This document analyzes whether stock returns in the Nigerian stock market follow a random walk distribution by testing the weak-form efficiency of the market. The study uses daily return data from 2010 to 2014 of the top 20 most active stocks on the Nigerian Stock Exchange. Autocorrelation and runs tests were performed and found that daily stock returns were randomly distributed, indicating the market is informationally efficient at the weak form level. This means past stock price information cannot be used to consistently earn abnormal returns. The study recommends further efforts to improve the market to attract more domestic and foreign investment.
Efficient market hypothesis and nigerian stock marketAlexander Decker
This document discusses a study that examined the weak-form efficient market hypothesis in the Nigerian stock market from 1986 to 2010. The researchers tested for stationarity using the Augmented Dickey Fuller and Philip Perron tests, and used serial auto-correlation and regression analysis to test for random walks in stock prices. The results showed that stock prices do not exhibit random walks and are not informationally efficient. The study recommends stronger regulation and policies to develop the Nigerian stock market and enhance its informational efficiency.
11.efficient market hypothesis and nigerian stock marketAlexander Decker
This document discusses a study that examined the weak-form efficient market hypothesis in the Nigerian stock market from 1986 to 2010. The researchers tested for stationarity using the Augmented Dickey Fuller and Philip Perron tests, and used serial auto-correlation and regression analysis to test for random walks in stock prices. The results showed that stock prices do not exhibit random walks and are not informationally efficient. The study recommends stronger regulation and policies to develop the Nigerian stock market and enhance its informational efficiency.
11.distribution of risk and return a statistical test of normality on dhaka s...Alexander Decker
This document summarizes a research study that examined the normal distribution of risk and return on the Dhaka Stock Exchange in Bangladesh. The study used statistical tests to analyze daily, weekly, and monthly returns calculated from three DSE indices over different time periods. The results found evidence of skewness and kurtosis in the returns, indicating they were not normally distributed and contradicting the assumption of random walk behavior. Additionally, inconsistencies were found between daily/weekly risk and return, suggesting additional return could be achieved without additional risk. The study aims to contribute to evaluating market efficiency assumptions in the Bangladesh capital market.
The effect of earnings announcement on share prices in ghanaAlexander Decker
This study examined the effect of earnings announcements on share prices on the Ghana Stock Exchange from 2010 to 2013. The researchers analyzed abnormal returns for 10 selected companies during a 21-day event window surrounding earnings announcement dates, using the market model and event study methodology. The results found that abnormal returns around earnings announcements were not statistically significant, inconsistent with the efficient market hypothesis. This suggests that the Ghana stock market does not efficiently incorporate earnings information into share prices at the time of announcements or immediately after. In conclusion, earnings announcements did not have a major effect on share prices of the selected companies at the time of the announcements or shortly after.
According to the EMH, stocks always trade at their fair value on stock exchanges, making it impossible for investors to either purchase undervalued stocks or sell stocks for inflated prices. As such, it should be impossible to outperform the overall market through expert stock selection or market timing, and that the only way an investor can possibly obtain higher returns is by purchasing riskier investments.
This document summarizes a study on trend following algorithms for technical trading in the stock market. It presents two trend following algorithms: 1) Static P&Q, which uses static values for parameters P and Q to determine when to enter and exit trades, and 2) Adaptive P&Q, which uses dynamically adjusted P and Q values. The algorithms were tested in a stock market simulation, and the Static P&Q algorithm achieved average monthly returns of 75.63%. However, performance degraded as market trend fluctuations increased, implying the need to pause trading during periods of high volatility.
Applications of Artificial Neural Network in Forecasting of Stock Market Indexpaperpublications3
Abstract: Prediction in any field is a challenging and unnerving process. Stock market is a promising financial investment that can generate great wealth. However, under the impact of Globalization Stock Market Prediction (SMP) accuracy has become more challenging and rewarding for the researchers and participants in the stock market. Artificial Neural Networks (ANN) have been found to be an efficient tool in modeling stock prices and quite a large number of studies have been done on it. ANN modeling of stock prices of selected stocks under NSE is attempted to predict the next day’s price. The network developed consists of one input layer, hidden layer and output layer with four, nine and one nodes respectively. The input being the closing price of the previous four days and output being the price for the next day. In the first section the adaptability of neural networks in stock market prediction is discussed, in the second section we discuss the traditional methods that were being used earlier for stock market prediction, in the third section we discuss the justification for using neural networks and how it is better over traditional methods, in the fourth section we discuss the basics of neural networks, section five gives an overview of data and methodology being used, in section six we have discussed the various forecasting errors methods to calculate the error, in section seven we have presented our results. The aim of this paper is to provide an overview of the application of artificial neural network in stock market prediction.
Efficient market hypothesis and nigerian stock marketAlexander Decker
This document discusses a study that examined the weak-form efficient market hypothesis in the Nigerian stock market from 1986 to 2010. The researchers tested for stationarity using the Augmented Dickey Fuller and Philip Perron tests, and used serial auto-correlation and regression analysis to test for random walks in stock prices. The results showed that stock prices do not exhibit random walks and are not informationally efficient. The study recommends stronger regulation and policies to develop the Nigerian stock market and enhance its informational efficiency.
11.efficient market hypothesis and nigerian stock marketAlexander Decker
This document discusses a study that examined the weak-form efficient market hypothesis in the Nigerian stock market from 1986 to 2010. The researchers tested for stationarity using the Augmented Dickey Fuller and Philip Perron tests, and used serial auto-correlation and regression analysis to test for random walks in stock prices. The results showed that stock prices do not exhibit random walks and are not informationally efficient. The study recommends stronger regulation and policies to develop the Nigerian stock market and enhance its informational efficiency.
11.distribution of risk and return a statistical test of normality on dhaka s...Alexander Decker
This document summarizes a research study that examined the normal distribution of risk and return on the Dhaka Stock Exchange in Bangladesh. The study used statistical tests to analyze daily, weekly, and monthly returns calculated from three DSE indices over different time periods. The results found evidence of skewness and kurtosis in the returns, indicating they were not normally distributed and contradicting the assumption of random walk behavior. Additionally, inconsistencies were found between daily/weekly risk and return, suggesting additional return could be achieved without additional risk. The study aims to contribute to evaluating market efficiency assumptions in the Bangladesh capital market.
The effect of earnings announcement on share prices in ghanaAlexander Decker
This study examined the effect of earnings announcements on share prices on the Ghana Stock Exchange from 2010 to 2013. The researchers analyzed abnormal returns for 10 selected companies during a 21-day event window surrounding earnings announcement dates, using the market model and event study methodology. The results found that abnormal returns around earnings announcements were not statistically significant, inconsistent with the efficient market hypothesis. This suggests that the Ghana stock market does not efficiently incorporate earnings information into share prices at the time of announcements or immediately after. In conclusion, earnings announcements did not have a major effect on share prices of the selected companies at the time of the announcements or shortly after.
According to the EMH, stocks always trade at their fair value on stock exchanges, making it impossible for investors to either purchase undervalued stocks or sell stocks for inflated prices. As such, it should be impossible to outperform the overall market through expert stock selection or market timing, and that the only way an investor can possibly obtain higher returns is by purchasing riskier investments.
This document summarizes a study on trend following algorithms for technical trading in the stock market. It presents two trend following algorithms: 1) Static P&Q, which uses static values for parameters P and Q to determine when to enter and exit trades, and 2) Adaptive P&Q, which uses dynamically adjusted P and Q values. The algorithms were tested in a stock market simulation, and the Static P&Q algorithm achieved average monthly returns of 75.63%. However, performance degraded as market trend fluctuations increased, implying the need to pause trading during periods of high volatility.
Applications of Artificial Neural Network in Forecasting of Stock Market Indexpaperpublications3
Abstract: Prediction in any field is a challenging and unnerving process. Stock market is a promising financial investment that can generate great wealth. However, under the impact of Globalization Stock Market Prediction (SMP) accuracy has become more challenging and rewarding for the researchers and participants in the stock market. Artificial Neural Networks (ANN) have been found to be an efficient tool in modeling stock prices and quite a large number of studies have been done on it. ANN modeling of stock prices of selected stocks under NSE is attempted to predict the next day’s price. The network developed consists of one input layer, hidden layer and output layer with four, nine and one nodes respectively. The input being the closing price of the previous four days and output being the price for the next day. In the first section the adaptability of neural networks in stock market prediction is discussed, in the second section we discuss the traditional methods that were being used earlier for stock market prediction, in the third section we discuss the justification for using neural networks and how it is better over traditional methods, in the fourth section we discuss the basics of neural networks, section five gives an overview of data and methodology being used, in section six we have discussed the various forecasting errors methods to calculate the error, in section seven we have presented our results. The aim of this paper is to provide an overview of the application of artificial neural network in stock market prediction.
This document summarizes the efficient market hypothesis (EMH) in three sentences:
The EMH states that market prices fully reflect all available public information and adjust instantly to new information. It has three forms - weak, semi-strong, and strong - with each form incorporating more types of information. Most research supports the weak and semi-strong forms, finding that historical data and public information are reflected in prices, but the strong form is not supported as non-public information can be used to earn excess returns.
Movement of Share Prices and Sectoral Analysis: A Reflection Through Interact...Waqas Tariq
Interaction in graphs gives the user with an advantage to analyze the data in greater depth. With the help of interactive graphics users can get better insight of the data in comparison to the static graphical tools. This paper introduces an interactive graphical tool consisting of two graphs, a line diagram complemented by a boxplot. The line diagram helps to understand how successive values of a variable are related to time and box plot can help the visual comparison of several such variables. Here the line diagram is used to visualize share prices of a company corresponding to a number of days and the boxplot displays the position of the Share price of all companies in a particular sector. An investor in share market needs to consider a number of factors before making any decision about investment. Some of the factors influencing the decision are the performance of the particular security in recent past, its position in terms of share price in its own sector. The graphical technique used in this software tool shall be helpful while making investment decision.
This document analyzes value versus growth investing styles through the lens of Dick Mayo's dilemma at GMO about whether to continue focusing on value stocks. It provides an in-depth comparison of the two styles, discussing their different focuses, investment philosophies, risks, typical stock features, and strategies. The document also analyzes whether value investing is still the right strategy given changing economic conditions that have favored growth stocks in recent years. Calculations are shown evaluating specific stocks like Cisco, CVS, and RR Donnelley to determine if they are over or undervalued based on criteria like P/E ratio, P/B ratio, and expected long-term returns.
This document discusses data mining methods and implementation of predictive data mining architecture for stock market prediction. It describes predicting unknown data values using classification, regression, and time series analysis. Two types of predictions discussed are stock market and environmental predictions. Stock market prediction aims to determine future company stock prices, while various parameters like return on investment are analyzed. The document also covers data mining techniques like descriptive and predictive mining, algorithms, and performance evaluation metrics like correct profitable trade signals and annual return on investment.
This document provides an overview of fundamental and technical analysis for investing in the Philippines. It discusses various fundamental analysis tools like price-earnings ratios, income statements, and balance sheets. It also covers technical analysis indicators like moving averages, support and resistance levels, and chart patterns. The document uses examples of companies listed on the Philippine Stock Exchange to demonstrate how to use these analytical tools and approaches for investment decisions in the local market.
Financial Analysis of Retail Business Organization: A Case of Wal-Mart Stores...Samsul Alam
The main objective of this study is to present the Walmart’s financial performance, making the important valuation of the company. The study used quantitative method using secondary sources. The finding of this descriptive study is that Walmart is the lucrative choice for the past, present and future investors with the estimation of terminal value at the end of the fiscal year 2026 estimated US $580 billion and the fundamental value of US $736 billion. The result shows that due to the emergence of stronger competitors and for being matured, Walmart is not performing as expected by investors, but its gigantic market size will make it capable of doing business profitably over a longer period of time. The ultimate decision given for the investors is to buy. The assumption is made on in-depth financial analysis with reliable data and calculation. The study has noteworthy importance to the financial market stakeholders.
Technical and fundamental analysis on stock market Babasab Patil
The document discusses technical and fundamental analysis of securities. It provides an overview of technical analysis concepts like Dow theory, Elliot waves, and moving averages. It also discusses fundamental analysis, including economic, industry, and company analysis. Key company analysis factors mentioned include management, annual reports, ratios, and cash flow. The document outlines objectives to conduct technical and fundamental analysis of selected Indian stock market securities. It describes the research methodology as involving secondary data analysis and a sample size of 10 stocks for technical analysis and 4 for fundamental analysis.
JUMPING RISK IN TAIWAN AND TAIEX OPTION RETURN IN TAIWAN ijcsit
With low-interest environment in recent years, investment of financial commodity was unable to meet the requirements of necessary paid by society. Therefore, the traditional financial tool were replacing with derivative financial commodity which were high risk, high lever, and high complex; including option, forward contract, futures, credit default swap, and collateralized debt obligations. Global Board Options Exchanges were founded in 1983 that S&PS00 (SPX) index option which launched by the Chicago Board Options Exchange (CBOE). Moreover CBOE was the option which target on trade index at the earliest, and CBOE was the most popular exchange with option trade. Taiwan Futures Exchange (TFE) launched Taiwan weighted index options (TXO) in December 2001 and, and launched stock options in 2003. Currently TXO was the most actively traded options market in Taiwan, but almost had no stock options trading volume due to the release of warrants market. However warrants market and individual stock options had higher homogeneous and better mobility to influence the stock options market. Although Taiwan options market started lately, develops quite fast, the option of Taiwan index was the sixth volume in the global select token name in 2013, that showed that Taiwan index options was a good target on the options-related research. Due to the globalization of financial markets, the single original market waved turn into the global storm which that affected financial asset prices were no longer continuous fluctuations, and it showed a leaps of change by the Butterfly Effect. Because the price process included continuity and discontinuity, the spread and jump process was more accurate than Brownian motion (BM). Currently the derivatives study biased on interest rate futures, foreign futures or foreign exchange futures options and Taiwan index futures options. By the way, the study about the jumping risks related to Taiwan index options effects is rare.
Fluctuations of Equity Share Price of the Selected Banks in Omanjournal ijrtem
ABSTRACT: Since Oman is experiencing a fall in the crude oil prices leading to the decline in share prices, there is a fear of an economic slowdown. Oman being heavily dependent on oil to fund its national budget, it may experience a crisis due to this situation. Capital Market Authority is the regulator or the governmental body which is responsible for governing the trading of securities in the Sultanate. Muscat Securities Market is the exchange where all the listed securities are traded. The Central Bank of Oman is in charge of keeping up the internal and external worth of the national money. It is also the single coordinated controller of Oman's finance related services industry. In this report we examine the equity shares price behaviour of six banks of Oman. These banks are listed in the MSM. A clear comparison between these will help to forecast their future prices. This will benefit the shareholders in understanding and also to make decisions regarding which bank to invest in to get maximum returns. Keywords: Stock Market, Behaviour of Equity Share Prices, Conventional and Islamic Banks.
Technical analysis and fundamental analysis are the two main approaches to analyzing securities in financial markets. Technical analysis examines past price movements to predict future prices, while fundamental analysis considers economic factors like financial statements. They differ in the data used - technical analysts look at charts while fundamental analysts examine financial reports. They also have different time horizons, with technical analysis focusing on weeks/days and fundamental analysis on years. While often viewed as opposing approaches, combining elements of both can provide benefits to analysis.
This document is a project report submitted by Rajat Jain for a post graduate diploma in management. The report focuses on conducting fundamental analysis of securities to suggest equity investments. It includes declarations, acknowledgements, an introduction on fundamental analysis and objectives. It discusses analyzing the economy, industry and companies. It also covers tools for fundamental analysis and profiles India Infoline, Tata Steel, Wipro and Sun Pharma to analyze their sectors, financials and make investment interpretations. The report aims to understand company performance, suggest investment opportunities and draw conclusions on the analysis.
Summer training project report on fluctuation of indian stock marketshailehpalrecha
This document is a summer training project report submitted by Rahul Jajoo to the Rajasthan Technical University. The report studies the fluctuations of the Indian stock market over the past two years under the supervision of Prabath Financial Services Limited. The objective is to understand the factors affecting stock prices and market trends to help investors make informed decisions. The report includes research methodology, analysis of market fluctuations, and conclusions about how this impacts the Indian economy.
This document summarizes a paper that examines behavioral finance versus the efficient market hypothesis and how they can be used to facilitate capital gains. It discusses how behavioral finance incorporates psychological factors that can lead to market inefficiencies and opportunities for gain, unlike the efficient market theory. The paper will analyze works supporting both theories to argue that incorporating behavioral finance can help assess if price movements reflect real changes in company value or irrational investor behavior.
Data Mining refers to the analysis of large amounts of data stored in computers. The Big Data
era is already present, with current sources indicating that more data have been created over the last two years
than they have been generated throughout the entire human history. Big Data involves data sets so large that
traditional data analysis methods are no longer usable due to the huge amount of data. Lacking or ignoring
the data structure is an extremely important aspect,
This document is a project report submitted as a requirement for an MBA degree. It analyzes selected commodities using fundamental and technical analysis. The objectives are to study the Indian commodity market, and analyze gold, silver, and copper. Fundamental analysis includes production, demand/supply, and volatility. Technical analysis uses charts and indicators. The scope is limited to metal indices on the commodity market. There are limitations due to the short time frame and that technical analysis is for the short-run while fundamental analysis is for long-run. The methodology includes collecting primary and secondary data, and presenting it using tables, charts and indicators to analyze the commodities.
This paper examines using different textual representations of financial news articles - bag of words, noun phrases, and named entities - to predict stock prices 20 minutes after an article is released, using support vector machines. The study finds that named entities outperform bag of words, the typical representation used. It introduces the topic of using textual analysis to predict stock prices and reviews prior literature on stock prediction using both fundamental and technical analysis approaches as well as textual data.
Technical analysis is a method of evaluating securities using statistical analysis of past market data like price and volume. It is used to identify patterns that can predict future price movements. Technical analysis uses tools like charts, indicators, and computer programs to analyze trends and identify trading opportunities. While technical analysis is widely used by traders, academics are divided on its effectiveness, with some studies supporting it and others finding the evidence inconclusive or inconsistent with market efficiency. Technical analysis is commonly used over shorter time frames by day traders, short-term investors, and hedgers seeking to manage risk.
Patience may be virtue, but impatience can frequently be profitable.
The attempt to determine future share price movement and its reliability by references to historical data.
This white paper discusses Discovery Patterns' analytic platform for unstructured big data that can discover expanded market sentiments. It defines key terms like prevailing and expanded market sentiment. It also describes Discovery Patterns' use of an ultra-granular industry context database called Industry Building Blocks, competitive analytic engines, and trend visualization engines to discover expanded market sentiments. Two case studies are presented that show how expanded market sentiments can provide insights into future market movements and identify investment opportunities when they diverge from prevailing sentiments.
Fundamental analysis evaluates a security's underlying value by examining related economic, financial and other qualitative and quantitative factors. It includes examining a company's financial statements and health, its management and competitive advantages, and its competitors and markets. Technical analysis, on the other hand, studies past stock price movements and patterns to predict future movements. Key fundamental ratios help analyze a company's profitability, operational efficiency, liquidity, leverage, and market performance. These ratios provide insights into a company's financial health and ability to operate and grow successfully.
A garch approach to measuring efficiency, a case study of nairobi securities ...Alexander Decker
This document discusses research analyzing the efficiency of the Nairobi Securities Exchange using a GARCH model. Previous studies of the exchange's efficiency using ordinary least squares regression methods yielded inconclusive results. The research first uses non-parametric methods to show that daily stock returns are non-random and dependent on previous returns. It then employs a GARCH(3,1) model, finding that the current return is determined by the mean return plus an error term that varies based on returns from the previous 3 days. This dependence on past returns signifies weak-form market inefficiency according to the research. Information and communication technologies are increasing access to information in Kenya's market but the study finds evidence it has not yet achieved weak-form efficiency
Discuss the differences between weak form, semi-strong form and strong form capital market efficiency, and critically evaluate the significance of the efficient market hypothesis (EMH) for the financial manager, using examples or cases in real-life.
This document summarizes the efficient market hypothesis (EMH) in three sentences:
The EMH states that market prices fully reflect all available public information and adjust instantly to new information. It has three forms - weak, semi-strong, and strong - with each form incorporating more types of information. Most research supports the weak and semi-strong forms, finding that historical data and public information are reflected in prices, but the strong form is not supported as non-public information can be used to earn excess returns.
Movement of Share Prices and Sectoral Analysis: A Reflection Through Interact...Waqas Tariq
Interaction in graphs gives the user with an advantage to analyze the data in greater depth. With the help of interactive graphics users can get better insight of the data in comparison to the static graphical tools. This paper introduces an interactive graphical tool consisting of two graphs, a line diagram complemented by a boxplot. The line diagram helps to understand how successive values of a variable are related to time and box plot can help the visual comparison of several such variables. Here the line diagram is used to visualize share prices of a company corresponding to a number of days and the boxplot displays the position of the Share price of all companies in a particular sector. An investor in share market needs to consider a number of factors before making any decision about investment. Some of the factors influencing the decision are the performance of the particular security in recent past, its position in terms of share price in its own sector. The graphical technique used in this software tool shall be helpful while making investment decision.
This document analyzes value versus growth investing styles through the lens of Dick Mayo's dilemma at GMO about whether to continue focusing on value stocks. It provides an in-depth comparison of the two styles, discussing their different focuses, investment philosophies, risks, typical stock features, and strategies. The document also analyzes whether value investing is still the right strategy given changing economic conditions that have favored growth stocks in recent years. Calculations are shown evaluating specific stocks like Cisco, CVS, and RR Donnelley to determine if they are over or undervalued based on criteria like P/E ratio, P/B ratio, and expected long-term returns.
This document discusses data mining methods and implementation of predictive data mining architecture for stock market prediction. It describes predicting unknown data values using classification, regression, and time series analysis. Two types of predictions discussed are stock market and environmental predictions. Stock market prediction aims to determine future company stock prices, while various parameters like return on investment are analyzed. The document also covers data mining techniques like descriptive and predictive mining, algorithms, and performance evaluation metrics like correct profitable trade signals and annual return on investment.
This document provides an overview of fundamental and technical analysis for investing in the Philippines. It discusses various fundamental analysis tools like price-earnings ratios, income statements, and balance sheets. It also covers technical analysis indicators like moving averages, support and resistance levels, and chart patterns. The document uses examples of companies listed on the Philippine Stock Exchange to demonstrate how to use these analytical tools and approaches for investment decisions in the local market.
Financial Analysis of Retail Business Organization: A Case of Wal-Mart Stores...Samsul Alam
The main objective of this study is to present the Walmart’s financial performance, making the important valuation of the company. The study used quantitative method using secondary sources. The finding of this descriptive study is that Walmart is the lucrative choice for the past, present and future investors with the estimation of terminal value at the end of the fiscal year 2026 estimated US $580 billion and the fundamental value of US $736 billion. The result shows that due to the emergence of stronger competitors and for being matured, Walmart is not performing as expected by investors, but its gigantic market size will make it capable of doing business profitably over a longer period of time. The ultimate decision given for the investors is to buy. The assumption is made on in-depth financial analysis with reliable data and calculation. The study has noteworthy importance to the financial market stakeholders.
Technical and fundamental analysis on stock market Babasab Patil
The document discusses technical and fundamental analysis of securities. It provides an overview of technical analysis concepts like Dow theory, Elliot waves, and moving averages. It also discusses fundamental analysis, including economic, industry, and company analysis. Key company analysis factors mentioned include management, annual reports, ratios, and cash flow. The document outlines objectives to conduct technical and fundamental analysis of selected Indian stock market securities. It describes the research methodology as involving secondary data analysis and a sample size of 10 stocks for technical analysis and 4 for fundamental analysis.
JUMPING RISK IN TAIWAN AND TAIEX OPTION RETURN IN TAIWAN ijcsit
With low-interest environment in recent years, investment of financial commodity was unable to meet the requirements of necessary paid by society. Therefore, the traditional financial tool were replacing with derivative financial commodity which were high risk, high lever, and high complex; including option, forward contract, futures, credit default swap, and collateralized debt obligations. Global Board Options Exchanges were founded in 1983 that S&PS00 (SPX) index option which launched by the Chicago Board Options Exchange (CBOE). Moreover CBOE was the option which target on trade index at the earliest, and CBOE was the most popular exchange with option trade. Taiwan Futures Exchange (TFE) launched Taiwan weighted index options (TXO) in December 2001 and, and launched stock options in 2003. Currently TXO was the most actively traded options market in Taiwan, but almost had no stock options trading volume due to the release of warrants market. However warrants market and individual stock options had higher homogeneous and better mobility to influence the stock options market. Although Taiwan options market started lately, develops quite fast, the option of Taiwan index was the sixth volume in the global select token name in 2013, that showed that Taiwan index options was a good target on the options-related research. Due to the globalization of financial markets, the single original market waved turn into the global storm which that affected financial asset prices were no longer continuous fluctuations, and it showed a leaps of change by the Butterfly Effect. Because the price process included continuity and discontinuity, the spread and jump process was more accurate than Brownian motion (BM). Currently the derivatives study biased on interest rate futures, foreign futures or foreign exchange futures options and Taiwan index futures options. By the way, the study about the jumping risks related to Taiwan index options effects is rare.
Fluctuations of Equity Share Price of the Selected Banks in Omanjournal ijrtem
ABSTRACT: Since Oman is experiencing a fall in the crude oil prices leading to the decline in share prices, there is a fear of an economic slowdown. Oman being heavily dependent on oil to fund its national budget, it may experience a crisis due to this situation. Capital Market Authority is the regulator or the governmental body which is responsible for governing the trading of securities in the Sultanate. Muscat Securities Market is the exchange where all the listed securities are traded. The Central Bank of Oman is in charge of keeping up the internal and external worth of the national money. It is also the single coordinated controller of Oman's finance related services industry. In this report we examine the equity shares price behaviour of six banks of Oman. These banks are listed in the MSM. A clear comparison between these will help to forecast their future prices. This will benefit the shareholders in understanding and also to make decisions regarding which bank to invest in to get maximum returns. Keywords: Stock Market, Behaviour of Equity Share Prices, Conventional and Islamic Banks.
Technical analysis and fundamental analysis are the two main approaches to analyzing securities in financial markets. Technical analysis examines past price movements to predict future prices, while fundamental analysis considers economic factors like financial statements. They differ in the data used - technical analysts look at charts while fundamental analysts examine financial reports. They also have different time horizons, with technical analysis focusing on weeks/days and fundamental analysis on years. While often viewed as opposing approaches, combining elements of both can provide benefits to analysis.
This document is a project report submitted by Rajat Jain for a post graduate diploma in management. The report focuses on conducting fundamental analysis of securities to suggest equity investments. It includes declarations, acknowledgements, an introduction on fundamental analysis and objectives. It discusses analyzing the economy, industry and companies. It also covers tools for fundamental analysis and profiles India Infoline, Tata Steel, Wipro and Sun Pharma to analyze their sectors, financials and make investment interpretations. The report aims to understand company performance, suggest investment opportunities and draw conclusions on the analysis.
Summer training project report on fluctuation of indian stock marketshailehpalrecha
This document is a summer training project report submitted by Rahul Jajoo to the Rajasthan Technical University. The report studies the fluctuations of the Indian stock market over the past two years under the supervision of Prabath Financial Services Limited. The objective is to understand the factors affecting stock prices and market trends to help investors make informed decisions. The report includes research methodology, analysis of market fluctuations, and conclusions about how this impacts the Indian economy.
This document summarizes a paper that examines behavioral finance versus the efficient market hypothesis and how they can be used to facilitate capital gains. It discusses how behavioral finance incorporates psychological factors that can lead to market inefficiencies and opportunities for gain, unlike the efficient market theory. The paper will analyze works supporting both theories to argue that incorporating behavioral finance can help assess if price movements reflect real changes in company value or irrational investor behavior.
Data Mining refers to the analysis of large amounts of data stored in computers. The Big Data
era is already present, with current sources indicating that more data have been created over the last two years
than they have been generated throughout the entire human history. Big Data involves data sets so large that
traditional data analysis methods are no longer usable due to the huge amount of data. Lacking or ignoring
the data structure is an extremely important aspect,
This document is a project report submitted as a requirement for an MBA degree. It analyzes selected commodities using fundamental and technical analysis. The objectives are to study the Indian commodity market, and analyze gold, silver, and copper. Fundamental analysis includes production, demand/supply, and volatility. Technical analysis uses charts and indicators. The scope is limited to metal indices on the commodity market. There are limitations due to the short time frame and that technical analysis is for the short-run while fundamental analysis is for long-run. The methodology includes collecting primary and secondary data, and presenting it using tables, charts and indicators to analyze the commodities.
This paper examines using different textual representations of financial news articles - bag of words, noun phrases, and named entities - to predict stock prices 20 minutes after an article is released, using support vector machines. The study finds that named entities outperform bag of words, the typical representation used. It introduces the topic of using textual analysis to predict stock prices and reviews prior literature on stock prediction using both fundamental and technical analysis approaches as well as textual data.
Technical analysis is a method of evaluating securities using statistical analysis of past market data like price and volume. It is used to identify patterns that can predict future price movements. Technical analysis uses tools like charts, indicators, and computer programs to analyze trends and identify trading opportunities. While technical analysis is widely used by traders, academics are divided on its effectiveness, with some studies supporting it and others finding the evidence inconclusive or inconsistent with market efficiency. Technical analysis is commonly used over shorter time frames by day traders, short-term investors, and hedgers seeking to manage risk.
Patience may be virtue, but impatience can frequently be profitable.
The attempt to determine future share price movement and its reliability by references to historical data.
This white paper discusses Discovery Patterns' analytic platform for unstructured big data that can discover expanded market sentiments. It defines key terms like prevailing and expanded market sentiment. It also describes Discovery Patterns' use of an ultra-granular industry context database called Industry Building Blocks, competitive analytic engines, and trend visualization engines to discover expanded market sentiments. Two case studies are presented that show how expanded market sentiments can provide insights into future market movements and identify investment opportunities when they diverge from prevailing sentiments.
Fundamental analysis evaluates a security's underlying value by examining related economic, financial and other qualitative and quantitative factors. It includes examining a company's financial statements and health, its management and competitive advantages, and its competitors and markets. Technical analysis, on the other hand, studies past stock price movements and patterns to predict future movements. Key fundamental ratios help analyze a company's profitability, operational efficiency, liquidity, leverage, and market performance. These ratios provide insights into a company's financial health and ability to operate and grow successfully.
A garch approach to measuring efficiency, a case study of nairobi securities ...Alexander Decker
This document discusses research analyzing the efficiency of the Nairobi Securities Exchange using a GARCH model. Previous studies of the exchange's efficiency using ordinary least squares regression methods yielded inconclusive results. The research first uses non-parametric methods to show that daily stock returns are non-random and dependent on previous returns. It then employs a GARCH(3,1) model, finding that the current return is determined by the mean return plus an error term that varies based on returns from the previous 3 days. This dependence on past returns signifies weak-form market inefficiency according to the research. Information and communication technologies are increasing access to information in Kenya's market but the study finds evidence it has not yet achieved weak-form efficiency
Discuss the differences between weak form, semi-strong form and strong form capital market efficiency, and critically evaluate the significance of the efficient market hypothesis (EMH) for the financial manager, using examples or cases in real-life.
This document presents an empirical test of the informational efficiency hypothesis on the Moroccan stock market. It begins with a literature review on the efficient market hypothesis and the three forms of market efficiency: weak, semi-strong, and strong. The authors then test for weak-form efficiency on the Moroccan stock market using daily MASI index data from 2002-2022. They employ runs tests, autocorrelation tests, unit root tests, and variance ratio tests. The results of all four tests show that the MASI index does not follow a random walk and reject the hypothesis of weak-form efficiency on the Moroccan stock market. The authors conclude that the Moroccan stock market is informationally inefficient in
Efficient market Hypothesis that explains the Capital asset pricing modelDr Yogita Wagh
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This study analyzes the efficiency of the Nigerian capital market by testing whether professionally managed funds can beat the market index. Monthly return data from 2007 to 2011 for five banks was used to test the strong form efficiency. The market model was used to estimate residuals and test if abnormal returns of managed portfolios were significantly different from zero. The results found the abnormal returns of professionally managed portfolios were insignificantly different from zero, indicating the Nigerian stock market is efficient in the strong form. The findings recommend fully computerizing the stock exchange and brokerages to maintain strong form efficiency through timely access to price-sensitive information.
10.Efficient Markets Hypothesis Clarke The Efficient Markets HypothesisNicole Heredia
The document discusses the efficient market hypothesis (EMH), which proposes that stock prices instantly reflect all available information and that it is impossible for investors to consistently outperform the overall market. It describes three versions of the EMH based on the type of information reflected in prices: weak form reflects past prices; semi-strong form reflects all public information; strong form reflects all public and private information. The key implication is that market prices should be trusted as they incorporate all known information, so securities are fairly priced on average. While prices are rational, changes are expected to be random and unpredictable.
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- It employs statistical tests like run tests and autocorrelation tests on monthly closing index values from 2000-2013 to analyze randomness and independence of stock price changes over time.
- The results of the statistical tests show that the NSE is inefficient in the weak form, as past stock price data can be used to predict future price movements, violating the random walk hypothesis of weak form efficiency.
The document discusses the efficient market hypothesis (EMH), which suggests that current stock prices fully reflect all available information and it is difficult to outperform the market consistently. It describes the three forms of market efficiency - weak, semi-strong, and strong - based on the types of information reflected in prices. The document also addresses some common misconceptions about the EMH, such as claims that successful investors disprove it or that analysis is pointless. Overall, the EMH asserts that markets are generally efficient but not perfectly so, and some investors can outperform by chance.
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The document discusses the Efficient Market Hypothesis (EMH) and different forms of market efficiency as it relates to insider trading. It provides an overview of the EMH, including its historical development and Fama's definitions of weak, semi-strong, and strong forms of market efficiency. Weak-form refers to efficiency based on past prices or returns. Semi-strong incorporates all public information. Strong-form suggests all private information is also reflected in prices. Evidence against full market efficiency is also presented.
The Efficient Markets Hypothesis Posits That `` Security...Samantha Randall
The Earned Income Tax Credit (EITC) provides tax credits to low-income households who work, potentially providing substantial income increases. To qualify, recipients must have labor income below certain thresholds, such as $33,241 for single mothers. Research shows the EITC increases labor supply for single mothers. However, complications may arise from the program. The analysis will examine the EITC's effectiveness at increasing single mothers' labor supply and potential issues using data from the Current Population Surveys from 1984 to 1996.
The document discusses the efficient market hypothesis (EMH), which states that stock prices already reflect all available public information, making it impossible for investors to outperform the market through strategies based on historical prices, economic news, or other public data. There are three forms of the EMH - weak, semi-strong, and strong - differing in the type of information believed to be reflected in prices. While several studies have found evidence supporting the EMH, others have found anomalies like value and small firm effects that appear to allow above-market returns. The validity of the EMH remains controversial.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
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A Critical Review Of The Market Efficiency ConceptAaron Anyaakuu
This document provides a critical review of the concept of market efficiency through 3 key points:
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3) It discusses differing views on market efficiency, with some arguing markets efficiently reflect information, while others point to anomalies and
Are good companies good stocks evidence from nairobi stock exchangeAlexander Decker
This document summarizes a research study that examined the relationship between company performance and stock performance on the Nairobi Stock Exchange. The study hypothesized that there would be a strong positive correlation between "good companies", defined as those with strong earnings and sales growth, and their stock performance. The researchers analyzed 32 listed companies using correlation analysis and descriptive statistics. The results indicated there is a strong positive correlation between good company performance and good stock performance on the NSE, supporting the hypothesis that good companies tend to be good stocks.
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The document discusses the efficient market hypothesis (EMH) which argues that stock prices reflect all available information. It defines three forms of market efficiency - weak, semi-strong, and strong - based on the types of information reflected in stock prices. The weak form states that prices reflect all historical price data, while the semi-strong form argues that prices immediately incorporate publicly available information. Empirical tests provide mixed support for the different forms of the EMH. The document also discusses potential market inefficiencies and anomalies that appear to contradict the EMH, such as the size effect and January effect.
: Security and Portfolio Analysis :Efficient market theoryRahulKaushik108
Key Concepts of Efficient market theory: Very Lucid presentation , very Useful for MBA student to understand the Concepts of Efficient Market theory( Random walk hypotheses ) .The key idea of the hypotheses is" no one can efficiently out predict the market" or in other terms, technical analysis or fundamental analysis can not beat "the naive buy and hold strategy".
This document summarizes a study that examines the stock price reaction to dividend announcements in the Nepalese stock market. The study analyzes 139 dividend announcements between 2000-2011, categorizing them as dividend initiations, increases, decreases, or no changes. The study tests the hypotheses that dividend changes will be associated with subsequent stock price movements in the same direction, and that firm-specific factors may influence the stock price reaction. Event study methodology is used to analyze abnormal stock returns around the announcement dates. Preliminary results found higher positive abnormal returns for dividend initiations and increases, and higher negative returns for decreases. The study aims to test the semi-strong form of market efficiency and the dividend signaling hypothesis in the Nepalese market
The document provides an overview of the efficient market hypothesis (EMH) and evidence related to it.
The EMH states that security prices fully reflect all available information. Empirical evidence is mixed but generally supports the idea. Studies show investment analysts and funds cannot consistently beat the market. Stock prices also reflect publicly available information.
However, some evidence contradicts the EMH. Small firms have abnormally high returns, and returns are higher in January. Market prices also sometimes overreact or are excessively volatile. New information is also not always immediately reflected in stock prices.
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How to Fix the Import Error in the Odoo 17Celine George
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A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
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it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
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ABSTRACT
This study tests the random walk theory in the Nigerian stock market by analyzing whether stock
returns follow a random walk distribution. The study employs the daily returns of the Top 20 most
performing stocks on the NSE for the period January 1st
2010 to December 31st
2014. Autocorrelation
and runs test were employed for hypothesis testing. Based on our analysis, we found that the daily
stock returns of the 20 most active stocks on the Nigerian stock market are randomly distributed
indicating that Nigerian Stock market is informational efficient at the weak form level. The
implication of the finding is that no one can fool the market consistently for a long time by trading on
the basis of past information such as historical stock prices. The study recommends that more efforts
should be made to reposition the market to attract more investible funds from domestic and foreign
investors.
Key Words: Capital Market, Efficiency level, Nigerian Stock Exchange, random walk
1. Introduction
Efficiency of markets has been a matter of considerable interest in the finance literature since the
introduction of Efficient Market Hypothesis (EMH) by Eugene Fama in 1965. Although market
efficiency are of three types: (Allocation efficiency, operational efficiency and informational or
pricing efficiency), the informational efficiency which is the behaviour of share prices and returns in
relation to the arrival of information in the market is the most widely studied, and as it generate the
most interest. Under informational efficiency prices provide accurate signals for resource allocation
so that firms can make productive investment decision and investors can choose among the securities
under the assumption that securities’ prices at any time fully reflect all available information.
The efficiency of the stock market is imperative in economic development as it provides the vehicle
for mobilizing savings and investment resources for developmental purposes. It also affords investors
opportunities to diversify their portfolio across a variety of assets leading to reduction in the cost of
capital.
Although information efficiency has been widely investigated (Shafi, 2014; Ayentimi, Mensah &
Naa-idar, 2013; Simons & Laryea, 2006), recent developments have created an illusion that market
efficiency will improve. In emerging stock markets, most empirical studies focused on weak form
efficiency (Afego, 2012; Gimba, 2012; Ayadi, 1984) which is the lowest out of the three forms of
informational efficiency. In Nigeria, the Nigerian Stock Exchange (NSE) has witnessed tremendous
growth in market capitalization, membership, value and volume traded as well as a tremendous
improvement in the information flow using information technology in recent times (Tijjani, 2010).
There has also been astronomic rise of the All Share Index (ASI) indicating investors’ confidence in
the stock market giving rise to the idea that its pricing efficiency might improve.
The main objective of this paper is to test the random walk theory in the NSE market in order to
assess whether it is weak form efficient. The paper is organised in four sections, the next section is a
review of literature; section 3 is the methodology adopted to address the problem; section 4 is data
analysis and results; and finally conclusion and recommendation are presented in section 5.
2. Literature Review
Market efficiency is a fundamental concept in finance; the concept was first introduced by Louis
Bachelier in 1900 in his thesis title the “the random character of stock market prices” (Dimson &
Mussavian, 1998). The concept has since been refined into Efficient Market Theory (EMT). EMT
posits that all information (past, present and even discounted future events) are reflected in market
price in an efficient market (Fama, 1991). The concept of market efficiency has three different types
(Brown, 2011); first an efficient market is a market in which resources are allocated to the most
deserving entities- “allocational efficiency”. Efficient market also means a market where the cost of
operation in it and the time taken to execute a deal is minimal- “operational efficiency”. The third and
most important type of market efficiency is the “information or pricing efficiency”. Information
efficiency is a market in which prices provide accurate signals for the allocation of resources. In this
market security prices always “fully reflect” available information. In an efficient market therefore,
3. IRA-International Journal of Management & Social Sciences
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current stock prices fully reflect all available information about the value of the firm; in that way no
one can fool the market by consistently making abnormal profits using public or even privately held
information. Fama (1991) posits that the level of information efficiency in the stock markets including
sophistication were not the same. He therefore considers market efficiency at three different forms or
levels.
a. Weak-form efficiency
The weak-form of the market efficiency asserts that the current price of shares fully incorporates all
past information such as historical prices. Thus, nobody can detect mispriced securities and
consistently beat the market for long time by analyzing past prices. The weak form efficiency got its
name for the reason that security prices are the most public as well as the most easily available pieces
of information (Clarke, Jandik & Mandelker, 2000). According to the weak form efficiency there will
be no need for trend analysis since security prices follow a random walk. That is, trends cannot be
predicted, thus, knowledge about the past cannot not help in realizing future returns.
b. Semi-strong form efficiency
The semi-strong-form of market efficiency suggests that the current price fully impound all
information in the public domain (past and current information). Public information includes not only
past prices, but also data reported in a company’s financial statements (annual reports, income
statements, periodic filings for the regulatory/governmental authorities such as SEC), earnings and
dividend announcements, announced merger plans, the financial situation of company’s competitors,
expectations regarding macroeconomic factors such as inflation and unemployment. It is expected that
markets cannot be efficient at the semi form except it is first proven efficient in the weak form.
c. Strong form efficiency
The strong form of market efficiency states that the current price fully incorporates all existing
information; both public and even privately held (sometimes called insider information). In other
words, the strong form market efficiency asserts that even a company’s management (insiders) cannot
systematically gain from inside information by buying company’s shares to follow what they see as a
very profitable purchase. Similarly, the members of the company’s research department are not able
to profit from the information about the new discovery they just completed. The rationale for strong-
form market efficiency is that the market anticipates, in an unbiased manner, future developments and
therefore the share price incorporate all available information and evaluates it in a much more
objective and informative way than the insiders. The three levels of market efficiency suggest that
investing is a fair game in which “you win some” and “you lose some”.
To what extent this is obtainable in Nigeria is tested in this paper with a focus on the weak-form level
of informational efficiency. As earlier stated the weak form level is the first level of efficiency and
that these levels are like the stair case; without attaining the lower (weak form) it is impossible to
attain the next level of efficiency (semi-strong and the strong in that order).
This paper adopts a random walk theory as a basis for hypothesising. The theory suggests that share
prices changes have the same distribution and are independent of each other, so that the past
movement or trend of a share price or returns cannot be used to predict its future price or returns
movement. The Random Walk model can be algebraically stated as follows:
pt = pt-1 +et+1 ------------------------------------------------------------------------------ (1)
Where
Pt is price in year t and Pt-1 = price in year t-1.
According to equation (1), price changes must be a response only to new information; since
information arrives randomly, share prices must also fluctuate unpredictably. The suggestion is that
profiting from price prediction is very difficult and unlikely. The main idea behind this is that price
changes with the arrival of new information. A market is said to be efficient if prices adjust quickly
and, on average, without bias to new information.
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503
Fama (1970) argued that the random walk theory is an extension of the expected return or fair game
model. Specifically, the fair game model indicates that the condition for having market equilibrium
can be stated in terms of expected returns while the random walk model gives the details of the
stochastic process generating returns. Therefore, he concluded that empirical tests of the random walk
theory are more powerful in support of the EMH than tests of the fair game model.
Empirical Review
The work of Samuel and Yacout (1981) is the first published empirical studies on the weak-form
efficiency of the Nigerian stock market. The study “Stock market in developing countries” employed
serial correlation test to examine weekly price series of 21 listed Nigerian Firms from July 1977 to
July 1979. The results show that the stock price changes are not serially correlated but follow a
random walk, thus accepting the weak-form market efficiency hypothesis.
Ayadi (1984) followed Samuel and Yacout (1981) and work tested the price behaviour of 30
securities quoted on the NSE between 1977 and 1980, using Monday closing prices of these shares
after adjusting for cash dividends and script issues. The results show that the share price movements
on the NSE follow a random walk.
Olowe (1999), carries out tests of weak form efficiency in Nigeria using monthly data on 59 randomly
selected securities from 1981-1992. He finds the Nigerian market to conform to weak-form efficiency
in joint Q-tests of partial autocorrelation coefficients for ten lags in the return data, though he argues
that poor informational flows and inefficient communications systems cast doubts on the ability of the
market to pass higher hurdles of efficiency. More recent works of Okpara (2010) also provide
evidence that the Nigerian stock market is weak-form efficient.
Okpara (2010) employed the runs test and the partial autocorrelation function as alternate forms of
research instrument to test the weak form efficiency of the Nigerian stock market. His results revealed
that the Nigerian stock market is efficient in the weak form and therefore follows a random walk
process. He concluded that the opportunity of making excess returns using fundamental or technical
analysis is not possible in Nigeria.
Contrary to the already cited works above, Akpan (1995) studied the informational efficiency of the
NSE including the risk implications of investing in the market, using time series data of stock market
price indices covering period of 1989 to 1992. His result shows evidence to reject the hypothesis of
weak form efficiency of NSE.
Nwosa and Oseni (2011) also investigated the weak-form efficient market hypothesis in Nigeria using
a sample data from 1986 - 2010. Adopting a serial auto-correlation and regression method of analysis,
they tested for stationarity using the Augmented Dickey Fuller and Philip Perron test. The result
showed that the variables are stationary at first differencing. The result of the serial auto-correlation
and regression analysis both revealed that the Nigeria stock market is informational inefficient, that is
stock prices do not exhibit random walk.
Gimba (2012) tested the weak form of market efficiency for the market index and five selected
individual stocks using weekly return data for the period of January 2007 to December 2009. Gimba
(2012) employed three different techniques which include autocorrelation, runs and variance ratio
tests. The results obtained from the autocorrelation indicate that the null hypothesis of random walk
conclusively rejected the market index and four out of the five selected individual stocks, even where
the returns were corrected for thin trading. The runs test and the variance ratio test under both
homoscedasticity and hetroscedasticity assumptions also failed to support the random walk hypothesis
for market index and all the selected individual stocks. Thus, he concluded that the Nigerian stock
market is weak -form inefficient. Similar results are found by Afego (2012).
Similar to other emerging markets, studies in Nigeria show conflicting evidence. To reposition the
market for global competition and enhance informational efficiency, many reforms have been done by
the NSE market including market digitization to promote prompt information transmission, and the
establishment of securities tribunal to prosecute erring members and participants in the market. In the
light of these new happenings in the Nigerian market, it is expected that the market will now be
5. IRA-International Journal of Management & Social Sciences
504
efficient at least at the lower level. This study substantiates this claim using more recent information
from 1st January 2010 to 31st December 2014. The study also employs both the non-parametric and
parametric tests to provide a more comprehensive and robust conclusions regarding informational
efficiency of the Nigerian Market.
3. Methodology
This study covers a period from January 1st
2010 to December, 31st
2014 for the Top 20 most
performing and actively traded stocks on the main floor of the NSE. The selected Top 20 performing
stocks is presented in Table 1.
Table 1: Top 20 Most Performing Stocks on Turnover in Nigeria
S/No COMPANY Value (₦)
1 ZENITH BANK 625,937,687.46
2 NIGERIAN BREWERIES 608,568,186.05
3 GUARANTY TRUST BANK 324,524,872.79
4 GUINNESS NIG. PLC 274,111,449.90
5 ACCESS BANK 272,258,986.71
6 UNITED BANK FOR AFRICA 95,045,407.63
7 UNILEVER 62,248,768.42
8 FIRST BANK OF NIGERIA HOLDING 55,179,668.10
9 FORTE OIL 37,460,508.99
10 UCAP 36,774,410.02
11 NESTLE PLC 26,760,219.52
12 OANDO PLC 22,798,682.34
13 DIAMOND BANK PLC 10,344,981.89
14 STANBIC IBTC 8,221,515.42
15 UACN 6,781,483.73
16 LARFARGE WAPCO 5,905,322.45
17 TRANSCORP 5,861,402.49
18 FLOURMILL 5,759,028.08
19 STERLIN BANK 5,097,447.75
20 FIDELITY BANK 3,291,300.31
The daily all shares price is the main data required for analysis and is obtained from the NSE. To test
the weak form efficiency of the NSE the daily returns determined by taking a natural logarithmic
transformation was employed. The time series of continuously compounded daily returns are
computed as follows:
Rt=
(𝑝 𝑡−𝑝 𝑡−1)
𝑝 𝑡−1
= log(r)/ log (Pt-1) = log(r) –log (Pt-1) . . . . . . . . . . . . . . (2)
The degree of randomness in stock returns in Nigeria was tested using autocorrelation and runs test.
Serial correlation or autocorrelation measure the correlation between different points in time. A
relatively high serial correlation indicates the predictability in stock returns. There are several ways of
testing for autocorrelation; the Ljung–Box Q-statistics which is widely used to test the weak form
efficiency is adopted in this study. If there is no serial correlation in the residuals, the autocorrelations
(AC) and partial autocorrelations (PAC) at all lags should be nearly zero, and all Q-statistics should
be insignificant with large p-values. In the event where data is suspected to be non-normally
distributed the runs tests is the most appropriate measure of random walk in stock prices or returns.
For this reason the runs test was performed to complement the Ljung-Box Q statistics. According to
Li (2008), serial correlation in stock returns is a necessary but not sufficient condition of market
efficiency hence needs a complementary test such as the runs test.
4. Analysis and Results
The data for this research are empirically analysed and results reported and discussed in this section.
The detailed results of the descriptive statistics for the daily share returns for the period from January
1st
2010 to 31st
December, 2014 are reported in Table 2.
6. IRA-International Journal of Management & Social Sciences
505
Table 2: Descriptive Statistics for the Daily Returns of the Sample Firms
Mean MAX MIN SD SKEW KURT Jarque-Bera P-Value N
ACCESS 0.0000 0.0417 -0.0455 0.0107 -0.0895 3.8129 35.6840 0.0000 1236
DIAMOND -0.0001 0.0414 -0.0458 0.0118 0.0448 3.7440 28.9220 0.0000 1236
FBNH -0.0002 0.0419 -0.0539 0.0100 -0.0063 4.6557 141.1917 0.0000 1236
FIDELITY -0.0001 0.0411 -0.0442 0.0115 0.0472 3.6453 21.9066 0.0000 1236
FLOURMIL 0.0000 0.0423 -0.0747 0.0100 -0.9467 10.4119 3013.8370 0.0000 1236
FORTE 0.0007 0.0424 -0.0457 0.0125 0.4573 5.6404 402.1174 0.0000 1236
GTB 0.0002 0.0396 -0.1137 0.0097 -2.3885 31.6681 43501.1300 0.0000 1236
GUINESS 0.0001 0.0423 -0.0397 0.0079 0.4447 7.7334 1194.6110 0.0000 1236
NB 0.0004 0.0273 -0.0400 0.0082 -0.1078 5.0537 219.6114 0.0000 1236
NESTLE 0.0005 0.0410 -0.0895 0.0077 -0.8043 23.0322 20799.6100 0.0000 1236
OANDO -0.0006 0.0423 -0.1761 0.0148 -1.5194 22.6021 20263.9900 0.0000 1236
STANBIC 0.0004 0.2487 -0.0413 0.0123 6.6224 134.7775 903347.6000 0.0000 1236
STERLINBANK 0.0003 0.0414 -0.0458 0.0137 -0.0401 2.8485 1.5122 0.4695 1236
TRANSCORP 0.0006 0.0424 -0.0458 0.0150 0.1418 3.9473 50.3551 0.0000 1236
UACN 0.0000 0.0414 -0.1077 0.0099 -1.5673 21.2002 17565.2100 0.0000 1236
UACP -0.0002 0.0422 -0.1046 0.0113 -1.0541 14.4561 6987.8470 0.0000 1236
UBA -0.0004 0.0422 -0.0996 0.0134 -0.4317 6.7215 751.6428 0.0000 1236
UNILEVER 0.0003 0.0422 -0.0446 0.0092 -0.0761 6.0595 483.2578 0.0000 1236
WAPCO 0.0003 0.0423 -0.0458 0.0089 -0.0149 7.0491 844.4029 0.0000 1236
ZENITH 0.0001 0.0421 -0.1076 0.0099 -1.0038 15.3208 8025.4370 0.0000 1236
Avg 0.0001 0.0514 -0.0706 0.0109 -0.1146 16.7190 51383.9938 0.0235 1236
An analysis of the table reveals several points. First, the table reveals that the average stock returns for
the 20 selected firms as well as the whole market is relatively low ranging from -0.06% (Oando) to
+0.07% (Forte). 20 percent of the firms have negative returns, another 20 percent have zero returns
while the remaining 60 percent have positive returns. Overall, the whole market has an insignificant
positive return. The table also indicates the minimum and maximum stock returns vary from -17.61%
to -3.97% and 3.96% to 24.87% for minimum and maximums respectively. The whole market has a
minimum of -2.41% and a maximum return of 2.3%. from the descriptive statistics it is also clear that
stock return on the NSE are not approximated to normal distribution since the Jarque-Bera’s p-values
are all lower than 0.05 (except with respect to Sterlin Bank.
The behaviour of daily returns series of the whole NSE over the period January 2010 to December
2014 is graphically stated in Figure 1.
Fig 1: Time Series Plot of Nigerian Stock Exchange daily returns
Specifically, Figure 1 indicates a high level of volatility associated with the returns in the NSE. The
figure is an indication that sock returns in NSE might be randomly distributed or alternatively might
be a weak form informational efficient.
-.03
-.02
-.01
.00
.01
.02
.03
250 500 750 1000
NSINDEX
LogReturns
Days
7. IRA-International Journal of Management & Social Sciences
506
The basic purpose in this study is to test whether NSE is informational weak-form efficient.
Alternatively stated, the study examines whether share returns on the NSE exhibit persistence which
may be exploited to predict future price changes. In this regards autocorrelation and runs tests were
conducted. The result of the autocorrelation test is presented in Table 3.
If there is no autocorrelation AC and PAC at all lags (lag 1, 2, 3 ... 15) should be equal to zero and Q-
statistics should be insignificant with large p-values. From the analysis it can be seen that 16
companies representing 80% of the sample have insignificant Q-statistic (p < 0.05) even at the 15th
lag. These statistics indicate that the stock return follows a random walk on the NSE. Restated
alternatively, since the p value is more than 5%, we cannot reject the null hypothesis that the stock
return on the NSE does not have serial correlation.
Table 3: Autocorrelation statistics for the daily returns of the sample firms
Company/Lags 1 2 3 4 5 … 10 … 15
ACCESS Q-Stat 0.0390 1.4064 6.7616 8.8348 9.7015 … 19.0640 … 22.9910
Prob 0.8430 0.4950 0.0800 0.0650 0.0840 … 0.0390 … 0.0840
DIAMOND Q-Stat 0.0270 0.2260 3.6971 6.7900 9.5073 … 13.0110 … 15.0400
Prob 0.8700 0.8930 0.2960 0.1470 0.0900 … 0.2230 … 0.4490
FBNH Q-Stat 0.3450 18.0150 30.9440 31.6610 31.6880 … 34.1590 … 42.7090
Prob 0.5570 0.0000 0.0000 0.0000 0.0000 … 0.0000 … 0.0000
FIDELITY Q-Stat 0.0341 1.3515 4.8000 10.5530 13.0560 … 17.7430 … 21.3620
Prob 0.8530 0.5090 0.1870 0.0320 0.0230 … 0.0590 … 0.1260
FLOURMILLS Q-Stat 0.0028 0.4580 1.4193 1.4193 2.2825 … 6.6242 … 12.9190
Prob 0.9580 0.7950 0.7010 0.8410 0.8090 … 0.7600 … 0.6090
FORTE Q-Stat 0.7850 3.1872 6.1304 7.1552 10.6130 … 18.3640 … 20.8650
Prob 0.3760 0.2030 0.1050 0.1280 0.0600 … 0.0490 … 0.1410
GTB Q-Stat 0.0012 3.2343 8.3253 8.7560 13.3340 … 24.0670 … 32.4390
Prob 0.9730 0.1980 0.0400 0.0670 0.0200 … 0.0070 … 0.0060
GUINESS Q-Stat 0.0470 2.1133 3.6846 3.7169 4.2028 … 12.1190 … 19.3550
Prob 0.8280 0.3480 0.2980 0.4460 0.5210 … 0.2770 … 0.1980
NB Q-Stat 0.0305 3.9211 7.0646 7.4160 7.9499 … 15.4970 … 22.3500
Prob 0.8610 0.1410 0.0700 0.1150 0.1590 … 0.1150 … 0.0990
NESTLE Q-Stat 0.0008 0.1029 0.4867 0.5339 1.0787 … 3.7776 … 12.2880
Prob 0.9780 0.9500 0.9220 0.9700 0.9560 … 0.9570 … 0.6570
OANDO Q-Stat 0.6155 3.5289 12.3620 13.3210 14.8950 … 22.8100 … 29.9690
Prob 0.4330 0.1710 0.0060 0.0100 0.0110 … 0.0110 … 0.0120
STERLIN BANK Q-Stat 0.0000 0.0002 1.1653 2.0968 3.3494 … 10.6000 … 12.1020
Prob 0.9980 1.0000 0.7610 0.7180 0.6460 … 0.3890 … 0.6710
STANBIC Q-Stat 0.0042 0.0823 0.2456 0.8012 3.9914 … 8.8902 … 10.9770
Prob 0.9480 0.9600 0.9700 0.9380 0.5510 … 0.5430 … 0.7540
TRANSCORP Q-Stat 0.0221 0.1809 0.3397 0.7227 2.8703 … 5.4709 … 16.2470
Prob 0.8820 0.9140 0.9520 0.9490 0.7200 … 0.8580 … 0.3660
UACN Q-Stat 0.0000 0.0026 0.0582 2.1166 2.9414 … 7.0267 … 15.0330
Prob 0.9990 0.9990 0.9960 0.7140 0.7090 … 0.7230 … 0.4490
UACP Q-Stat 0.0000 0.0308 3.1543 3.2889 4.0631 … 6.0364 … 10.4240
Prob 0.9960 0.9850 0.3680 0.5110 0.5400 … 0.8120 … 0.7920
UBA Q-Stat 0.1503 1.6566 2.9030 5.7827 5.8159 … 12.7860 … 16.5950
Prob 0.6980 0.4370 0.4070 0.2160 0.3250 … 0.2360 … 0.3440
UNILEVER Q-Stat 0.0000 0.0000 0.5485 3.4013 3.4265 … 5.5687 … 15.3500
Prob 0.9950 1.0000 0.9080 0.4930 0.6350 … 0.8500 … 0.4270
WAPCO Q-Stat 0.0535 4.5810 4.6666 4.9935 5.8672 … 18.6510 … 19.5400
Prob 0.8170 0.1010 0.1980 0.2880 0.3190 … 0.0450 … 0.1900
ZENITH Q-Stat 0.0264 8.0600 14.6980 16.1790 16.3160 … 26.1350 … 33.4430
Prob 0.8710 0.0180 0.0020 0.0030 0.0060 … 0.0040 … 0.0040
Avg Q-Stat 0.1092 2.6070 5.6727 6.9770 8.3475 … 14.4200 … 20.0999
Prob 0.8367 0.5559 0.4134 0.3826 0.3592 … 0.3479 … 0.3189
A complementary test was conducted using the runs test since daily returns of the selected samples
are not normally distribution (see Table 2). The results of the runs test are presented in Table 4.
8. IRA-International Journal of Management & Social Sciences
507
Table 4: Results for the Runs Tests
Firms Cases < 0 Cases ≥ 0 Total Cases Number of Runs Za
p-value
ACCESS 554 682 1236 557 -3.1855 0.0014
DIAMOND 555 681 1236 555 -3.3112 0.0009
FBNH 583 653 1236 547 -3.9976 0.0001
FIDELITY 558 678 1236 635 1.2539 0.2099
FLOURMILLS 296 940 1236 414 -2.9090 0.0036
FORTE 236 1000 1236 303 -7.3604 0.0000
GTB 558 678 1236 598 -0.8718 0.3833
GUINESS 313 923 1236 382 -6.5077 0.0000
NB 470 766 1236 504 -4.8034 0.0000
NESTLE 227 1009 1236 297 -7.0851 0.0000
OANDO 551 685 1236 510 -5.8588 0.0000
STANBIC 445 791 1236 551 -1.2086 0.2268
STERLINBANK 532 704 1236 577 -1.7430 0.0813
TRANSCORP 433 803 1236 408 -9.7290 0.0000
UACP 277 959 1236 404 -2.1972 0.0280
UBA 585 651 1236 562 -3.1526 0.0016
UNILEVER 336 900 1236 481 -0.6701 0.5028
WAPCO 366 870 1236 500 -1.1089 0.2675
ZENITH 553 683 1236 597 -0.8726 0.3829
UACN 351 885 1236 482 -1.5149 0.1298
Avg 439 797 1236 493 -3.3417 0.1110
Table 4 indicates that the stock returns of the sample companies follow a random walk. Out of the
1236 days, 439 have negative returns on average and 797 have positive or zero returns. Since the
calculated p-values are more than the critical value (0.05), we fail to reject that the order of stock
returns on the NSE follow a random walk. These results should however be interpreted with caution,
since the zero values might be capable of influencing the results.
These results contrast the earlier findings by Afego (2012) in Nigeria. The implication is that prices in
the market do not follow a predetermined pattern. The overall results suggest that NSE is
informational weak-form efficient since the returns are random. The results suggest that it is difficult
to consistently outperform (fool) the stock market by simply studying past information on the NSE
because such information is already impounded in security prices. This result differs from the earlier
studies which suggest that the market is not efficient even at the weak form. Reasons for these new
findings might be the following: (1) developments in the NSE (such as the computerisation of the
market) make trading and transmission of information in real time. Participants send and receive
information about the market almost instantaneously. (2) The setting up of securities tribunal has
facilitated quick dispensation of justice or instils discipline in the market.
5. Conclusion
Market efficiency is a fundamental concept in finance. It is a concept bothering on fair play in the
securities market. Because the concept is so important it has been extensively tested from time to time
to see if the market is informationally efficient or not. The concept of market efficiency encompasses
three things; information (pricing) efficiency, operational efficiency and allocational efficiency. Our
study focuses on informational efficiency. Fama (1970) states that the information efficiency is more
important and consists of three levels: the weak form, the semi-strong and strong-form efficiency.
This study tests the weak form market efficiency. Based on our analysis using the autocorrelation and
runs test, the study found that the daily stock returns of the 20 most active stocks on the Nigerian
stock market are randomly distributed. This study therefore concludes that the NSE is informational
efficient in the weak form.
9. IRA-International Journal of Management & Social Sciences
508
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