scholarly journals A Neural Networks Adoption Framework for Predicting Stock Market Trends: Case of the Zimbabwe Stock Exchange

2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Ezekiel Tinashe Mukanga
Keyword(s):  
Neural Networks ◽  
Stock Market ◽  
Stock Exchange ◽  
Market Trends

Analysis on Indian Stock Market Prediction Using Deep Learning Models

2021 ◽  
pp. 80-90
Author(s):  
Kalaivani Karuppiah ◽  
Umamaheswari N. ◽  
Venkatesh R.
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Stock Market ◽  
Multilayer Perceptron ◽  
Stock Exchange ◽  
Market Price ◽  
Learning Models ◽  
Stock Market Prediction ◽  
Indian Stock Market

The neural network is one of the best data mining techniques that have been used by researchers in different areas for the past 10 years. Analysis on Indian stock market prediction using deep learning models plays a very important role in today's economy. In this chapter, various deep learning architectures such as multilayer perceptron, recurrent neural networks, long short -term memory, and convolutional neural network help to predict the stock market prediction. There are two different stock market price companies, namely National Stock Exchange and New York Stock Exchange, are used for analyzing the day-wise closing price used for comparing different techniques such as neural network, multilayer perceptron, and so on. Both the NSE and NYSE share their common details, and they are compared with various existing models. When compared with the previous existing models, neural networks obtain higher accuracy, and their experimental result is shown in betterment compared with existing techniques.

Predicting Stock Price Using Artificial Neural Networks

2019 ◽  
Vol 118 (8) ◽  
pp. 96-117
Author(s):  
Dr. Nigama. K ◽  
Dr. R Alamelu ◽  
Dr. S. Selvabaskar ◽  
Dr. K.G. Prasanna Sivagami
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Explicit Knowledge ◽  
Stock Exchange ◽  
Market Price ◽  
Financial Investment ◽  
Artificial Neural

Stock market facilitates the economic activities that contribute to a nation’s growth and prosperity. This is viewed as one of the lucrative avenues for financial investment. Although the stock market is a thrilling and potential opportunity to grow one’s money, it brings along with it certain challenges, because, there is no universal rule that suggests profitable investments.  Investors, corporate and advisors employ several techniques like fundamental and technical analysis, trend analysis and other analysis to suggest stocks that will give best yields but such tools are neither consistent nor foolproof in the prediction of stock prices. But human exertions to convert the tacit knowledge into explicit knowledge has never found any alternate. More, the uncertainties, more the efforts to know them with certainty.  Digital economy with its advanced technological tools aids the pursuit of not only understanding uncertainties but also predicting the future with maximum precision. The most prominent techniques in the technological realm includes the usage of artificial neural networks (ANNs) and Genetic Algorithms. This paper discusses the stock prices forecasting ability of Bombay stock exchange trend using genetically evolved neural networks, the input being the closing price of the previous five years and output being the price for the next day. Risk (Standard deviation), Average Return, variance and Market price are chosen as indicators of the performance. The objective of this study is to give an overview of the application of artificial neural network in predicting stock market.

scholarly journals The Reversal of Stock Market Trends as a Behavioral Bias: Evidence from Tunisian Stock Exchange

2016 ◽  
Vol 6 (2) ◽  
pp. 13 ◽  
Author(s):  
Mondher Kouki ◽  
Mosbeh Hsini
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Empirical Investigation ◽  
Stock Exchange ◽  
Empirical Test ◽  
Past Performance ◽  
Behavioral Bias ◽  
Portfolio Returns ◽  
Explanatory Factors ◽  
Market Trends

This paper examines the behavioral bias in Tunisia, a country with a small stock market in terms of capital, but surprisingly dynamic in comparison to other emerging markets. Our study is consistent with Jegadeesh & Titman (1993)’ approach as presented to highlight an analysis of  such reversal phenomena of portfolio returns, and provides explanatory factors  to the so-called market trends reversal. The empirical investigation is based on a weekly database for a period from January 2002 to January 2013 related to stock prices and index values of market capitalization (TUNINDEX). The empirical test demonstrates the existence of winner-loser phenomenon in accordance with over-reaction hypothesis stating that portfolios with the worst past performance outperform, during the subsequent periods, those having produced best past performance and vice versa. 

Performances of liquidity factors in the stock market cycle: evidence from the Tokyo Stock Exchange

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xin Zhong
Keyword(s):  
Asset Pricing ◽  
Stock Market ◽  
Stock Exchange ◽  
Regression Method ◽  
Pricing Model ◽  
Content Type ◽  
Asset Pricing Model ◽  
Long Run ◽  
Market Trends ◽  
Japanese Stock Market

PurposeThe purpose of this study is to examine the performances of liquidity factors in the stock market cycle. It aims to investigate whether the contribution of liquidity factors changes with stock market trends.Design/methodology/approachSix liquidity proxies and two-factor construction methods are compared in this study. The spanning regression method was applied to examine the contribution of liquidity factors to the asset pricing model, while the Fama and MacBeth regression method was used for examining the pricing power of liquidity factors.FindingsThe result shows that liquidity factors are accretive to models explaining returns in bull markets but not accretive to models in bear markets. The most appropriate method of constructing liquidity factors in the Japanese stock market has also been clarified.Originality/valueIn the Japanese stock market, there has never been a comprehensive test of the role of the liquidity risk factor in different market trends using the long-run data. This study helps with identifying the importance of liquidity pricing risk in different market trends. It also fills the gaps by comparing liquidity factors that are constructed through different methods and proxies and provides evidence for further confirming the correct asset pricing model in the future.

scholarly journals Stock Prediction using Neural Networks and Time Series Analysis Methods

2020 ◽  
pp. 546-551
Author(s):  
Mohammad Pardaz Banu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Stock Market ◽  
Stock Price ◽  
Financial Systems ◽  
Stock Market Forecasting ◽  
Network Methods ◽  
Market Trends ◽  
Stock Market Investors

The stock market is considered to be one of the most highly complex financial systems which consist of various components or stocks, the price of which fluctuates greatly with respect to time. Stock market forecasting involves uncovering the market trends with respect to time. All the stock market investors aim to maximize the returns over their investments and minimize the risks associated. There are time series methods such as AR, MA, SARIMAX developed to predict the stock price but neural network methods such as CNN, LSTM also used to predict the stock price. This research paper describes the prediction of stock market using neural network alogorithms and also few time series methods.

scholarly journals Role of Stock Exchanges in Economic Development of Uzbekistan

2016 ◽  
Vol 10 (1) ◽  
pp. 172 ◽  
Author(s):  
Ulugbek Khalikov
Keyword(s):  
Economic Development ◽  
Stock Market ◽  
Stock Exchange ◽  
Stock Exchanges ◽  
Investment Climate ◽  
Stock Market Development ◽  
Documentary Analysis ◽  
Market Trends ◽  
Recent Government

The paper is devoted to study the contemporary role of investments to economic development in the context of Uzbek stock exchange. The comparative analysis of economic development and stock market trends in Uzbekistan, Kazakhstan and Russia for the period of 2000-2015 are conducted using documentary analysis, quantitative and qualitative analysis, and other statistical methods of research.The results reveal that Uzbekistan has made notable change in regulation and improvement of investment climate and has stable economic development trends for the studied period. However, Stock market development in Uzbekistan remains weak and recent government effort to accelerate privatization is expected to boost the market and support foreign investments attraction.

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