Deep Neural Network and Time Series Approach for Finance Systems

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.

Design of Financial Risk Control Model Based on Deep Learning Neural Network

“…For biological neural networks, different neurons are connected to each other. The neural network in deep learning enables machines to imitate human activities such as audio-visual and thinking [ 8 ]. The scope of its role is shown in Figure 2 .…”

Regional Entrepreneurship, Business Environment, and High-Quality Economic Development: An Empirical Analysis of Nine Urban Agglomerations in China

“…The relationship between the business environment, the scale of financial services, innovation, regional entrepreneurship, and high-quality economic development is complex. In theory, the business environment, the scale of financial services, and the improvement of the entrepreneurial environment will improve the regional entrepreneurial situation and enhance economic development ( Radović-Marković et al, 2019 ; Jang et al, 2020 ; Pradhan et al, 2020 ; Srivastava et al, 2021 ). However, the study found that due to regional heterogeneity, not all of the above three factors have a positive effect on regional entrepreneurship and quality economic development.…”

“…Many scholars have studied entrepreneurial development depending on the business environment to increase the number of entrepreneurs and the rate of business growth ( Sattari and Mehrabi, 2016 ; Khan et al, 2019 ; Radović-Marković et al, 2019 ; Jang et al, 2020 ). Other researchers have explored the positive effects of financial development on promoting entrepreneurship ( Civera et al, 2017 ; Léon, 2019 ; Hommel and Bican, 2020 ; Liu et al, 2020 ; Srivastava et al, 2021 ; Charfeddine and Zaouali, 2022 ). In addition, the development of innovation has contributed significantly to entrepreneurship and economic growth ( Huggins and Thompson, 2015 ; Ferreira et al, 2017 ; Pounder, 2019 ; Pradhan et al, 2020 ).…”

A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction

“…Recently, hybrid deep learning methods have been proposed to improve the prediction performance of stock market trends. Srivastava, Zhang [45] developed a hybrid model called RCNN, which combines RNN and CNN by exploiting the advantages of both models. Their experiments showed that the combined hybrid system had a positive impact on the performance of the model when text data and technical indicators were used as input data, and the proposed model performed better than the CNN model.…”