An Empirical Study on Efficient Market Hypothesis: The Case of Indian Capital Markets

2014 ◽  
Vol 1 (2) ◽  
Author(s):  
Anjala Kalsie
Keyword(s):  
Time Series ◽  
Empirical Study ◽  
Time Series Analysis ◽  
Unit Root ◽  
Weak Form ◽  
Efficient Market Hypothesis ◽  
Efficient Markets ◽  
Test Unit ◽  
Unit Root Testing ◽  
Univariate Time Series

The objective of this paper is to study the efficiency of Indian stock markets during the period 2001-2011. The weak form of efficient markets is extensively tested using NIFTY and 6 major NSE sectoral indices Pharma, IT, MNC, Bank, FMCG and Nifty Junior. Univariate time series analysis of indices returns is carried using tests for randomness / non-stationarity - runs test, unit root testing. ACF, correlograms and other relevant statistical methods. The study concludes that Indian markets are inefficient in its weak form for the study period.

scholarly journals THE WEAK FORM MARKET EFFICIENCY IN THE MSCI ETF INDICES: CONVENTIONAL AND THE FOURIER UNIT ROOT TEST ON THE DEVELOPED AND DEVELOPING COUNTRIES

2020 ◽  
Vol 8 (4) ◽  
pp. 409-423
Author(s):  
Sümeyra GAZEL
Keyword(s):  
Developing Countries ◽  
Unit Root ◽  
Structural Break ◽  
Unit Root Test ◽  
Weak Form ◽  
Efficient Market Hypothesis ◽  
Developed Countries ◽  
Exchange Traded Funds ◽  
Unit Root Testing ◽  
Daily Data

In this study, weak form efficiency of the Exchange Traded Funds (ETF) in the Morgan Stanley Capital International (MSCI) Index of developed and developing countries is tested. The Fourier Unit Root test, which does not lose its predictive power in terms of structural break date, number and form, is used on daily data. Also, conventional unit root tests are used for comparison between two different tests. Analysis results indicate common findings in some countries for both unit root testing. However, the Fourier unit root test results relatively more support the assumption of efficient market hypothesis that developed countries may be more efficient than developing countries.

scholarly journals Testing the efficient market hypothesis on the Nairobi Securities Exchange

2016 ◽  
Vol 13 (3) ◽  
pp. 75-83 ◽  
Author(s):  
Josephine Njuguna
Keyword(s):  
Unit Root ◽  
Serial Correlation ◽  
Weak Form ◽  
Efficient Market Hypothesis ◽  
Unit Root Tests ◽  
Test Results ◽  
Test Unit ◽  
Efficient Market ◽  
Correlation Test ◽  
Index Data

This paper tests the weak-form of the efficient market hypothesis (EMH) of the Nairobi Securities Exchange (NSE) using daily and weekly index data from the NSE 20 share index over the period, January 2001 to January 2015 and the NSE All Share Index (ASI) from its initiation, in February 2008 to January 2015. To test weak-form efficiency in this market, this study uses the serial correlation test, unit root tests (ADF and Phillips-Perron) and runs test. Results indicate that we cannot accept the EMH for the NSE using the serial correlation test, unit root tests and the runs test. Overall, the Kenyan market is found to not be weak-form efficient

scholarly journals Forecasting of Economic Indicators (Production, Consumption, Population) of Wheat Crop (A Case Study)

2021 ◽  
Vol 8 (6) ◽  
pp. 38-46
Author(s):  
Mohammad Karim Ahmadzai
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Wheat Crop ◽  
Series Analysis ◽  
The People ◽  
Self Sufficiency ◽  
Univariate Time Series ◽  
Short Run

Wheat is the most important food crop in Afghanistan, whether consumed by the bulk of the people or used in various sectors. The problem is that Afghanistan has a significant shortfall of wheat between domestic production and consumption. Thus, the present study looks at the issue of meeting self-sufficiency for the whole population due to wheat shortages. To do so, we employ time series analysis, which can produce a highly exact short-run prediction for a significant quantity of data on the variables in question. The ARIMA models are versatile and widely utilised in univariate time series analysis. The ARIMA model combines three processes: I the auto-regressive (AR) process, (ii) the differencing process, and (iii) the moving average (MA) process. These processes are referred to as primary univariate time series models in statistical literature and are widely employed in various applications. Where predicting future wheat requirements is one of the most important tools that decision-makers may use to assess wheat requirements and then design measures to close the gap between supply and consumption. The present study seeks to forecast Production, Consumption, and Population for the period 2002-2017 and estimate the values of these variables between 2002 and 2017. (2018-2030).  

Recent Mortality Trends in the Spanish Population

2002 ◽  
Vol 8 (4) ◽  
pp. 757-786 ◽  
Author(s):  
A. Felipe ◽  
M. Guillen ◽  
A. M. Perez-Marin
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Spanish Population ◽  
Mortality Trends ◽  
Calendar Time ◽  
Series Analysis ◽  
Univariate Time Series ◽  
Using Data ◽  
The Way

ABSTRACTOur research deals with the way that calendar time affects mortality patterns in the Spanish population, and how this information can be used to elaborate predictions. A description of the observed mortality evolution has been worked out using data from 1975 to 1993. We have used Heligman-Pollard Law number two to model the evolution of Spanish mortality over the period and using univariate time series analysis, we have obtained a prognosis for years 1994 to 2010.

scholarly journals Random Walk Hypothesis for Stock Prices in Fiji

2021 ◽  
Vol 13 (2) ◽  
pp. 79-88
Author(s):  
Janesh Sami
Keyword(s):  
Random Walk ◽  
Stock Market ◽  
Unit Root ◽  
Stock Prices ◽  
Structural Break ◽  
Weak Form ◽  
Efficient Market Hypothesis ◽  
Random Walk Hypothesis ◽  
Augmented Dickey Fuller ◽  
Historical Movement

The main goal of this paper is to investigate the random walk hypothesis in Fiji using monthly data from January 2000 to October 2017. Applying augmented Dickey Fuller (ADF 1979, 1981) and Phillips-Perron (1988), Zivot-Andrews (1992), and Narayan and Popp (2010) unit root tests, this study finds that stock prices is best characterized as non-stationary. The estimated multiple structural break dates in the stock prices corresponds with devaluation of Fijian dollar by 20 percent in 2009 and General Elections in September 2014, which Fiji First Party won by majority votes. The empirical results indicate that stock prices are best characterized as a unit root (random walk) process, indicating that the weak-form efficient market hypothesis holds in Fiji’s stock market. Hence, it will be difficult to predict future returns based on historical movement of stock prices in Fiji’s stock market.

scholarly journals Using ARIMA Models to Forecasting of Economic Variables of Wheat Crop in Afghanistan

2020 ◽  
pp. 1-21
Author(s):  
M. Karim Ahmadzai ◽  
Moataz Eliw
Keyword(s):  
Time Series ◽  
Correlation Function ◽  
Time Series Analysis ◽  
Partial Correlation ◽  
Wheat Crop ◽  
Prediction Methods ◽  
Arima Models ◽  
Self Sufficiency ◽  
Univariate Time Series ◽  
Production And Consumption

Wheat is considered the main food crops in Afghanistan, whether to use it for majority of the population consumption or to use it in some industries and others. Problem: Afghanistan suffers from a large gap between production and consumption, so the current research investigates the problem arising from a shortage of wheat production to meet self-sufficiency of the population. Methods: The time series analysis can provide short-run forecast for sufficiently large amount of data on the concerned variables very precisely. In univariate time series analysis, the ARIMA models are flexible and widely used. The ARIMA model is the combination of three processes: (i) Autoregressive (AR) process, (ii) Differencing process and (iii) Moving-Average (MA) process. These processes are known in statistical literature as main univariate time series models and are commonly used in many applications. Where, Estimation of future wheat requirement is one of the essential tools that may help decision-makers to determine wheat needs and then developing plans that help reduce the gap between production and consumption. A solid strategy that widely applying of improved seeds and fertilizers, an effective research and extension system for better crop management is necessary to eliminate this gap for self-sufficiency in wheat production, besides providing the necessary financial sums for that. Where most prediction methods are valid for one-year prediction. However, moving prediction methods have been found to measure and predict the future movement of the dependent variable. Aims: The current research aims to prediction for Area, Productivity, Production, Consumption and Population over the period (2002-2017), to estimate the values of these variables in the period of (2018-2030). Results: The results showed that through the drawing of the historical data for Planted area, Productivity, Production, Consumption and Population of wheat crop it was evident that the series data is not static due to an increasing or a decreasing of general trend, which means the instability of the average, by using Auto-correlation function (ACF) and Partial Correlation Function to detect the stability of the time series, The results showed also, the significance of Autocorrelation coefficient and partial correlation coefficient values, which indicates that the time series is not static.

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