Search results for: daily stock price data

Authors: Pradyumna Mohanty

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The paper aims to explore whether accounting information of Indian companies in the Steel sector are value relevant or not. Ohlson’s model which usually takes into consideration book value per share (BV) and earnings per share (EARN) has been used and the same has been expanded to include two more variables such as cash flow from operations (CFO) and return on equity (ROE). The data were collected from CMIE-Prowess data base in respect of BSE-listed steel companies and the time frame spans from 2010 to 2014. OLS regression has been used to test the value relevance of these accounting numbers. Results indicate that both CFO and BV are having significant influence on the stock price in two out of five years of study. But, BV is emerging as the most significant and highly value relevant of all the four variables during the entire period of study.

Keywords: value relevance, accounting information, book value per share, earnings per share

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Authors: O. Benabdeljelil, A. Karioun, S. Amami, R. Rouger, M. Hamidine

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A model for preventing the risks related to climate conditions in the agricultural sector is presented. It will determine the yearly optimum premium to be paid by a producer in order to reach his required turnover. The model is based on both climatic stability and 'soft' responses of usually grown species to average climate variations at the same place and inside a safety ball which can be determined from past meteorological data. This allows the use of linear regression expression for dependence of production result in terms of driving meteorological parameters, the main ones of which are daily average sunlight, rainfall and temperature. By simple best parameter fit from the expert table drawn with professionals, optimal representation of yearly production is determined from records of previous years, and yearly payback is evaluated from minimum yearly produced turnover. The model also requires accurate pricing of commodity at N+1. Therefore, a pricing model is developed using 3 state variables, namely the spot price, the difference between the mean-term and the long-term forward price, and the long-term structure of the model. The use of historical data enables to calibrate the parameters of state variables, and allows the pricing of commodity. Application to beet sugar underlines pricer precision. Indeed, the percentage of accuracy between computed result and real world is 99,5%. Optimal premium is then deduced and gives the producer a useful bound for negotiating an offer by insurance companies to effectively protect its harvest. The application to beet production in French Oise department illustrates the reliability of present model with as low as 6% difference between predicted and real data. The model can be adapted to almost any agricultural field by changing state parameters and calibrating their associated coefficients.

Keywords: agriculture, production model, optimal price, meteorological factors, 3-factor model, parameter calibration, forward price

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Authors: Nur Sulihatimarsyila Abd Wafti, Harrison Lik Nang Lau, Nabilah Kamaliah Mustaffa, Nur Azreena Idris

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The use of biolubricant has gained its popularity over the last decade. Base stock produced using methyl ester and trimethylolethane (TME) can be potentially used for biolubricant production due to its biodegradability, non-toxicity and good thermal stability. The synthesis of biolubricant base stock e.g. triester (TE) via transesterification of palm methyl ester and TME in the presence of sodium methoxide as the catalyst was conducted. Factors influencing the reaction conditions were investigated including reaction time, temperature and pressure. The palm-based biolubricant base stock produced was analysed for its monoester (ME), diester (DE) and TE contents using gas chromatography as well as its lubricating properties such as viscosity, viscosity index, oxidation stability, and density. The resulting base stock containing 90 wt% TE was successfully synthesized.

Keywords: biolubricant, methyl ester, triester transesterification, lubricating properties

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Authors: George Hadjinicola, Andreas Soteriou

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This paper examines the relationship between the number of price promotions that a firm should conduct per year and the level of safety stocks that the firm should maintain. Price promotions result in temporary sales increases, which affect the operations function through (1) an increase in the quantities demanded and (2) an increase in safety stocks required to maintain the desired service level. We propose a modeling framework where both price promotions and improved service levels, operationalized through higher safety stocks, can affect sales. We treat the annual number of promotions as a decision variable. We identify market conditions where the operations function, through improved safety stocks, can complement price promotions or even play the leading role in sales increases.

Keywords: price promotions, safety stocks, marketing/operations interface, mathematical model

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Authors: Fatin Fadhilah Hasib, Dewi Nuraini, Nisful Laila, Muhammad Madyan

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A big political event such as Cabinet reshuffle mostly can affect the stock price positively or negatively, depend on the perception of each investor and potential investor. This study aims to analyze the movement of the market and trading activities which respect to an event using event study method. This method is used to measure the movement of the stock exchange in which abnormal return can be obtained by investor related to the event. This study examines the differences of reaction on abnormal return and trading volume activity from the companies listed in the Jakarta Islamic Index (JII), before and after the announcement of the Cabinet Work Volume II on 27 July 2016. The study was conducted in observation of 21 days in total which consists of 10 days before the event and 10 days after the event. The method used in this study is event study with market adjusted model method that observes market reaction to the information of an announcement or publicity events. The Results from the study showed that there is no significant negative nor positive reaction at the abnormal return and abnormal trading before and after the announcement of the cabinet reshuffle. It is indicated by the results of statistical tests whose value not exceeds the level of significance. Stock exchange of the JII just reflects from the previous stock prices without reflecting the information regarding to the Cabinet reshuffle event. It can be concluded that the capital market is efficient with a weak form.

Keywords: abnormal return, abnormal trading volume activity, event study, political event

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Authors: Adel M. Qatawneh

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This paper aimed to determine the importance of knowledge innovation for external audit on anti-corruption in the entire Jordanian bank companies are listed in Amman Stock Exchange (ASE). The study importance arises from the need to recognize the Knowledge innovation for external audit and anti-corruption as the development in the world of business, the variables that will be affected by external audit innovation are: reliability of financial data, relevantly of financial data, consistency of the financial data, Full disclosure of financial data and protecting the rights of investors to achieve the objectives of the study a questionnaire was designed and distributed to the society of the Jordanian bank are listed in Amman Stock Exchange. The data analysis found out that the banks in Jordan have a positive importance of Knowledge innovation for external audit on anti-corruption. They agree on the benefit of Knowledge innovation for external audit on anti-corruption. The statistical analysis showed that Knowledge innovation for external audit had a positive impact on the anti-corruption and that external audit has a significantly statistical relationship with anti-corruption, reliability of financial data, consistency of the financial data, a full disclosure of financial data and protecting the rights of investors.

Keywords: knowledge innovation, external audit, anti-corruption, Amman Stock Exchange

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Authors: Fatemeh Norouzi

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Today, financial performance has become one of the important issues in accounting and auditing that companies and their managers have paid attention to this issue and for this reason to the variables that are influential in this field. One of the things that can affect financial performance is corporate governance, which is examined in this research, although some things such as issues related to auditing can also moderate this relationship; Therefore, this research has been conducted with the aim of investigating the relationship between corporate governance and financial performance with regard to the moderating role of feedback and internal control weakness. The research is practical in terms of purpose, and in terms of method, it has been done in a post-event descriptive manner, in which the data has been analyzed using stock market data. Data collection has been done by using stock exchange data which has been extracted from the website of the Iraqi Stock Exchange, the statistical population of this research is all the companies admitted to the Iraqi Stock Exchange. . The statistical sample in this research is considered from 2014 to 2021, which includes 34 companies. Four different models have been considered for the research hypotheses, which are eight hypotheses, in this research, the analysis has been done using EXCEL and STATA15 software. In this article, collinearity test, integration test ,determination of fixed effects and correlation matrix results, have been used. The research results showed that the first four hypotheses were rejected and the second four hypotheses were confirmed.

Keywords: size of the board of directors, duality of the CEO, financial performance, internal control weakness

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Authors: Serpil Türkyılmaz, Mesut Balıbey

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In this study, long memory properties in volatility of Turkey Stock Market are being examined through the FIGARCH, FIEGARCH and FIAPARCH models under different distribution assumptions as normal and skewed student-t distributions. Furthermore, structural changes in volatility of Turkey Stock Market are investigated. The results display long memory property and the presence of asymmetric effects of shocks in volatility of Turkey Stock Market.

Keywords: FIAPARCH model, FIEGARCH model, FIGARCH model, structural break

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Authors: Sayedramin Hashemianesfehani, Seyed Hossein Hosseinilargani

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In this study, we attempt to realize that to what extent the increase in selected commodities in Iran Market is originated from the implementation of the targeted subsidies law. Hence, an econometric model based on existing theories of increasing and transferring prices in order to transferring inflation is developed. In other words, world price index and virtual variables defined for targeted subsidies has significant and positive impact on the producer price index. The obtained results indicated that the targeted subsidies act in Iran has influential long and short-term impacts on producer price indexes. Finally, world prices of dairy products and dairy price with respect to major parameters is carried out to obtain some managerial ‎results.

Keywords: econometric models, targeted subsidies, consumer price index (CPI), producer price index (PPI)

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Authors: Mahdi Karazmodeh, Pooyan Jafari

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One of the most interesting topics in finance is the relation between stock prices and exchange rates. During the past decades different stock markets in different countries have been the subject of study for researches. The volatilities of exchange rates and its effect on stock prices during the past 10 years have continued to be an attractive research topic. The subject of this study is one of the most important indices, FTSE 100. 20 firms with the highest market capitalization in 5 different industries are chosen. Firms are included in oil and gas, mining, pharmaceuticals, banking and food related industries. 5 different criteria have been introduced to evaluate the relationship between stock markets and exchange rates. Return of market portfolio, returns on broad index of Sterling are also introduced. The results state that not all firms are sensitive to changes in exchange rates. Furthermore, a Granger Causality test has been run to observe the route of changes between stock prices and foreign exchange rates. The results are consistent, to some level, with the previous studies. However, since the number of firms is not large, it is suggested that a larger number of firms being used to achieve the best results. However results showed that not all firms are affected by foreign exchange rates changes. After testing Granger Causality, this study found out that in some industries (oil and gas, pharmaceuticals), changes in foreign exchange rate will not cause any changes in stock prices (or vice versa), however, in banking sector the situation was different. This industry showed more reaction to these changes. The results are similar to the ones with Richards and Noel, where a variety of firms in different industries were evaluated.

Keywords: stock prices, foreign exchange rate, exchange rate exposure, Granger Causality

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Authors: Anwar Hussain, Ahmed Imran, Farida Faisal, Fatima Sultana

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The present research evaluates a comparative assessment of dividend policy and share price across the South Asian countries including Pakistan, India and Sri-Lanka over the period of 2010 to 2014. Academic writers found that dividend policy and share price relationship is not same in south Asian market due to different reasons. Moreover, Panel Models used = for the evaluation of current study. In addition, Redundant fixed effect Likelihood and Hausman test used for determine of Common, Fixed and Random effect model. Therefore Indian market dividend policies play a fundamental role and significant impact on Market Share Prices. Although, present research found that different as compared to previous study that dividend policy have no impact on share price in Sri-Lanka and Pakistan.

Keywords: dividend policy, share price, South Asian countries, panel data analysis, theories and parameters of dividend

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Authors: Almoutassem Bellah Nasser

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This paper is aimed at calculating the intangible assets of Jordanian economy. This effort is a response to the demand from corporations for these services which reflects a perceived gap in internal and external financial reporting on intangible investments. The main conclusion of the paper is to suggest that the way forward to a standardized, more comparable approach to measuring intangible capital is to employ CIV method of valuation. Published macroeconomic data traditionally exclude most intangible investment from measured GDP. This situation is beginning to change as some attempts have been made to measure the amount of intangible assets. It was found that intangible assets account for $164.20 million in all the listed companies of Jordan. All this money does not appear on the balance sheets of these companies and hence requires special attention of policy makers for better utilization.

Keywords: intangible capital, stock prices, Amman Stock Exchange

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Authors: M. Stoklasa, H. Starzyczna, K. Matusinska

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This article deals with the perceived quality of regional products in the Moravian-Silesian region in the Czech Republic. Research was focused on finding out what do consumers perceive as a quality product and what characteristics make a quality product. The data were obtained by questionnaire survey and analysed by IBM SPSS. From the thousands of respondents the representative sample of 719 for MS region was created based on demographic factors of gender, age, education and income. The research analysis disclosed that consumers in MS region are still price oriented and that the preference of quality over price does not depend on regional brand knowledge.

Keywords: regional brands, quality products, characteristics of quality, quality over price

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Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang

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In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.

Keywords: deep learning, convolutional neural network, LSTM, housing prediction

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Authors: Prateep Wajeetongratana

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The objectives of the research were to examine that how management accounting techniques were perceived and used by companies listed on the stock exchange and to investigate similarities or differences of management accounting practices between companies listed on the stock exchange and Thai SMEs. Descriptive and inferential statistics were employed. The finding found that almost all of the companies used traditional management accounting techniques more than advanced management accounting techniques. Four management accounting techniques having no significant association with business characteristic were standard costing, job order costing, process costing. The barriers that Thai SMEs encountered were a lack of proper accounting system and the insufficient knowledge in management accounting of the accountants. The comparison results revealed that both companies listed on the stock exchange and Thai SMEs used traditional management accounting techniques more than advanced techniques.

Keywords: companies listed on the stock exchange, financial budget, management accounting, operating budget

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Authors: Frankie Burgos, Emely Munar, Conrado Basa

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This paper aimed at developing an artificial neural network (ANN) model specifically for the Philippine Stock Exchange index closing value. The inputs to the ANN are US Dollar and Philippine Peso(USD-PHP) exchange rate, GDP growth of the country, quarterly inflation rate, 10-year bond yield, credit rating of the country, previous open, high, low, close values and volume of trade of the Philippine Stock Exchange Index (PSEi), gold price of the previous day, National Association of Securities Dealers Automated Quotations (NASDAQ), Standard and Poor’s 500 (S & P 500) and the iShares MSCI Philippines ETF (EPHE) previous closing value. The target is composed of the closing value of the PSEi during the 627 trading days from November 3, 2011, to May 30, 2014. MATLAB’s Neural Network toolbox was employed to create, train and simulate the network using multi-layer feed forward neural network with back-propagation algorithm. The results satisfactorily show that the neural network developed has the ability to model the PSEi, which is affected by both internal and external economic factors. It was found out that the inputs used are the main factors that influence the movement of the PSEi closing value.

Keywords: artificial neural networks, artificial intelligence, philippine stocks exchange index, stocks trading

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Authors: Katerina Kormusheva

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Pricing plays a pivotal role in the marketing discipline as it directly influences consumer perceptions, purchase decisions, and overall market positioning of a product or service. This paper seeks to expand current knowledge in the area of discriminatory and differential pricing, a main area of marketing research. The methodology includes developing a framework and a model for determining how many price points to implement in differential pricing. We focus on choosing the levels of differentiation, derive a function form of the model framework proposed, and lastly, test it empirically with data from a large-scale marketing pricing experiment of services in telecommunications.

Keywords: marketing, differential pricing, price points, optimization

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Authors: Charles Lee

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The Principal Orthogonal Decomposition (POD) technique has been used as a model reduction tool for many applications in engineering and science. In principle, one begins with an ensemble of data, called snapshots, collected from an experiment or laboratory results. The beauty of the POD technique is that when applied, the entire data set can be represented by the smallest number of orthogonal basis elements. It is the such capability that allows us to reduce the complexity and dimensions of many physical applications. Mathematical formulations and numerical schemes for the POD method will be discussed along with applications in NASA’s Deep Space Large Antenna Arrays, Satellite Image Reconstruction, Cancer Detection with DNA Microarray Data, Maximizing Stock Return, and Medical Imaging.

Keywords: reduced-order methods, principal component analysis, cancer detection, image reconstruction, stock portfolios

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Authors: Changju Lee, Seungmo Ku, Sondo Kim, Woojin Chang

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How to detecting financial bubble? Addressing this simple question has been the focus of a vast amount of empirical research spanning almost half a century. However, financial bubble is hard to observe and varying over the time; there needs to be more research on this area. In this paper, we used abnormal difference between common stocks price and those preferred stocks price to explain financial bubble. First, we proposed the ‘W-index’ which indicates spread between common stocks and those preferred stocks in stock market. Second, to prove that this ‘W-index’ is valid for measuring financial bubble, we showed that there is an inverse relationship between this ‘W-index’ and S&P500 rate of return. Specifically, our hypothesis is that when ‘W-index’ is comparably higher than other periods, financial bubbles are added up in stock market and vice versa; according to our hypothesis, if investors made long term investments when ‘W-index’ is high, they would have negative rate of return; however, if investors made long term investments when ‘W-index’ is low, they would have positive rate of return. By comparing correlation values and adjusted R-squared values of between W-index and S&P500 return, VIX index and S&P500 return, and TED index and S&P500 return, we showed only W-index has significant relationship between S&P500 rate of return. In addition, we figured out how long investors should hold their investment position regard the effect of financial bubble. Using this W-index, investors could measure financial bubble in the market and invest with low risk.

Keywords: financial bubble detection, future return, forecasting, pairs trading, preferred stocks

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Authors: Yungtai Lo

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The current study follows an educational intervention on bottle-weaning to simultaneously evaluate the effect of the bottle-weaning intervention on reducing bottle use, daily milk intake from bottles, and daily energy intake in toddlers aged 11 to 13 months. A shared parameter model and a random effects model are used to jointly model bottle use, daily milk intake from bottles, and daily energy intake. We show in the two joint models that the bottle-weaning intervention promotes bottleweaning, and reduces daily milk intake from bottles in toddlers not off bottles and daily energy intake. We also show that the odds of drinking from a bottle were positively associated with the amount of milk intake from bottles and increased daily milk intake from bottles was associated with increased daily energy intake. The effect of bottle use on daily energy intake is through its effect on increasing daily milk intake from bottles that in turn increases daily energy intake.

Keywords: two-part model, semi-continuous variable, joint model, gamma regression, shared parameter model, random effects model

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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Current real-estate value estimation, difficult for laymen, usually is performed by specialists. This paper presents an automated estimation process based on big data and machine-learning technology that calculates influences of building conditions on real-estate price measurement. The present study analyzed actual building sales sample data for Nonhyeon-dong, Gangnam-gu, Seoul, Korea, measuring the major influencing factors among the various building conditions. Further to that analysis, a prediction model was established and applied using RapidMiner Studio, a graphical user interface (GUI)-based tool for derivation of machine-learning prototypes. The prediction model is formulated by reference to previous examples. When new examples are applied, it analyses and predicts accordingly. The analysis process discerns the crucial factors effecting price increases by calculation of weighted values. The model was verified, and its accuracy determined, by comparing its predicted values with actual price increases.

Keywords: apartment complex, big data, life-cycle building value analysis, machine learning

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Authors: Muhammad Tariq, Hammad Tahir, Fawwad Mahmood Butt

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Purpose: This study attempts to examine the best forecasting methodologies in the time series. The time series forecasting models such as VAR, ARCH and the ARMA are considered for the analysis. Methodology: The Bench Marks or the parameters such as Adjusted R square, F-stats, Durban Watson, and Direction of the roots have been critically and empirically analyzed. The empirical analysis consists of time series data of Consumer Price Index and Closing Stock Price. Findings: The results show that the VAR model performed better in comparison to other models. Both the reliability and significance of VAR model is highly appreciable. In contrary to it, the ARCH model showed very poor results for forecasting. However, the results of ARMA model appeared double standards i.e. the AR roots showed that model is stationary and that of MA roots showed that the model is invertible. Therefore, the forecasting would remain doubtful if it made on the bases of ARMA model. It has been concluded that VAR model provides best forecasting results. Practical Implications: This paper provides empirical evidences for the application of time series forecasting model. This paper therefore provides the base for the application of best time series forecasting model.

Keywords: forecasting, time series, auto regression, ARCH, ARMA

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Authors: S. K. Ashiquer Rahman

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Recently, oil has become more influential in almost every economic sector as a key material. As can be seen from the news, when there are some changes in an oil price or OPEC announces a new strategy, its effect spreads to every part of the economy directly and indirectly. That’s a reason why people always observe the oil price and try to forecast the changes of it. The most important factor affecting the price is its supply which is determined by the number of wildcats drilled. Therefore, a study about the number of wellheads and other economic variables may give us some understanding of the mechanism indicated by the amount of oil supplies. In this paper, we will consider a relationship between the number of wellheads and three key factors: the price of the wellhead, domestic output, and GNP constant dollars. We also add trend variables in the models because the consumption of oil varies from time to time. Moreover, this paper will use an econometrics method to estimate parameters in the model, apply some tests to verify the result we acquire, and then conclude the model.

Keywords: price, domestic output, GNP, trend variable, wildcat activity

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Authors: Boulis M. Ibrahim, Iordanis A. Kalaitzoglou

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A micro structural pricing model is proposed in which price components account for learning by incorporating changing expectations of the trading intensity and the risk level of incoming trades. An analysis of European carbon futures transactions finds expected trading intensity to increase the information component and decrease the liquidity component of price changes, but at different rates. Among the results, the expected persistence in trading intensity explains the majority of the auto correlations in the level and the conditional volatility of price changes, helps predict hourly patterns in the bid–ask spread and differentiates between the impact of buy versus sell and continuing versus reversing trades.

Keywords: CO2 emission allowances, market microstructure, duration, price discovery

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Authors: Asiye Aydilek, Harun Aydilek

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We investigate the costs of various suboptimal allocations in housing, consumption, bond and stock holdings of a retiree in a setting with recursive utility, considering the extensive empirical evidence that investors make suboptimal decisions in different ways. We find that suboptimal stock holdings impose only modest costs on the retiree. This may have a merit in explaining the limited stock investment in the data. The cost of suboptimal bond holdings is higher than that of stocks, but still small. This may partially explain why many more people hold bonds compared to stocks. We find that positive deviations from the optimal level are less costly relative to the negative ones in suboptimal housing allocations. This may help us to clarify why the elderly are over consuming housing, as seen in the housing data. The cost of suboptimal consumption is quite high and the highest of all. Our paper suggests that, in terms of welfare, the decisions of how much of liquid wealth to use for consumption and for saving are more important than the decision about the composition of liquid savings. Suboptimal stock holdings are twice more costly in power utility and suboptimal bond holdings are twenty times more costly in recursive utility. Recursive utility is superior to power utility in terms of rationalizing many people's preference for bonds instead of stocks in investment.

Keywords: housing, recursive utility, retirement, suboptimal decisions, welfare cost

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Authors: Gladie Lui

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The global credit crisis of 2008 aroused renewed interest in the effectiveness of corporate governance mechanisms to safeguard investor interests. In this paper, we measure the effect of the crisis from 2008 to 2009 on the stock performance of 976 Hong Kong-listed companies and examine its link to corporate governance mechanisms. It is evident that the crisis and the economic downturn affected different industries. Empirical results show that firms with an independent board and a high concentration of ownership and management ownership had lower abnormal stock returns, but a lower price volatility during the global financial crisis. These results highlight that no single corporate governance mechanism is fit for all types of financial crises and time frames. To strengthen investors’ confidence in the ability of companies to deal with such swift financial catastrophes, companies should enhance the dynamism and responsiveness of their governance mechanisms in times of turbulence.

Keywords: board of directors, capital market, corporate governance, financial crisis

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Authors: Ernawati Mustafa Kamal, Hasnanywati Hassan, Atasya Osmadi

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The housing industry is crucial for sustainable development of every country. Housing is a basic need that can enhance the quality of life. Owning a house is therefore the main aim of individuals. However, affordability has become a critical issue towards homeownership. In recent years, housing price in the main cities has increased tremendously to unaffordable level. This paper investigates factors influencing the housing price from developer’s perspective and provides recommendation on strategies to tackle this issue. Online and face-to-face survey was conducted on housing developers operating in Penang, Malaysia. The results indicate that (1) location; (2) macroeconomics factor; (3) demographic factors; (4) land/zoning and; (5) industry factors are the main factors influencing the housing price. This paper contributes towards better understanding on developers’ view on how the housing price is determined and form a basis for government to help tackle the housing affordability issue.

Keywords: factors influence, house price, housing developers, Malaysia

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Authors: Chen-Yin Kuo, Yung-Hsin Lee

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Previous studies applying residual income valuation (RIV) model generally use panel data and single-equation model to forecast stock prices. Unlike these, this paper uses Taiwan longitudinal data to estimate multi-equation time-series models such as Vector Autoregressive (VAR), Vector Error Correction Model (VECM), and conduct out-of-sample forecasting. Further, this work assesses their forecasting performance by two instruments. In favor of extant research, the major finding shows that VECM outperforms other three models in forecasting for three stock sectors over entire horizons. It implies that an error correction term containing long-run information contributes to improve forecasting accuracy. Moreover, the pattern of composite shows that at longer horizon, VECM produces the greater reduction in errors, and performs substantially better than VAR.

Keywords: residual income valuation model, vector error correction model, out of sample forecasting, forecasting accuracy

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Authors: Mojo Mengistu Gelasso

Abstract:

The tropical forest is considered the most important forest ecosystem for mitigating climate change by sequestering a high amount of carbon. The potential carbon stock of the forest can be influenced by many factors. Therefore, studying these factors is crucial for understanding the determinants that affect the potential for woody carbon storage in the forest. This study was conducted to evaluate the potential for woody carbon stock and how it varies based on plant community types, as well as along altitudinal, slope, and aspect gradients in the Munessa dry Afromontane forest. Vegetation data was collected using systematic sampling. Five line transects were established at 100 m intervals along the altitudinal gradient between two consecutive transect lines. On each transect, 10 quadrats (20 x 20 m), separated by 200 m, were established. The woody carbon was estimated using an appropriate allometric equation formulated for tropical forests. The data was analyzed using one-way ANOVA in R software. The results showed that the total woody carbon stock of the Munessa forest was 210.43 ton/ha. The analysis of variance revealed that woody carbon density varied significantly based on environmental factors, while community types had no significant effect. The highest mean carbon stock was found at middle altitudes (2367-2533 m.a.s.l), lower slopes (0-13%), and west-facing aspects. The Podocarpus falcatus-Croton macrostachyus community type also contributed a higher woody carbon stock, as larger tree size classes and older trees dominated it. Overall, the potential for woody carbon sequestration in this study was strongly associated with environmental variables. Additionally, the uneven distribution of species with larger diameter at breast height (DBH) in the study area might be linked to anthropogenic factors, as the current forest growth indicates characteristics of a secondary forest. Therefore, our study suggests that the development and implementation of a sustainable forest management plan is necessary to increase the carbon sequestration potential of this forest and mitigate climate change.

Keywords: munessa forest, woody carbon stock, environmental factors, climate mitigation

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Authors: Mengistu Gelasso Mojo

Abstract:

The tropical forest is considered the most important forest ecosystem for mitigating climate change by sequestering a high amount of carbon. The potential carbon stock of the forest can be influenced by many factors. Therefore, studying these factors is crucial for understanding the determinants that affect the potential for woody carbon storage in the forest. This study was conducted to evaluate the potential for woody carbon stock and how it varies based on plant community types, as well as along altitudinal, slope, and aspect gradients in the Munessa dry Afromontane forest. Vegetation data was collected using systematic sampling. Five line transects were established at 100 m intervals along the altitudinal gradient between two consecutive transect lines. On each transect, 10 quadrats (20 x 20 m), separated by 200 m, were established. The woody carbon was estimated using an appropriate allometric equation formulated for tropical forests. The data was analyzed using one-way ANOVA in R software. The results showed that the total woody carbon stock of the Munessa forest was 210.43 ton/ha. The analysis of variance revealed that woody carbon density varied significantly based on environmental factors, while community types had no significant effect. The highest mean carbon stock was found at middle altitudes (2367-2533 m.a.s.l), lower slopes (0-13%), and west-facing aspects. The Podocarpus falcatus-Croton macrostachyus community type also contributed a higher woody carbon stock, as larger tree size classes and older trees dominated it. Overall, the potential for woody carbon sequestration in this study was strongly associated with environmental variables. Additionally, the uneven distribution of species with larger diameter at breast height (DBH) in the study area might be linked to anthropogenic factors, as the current forest growth indicates characteristics of a secondary forest. Therefore, our study suggests that the development and implementation of a sustainable forest management plan is necessary to increase the carbon sequestration potential of this forest and mitigate climate change.

Keywords: munessa forest, woody carbon stock, environmental factors, climate mitigation

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