Articlediamond2021

Deep Neural Network and Time Series Approach for Finance Systems

Praveen Ranjan Srivastava · Zuopeng Zhang · Prajwal Eachempati

54 citations0/0 files storedJournal of Organizational and End User Computing

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Financial Analysis and Corporate Governance

Abstract

The stock market is an aggregation of investor sentiment that affects daily changes in stock prices. Investor sentiment remained a mystery and challenge over time, inviting researchers to comprehend the market trends. The entry of behavioral scientists in and around the 1980s brought in the market trading's human dimensions. Shortly after that, due to the digitization of exchanges, the mix of traders changed as institutional traders started using algorithmic trading (AT) on computers. Nevertheless, the effects of investor sentiment did not disappear and continued to intrigue market researchers. Though market sentiment plays a significant role in timing investment decisions, classical finance models largely ignored the role of investor sentiment in asset pricing. For knowing if the market price is value-driven, the investor would isolate components of irrationality from the price, as reflected in the sentiment. Investor sentiment is an expression of irrational expectations of a stock's risk-return profile that is not justified by available information. In this context, the paper aims to predict the next-day trend in the index prices for the centralized Indian National Stock Exchange (NSE) deploying machine learning algorithms like support vector machine, random forest, gradient boosting, and deep neural networks. The training set is historical NSE closing price data from June 1st, 2013-June 30th, 2020. Additionally, the authors factor technical indicators like moving average (MA), moving average convergence-divergence (MACD), K (%) oscillator and corresponding three days moving average D (%), relative strength indicator (RSI) value, and the LW (R%) indicator for the same period. The predictive power of deep neural networks over other machine learning techniques is established in the paper, demonstrating the future scope of deep learning in multi-parameter time series prediction.

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References

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