A Hybrid PSO-Fuzzy Based Algorithm for Clustering Indian Stock Market Data

2017 ◽  
pp. 475-487 ◽  
Author(s):  
Somnath Mukhopadhyay ◽  
Tamal Datta Chaudhuri ◽  
J. K. Mandal
Keyword(s):  
Stock Market ◽  
Market Data ◽  
Indian Stock Market

Clustering Indian stock market data for portfolio management

2010 ◽  
Vol 37 (12) ◽  
pp. 8793-8798 ◽  
Author(s):  
S.R. Nanda ◽  
B. Mahanty ◽  
M.K. Tiwari
Keyword(s):  
Stock Market ◽  
Portfolio Management ◽  
Market Data ◽  
Indian Stock Market

scholarly journals Application of Machine Learning Algorithm in Indian Stock Market Data

2014 ◽  
Vol 16 (3) ◽  
pp. 06-10
Author(s):  
Sanjaya kumar Sen ◽  
◽  
Dr.Subhendu kumar Pani
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Market Data ◽  
Indian Stock Market

scholarly journals An intelligent indian stock market forecasting system using LSTM deep learning

2021 ◽  
Vol 21 (2) ◽  
pp. 1082
Author(s):  
K Kumar ◽  
Dattatray P. Gandhmal
Keyword(s):  
Deep Learning ◽  
Stock Market ◽  
Optimization Algorithm ◽  
Learning Algorithm ◽  
Stock Exchange ◽  
Market Data ◽  
Deep Learning Algorithm ◽  
Area Of Interest ◽  
Accuracy Of Prediction ◽  
Indian Stock Market

<p><span>Stock market data is considered to be one of the chaotic data in nature. Analyzing the stock market and predicting the stock market has been the area of interest among the researchers for a long time. In this paper, we have stepped forward and used a deep learning algorithm with classification to predict the behavior of the stock market. LSTM deep learning algorithm is used with an optimization algorithm to formulate the hyperparameters. To further improve the accuracy of prediction the stock data is first given to a classification algorithm to reduce the number of input parameters. In this research Technical indicators are subjected to classification and deep LSTM algorithm which are both integrated to improve the accuracy of prediction. Deep LSTM hyperparameters are trained using the optimization algorithm. In this paper infosys and zensar stocks data is collected from the Indian stock market data i.e. both national stock exchange (NSE) and bombay stock exchange (BSE). The proposed approach is applied on infosys and zensar share values, the prediction accuracy obtained by employing this integrated approach of classification and LSTM has given a prominent value of MSE and RMSE as 1.034 and 1.002 respectively. </span></p>

Market Efficiency of Indian Stock Market – A Study of Bonus Announcement in Bombay Stock Exchange

2011 ◽  
Vol 2 (1) ◽  
pp. 45-49
Author(s):  
M. Bharath M. Bharath ◽  
◽  
Dr. H. Shankar Dr. H. Shankar
Keyword(s):  
Stock Market ◽  
Market Efficiency ◽  
Stock Exchange ◽  
Indian Stock Market

Insider Trading: A White-Collar Crime and its Impact on Share Market

Think India ◽  
2014 ◽  
Vol 17 (3) ◽  
pp. 22-24
Author(s):  
Sreekumar Ray
Keyword(s):  
Stock Market ◽  
Insider Trading ◽  
White Collar Crime ◽  
White Collar ◽  
Adverse Impact ◽  
High Ranking ◽  
Internal Information ◽  
Indian Stock Market ◽  
The Impact

Since inception, the growth of the Indian stock market has been constrained through unethical, illegal and self-actualized activities of swanky persons involved in different capacities in the market. The stock market was trying to retrieve itself from the devastating effect of Harshad Mehta share market scam, when within a gap of ten years it was once again pushed into the darkness of the dungeon by another demon-child of the country- Ketan Parekh. Corporations have been looted by the insider traders, diversifying internal information to an external in lieu of cash. Investigations in the majority cases have proved the involvement of the high ranking officers of the companies in the crime, sophistically referred to as white-collar crime. It has an adverse impact on the growth and sustainability of the share market. Under the light of the above issue, this paper endeavors to study the impact of such crime on the share market. It focuses on the mechanism behind the insider-trading, its impact on the share market and the regulators supervision on the issue. Finally, suggestions have been provided which will contribute towards the dream of every Indian-a fraud-free share market focusing towards the overall development of the country.

Determinants of Trading Decision: An Experiential Examination

GIS Business ◽  
2018 ◽  
Vol 13 (1) ◽  
pp. 1-9
Author(s):  
Gunjan Sharma ◽  
Tarika Singh ◽  
Suvijna Awasthi
Keyword(s):  
Stock Market ◽  
Capital Markets ◽  
Capital Market ◽  
Capital Mobility ◽  
Influential Factors ◽  
Herd Behavior ◽  
Human Beings ◽  
The Past ◽  
The Government ◽  
Indian Stock Market

In the midst of increasing globalization, the past two decades have observed huge inflow of outside capital in the shape of direct and portfolio investment. The increase in capital mobility is due to contact between the different economies across the globe. The growing liberalization in the capital market leads to the growth of various financial products and services. Over the past decade, the Indian capital market has witnessed numerous changes in the direction of developing the capital markets more robust. With the growing Indian economy, the larger inflow of funds has been fetched into the capital markets. The government is continuously working on investor’s education in order to increase retail participation in the Indian stock market. The habits of the risk-averse middle class have been changing where these investors started participating in the Indian stock market. It is an explored fact that human beings are irrational and considering this fact becomes imperative to investigate factors that influence the trading decisions. In this research, ‘an attempt has been made to investigate various factors that affect the individual trading decision’. The data has been collected from various stockbroking firms and from clients of those stockbroking firms their opinions were recorded by means of a questionnaire. Data collected through the structured questionnaire, 33 questions were prepared which was given to the 330 respondents on the basis of convenience sampling out of which 220 individuals filled questionnaire, the total of 200 questionnaires was included in the study after eliminating the incomplete questionnaire. Various factors are being explored from the literature and then with the help of factor analysis some of the most influential factors have been explored. Factors like overconfidence, optimism, cognitive bias, herd behavior, advisory effect, and idealism are the factors which influenced the trading decision of the investors the most. Such kind of a study is contributing in the area of behavioral finance as a trading decision is an important aspect while investing in the stock market. And this kind of study would be helping and assisting financial advisors to strategies for their clients in making the right allocation and also the policy maker and market regulators to come up with better reforms for the Indian stock markets.

Relationship between Foreign Portfolio Investments (FPI), Domestic Institutional Investors, and Stock Market Returns in India

GIS Business ◽  
2017 ◽  
Vol 12 (6) ◽  
pp. 1-9
Author(s):  
Dhananjaya Kadanda ◽  
Krishna Raj
Keyword(s):  
Stock Market ◽  
Institutional Investors ◽  
Negative Relationship ◽  
Trading Strategies ◽  
Market Returns ◽  
Stock Market Returns ◽  
Foreign Portfolio Investment ◽  
Stock Market Return ◽  
Indian Stock Market ◽  
Feedback Trading

The present article attempts to understand the relationship between foreign portfolio investment (FPI), domestic institutional investors (DIIs), and stock market returns in India using high frequency data. The study analyses the trading strategies of FPIs, DIIs and its impact on the stock market return. We found that the trading strategies of FIIs and DIIs differ in Indian stock market. While FIIs follow positive feedback trading strategy, DIIs pursue the strategy of negative feedback trading which was more pronounced during the crisis. Further, there is negative relationship between FPI flows and DII flows. The results indicate the importance of developing strong domestic institutional investors to counteract the destabilising nature FIIs, particularly during turbulent times.

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