Search results for: the stock market

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: Damien Kunjal, Faeezah Peerbhai

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Despite the increasing popularity of exchange-traded funds (ETFs), ETF investment choices may not always be rational. Excess trading volume, misevaluations of securities, and excess return volatility present in financial markets can be attributed to the influence of the overconfidence bias. Whilst previous research has explored the overconfidence bias in stock markets; this study focuses on trading in ETF markets. Therefore, the objective of this study is to investigate the presence of investor overconfidence in the South African ETF market. Using vector autoregressive models, the lead-lag relationship between market turnover and the market return is examined for the market of South African ETFs tracking domestic benchmarks and for the market of South African ETFs tracking international benchmarks over the period November 2000 till August 2019. Consistent with the overconfidence hypothesis, a positive relationship between current market turnover and lagged market return is found for both markets, even after controlling for market volatility and cross-sectional dispersion. This relationship holds for both market and individual ETF turnover suggesting that investors are overconfident when trading in South African ETFs tracking domestic benchmarks and South African ETFs tracking international benchmarks since trading activity depends on past market returns. Additionally, using the global recession as a structural break, this study finds that investor overconfidence is more pronounced after the global recession suggesting that investors perceive ETFs as risk-reducing assets due to their diversification benefits. Overall, the results of this study indicate that the overconfidence bias has a significant influence on ETF investment choices, therefore, suggesting that the South African ETF market is inefficient since investors’ decisions are based on their biases. As a result, the effect of investor overconfidence can account for the difference between the fair value of ETFs and its current market price. This finding has implications for policymakers whose responsibility is to promote the efficiency of the South African ETF market as well as ETF investors and traders who trade in the South African ETF market.

Keywords: exchange-traded fund, market return, market turnover, overconfidence, trading activity

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Authors: Kwabena Asare, Ezekiel N. N. Nortey, Felix O. Mettle

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Modelling of extreme events has always been of interest in fields such as hydrology and meteorology. However, after the recent global financial crises, appropriate models for modelling of such rare events leading to these crises have become quite essential in the finance and risk management fields. This paper models the extreme values of the Ghana Stock Exchange All-Shares indices (2000-2010) by applying the Extreme Value Theory to fit a model to the tails of the daily stock returns data. A conditional approach of the EVT was preferred and hence an ARMA-GARCH model was fitted to the data to correct for the effects of autocorrelation and conditional heteroscedastic terms present in the returns series, before EVT method was applied. The Peak Over Threshold (POT) approach of the EVT, which fits a Generalized Pareto Distribution (GPD) model to excesses above a certain selected threshold, was employed. Maximum likelihood estimates of the model parameters were obtained and the model’s goodness of fit was assessed graphically using Q-Q, P-P and density plots. The findings indicate that the GPD provides an adequate fit to the data of excesses. The size of the extreme daily Ghanaian stock market movements were then computed using the Value at Risk (VaR) and Expected Shortfall (ES) risk measures at some high quantiles, based on the fitted GPD model.

Keywords: extreme value theory, expected shortfall, generalized pareto distribution, peak over threshold, value at risk

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Authors: Alexander Marx

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Measuring and mitigating market risk is essential for the stability of enterprises, especially for major banking corporations and investment bank firms. To employ these risk measurement and mitigation processes, the Value at Risk (VaR) is the most commonly used risk metric by practitioners. In the past years, we have seen significant weaknesses in the predictive performance of the VaR in times of financial market crisis. To address this issue, the purpose of this study is to investigate the value-at-risk (VaR) estimation models and their predictive performance by applying a series of backtesting methods on the stock market indices of the G7 countries (Canada, France, Germany, Italy, Japan, UK, US, Europe). The study employs parametric, non-parametric, and semi-parametric VaR estimation models and is conducted during three different periods which cover the most recent financial market crisis: the overall period (2006–2022), the global financial crisis period (2008–2009), and COVID-19 period (2020–2022). Since the regulatory authorities have introduced and mandated the Conditional Value at Risk (Expected Shortfall) as an additional regulatory risk management metric, the study will analyze and compare both risk metrics on their predictive performance.

Keywords: value at risk, financial market risk, banking, quantitative risk management

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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: Taufik Nugroho, Firsty Dzainuurahmana, Tika Widiastuti

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The polemic about the salt crisis re-emerged, this is a classic problem in Indonesia and is still a homework that is not finished yet. This salt crisis occurs due to low productivity of salt commodities that have not been able to meet domestic demand and lack of salt productivity caused by several factors. One of the biggest factors of the crisis is the weather anomaly that disrupts salt production, less supportive technology and price stability. This study will try to discuss the salt scarcity and crisis solution in Islamic view. As for the conclusion of this study is the need for equilibrium or balancing between demand and supply, need to optimize the role of the government as Hisbah to maintain the balance of market mechanisms and prepare the stock system of salt stock by buying farmers products at reasonable prices then storing them.

Keywords: crisis, Islamic solution, scarcity, salt

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Authors: Marjan Golmaryami, Marzieh Behzadi

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Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too.

Keywords: artificial neural network (ANN), bat algorithm, particle swarm optimization algorithm (PSO), stock exchange

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Authors: Jasman Tuyon, Zamri Ahmad

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Behavioral asset pricing research has been gaining momentum since in 1990s. However, it is still incomplete and has been criticized for some philosophical, theoretical and model specification limitations. Due to these drawbacks, investors’ behaviors as a source of risk in behavioral asset pricing modeling still remains disputable. This paper aims to address these issues with an alternative perspective based on behavioral finance paradigm. Specifically, this paper proposes a theoretical linkages of both fundamental and behavioral risks on stock prices formation and an extension of the multifactor stock pricing model by combining multi-factor fundamentals and behavioral risks factors.

Keywords: behavioral finance, multifactor asset pricing, behavioral risks, fundamental risks

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Authors: Zahra Hatami, Hesham Ali, David Volkman

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Selecting a portfolio for investing is a crucial decision for individuals and legal entities. In the last two decades, with economic globalization, a stream of financial innovations has rushed to the aid of financial institutions. The importance of selecting stocks for the portfolio is always a challenging task for investors. This study aims to create a financial network to identify optimal portfolios using network centralities metrics. This research presents a community detection technique of superior stocks that can be described as an optimal stock portfolio to be used by investors. By using the advantages of a network and its property in extracted communities, a group of stocks was selected for each of the various time periods. The performance of the optimal portfolios compared to the famous index. Their Sharpe ratio was calculated in a timely manner to evaluate their profit for making decisions. The analysis shows that the selected potential portfolio from stocks with low centrality measurement can outperform the market; however, they have a lower Sharpe ratio than stocks with high centrality scores. In other words, stocks with low centralities could outperform the S&P500 yet have a lower Sharpe ratio than high central stocks.

Keywords: portfolio management performance, network analysis, centrality measurements, Sharpe ratio

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Authors: Abdullah Almulhim

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The International Accounting Standards Board (IASB) was established in 2001 to develop International Financial Reporting Standards (IFRS) that bring transparency, accountability, and efficiency to financial markets around the world. In addition, the IFRS provide a unified accounting language, which is especially important in the era of globalization. However, the establishment of a single set of high-quality international accounting standards is a matter of growing importance, as participants in the increasingly integrated world capital market demand comparability and transparency of financial reporting worldwide. Saudi Arabia became the 149th member of the World Trade Organization (WTO) on 11 December 2005, which has increased the need to convert to IFRS. Currently, the Saudi Arabian Monetary Authority (SAMA) requires banks and insurance companies in Saudi Arabia to report under IFRS Standards. However, until the end of 2016, SOCPA standards were applied to all other companies, listed and unlisted. From 2017, listed Saudi companies would be required to report under IFRS Standards as adopted by SOCPA effective 2017. This paper is to investigate the expected benefits gained and highlight the challenges faced by adopting IFRS by the listed companies in the Saudi Stock Exchange. Questionnaires were used as the main method of data collection. They were distributed to listed companies in the Saudi Capital Market. Data obtained through the questionnaires have been imported into SPSS statistical software for analysis. The expected results of this study will show the benefits of adopting IFRS by Saudi Listed Companies. However, this study will investigate the challenges faced by adopting IFRS by the listed companies in the Saudi Arabian Stock Market. Findings will be discussed later upon completion of initial analysis.

Keywords: challenges, IAS, IFRS, opportunities, Saudi, SOCPA

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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: Bel Abed Ines Mariem

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Different stock markets keep changing their exchange structure for the only purpose of improving the functioning of their markets. This paper investigates the effects of the transfer from one trading category to another in the Tunisian Stock Exchange on market price and liquidity. The sample consists of 40 securities transferred from call auction to continuous auction and conversely during the period between 2004 and 2013. The methodology used is the event study. Empirical results show an interesting phenomenon observed; stocks transferred to the call system have experienced an improvement on their price and liquidity especially for less liquid ones. However, price and liquidity for stocks transferred from call system to continuous system have decreased.

Keywords: microstructure, call auction, continuous auction, price, liquidity and event study

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Authors: Marc S. Paolella, Pawel Polak

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The paper proposes a framework for large-scale portfolio optimization which accounts for all the major stylized facts of multivariate financial returns, including volatility clustering, dynamics in the dependency structure, asymmetry, heavy tails, and non-ellipticity. It introduces a so-called risk fear portfolio strategy which combines portfolio optimization with active risk monitoring. The former selects optimal portfolio weights. The latter, independently, initiates market exit in case of excessive risks. The strategy agrees with the stylized fact of stock market major sell-offs during the initial stage of market downturns. The advantages of the new framework are illustrated with an extensive empirical study. It leads to superior multivariate density and Value-at-Risk forecasting, and better portfolio performance. The proposed risk fear portfolio strategy outperforms various competing types of optimal portfolios, even in the presence of conservative transaction costs and frequent rebalancing. The risk monitoring of the optimal portfolio can serve as an early warning system against large market risks. In particular, the new strategy avoids all the losses during the 2008 financial crisis, and it profits from the subsequent market recovery.

Keywords: comfort, financial crises, portfolio optimization, risk monitoring

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Authors: Hamid Rostami Jaz, Kamran Ameri Siahooei

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Up to now different methods have been used to forecast the index returns and the index rate. Artificial intelligence and artificial neural networks have been one of the methods of index returns forecasting. This study attempts to carry out a comparative study on the performance of different Radial Base Neural Network and Feed-Forward Perceptron Neural Network to forecast investment returns on the index. To achieve this goal, the return on investment in Tehran Stock Exchange index is evaluated and the performance of Radial Base Neural Network and Feed-Forward Perceptron Neural Network are compared. Neural networks performance test is applied based on the least square error in two approaches of in-sample and out-of-sample. The research results show the superiority of the radial base neural network in the in-sample approach and the superiority of perceptron neural network in the out-of-sample approach.

Keywords: exchange index, forecasting, perceptron neural network, Tehran stock exchange

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Authors: Deniz Peksen

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This thesis aims to predict trend movement of closing price of stock and to maximize portfolio by utilizing the predictions. In this context, the study aims to define a stock portfolio strategy from models created by using Logistic Regression, Gradient Boosting and Random Forest. Recently, predicting the trend of stock price has gained a significance role in making buy and sell decisions and generating returns with investment strategies formed by machine learning basis decisions. There are plenty of studies in the literature on the prediction of stock prices in capital markets using machine learning methods but most of them focus on closing prices instead of the direction of price trend. Our study differs from literature in terms of target definition. Ours is a classification problem which is focusing on the market trend in next 20 trading days. To predict trend direction, fourteen years of data were used for training. Following three years were used for validation. Finally, last three years were used for testing. Training data are between 2002-06-18 and 2016-12-30 Validation data are between 2017-01-02 and 2019-12-31 Testing data are between 2020-01-02 and 2022-03-17 We determine Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate as benchmarks which we should outperform. We compared our machine learning basis portfolio return on test data with return of Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate. We assessed our model performance with the help of roc-auc score and lift charts. We use logistic regression, Gradient Boosting and Random Forest with grid search approach to fine-tune hyper-parameters. As a result of the empirical study, the existence of uptrend and downtrend of five stocks could not be predicted by the models. When we use these predictions to define buy and sell decisions in order to generate model-based-portfolio, model-based-portfolio fails in test dataset. It was found that Model-based buy and sell decisions generated a stock portfolio strategy whose returns can not outperform non-model portfolio strategies on test dataset. We found that any effort for predicting the trend which is formulated on stock price is a challenge. We found same results as Random Walk Theory claims which says that stock price or price changes are unpredictable. Our model iterations failed on test dataset. Although, we built up several good models on validation dataset, we failed on test dataset. We implemented Random Forest, Gradient Boosting and Logistic Regression. We discovered that complex models did not provide advantage or additional performance while comparing them with Logistic Regression. More complexity did not lead us to reach better performance. Using a complex model is not an answer to figure out the stock-related prediction problem. Our approach was to predict the trend instead of the price. This approach converted our problem into classification. However, this label approach does not lead us to solve the stock prediction problem and deny or refute the accuracy of the Random Walk Theory for the stock price.

Keywords: stock prediction, portfolio optimization, data science, machine learning

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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: 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: Feijoo E. Colomine Duran, Carlos E. Peñaloza Corredor

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There modeling component selection financial investment (Portfolio) a variety of problems that can be addressed with optimization techniques under evolutionary schemes. For his feature, the problem of selection of investment components of a dichotomous relationship between two elements that are opposed: The Portfolio Performance and Risk presented by choosing it. This relationship was modeled by Markowitz through a media problem (Performance) - variance (risk), ie must Maximize Performance and Minimize Risk. This research included the study and implementation of multi-objective evolutionary techniques to solve these problems, taking as experimental framework financial market equities Colombia Stock Exchange between 2009-2015. Comparisons three multiobjective evolutionary algorithms, namely the Nondominated Sorting Genetic Algorithm II (NSGA-II), the Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Indicator-Based Selection in Multiobjective Search (IBEA) were performed using two measures well known performance: The Hypervolume indicator and R_2 indicator, also it became a nonparametric statistical analysis and the Wilcoxon rank-sum test. The comparative analysis also includes an evaluation of the financial efficiency of the investment portfolio chosen by the implementation of various algorithms through the Sharpe ratio. It is shown that the portfolio provided by the implementation of the algorithms mentioned above is very well located between the different stock indices provided by the Colombia Stock Exchange.

Keywords: finance, optimization, portfolio, Markowitz, evolutionary algorithms

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Authors: Wee Tong Liaw, Elaine Wong Yee Sing

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The main objective and focus of this study are to establish the significance of a sustained health promoting workplace on stock and portfolio returns focusing on companies listed on the Singapore stock exchange, using a two-factor model comprising of the single factor CAPM and a 'health promoting workplace' factor. The 'health promoting workplace' factor represents the excess returns derived between two portfolios of component stocks that, when combined, would represent a top tier stock market index in Singapore, namely the STI index. The first portfolio represents companies that are independently assessed by the Singapore’s Health Award, SHA, to have a sustained and comprehensive health promoting workplace (SHA-STI portfolio) and the second portfolio represents companies that had not been independently assessed (Non-SHA STI portfolio). Since 2001, many companies in Singapore have voluntarily participated in the bi-annual Singapore HEALTH Award initiated by the Health Promotion Board of Singapore (HPB). The Singapore HEALTH Award (SHA), is an industry-wide award and assessment process. SHA assesses and recognizes employers in Singapore for implementing a comprehensive and sustainable health promotion programme at their workplaces. When using a ten year holding period instead of a one year holding period, excess returns in the SHA-STI portfolio over Non-SHA STI portfolio were consistently being observed over all test periods, during 2001 to 2013. In addition, when applied to the SHA-STI portfolio, results from the Two Factor Model consistently revealed higher explanatory powers across all test periods for the portfolio as well as all the individual component stocks in SHA-STI portfolio, than the single factor CAPM model. However, with respect to attaining higher level of achievement in the Singapore Health Award, this study did not show any incentive for selecting listed companies that have achieved a higher level of award. Results from this study would give further insights to investors and fund managers alike who intend to consider health promoting workplace as a risk factor in their stock or portfolio selection process, in particular for investors who have a preference for STI’s component stocks and with a longer investment horizon. Key micro factors like management abilities, business development strategies and production capabilities that meet the needs of market would create the demand for a company’s product(s) or service(s) and consequently contribute to its top line and profitability. Thereafter, the existence of a sustainable health promoting workplace would be a key catalytic factor in sustaining a productive workforce needed to support the continued success of a profitable business.

Keywords: asset pricing model, company's performance, stock returns, financial risk factor, sustained health promoting workplace

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Authors: Charles-Olivier Amédée-Manesme, Benoit Faye, Eric Le Fur

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In this paper, the heterogeneity of the Bordeaux Legends 50 wine market price segment is addressed. For this purpose, quantile regression is applied – with market segmentation based on wine bottle price quantile – and the hedonic price of wine attributes is computed for various price segments of the market. The approach is applied to a major privately held data set which consists of approximately 30,000 transactions over the 2003–2014 period. The findings suggest that the relative hedonic prices of several wine attributes differ significantly among deciles. In particular, the elasticity coefficient of the expert ratings shows strong variation among prices. If - as suggested in the literature - expert ratings have a positive influence on wine price on average, they have a clearly decreasing impact over the quantiles. Finally, the lower the wine price, the higher the potential for price appreciation over time. Other variables such as chateaux or vintage are also shown to vary across the distribution of wine prices. While enhancing our understanding of the complex market dynamics that underlie Bordeaux wines’ price, this research provides empirical evidence that the QR approach adequately captures heterogeneity among wine price ranges, which simultaneously applies to wine stock, vintage and auctions’ house.

Keywords: hedonics, market segmentation, quantile regression, heterogeneity, wine economics

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Authors: Meltem Gurunlu

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One of the central problems in corporate finance is the allocation of funds. This usually takes two forms: allocation of funds across firms in an economy or allocation of funds across projects or business units within a firm. The first one is typically related to the external markets (the bond market, the stock market, banks and finance companies) whereas the second form of the capital allocation is related to the internal capital markets in which corporate headquarters allocate capital to their business units. (within-group transfers, within-group credit markets, and within-group equity market). The main aim of this study is to investigate the nature of capital allocation dynamics by comparing the relevant studies carried out on external and internal capital markets with paying special significance to the business groups.

Keywords: internal capital markets, external capital markets, capital structure, capital allocation, business groups, corporate finance

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Authors: Morten Brøgger, Kim Wittchen

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Focus on reducing energy consumption in existing buildings at large scale, e.g. in cities or countries, has been increasing in recent years. In order to reduce energy consumption in existing buildings, political incentive schemes are put in place and large scale investments are made by utility companies. Prioritising these investments requires a comprehensive overview of the energy consumption in the existing building stock, as well as potential energy-savings. However, a building stock comprises thousands of buildings with different characteristics making it difficult to model energy consumption accurately. Moreover, the complexity of the building stock makes it difficult to convey model results to policymakers and other stakeholders. In order to manage the complexity of the building stock, building archetypes are often employed in building stock energy models (BSEMs). Building archetypes are formed by segmenting the building stock according to specific characteristics. Segmenting the building stock according to building type and building age is common, among other things because this information is often easily available. This segmentation makes it easy to convey results to non-experts. However, using a single archetypical building to represent all buildings in a segment of the building stock is associated with loss of detail. Thermal characteristics are aggregated while other characteristics, which could affect the energy efficiency of a building, are disregarded. Thus, using a simplified representation of the building stock could come at the expense of the accuracy of the model. The present study evaluates the accuracy of a conventional archetype-based BSEM that segments the building stock according to building type- and age. The accuracy is evaluated in terms of the archetypes’ ability to accurately emulate the average energy demands of the corresponding buildings they were meant to represent. This is done for the buildings’ energy demands as a whole as well as for relevant sub-demands. Both are evaluated in relation to the type- and the age of the building. This should provide researchers, who use archetypes in BSEMs, with an indication of the expected accuracy of the conventional archetype model, as well as the accuracy lost in specific parts of the calculation, due to use of the archetype method.

Keywords: building stock energy modelling, energy-savings, archetype

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Authors: Rebeca Ramirez Acosta, Sebastian Lenhoff

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Decision makers worldwide need to determine if the implementation of a new market mechanism will contribute to the sustainability and resilience of the power system. Due to smart grid technologies, new products in the distribution and transmission system can be traded; however, the impact of changing a market rule will differ between several regions. To test systematically those impacts, a market categorization has been compiled and organized in a smart grid market testing toolbox. This toolbox maps all actual energy products and sets the basis for running a co-simulation test with the new rule to be implemented. It will help to measure the impact of the new rule, based on the sustainable and resilience indicators.

Keywords: co-simulation, electricity market, smart grid market, market testing

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Authors: Zane Turner, Kevin Labille, Susan Gauch

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Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We present a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: computational finance, sentiment analysis, sentiment lexicon, stock movement prediction

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Authors: Zane Turner, Kevin Labille, Susan Gauch

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Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We introduce a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: computational finance, sentiment analysis, sentiment lexicon, stock movement prediction

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Authors: A. Hidalgo, A. Desportes, E. Bonin, A. Kadaoui, T. Bouaricha

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Present paper is concerned with portfolio management with Black-Litterman (B-L) model. Considered stocks are exclusively limited to large companies stocks on US market. Results obtained by application of the model are presented. From analysis of collected Dow Jones stock data, remarkable explicit analytical expression of optimal B-L parameter τ, which scales dispersion of normal distribution of assets mean return, is proposed in terms of standard deviation of covariance matrix. Implementation has been developed in Matlab environment to split optimization in Markovitz sense from specific elements related to B-L representation.

Keywords: Black-Litterman, Markowitz, market data, portfolio manager opinion

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Authors: Bengan Simbarashe Manwere

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The entire African continent, comprising 55 countries, holds a 2% share of the global carbon market. The World Bank attributes the continent’s insignificant share and participation in the carbon market to the limited access to electricity. Approximately 800 million people spread across 47 African countries generate as much power as Spain, with a population of 45million. Only South Africa and North Africa have carbon-reduction investment opportunities on the continent and dominate the 2% market share of the global carbon market. On the back of the 2015 Paris Agreement, South Africa signed into law the Carbon Tax Act 15 of 2019 and the Customs and Excise Amendment Act 13 of 2019 (Gazette No. 4280) on 1 June 2019. By these laws, South Africa was ushered into the league of active global carbon market players. By increasing the cost of production by the rate of R120/tCO2e, the tax intentionally compels the internalization of pollution as a cost of production and, relatedly, stimulate investment in clean technologies. The first phase covered the 1 June 2019 – 31 December 2022 period during which the tax was meant to escalate at CPI + 2% for Scope 1 emitters. However, in the second phase, which stretches from 2023 to 2030, the tax will escalate at the inflation rate only as measured by the consumer price index (CPI). The Carbon Tax Act provides for carbon allowances as mitigation strategies to limit agents’ carbon tax liability by up to 95% for fugitive and process emissions. Although the June 2019 Carbon Tax Act explicitly makes provision for a carbon trading scheme (CTS), the carbon trading regulations thereof were only finalised in December 2020. This points to a delay in the establishment of a carbon trading scheme (CTS). Relatedly, emitters in South Africa are not able to benefit from the 95% reduction in effective carbon tax rate from R120/tCO2e to R6/tCO2e as the Johannesburg Stock Exchange (JSE) has not yet finalized the establishment of the market for trading carbon credits. Whereas most carbon trading schemes have been designed and constructed from the beginning as new tailor-made systems in countries the likes of France, Australia, Romania which treat carbon as a financial product, South Africa intends, on the contrary, to leverage existing trading infrastructure of the Johannesburg Stock Exchange (JSE) and the Clearing and Settlement platforms of Strate, among others, in the interest of the Paris Agreement timelines. Therefore the carbon trading scheme will not be constructed from scratch. At the same time, carbon will be treated as a commodity in order to align with the existing institutional and infrastructural capacity. This explains why the Carbon Tax Act is silent about the involvement of the Financial Sector Conduct Authority (FSCA).For South Africa, there is need to establish they equilibrium stability of the CTS. This is important as South Africa is an innovator in carbon trading and the successful trading of carbon credits on the JSE will lead to imitation by early adopters first, followed by the middle majority thereafter.

Keywords: carbon trading scheme (CTS), Johannesburg stock exchange (JSE), carbon tax act 15 of 2019, South Africa

Procedia PDF Downloads 34

Authors: Yangrae Cho, Jinseok Kim, Yongtae Park

Abstract:

Mobile app service ecosystem has been abruptly emerged, explosively grown, and dynamically transformed. In contrast with product markets in which product sales directly cause increment in firm’s income, customer’s usage is less visible but more valuable in service market. Especially, the market situation with cutthroat competition in mobile app store makes securing and keeping of users as vital. Although a few service firms try to manage their apps’ usage patterns by fitting on S-curve or applying other forecasting techniques, the time series approaches based on past sequential data are subject to fundamental limitation in the market where customer’s attention is being moved unpredictably and dynamically. We therefore propose a new conceptual approach for detecting usage pattern of mobile app service with Hidden Markov Model (HMM) which is based on the dual stochastic structure and mainly used to clarify unpredictable and dynamic sequential patterns in voice recognition or stock forecasting. Our approach could be practically utilized for app service firms to manage their services’ lifecycles and academically expanded to other markets.

Keywords: mobile app service, usage pattern, Hidden Markov Model, pattern detection

Procedia PDF Downloads 306

Authors: Lynmar M. Didal, Ramises G. Manzano Jr., Jacque Bon-Isaac C. Aboy

Abstract:

This study analyzes the performance of global minimum variance and naive portfolios across different economic periods, using monthly stock returns from the Philippine Stock Exchange Index (PSEI), S&P 500, and Dow Jones Industrial Average (DOW). The performance is evaluated through the Sharpe ratio, Sortino ratio, Jensen’s Alpha, Treynor ratio, and Information ratio. Additionally, the study investigates the impact of short selling on portfolio performance. Six-time periods are defined for analysis, encompassing events such as the global financial crisis and the COVID-19 pandemic. Findings indicate that the Naive portfolio generally outperforms the GMV portfolio in the S&P 500, signifying higher returns with increased volatility. Conversely, in the PSEI and DOW, the GMV portfolio shows more efficient risk-adjusted returns. Short selling significantly impacts the GMV portfolio during mid-GFC and mid-COVID periods. The study offers insights for investors, suggesting the Naive portfolio for higher risk tolerance and the GMV portfolio as a conservative alternative.

Keywords: portfolio performance, global minimum variance, naïve portfolio, risk-adjusted metrics, short-selling

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Authors: Cameron Truong, Mikhail Bhatia, Yangyang Chen, Viet Nga Cao

Abstract:

Using a sample of U.S. firms between 1996 and 2011, this paper documents a positive association between options trading volume and future stock price crash risk. This relation is evidently more pronounced among firms with higher information asymmetry, business uncertainty, and short-sale constraints. In a dichotomous cross-sectional setting, we also document that firms with options trading have higher future crash risk than firms without options trading. We further show in a difference-in-difference analysis that firms experience an increase in crash risk immediately after the listing of options. The results suggest that options traders are able of identifying bad news hoarding by management and choose to trade in a liquid options market in anticipation of future crashes.

Keywords: bad news hoarding, cross-sectional setting, options trading, stock price crash

Procedia PDF Downloads 419