scholarly journals Modelling the Behaviour of Technicians and Fundamentalists in the Shanghai Stock Market

2009 ◽  
Author(s):  
Imad A. Moosa ◽  
Larry CF Li
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Price Formation ◽  
Chinese Stock Market ◽  
Price Effect ◽  
Daily Data ◽  
The Government

This paper provides empirical evidence on the role of fundamentalists and technicians in the Chinese stock market. Three econometric models are used to differentiate the stock price effect between the actions of traders who act on the basis of fundamental analysis and those acting on the basis of technical analysis. The models are estimated using randomly selected monthly and daily data on the stock prices of one hundred companies listed on the Shanghai Stock Exchange. The results reveal that both fundamentalists and technicians have roles to play in stock price formation, although technicians appear to play a more important role. This result holds even if the government intervention is allowed for. Some explanations are presented for the dominance of technicians.  

scholarly journals Investor Sentiment and Stock Price Crash Risk in the Chinese Stock Market

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Binghui Wu ◽  
Yuanman Cai ◽  
Mengjiao Zhang
Keyword(s):  
Comparative Analysis ◽  
Stock Market ◽  
Stock Price ◽  
Stock Exchange ◽  
Investor Sentiment ◽  
Crash Risk ◽  
Research Approach ◽  
Chinese Stock Market ◽  
The Impact ◽  
A Share

This paper uses the partial least squares method to construct the investor sentiment index in Chinese stock market. The Shanghai Stock Exchange 180 Index and the Shenzhen Stock Exchange 100 Index are used as samples. From the perspectives of holistic sentiment and heterogeneous sentiment, this paper studies the impact of investor sentiment on stock price crash risk. The results show that investor sentiment can significantly affect stock price crash risk in Shanghai and Shenzhen A-share markets, especially in the Shenzhen A-share market no matter from which perspective. And investor pessimism has a greater impact on stock price crash risk in the Shenzhen A-share market from the perspective of heterogeneous sentiment. Compared with the available researches, this paper makes two contributions: (i) the comparative analysis is adopted to discuss the differences between Shanghai and Shenzhen A-share markets, abandoning the research approach that takes the two markets as a whole in existing literature, and (ii) this paper not only studies the impact of investor holistic sentiment on stock price crash risk from a macro perspective, but also adds a more micro heterogeneous sentiment and conducts a comparative analysis.

Performance and development of a thin stock market: the Stockholm Stock Exchange 1912–2017

2020 ◽  
pp. 1-19
Author(s):  
Kristian Rydqvist ◽  
Rong Guo
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Stock Selection ◽  
Data Set ◽  
Transaction Prices ◽  
Limit Orders ◽  
A Value

We estimate historical stock returns for Swedish listed companies in a newly constructed data set of daily stock prices that spans more than 100 years. Stock returns exhibit all the familiar characteristics. The growth of the public sector depressed the stock market, and the process of globalization revitalized it. Banks played an important role in the early development of the stock market. There was little trading in the past, and we examine the effects on return measurement from missing data. Stock selection and the replacement of missing transaction prices through search back procedures or limit orders make little difference to a value-weighted stock price index, while ignoring the price effects of capital operations makes a big difference.

Impact on Stock Price by the Inclusion to and Exclusion from CNX Nifty Index

2012 ◽  
Vol 13 (1) ◽  
pp. 39-50 ◽  
Author(s):  
M. Selvam ◽  
G. Indhumathi ◽  
J. Lydia
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Research Study ◽  
Stock Exchange ◽  
Valuation Effect ◽  
Index Maintenance ◽  
Indian Stock Market ◽  
The Impact ◽  
Indian Perspective

Changes in an index are a regular phenomenon and they take place due to the inclusion and exclusion of stocks from the index. The inclusion or exclusion of stocks creates great impact on the value of the firm. However, these changes are simply a short-lived event with no permanent valuation effect. The present research study analyzed the impact of the inclusion into and exclusion of certain stocks from National Stock Exchange (NSE) S&P CNX Nifty index with Indian perspective. The study provides evidence on whether the announcements of Nifty index maintenance committee have any information content. This will also demonstrate the efficiency of Indian stock market with particular reference to NSE. The study revealed that on an average, no permanent effects were observed on stock prices. It is also found from the study that the NSE reacted unfavourably to the inclusion and exclusion of stocks and it is impossible to earn any excess returns where the particular stocks are included or excluded from the index.

scholarly journals THE MEDIATING ROLE OF DEBT AND DIVIDEND POLICY ON THE EFFECT PROFITABILITY TOWARD STOCK PRICE

2020 ◽  
Vol 22 (1) ◽  
pp. 1-10
Author(s):  
Akhmadi Akhmadi ◽  
Nurohman Nurohman ◽  
Robiyanto Robiyanto
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Dividend Policy ◽  
Stock Exchange ◽  
Sampling Technique ◽  
Return On Equity ◽  
Mediating Role ◽  
Debt Policy ◽  
Positive Effect

This study aimed to obtain an empirical explanation of the role of debt policy and dividend policy as variables mediating the influence of profitability on stock prices. This study used six mining companies listed on the Indonesia Stock Exchange (IDX) during the period of 2012–2016 as samples, hence there were 30 observational data. The sampling technique in this study was purposive sampling. This study found that profitability had a positive effect on stock prices, but the increasing profitability would not necessarily reduce the debt policy. The increasing profitability did not significantly increase the dividend policy, however, increasing dividend policy would increase the stock prices. The results also proved that debt and dividend policy did not mediate the influence of return on equity on the stock prices.

scholarly journals A Robust Predictive Model for Stock Price Forecasting

2021 ◽  
Author(s):  
Jaydip Sen ◽  
Tamal Datta Chaudhuri
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Research Work ◽  
Efficient Market Hypothesis ◽  
Price Forecasting ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Daily Data ◽  
Functional Forms

Prediction of future movement of stock prices has been the subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannot be predicted accurately. On the other hand, there are propositions that have shown that, if appropriately modelled, stock prices can be predicted fairly accurately. The latter have focused on choice of variables, appropriate functional forms and techniques of forecasting. This work proposes a granular approach to stock price prediction by combining statistical and machine learning methods with some concepts that have been advanced in the literature on technical analysis. The objective of our work is to take 5 minute daily data on stock prices from the National Stock Exchange (NSE) in India and develop a forecasting framework for stock prices. Our contention is that such a granular approach can model the inherent dynamics and can be fine-tuned for immediate forecasting. Six different techniques including three regression-based approaches and three classification-based approaches are applied to model and predict stock price movement of two stocks listed in NSE - Tata Steel and Hero Moto. Extensive results have been provided on the performance of these forecasting techniques for both the stocks.

scholarly journals Stock Market Prices Prediction using Random Forest and Extra Tree Regression

2019 ◽  
Vol 8 (3) ◽  
pp. 1224-1228
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Research Area ◽  
Support Vector ◽  
Market Prices ◽  
Machine Learning Approach ◽  
Innovative Work

Prediction of Stock price is now a day’s an existing and interesting research area in financial and academic sectors to know the scale of economies. There did not exists any significant set of rules to estimate and predict the scale of share in the stock exchange. Many evolutionary technologies are existing such as technical, fundamental, time, statistical and series analysis which help us to attempt the prediction process, but none of the methods are proved as reliable and accurate tool to the society in the estimation of stock exchange or share market scales. Here in this paper we attempted to do innovative work through Machine Learning approach to predict or sense the behaviour tracking of the stock market sensex. Linear regression, Support Vector regression, Decision Tree, Ramdom Forest Regressor and Extra Tree Regressor are the Machine Learning models implemented effectively in predicting the stock prices and define the activity between the exchanges the securities between the buyers and sellers. We predicted the price of the stock based on the closing value and stock price. An algorithm with high accuracy we do the process of comparison for the accuracy of each of the model and finally is considered as better algorithm for predicting stock price. As share market is a vague domain we cannot predict the conditions occur, and also share market can never be predicted, this job can be done easily and technically through this work and the main aim of this paper is to apply algorithms in Machine Learning in predicting the stock prices.

scholarly journals FUNDAMENTAL AND TECHNICAL ANALYSIS OF THE STOCK PRICE

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Denis Spahija ◽  
Seadin Xhaferi
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Stock Exchanges ◽  
Full Time ◽  
Equal Opportunities ◽  
Factors Affecting ◽  
The Past ◽  
Real Value

Trading with stocks in developed market conditions for some is fun, for others it is a way to preserve the real value of the asset, while for the most is a challenge to gain bigger profits quickly and easily. Dreams on stock market alchemy rely on the development and upgrading of special systems whose ultimate goal is to uncover stock price secrets and their changes. What are the chances of this happening? Chances are minimal, according to experiences from the world’s leading stock exchanges in the past. The stock market complexity, the number and unpredictability of factors affecting stock prices and unexpected changes or stability do not give much hope to those who know what’s going to happen in the future. In such endeavors there are equal opportunities for both stock exchange experts and full-time amateurs. For all this, if the stock market cannot be defeated or deceived, then it is better to join it. So this means: to create a diversified portfolio of securities that provides a safe income, slightly higher than annual inflation, minimizing the risk.

scholarly journals Company fundamentals and stock price movements: The role of crude oil prices – Evidence from Nigerian Stock Exchange (NSE)

2020 ◽  
Vol 3 (1) ◽  
pp. 1-13
Author(s):  
Tijjani Bashir Musa
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Panel Study ◽  
Oil Prices ◽  
Secondary Source ◽  
Linear Regression Models ◽  
Price Movements ◽  
Stock Price Movements

This study analyzed company fundamentals on how it relates and predict stock price movements and the extent of the role of oil prices in moderating the influence of these company fundamentals in stock price movements. The study covered the period of 2014 to 2018. The study is a panel study. A total of 132 companies were sampled from 196 companies listed on the Nigerian Stock Exchange (NSE) as of December 2018. Data were collected from a secondary source. Multiple linear regression models were used to analyze the data. The study found that a relationship exists between selected companies' fundamentals and stock prices, and oil prices moderate the relationship. But EPS and Working Capital have high predictive power on stock price movements but moderating with oil prices the influence reduces significantly. The study recommends among others that Managers of companies in Nigeria should formulate policies and exert effort geared towards improving company fundamentals in the event of oil prices increases.

scholarly journals The Effect of Financial Institutions Banking Soundness Against Stock Price

2019 ◽  
pp. 045-052
Author(s):  
Abu Bakar Akbar
Keyword(s):  
Financial Institutions ◽  
Stock Prices ◽  
Commercial Banks ◽  
Stock Price ◽  
Stock Exchange ◽  
Linear Regression Analysis ◽  
Capital Adequacy ◽  
Coefficient Of Determination ◽  
Measurement Tool ◽  
The Government

This study was conducted to determine the effect of the soundness of banking financial institutions variable as measured by Return on Assets, Net Interest Margin, and Capital Adequacy Ratio on share prices at government banks in the Indonesia Stock Exchange for the period 2008-2015. The population of this study is the Government commercial banks consisting of BRI Bank, BNI Bank, BTN Bank, and Bank Mandiri. The sample of this study is the assessment of the soundness of government commercial banks on stock prices, with the scope of the assessment covering the factors of ROA, NIM, and CAR. The sample was obtained through the publication of 2008-2015 annual financial statements totaling 32 data. The analysis technique uses multiple linear regression analysis, while the measurement tool uses t test, F test, and the coefficient of determination (R2) test. The results showed that the ROA variable had a positive and significant effect on stock prices, the NIM variable had a negative and significant effect on stock prices, the CAR variable had a positive and significant effect on stock prices at government commercial banks in the Indonesia Stock Exchange.

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.

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