scholarly journals An analysis of equity share price movement of selected companies listed in nse

2012 ◽  
Vol 1 (3) ◽  
pp. 246-252
Author(s):  
CHITRA R ◽  
SATHYA M
Keyword(s):  
Average Rate ◽  
Moving Average ◽  
Secondary Data ◽  
Relative Strength ◽  
Rate Of Change ◽  
Share Price ◽  
Price Movement ◽  
Share Prices ◽  
The Past ◽  
Data Collections

The stock market has an important role in the allocation of resources, both directly as a source of funds and as a determinant of firm’s value and borrowing capacity. This study we made a study and analysis on Equity share price behaviour of selected companies listed in NSE India. The Purpose of the study is, attempted to test the equity price movements of IT,Pharmaceuticals, Banking and Automobiles sectors. These are taken as sample sectors. The objective of the study is to analyze the Equity share price movement of selected companies listed in NSE, India and to analyze the movements of the high pricedequity shares of four sectors for the past 3 years. Data collections are to be used in the study is secondary data. The study usestools such as Simple Moving Average, Rate of Change, Relative Strength Index, Stochastic oscillator and Trend analysis. TheMonthly share prices of above mentioned companies were taken for a period of three years from January 2009 to Dec 2011. Theclosing prices of share prices were taken. All four sectors registered tremendous growth in the past 3 years, and as per theanalysis all these sectors companies were predicted as best to invest for better rate of return for the investors.

scholarly journals Stock Price Movement through Technical Analysis: Empirical Evidence from the Information Technology (IT) Sector

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Shalini Singh ◽  
Anindita Chakraborty
Keyword(s):  
Stock Price ◽  
Moving Average ◽  
Secondary Data ◽  
Technical Analysis ◽  
Future Price ◽  
Analysis Tool ◽  
Price Movement ◽  
Technical Indicators ◽  
Share Prices ◽  
The Future

<em>Technical analysis forecasts the future asset prices with the use of their historical prices, trading volumes, market action and primarily through the uses of charts that predicts the future price trends. Technical analysis guides the investor to track the market with different indicators which is convenient for their study. Technical indicators aids to analyse the short-term price movement of the shares, most importantly it indicates the turning point and helps in projecting the price movement. This paper is prepared to employ the technical analysis tool to IT index companies. Indicators have been analysed using share prices of companies for 1 years, i.e., from January 2015- December 2015. Study is performed using secondary data, which has been collected from NSE website. The Technical Indicators used for the study are Bollinger Bands and MACD (Moving Average Convergence and Divergence). The purpose of the study is to find the best technical indicator to analyse the share prices.</em>

scholarly journals Analysis of Nifty Movement on Share Prices of Selected Construction Companies

2012 ◽  
Vol 1 (3) ◽  
pp. 59-65
Author(s):  
M. Gomathi ◽  
Dr.S. Nirmala
Keyword(s):  
Stock Prices ◽  
Moving Average ◽  
Technical Analysis ◽  
Relative Strength ◽  
Rate Of Change ◽  
Construction Sector ◽  
Strength Index ◽  
Share Prices ◽  
Construction Companies ◽  
Price Movements

This study aims at analyzing and predicting the price movements of construction companies stocks contributing to the NIFTY50 Index. To analyze the volatility of telecom stock and understand the behavior of stock prices in construction sector stocks i.e. (JP ASSOCIATES LIMITED, DLF LIMITED, GAMMON INDIA LIMITED, PUNJ LLOYD LIMITED, HCC LIMITED). The data for these stocks are collected from magazines, newspaper and websites. The stocks are analyzed by monitoring their respective price movements using technical tools. The technical tools used in this study are Exponential moving average, Relative strength index, Rate of change, MACD. Using these tools the trend over the recent past was deciphered. The expected trend in the immediate future was also predicted. Technical Analysis studies the price and volume movement in the market and predicts the future. It helps in identifying that the best time to buy and sell equity. Technical Analysis is a method of evaluating equities by analyzing the statistics generated by market activity, such as past prices and volume.

scholarly journals Prediction of Stock Price Movements using Monte Carlo Simulation

2019 ◽  
Vol 8 (12) ◽  
pp. 2012-2016
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Stock Price ◽  
Narrow Band ◽  
Share Price ◽  
Share Prices ◽  
The Past ◽  
Price Movements ◽  
A Minor ◽  
Random Behaviour

Monte Carlo Simulation depends on random behaviour of events. When a variable takes values at random and becomes highly unpredictable due to its nature of randomness, the property of random numbers is made use of for predicting the future values that the variable may take. This property can be made use of for predicting share price movements, when the past share prices exhibit random behaviour, without exhibiting high fluctuations. This article explains the methodology of using Monte Carlo Simulation for predicting share price movements and explains the process with the help of an illustration taking the monthly share price data of ITC Limited for a period of 36 months, where the share prices have moved within a narrow band. Findings of the analysis show that it works well and that the method of prediction is reasonably accurate, showing only a minor deviation from the actual prices.

Market Fundamentals and Stock Pricing in Nigeria

2015 ◽  
pp. 91-108
Author(s):  
Chukwuma Agu ◽  
Anthony Orji
Keyword(s):  
Secondary Data ◽  
Relative Price ◽  
Share Price ◽  
Share Prices ◽  
Macroeconomic Indicators ◽  
Market Fundamentals ◽  
External Sector ◽  
Key Indicators ◽  
The Relationship ◽  
Fundamental Values

This chapter investigates the relationship between stock pricing and behaviour of the stock market on one hand and micro and macroeconomic fundamentals in the Nigerian economy on the other from 1980-2009 using both primary and secondary data. Results from the primary survey indicate that the key drivers of share prices were neither broad macroeconomic indicators nor key indicators of the health of the firm. Prices were clearly shown to be much above levels that could have been determined by such indicators as posted profits of firms, amounts paid out as dividend and its regularity. Secondary data analysis equally show that the relationship between actual levels of the all share price index for the period of our analysis and during the financial crisis were not driven by “expected” variables. While its fundamental values are driven by monetary and relative price variables, actual values are driven by external sector variables and prices.

scholarly journals Indian Stock Markets Data Analysis and Prediction using Macroeconomics Indictors in Machine Learning

2020 ◽  
Vol 9 (10) ◽  
pp. 484-486
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Moving Average ◽  
Relative Strength ◽  
Rate Of Change ◽  
Random Forest Algorithm ◽  
Strength Index ◽  
Training Set ◽  
Trend Prediction ◽  
Unique Role

Machine Learning plays a unique role in the world of stock market when it comes to the trend prediction. Machine learning library MLIB helps in determining the future values of stocks. With the help of this research one can find the ups and downs of stock market by providing a signal for the same and done by analyzing the previous stock data. This study is based on analysis of stock data from 2000 to 2009 which includes top fifty companies of various sectors from all over India. Six stock data indicators known as, Bollinger Band, Relative Strength Index(RSI), Stochastic Oscillator, Williams % R, Moving Average Convergence Divergence (MACD), Rate of Change applied on the nineteen years of stock data then results of these indicators are compiled and finally with the use of machine learning libraries like Numpy, Pandas, Matplotlib, Sklearn a random forest algorithm is applied on the compiled result to predict the stock movement , these libraries which splits the results into two sets training set and testing set which also boost up the result and gives you the better prediction.

scholarly journals Underestimating risk leading to the collapse of a market leader in tourism

2022 ◽  
Vol 132 ◽  
pp. 01011
Author(s):  
Jana Janíková ◽  
Marek Vochozka ◽  
Martin Votava
Keyword(s):  
Stock Prices ◽  
Negative Impact ◽  
Moving Average ◽  
Share Price ◽  
Travel Agency ◽  
Effect Analysis ◽  
Share Prices ◽  
Cause And Effect Analysis ◽  
Market Leader ◽  
The Impact

The topic of this paper, underestimating risk leading to the collapse of the market leader in tourism, is demonstrated on the example of the British travel agency Thomas Cook, which at one time was one of the oldest and largest travel agencies in the world. The aim of this paper is to analyze the development of the stock prices of Thomas Cook from May 13, 2018 to May 19, 2019 and the factors that had an impact on the share price of this company in the monitored period. The base source of data are the share prices of the travel agency Thomas Cook in the specified period from May 13, 2018 to May 19, 2019 published by MarketWatch. A statistical description of time series is used, a moving average trend line is displayed, and a cause-and-effect analysis evaluating the impact of the published information on the value of Thomas Cook’s stocks is carried out. The general lesson for companies resulting from this contribution is that every negative event, announcement or piece of information has a negative impact on the value of a company’s shares and a collapse could happen even to the leader of a given industry. The collapse of Thomas Cook provides lessons for companies doing business in tourism, so that in the event of a planned merger, a suitable company is selected, the company’s funds are under control and development trends in the field are monitored.

Trading rules and excess returns: evidence from Turkey

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Massoud Metghalchi ◽  
Nazif Durmaz ◽  
Peggy Cloninger ◽  
Kamvar Farahbod
Keyword(s):  
Moving Average ◽  
Relative Strength ◽  
Rate Of Change ◽  
Multiple Time ◽  
Excess Returns ◽  
Strength Index ◽  
Technical Trading Rules ◽  
Trading Rules ◽  
Content Type ◽  
Practical Implications

Purpose This paper aims to investigate popular technical trading rules (TTRs) applied to the FTSE Turkish all-cap and small-cap indexes from September 23, 2003 to August 9, 2019 to determine rules that produce net excess returns over the Buy-and-Hold strategy (B&H). Design/methodology/approach Five TTRs, namely, simple moving average, relative strength index, moving average convergence divergence, momentum, and rate of change, are applied, singly (one indicator) and in combination (two indicators) for multiple time periods. Findings For the small-cap index, some TTRs – including the famous Golden Cross, when the 50-day moving average rises above 200-day moving average – produced net annual excess returns (NAERs) over the B&H strategy, for the entire period and each sub-period, after accounting for risk and transaction costs. Results were mixed for the large-cap index. The results support Cakici and Topyan (2013). Research limitations/implications This study investigates several indicators, but future studies should examine others, especially based on volume and price. Practical implications Investors in the FTSE Turkish small-cap index may use some trading rules to earn NAERs over the B&H strategy. Originality/value This research is important because it addresses a gap in the research by examining numerous TTRs in the Turkish stock market. Studies of TTRs in Turkey are scarce.

scholarly journals Modeling the Dependence Structure of Share Prices among Three Chinese City Banks

2018 ◽  
Vol 11 (4) ◽  
pp. 57 ◽  
Author(s):  
Guizhou Liu ◽  
Xiao-Jing Cai ◽  
Shigeyuki Hamori
Keyword(s):  
Global Financial Crisis ◽  
The Other ◽  
Share Price ◽  
Dependence Structure ◽  
Copula Models ◽  
Share Prices ◽  
The Past ◽  
Student’S T ◽  
The Global Financial Crisis ◽  
Dependence Structures

We study the dependence structure of share price returns among the Beijing Bank, Ningbo Bank, and Nanjing Bank using copula models. We use the normal, Student’s t, rotated Gumbel, and symmetrized Joe-Clayton (SJC) copula models to estimate the underlying dependence structure in two periods: one covering the global financial crisis and the other covering the domestic share market crash in China. We show that Beijing Bank is less dependent on the other two city banks than Nanjing Bank, which is dependent on the other two in share price extreme returns. We also observe a major decrease of dependency from 2007 to 2018 in three one-to-one dependence structures. Interestingly, contrary to recent literatures, Ningbo Bank and Nanjing Bank tend to be more dependent on each other in positive returns than in negative returns during the past decade. We also show the dynamic dependence structures among three city banks using time-varying copula.

scholarly journals Pengaruh Likuiditas terhadap Harga Saham PT. Jasa Marga (Persero) Tbk

2020 ◽  
Vol 9 (2) ◽  
pp. 152
Author(s):  
Nadia Lestari ◽  
Aris Munandar
Keyword(s):  
Stock Price ◽  
Linear Regression Analysis ◽  
Secondary Data ◽  
Sampling Technique ◽  
Share Price ◽  
Financial Statement ◽  
Current Ratio ◽  
Test Analysis ◽  
Share Prices ◽  
Independent Variable

This study aims to determine whether there is a significant effect between Liquidity on Share Prices at PT. Jasa Marga (Persero) Tbk. Liquidity as the independent variable while the Stock Price as a Bound Variable. The cause of Jasa Marga's share price decline is because the company will enter an expansion cycle. This increase was due to the increase in debt to the Bank to finance land bailouts to the Subsidiaries which was quite high. This condition is considered to have influenced the downward trend in share prices of PT. Jasa Marga (Persero) Tbk. This type of research is associative using secondary data, the population used is financial statement data for 12 years starting in 2007 - 2018 while the sample in this study is financial statement data for 5 years, namely in 2014 - 2018. The sampling technique using Purposive Sampling. Collection techniques using documentation and study of literature. Data analysis techniques using Current Ratio, Simple Linear Regression Analysis, Simple Correlation Coefficient, Simple Determination Coefficient, and One-Sample t-Test Analysis. The results of the Current Ratio value have fluctuated for 5 years, but losses have increased very high in 2015, 2016, and 2018. Current Ratio has a significant effect on the Stock Price

scholarly journals Technical analysis on select stocks of banking sector

2016 ◽  
Vol 6 (3) ◽  
pp. 231-242
Author(s):  
Sharmila R ◽  
Kavitha R ◽  
Ananthi S
Keyword(s):  
Banking Sector ◽  
Weather Forecasting ◽  
Stock Exchange ◽  
Moving Average ◽  
Technical Analysis ◽  
Relative Strength ◽  
Rate Of Change ◽  
Strength Index ◽  
Price Movements ◽  
Over Time

Technical Analysis is the forecasting of future financial price movements based on an examination of past price movements. Like weather forecasting, technical analysis does not result in absolute predictions about the future. Instead, technical analysis can help investors anticipate what is “likely” to happen to prices over time. Technical analysis uses a wide variety of charts that show price over time. This study is based on the analysis of four Nifty Bank Index stocks namely Axis Bank, Bank of Baroda, State Bank of India and ICICI bank listed in National Stock Exchange. Technical indicators such as Relative strength index (RSI), Rate of change (ROC) and Moving Average (MA) are used in the study. This paper aims at carrying out Technical Analysis of the securities of the selectedbanking stocks and to assist investment decisions in this Indian Market.

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