 2020

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