Search results for: daily stock price data

Authors: Tarek Sadraoui, Ahlem Nasr Othman

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Our research deal with studying and testing the effects of social media on the cryptocurrency price during the period 2020-2023. The rise of the phenomena of cryptocurrency in the world raises questions about the importance of sentiment analysis of social media on the price of the cryptocurrency. Using panel data, we show that the positive and negative twits have a positive and statistically significant impact on the price of the cryptocurrency, and neutral twits have exerted a negative and significant effect on the cryptocurrency price. Specifically, we determine the causal relationship, short-term and long-term relationship with ARDL approach between the cryptocurrency price and social media using the Granger causality test.

Keywords: social media, Twitter, Google trend, panel, cryptocurrency

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Authors: Abderrazek Ben Maatoug, Ibrahim Fatnassi, Wassim Ben Ayed

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In this paper we investigate the issue of market risk quantification for Middle East and North Africa (MENA) Islamic market equity. We use Value-at-Risk (VaR) as a measure of potential risk in Islamic stock market, for long and short position, based on Riskmetrics model and the conditional parametric ARCH class model volatility with normal, student and skewed student distribution. The sample consist of daily data for the 2006-2014 of 11 Islamic stock markets indices. We conduct Kupiec and Engle and Manganelli tests to evaluate the performance for each model. The main finding of our empirical results show that (i) the superior performance of VaR models based on the Student and skewed Student distribution, for the significance level of α=1% , for all Islamic stock market indices, and for both long and short trading positions (ii) Risk Metrics model, and VaR model based on conditional volatility with normal distribution provides the best accurate VaR estimations for both long and short trading positions for a significance level of α=5%.

Keywords: value-at-risk, risk management, islamic finance, GARCH models

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Authors: Jorge M. Uribe

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We propose a methodology for setting the price of an insurance contract targeted to manage the risk associated with weather conditions that affect variable renewable energy generation. The methodology relies on conditional quantile regressions to estimate the weather risk of a solar panel. It is illustrated using real daily radiation and weather data for three cities in Spain (Valencia, Barcelona and Madrid) from February 2/2004 to January 22/2019. We also adapt the concepts of value at risk and expected short fall from finance to this context, to provide a complete panorama of what we label as weather risk. The methodology is easy to implement and can be used by insurance companies to price a contract with the aforementioned characteristics when data about similar projects and accurate cash flow projections are lacking. Our methodology assigns a higher price to an insurance product with the stated characteristics in Madrid, compared to Valencia and Barcelona. This is consistent with Madrid showing the largest interquartile range of operational deficits and it is unrelated to the average value deficit, which illustrates the importance of our proposal.

Keywords: insurance, weather, vre, risk

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Authors: Kawsar Ahmed, Hong Wang

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The study's goal was to estimate the causal mediation impact of tobacco tax before and after price hikes among low-income male smokers, with a particular emphasis on the effect estimating pathways framework for continuous and dichotomous variables. From July to December 2021, a cross-sectional investigation of observational data (n=739) was collected from Bangladeshi low-wage smokers. The Quasi-Bayesian technique, binomial probit model, and sensitivity analysis using a simulation of the computational tools R mediation package had been used to estimate the effect. After a price rise for tobacco products, the average number of cigarettes or bidis sticks taken decreased from 6.7 to 4.56. Tobacco product rising prices have a direct effect on low-income people's decisions to quit or lessen their daily smoking habits of Average Causal Mediation Effect (ACME) [effect=2.31, 95 % confidence interval (C.I.) = (4.71-0.00), p<0.01], Average Direct Effect (ADE) [effect=8.6, 95 percent (C.I.) = (6.8-0.11), p<0.001], and overall significant effects (p<0.001). Tobacco smoking choice is described by the mediated proportion of income effect, which is 26.1% less of following price rise. The curve of ACME and ADE is based on observational figures of the coefficients of determination that asses the model of hypothesis as the substantial consequence after price rises in the sensitivity analysis. To reduce smoking product behaviors, price increases through taxation have a positive causal mediation with income that affects the decision to limit tobacco use and promote low-income men's healthcare policy.

Keywords: causal mediation analysis, directed acyclic graphs, tobacco price policy, sensitivity analysis, pathway estimation

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Authors: Liuyao Chai, Colin Clark

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Analysis of 574 participants in a simulated two-person distributive negotiation revealed that the first price 245 (42.7%) of these participants actually asked/offered for the item under negotiation (a used car) differed from the first price they previously stated they intended to ask/offer during their negotiation. This discrepancy between a negotiator’s intended first asking/offer price and his/her actual first asking/offer price had a significant and economically consequential impact on both the course and the outcomes of the negotiations studied. Participants whose actual first price remained the same as their intended first price tended to secure better negotiation outcomes. Moreover, participants who changed their intended first price tended to obtain relatively lower outcomes regardless of whether their modified first announced price had created a negotiating position that was ‘stronger’ or ‘weaker’ than if they had opened with their intended first price. Subsequent investigation of over twenty negotiation behaviours and pre-negotiation perceptual variables within this dataset indicated that the three types of first price announcers—i.e. intended first asking/offer price ‘weakeners’, ‘maintainers’ and ‘strengtheners’— comprised persons who tended to have significantly different pre-negotiation perceptions and behaved in systematically different ways during their negotiation. Typically, the most negative, outcome-compromising consequences of changing, weakening or strengthening an intended first price occurred at the very beginning of a negotiation when participants exchanged their actual first asking/offer prices.

Keywords: business communication, negotiation, persuasion, intended first asking/offer prices, bargaining

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Authors: Najlae Bendou, Jean-Jacques Lilti, Khalid Elbadraoui

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In this paper, we examine stock market integration of emerging markets around the global financial turmoil of 2007-2008. Following Pukthuanthong and Roll (2009), we measure the integration of 46 emerging countries using the adjusted R-square from the regression of each country's daily index returns on global factors extracted from the covariance matrix computed using dollar-denominated daily index returns of 17 developed countries. Our sample surrounds the global financial crisis and ranges between 2000 and 2018. We analyze results using four cohorts of emerging countries: East Asia & Pacific and South Asia, Europe & Central Asia, Latin America & Caribbean, Middle East & Africa. We find that the level of integration of emerging countries increases at the commencement of the crisis and during the booming phase of the business cycles. It reaches a maximum point in the middle of the crisis and then tends to revert to its pre-crisis level. This pattern tends to be common among the four geographic zones investigated in this study. Finally, we investigate the determinants of stock market integration of emerging countries in our sample using panel regressions. Our results suggest that the degree of stock market integration of these countries should be put into perspective by some macro-economic factors, such as the size of the equity market, school enrollment rate, international liquidity level, stocks traded volume, tax revenue level, imports and exports volumes.

Keywords: correlations, determinants of integration, diversification, emerging markets, financial crisis, integration, markets co-movement, panel regressions, r-square, stock markets

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Authors: Philemon Rakoto

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This paper tests the improvement of financial reporting quality when firms award stock options to their executives. The originality of this study is that we introduce the moderating effect of business ethics in the model. The sample is made up of 116 Canadian high-technology firms with available data for the fiscal year ending in 2012. We define the quality of financial reporting as the value relevance of accounting information as developed by Ohlson. Our results show that executive stock option award alone does not improve the quality of financial reporting. Rather, the quality improves when a firm awards stock options to its executives and investors perceive that the level of business ethics in that firm is high.

Keywords: business ethics, Canada, high-tech firms, stock options, value relevance

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Authors: Simin Eydivand, Ali Ghanadieslami, Reza Amiri

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Natural gas liquids (NGLs) are light hydrocarbons that are dissolved in associated or non‐associated natural gas in a hydrocarbon reservoir and are produced within a gas stream. There are different ways to calculate the price of NGL. In this study, a spreadsheet calculation method is used for calculation of NGL price with an attractive economy of IRR 25%. For a typical NGL Plant with 3,200,000 t/y capacity of investment and operation of 90% capacity to have IRR 25%, the price of NGL is calculated 277 $/t.

Keywords: natural gas liquid, NGL, LPG, price, NGL fractionation, NF, investment, IRR, NPV

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Authors: Mojtaba Rezaei, Elham Heydari

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This paper highlights the empirical results of analyzing the correlation between accounting information and systematic risk. This association is analyzed among financial ratios and systematic risk by considering the financial statement of 39 companies listed on the Tehran Stock Exchange (TSE) for five years (2014-2018). Financial ratios have been categorized into four groups and to describe the special features, as representative of accounting information we selected: Return on Asset (ROA), Debt Ratio (Total Debt to Total Asset), Current Ratio (current assets to current debt), Asset Turnover (Net sales to Total assets), and Total Assets. The hypotheses were tested through simple and multiple linear regression and T-student test. The findings illustrate that there is no significant relationship between accounting information and market risk. This indicates that in the selected sample, historical accounting information does not fully reflect the price of stocks.

Keywords: accounting information, market risk, systematic risk, stock return, efficient market hypothesis, EMH, Tehran stock exchange, TSE

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Authors: Fatma Ismailia, Malek Saihi

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This research provides an explanation of exchange incentives on the Tunis stock market from a behavioural point of view. The elucidation of the anomalies of excessive volume of transactions and that of excessive volatility cannot be done without the recourse to the psychological aspects of investors. The excessive confidence has been given the predominant role for the explanation of these phenomena. Indeed, when investors store increments, they become more confident about the precision of their private information and their exchange activities then become more aggressive on the subsequent periods. These overconfident investors carry out the intensive exchanges leading to an increase of securities volatility. The objective of this research is to identify whether the trading volume and the excessive volatility of securities observed on the Tunisian stock market come from the excessive exchange of overconfident investors. We use a sample of daily observations over the period January 1999 - October 2007 and we relied on various econometric tests including the VAR model. Our results provide evidence on the importance to consider the bias of overconfidence in the analysis of Tunis stock exchange specificities. The results reveal that the excess of confidence has a major impact on the trading volume while using daily temporal intervals.

Keywords: overconfidence, trading volume, efficiency, rationality, anomalies, behavioural finance, cognitive biases

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Authors: Norm Josephy, Lucy Kimball, Victoria Steblovskaya

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Hedging of a portfolio of European options under proportional transaction costs is considered. Our discrete time financial market model extends the binomial market model with transaction costs to the case where the underlying stock price ratios are distributed over a bounded interval rather than over a two-point set. An optimal hedging strategy is chosen from a set of admissible non-self-financing hedging strategies. Our approach to optimal hedging of a portfolio of options is based on theoretical foundation that includes determination of a no-arbitrage option price interval as well as on properties of the non-self-financing strategies and their residuals. A computational algorithm for optimizing an investor relevant criterion over the set of admissible non-self-financing hedging strategies is developed. Applicability of our approach is demonstrated using both simulated data and real market data.

Keywords: extended binomial model, non-self-financing hedging, optimization, proportional transaction costs

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Authors: Xinyi Xu

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This study conducts a detailed examination of Asian stock markets over the period from 2008 to 2023, with a focus on the dynamics of cointegration and their relevance for long-term investment strategies. Specifically, we assess the co-movement and potential for pairs trading—a strategy where investors take opposing positions on two stocks, indices, or financial instruments that historically move together. For example, we explore the relationship between the Nikkei 225 (N225), Japan’s benchmark stock index, and the Straits Times Index (STI) of Singapore, as well as the relationship between the Korea Composite Stock Price Index (KS11) and the STI. The methodology includes tests for normality, stationarity, cointegration, and the application of Vector Error Correction Modeling (VECM). Our findings reveal significant long-term relationships between these pairs, indicating opportunities for pairs trading strategies. Furthermore, the research underscores the challenges posed by model instability and the influence of major global incidents, which are identified as structural breaks. These findings pave the way for further exploration into the intricacies of financial market dynamics.

Keywords: normality tests, stationarity, cointegration, VECM, pairs trading

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Authors: Murat Yildiz, Burcu Yildiz

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Price Cap Regulation is a form of economic regulation designed in the 1980s in the United Kingdom. Price cap regulation sets a cap on the price that the utility provider can charge. The cap is set according to several economic factors, such as the price cap index, expected efficiency savings and inflation. It has been used by several countries as a regulatory regime in several sectors. Container port privatization is still in early stages in some countries. Lack of a general framework can be an impediment to privatization. This paper aims a general framework to comprising decisions to be made for variables which are able to accommodate the variety of container terminals. Several approaches that may be needed as well as a passage between approaches.

Keywords: Price Cap Regulation, ports privatization, container terminal price regime, earning sharing

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Authors: Ahmad Awajan, Sadam Al Wadi

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In this study, a hybrid method based on Empirical Mode Decomposition and Holt-Winter (EMD-HW) is used to forecast Amman stock market data. First, the data are decomposed by EMD method into Intrinsic Mode Functions (IMFs) and residual components. Then, all components are forecasted by HW technique. Finally, forecasting values are aggregated together to get the forecasting value of stock market data. Empirical results showed that the EMD- HW outperform individual forecasting models. The strength of this EMD-HW lies in its ability to forecast non-stationary and non- linear time series without a need to use any transformation method. Moreover, EMD-HW has a relatively high accuracy comparing with eight existing forecasting methods based on the five forecast error measures.

Keywords: Holt-Winter method, empirical mode decomposition, forecasting, time series

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Authors: Salako Rotimi, Oshungade Stephen, Ojewoye Opeyemi

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The maintenance of price stability is one of the macroeconomic challenges facing Nigeria as a nation. This paper attempts to build a co-integration multivariate time series model for inflation movement in Nigeria using data extracted from the abstract of statistics of the Central Bank of Nigeria (CBN) from 2008 to 2017. The Johansen cointegration test suggests at least one co-integration vector describing the long run relationship between Consumer Price Index (CPI), Food Price Index (FPI) and Non-Food Price Index (NFPI). All three series show increasing pattern, which indicates a sign of non-stationary in each of the series. Furthermore, model predictability was established with root-mean-square-error, mean absolute error, mean average percentage error, and Theil’s unbiased statistics for n-step forecasting. The result depicts that the long run coefficient of a consumer price index (CPI) has a positive long-run relationship with the food price index (FPI) and non-food price index (NFPI).

Keywords: economic, inflation, model, series

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Authors: Khalid Mujaljal, Hassan Alhajhoj

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This paper empirically investigates the influence of oil price fluctuations on the key macroeconomic variables of the Kingdom of Saudi Arabia using unrestricted VAR methodology. Two analytical tools- Granger-causality and variance decomposition are used. The Granger-causality test reveals that almost all specifications of oil price shocks significantly Granger-cause GDP and demonstrates evidence of causality between oil price changes and money supply (M3) and consumer price index percent (CPIPC) in the case of positive oil price shocks. Surprisingly, almost all specifications of oil price shocks do not Granger-cause government expenditure. The outcomes from variance decomposition analysis suggest that positive oil shocks contribute about 25 percent in causing inflation in the country. Also, contribution of symmetric linear oil price shocks and asymmetric positive oil price shocks is significant and persistent with 25 percent explaining variation in world consumer price index till end of the period.

Keywords: Granger causality, oil prices changes, Saudi Arabian economy, variance decomposition

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Authors: Minseo Jo

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The purpose of this paper is to examine the factors in the hedonic price models, that has significance impact in determining the price of apartments. There are many variables employed in the hedonic price models and their effectiveness vary differently according to the researchers and the regions they are analysing. In order to consider various conditions, the meta-regression analysis has been selected for the study. In this paper, four meta-independent variables, from the 65 hedonic price models to analysis. The factors that influence the prices of apartments, as well as including factors that influence the prices of apartments, regions, which are divided into two of the research performed, years of research performed, the coefficients of the functions employed. The covariance between the four meta-variables and p-value of the coefficients and the four meta-variables and number of data used in the 65 hedonic price models have been analyzed in this study. The six factors that are most important in deciding the prices of apartments are positioning of apartments, the noise of the apartments, points of the compass and views from the apartments, proximity to the public transportations, companies that have constructed the apartments, social environments (such as schools etc.).

Keywords: hedonic price model, housing price, meta-regression analysis, characteristics

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Authors: Mohammad Azam

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The purpose of this study is to investigate risk and real excess portfolio returns using inflation adjusted risk-free rates, a measuring technique that focuses on the momentum augmented Fama-French six-factor model and use monthly data from 1994 to 2022. With the exception of profitability, the data show that market, size, value, momentum, and investment factors are all strongly associated to excess portfolio stock returns using ordinary lease square regression technique. According to the Gibbons, Ross, and Shanken test, the momentum augmented Fama-French six-factor model outperforms the market. This technique discovery may be utilised by academics and professionals to acquire an in-depth knowledge of the Pakistan Stock Exchange across a broad stock pattern for investing decisions and portfolio construction.

Keywords: real excess portfolio returns, momentum augmented fama & french five-factor model, GRS-test, pakistan stock exchange

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Authors: Suraj Mehrotra

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The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

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Authors: Aristeidis Samitas, Evangelos Vasileiou

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The aim of this study is to examine the month and the trading month effect under changing financial trends. We choose the Greek stock market to implement our assumption because there are clear and long term periods of financial growth and recession. Daily financial data from Athens Exchange General Index for the period 2002-2012 are considered. The paper employs several linear and non-linear models, although the TGARCH asymmetry model best fits in this sample and for this reason we mainly present the TGARCH results. Empirical results show that changing economic and financial conditions influences the calendar effects. Especially, the trading month effect totally changes in each fortnight according to the financial trend. On the other hand, in Greece the January effect exists during the growth periods, although it does not exist when the financial trend changes. The findings are helpful to anybody who invest and deals with the Greek stock market. Moreover, they may pave the way for an alternative calendar anomalies research approach, so it may be useful to investors who take into account these anomalies when they draw their investment strategy.

Keywords: month effect, trading month effect, economic cycles, crisis

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Authors: S. Avdasheva, D. Tsytsulina

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In many regulated industries over the world price cap as a method of price regulation replaces cost-plus pricing. It is a kind of incentive regulation introduced in order to enhance productive efficiency by strengthening sellers’ incentives for cost reduction as well as incentives for more efficient pricing. However pricing under cap is not neutral for competition in the market. We consider influence on competition on the markets where benchmark for cap is chosen from when sellers are multi-market. We argue that the impact of price cap regulation on market competition depends on the design of cap. More specifically if cap for one (regulated) market depends on the price of the supplier in other (non-regulated) market, there is sub-type of price cap regulation (known in Russian tariff regulation as ‘netback minus’) that enhance incentives to collude in non-regulated market.

Keywords: price regulation, competition, collusion

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Authors: Engy Eissa, Dina M. Yousri

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COVID-19 has hit massively the world economy, financial markets and even societies’ livelihood. The infectious disease caused by the most recently discovered coronavirus was claimed responsible for a shrink in the global economy by 4.4% in 2020. Shortly after the first case in Wuhan was identified, a quick surge in the number of confirmed cases in China was evident and a vast spread worldwide is recorded with cases surpassing the 500,000 cases. Irrespective of the disease’s trajectory in each country, a call for immediate action and prompt government intervention was needed. Given that there is no one-size-fits-all approach across the world, a number of containment and adoption policies were embraced. It was starting by enforcing complete lockdown like China to even stricter policies targeted containing the spread of the virus, augmenting the efficiency of health systems, and controlling the economic outcomes arising from this crisis. Hence, this paper has three folds; first, it examines the impact of containment policies taken by governments on controlling the number of cases and deaths in the given countries. Second, to assess the ramifications of COVID-19 on financial markets measured by stock returns. Third, to study the impact of containment policies measured by the government response index, the stringency index, the containment health index, and the economic support index on financial markets performance. Using a sample of daily data covering the period 31st of January 2020 to 15th of April 2021 for the 10 most hit countries in wave one by COVID-19 namely; Brazil, India, Turkey, Russia, UK, USA, France, Germany, Spain, and Italy. The aforementioned relationships were tested using Panel VAR Regression. The preliminary results showed that the number of daily deaths had an impact on the stock returns; moreover, the health containment policies and the economic support provided by the governments had a significant effect on lowering the impact of COVID-19 on stock returns.

Keywords: COVID-19, government policies, stock returns, VAR

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Authors: Julio Albuja, David Zaldumbide

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Data analysis is an important step before taking a decision about money. The aim of this work is to analyze the factors that influence the final price of the houses through data mining algorithms. To our best knowledge, previous work was researched just to compare results. Furthermore, before using the data of the data set, the Z-Transformation were used to standardize the data in the same range. Hence, the data was classified into two groups to visualize them in a readability format. A decision tree was built, and graphical data is displayed where clearly is easy to see the results and the factors' influence in these graphics. The definitions of these methods are described, as well as the descriptions of the results. Finally, conclusions and recommendations are presented related to the released results that our research showed making it easier to apply these algorithms using a customized data set.

Keywords: algorithms, data, decision tree, transformation

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Authors: Muhammad Zia Ullah Baig, Atiq Uz Zama

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The motivation of the project is to facilitate the governance body, as well as the common man in his/her daily life consuming product rates, to easily monitor the expense, to control the budget with the help of single SMS (message), e-mail facility, and to manage governance body by task management system. The system will also be capable of finding irregularities being done by the concerned department in mitigating the complaints generated by the customer and also provide a solution to overcome problems. We are building a system that easily controls the price control system of any country, we will feeling proud to give this system free of cost to Indian Government also. The system is able to easily manage and control the price control department of government all over the country. Price control department run in different cities under City District Government, so the system easily run in different cities with different SMS Code and decentralize Database ensure the non-functional requirement of system (scalability, reliability, availability, security, safety). The customer request for the government official price list with respect to his/her city SMS code (price list of all city available on website or application), the server will forward the price list through a SMS, if the product is not available according to the price list the customer generate a complaint through an SMS or using website/smartphone application, complaint is registered in complaint database and forward to inspection department when the complaint is entertained, the inspection department will forward a message about the complaint to customer. Inspection department physically checks the seller who does not follow the price list, but the major issue of the system is corruption, may be inspection officer will take a bribe and resolve the complaint (complaint is fake) in that case the customer will not use the system. The major issue of the system is to distinguish the fake and real complain and fight for corruption in the department. To counter the corruption, our strategy is to rank the complain if the same type of complaint is generated the complaint is in high rank and the higher authority will also notify about that complain, now the higher authority of department have reviewed the complaint and its history, the officer who resolve that complaint in past and the action against the complaint, these data will help in decision-making process, if the complaint was resolved because the officer takes bribe, the higher authority will take action against that officer. When the price of any good is decided the market/former representative is also there, with the mutual understanding of both party the price is decided, the system facilitate the decision-making process. The system shows the price history of any goods, inflation rate, available supply, demand, and the gap between supply and demand, these data will help to allot for the decision-making process.

Keywords: price control, goods, government, inspection, department, customer, employees

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Authors: Li Li, Kai-Hsuan Chu

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It is well known that the real estate price depends on a lot of factors. Each house current value is dependent on the location, room number, transportation, living convenience, year and surrounding environments. Although, there are different experienced models for housing agent to estimate the price, it is a case by case study without overall dynamic variation investigation. However, many economic parameters may more or less influence the real estate price variation. Here, the influences of most macroeconomic parameters on real estate price are investigated individually based on least-square scheme and grey correlation strategy. Then those parameters are classified into leading indices, simultaneous indices and laggard indices. In addition, the leading time period is evaluated based on least square method. The important leading and simultaneous indices can be used to establish an artificial intelligent neural network model for real estate price variation prediction. The real estate price variation of Taipei, Taiwan during 2005 ~ 2017 are chosen for this research data analysis and validation. The results show that the proposed method has reasonable prediction function for real estate business reference.

Keywords: real estate price, least-square, grey correlation, macroeconomics

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Authors: Admassu Addi, Sebesebe Demissew, Teshome Soromessa, Zemede Asfaw

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This study measures the carbon stock of the Moist Afromontane Gesha-Sayilem forest found in Gesha and Sayilem District in southwest Ethiopia. A stratified sampling method was used to identify the number of sampling point through the Global Positioning System. A total of 90 plots having nested plots to collect tree species and soil data were demarcated. The results revealed that the total carbon stock of the forest was 362.4 t/ha whereas the above ground carbon stock was 174.95t/ha, below ground litter, herbs, soil, and dead woods were 34.3,1.27, 0.68, 128 and 23.2 t/ha (up to 30 cm depth) respectively. The Gesha- Sayilem Forest is a reservoir of high carbon and thus acts as a great sink of the atmospheric carbon. Thus conservation of the forest through introduction REDD+ activities is considered an appropriate action for mitigating climate change.

Keywords: carbon sequestration, carbon stock, climate change, allometric, Ethiopia

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Authors: The Danh Phan

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House price forecasting is a main topic in the real estate market research. Effective house price prediction models could not only allow home buyers and real estate agents to make better data-driven decisions but may also be beneficial for the property policymaking process. This study investigates the housing market by using machine learning techniques to analyze real historical house sale transactions in Australia. It seeks useful models which could be deployed as an application for house buyers and sellers. Data analytics show a high discrepancy between the house price in the most expensive suburbs and the most affordable suburbs in the city of Melbourne. In addition, experiments demonstrate that the combination of Stepwise and Support Vector Machine (SVM), based on the Mean Squared Error (MSE) measurement, consistently outperforms other models in terms of prediction accuracy.

Keywords: house price prediction, regression trees, neural network, support vector machine, stepwise

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Authors: Darren Zou

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Forecasting the stock market is a very challenging task. Various economic indicators such as GDP, exchange rates, interest rates, and unemployment have a substantial impact on the stock market. Time series models are the traditional methods used to predict stock market changes. In this paper, a machine learning method, Bayesian Additive Regression Tree (BART) is used in predicting stock market indexes based on multiple economic indicators. BART can be used to model heterogeneous treatment effects, and thereby works well when models are misspecified. It also has the capability to handle non-linear main effects and multi-way interactions without much input from financial analysts. In this research, BART is proposed to provide a reliable prediction on day-to-day stock market activities. By comparing the analysis results from BART and with time series method, BART can perform well and has better prediction capability than the traditional methods.

Keywords: BART, Bayesian, predict, stock

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Authors: H. D. Ibrahim, H. C. Chinwenyi, T. Danjuma

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An option is defined as a financial contract that provides the holder the right but not the obligation to buy or sell a specified quantity of an underlying asset in the future at a fixed price (called a strike price) on or before the expiration date of the option. This paper examined two approaches for derivation of Partial Differential Equation (PDE) options price valuation formula for the Heston stochastic volatility model. We obtained various PDE option price valuation formulas using the riskless portfolio method and the application of Feynman-Kac theorem respectively. From the results obtained, we see that the two derived PDEs for Heston model are distinct and non-unique. This establishes the fact of incompleteness in the model for option price valuation.

Keywords: Black-Scholes partial differential equations, Ito process, option price valuation, partial differential equations

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Authors: Anna M. Pretorius

Abstract:

The emerging markets category for portfolio investment was introduced in 1986 in an attempt to promote capital market development in less developed countries. Investors traditionally diversified their portfolios by investing in different developed markets. However, high growth opportunities forced investors to consider emerging markets as well. Examples include the rapid growth of the “Asian Tigers” during the 1980s, growth in Latin America during the 1990s and the increased interest in emerging markets during the global financial crisis. As such, portfolio flows to emerging markets have increased substantially. In 2002 7% of all equity allocations from advanced economies went to emerging markets; this increased to 20% in 2012. The stronger links between advanced and emerging markets led to increased synchronization of asset price movements. This increased level of stock market integration for emerging markets is confirmed by various empirical studies. Against the background of increased interest in emerging market assets and the increasing level of integration of emerging markets, this paper focuses on the determinants of stock market integration of emerging market countries. Various studies have linked the level of financial market integration with specific economic variables. These variables include: economic growth, local inflation, trade openness, local investment, budget surplus/ deficit, market capitalization, domestic bank credit, domestic institutional and legal environment and world interest rates. The aim of this study is to empirically investigate to what extent trade-related determinants have an impact on stock market integration. The panel data sample include data of 16 emerging market countries: Brazil, Chile, China, Colombia, Czech Republic, Hungary, India, Malaysia, Pakistan, Peru, Philippines, Poland, Russian Federation, South Africa, Thailand and Turkey for the period 1998-2011. The integration variable for each emerging stock market is calculated as the explanatory power of a multi-factor model. These factors are extracted from a large panel of global stock market returns. Trade related explanatory variables include: exports as percentage of GDP, imports as percentage of GDP and total trade as percentage of GDP. Other macroeconomic indicators – such as market capitalisation, the size of the budget deficit and the effectiveness of the regulation of the securities exchange – are included in the regressions as control variables. An initial analysis on a sample of developed stock markets could not identify any significant determinants of stock market integration. Thus the macroeconomic variables identified in the literature are much more significant in explaining stock market integration of emerging markets than stock market integration of developed markets. The three trade variables are all statistically significant at a 5% level. The market capitalisation variable is also significant while the regulation variable is only marginally significant. The global financial crisis has highlighted the urgency to better understand the link between the financial and real sectors of the economy. This paper comes to the important finding that, apart from the level of market capitalisation (as financial indicator), trade (representative of the real economy) is a significant determinant of stock market integration of countries not yet classified as developed economies.

Keywords: emerging markets, financial market integration, panel data, trade

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