Search results for: stock investment analysis

Authors: Pallavi Kishore, Mariam Aslam

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The sharp decline in oil prices that started in 2014 affected most economies in the world either positively or negatively. In some economies, particularly the oil exporting countries, the effects were felt immediately. The Gulf Cooperation Council’s (GCC henceforth) countries are oil and gas-dependent with the largest oil reserves in the world. UAE (United Arab Emirates) has been striving to diversify away from oil and expects higher non-oil growth in 2018. These two factors, falling oil prices and the economy strategizing away from oil dependence, make a compelling case to study the financial performance of various sectors in the economy. Among other sectors, the insurance sector is widely recognized as an important indicator of the health of the economy. An expanding population, surge in construction and infrastructure, increased life expectancy, greater expenditure on automobiles and other luxury goods translate to a booming insurance sector. A slow-down of the insurance sector, on the other hand, may indicate a general slow-down in the economy. Therefore, a study on the insurance sector will help understand the general nature of the current economy. This study involves calculations and comparisons of ratios pre and post the fall in oil prices in the insurance sector in the UAE. A sample of 33 companies listed on the official stock exchanges of UAE-Dubai Financial Market and Abu Dhabi Stock Exchange were collected and empirical analysis employed to study the financial performance pre and post fall in oil prices. Ratios were calculated in 5 categories: Profitability, Liquidity, Leverage, Efficiency, and Investment. The means pre- and post-fall are compared to conclude that the profitability ratios including ROSF (Return on Shareholder Funds), ROCE (Return on Capital Employed) and NPM (Net Profit Margin) have all taken a hit. Parametric tests, including paired t-test, concludes that while the fall in profitability ratios is statistically significant, the other ratios have been quite stable in the period. The efficiency, liquidity, gearing and investment ratios have not been severely affected by the fall in oil prices. This may be due to the implementation of stronger regulatory policies and is a testimony to the diversification into the non-oil economy. The regulatory authorities can use the findings of this study to ensure transparency in revealing financial information to the public and employ policies that will help further the health of the economy. The study will also help understand which areas within the sector could benefit from more regulations.

Keywords: UAE, insurance sector, ratio analysis, oil price, profitability, liquidity, gearing, investment, efficiency

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Authors: Uche A. Ibekwe

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Pension funds are pooled assets which are established to provide income for retirees. The funds are usually regulated to check excessive risk taking by fund managers. In Nigeria, the current defined contribution (DC) pension scheme appears to contain some overly stringent restrictions which might be hampering its successful implementation. Notable among these restrictions is the 25 percent maximum limit on investment in ordinary shares of quoted companies. This paper examines the extent to which these restrictions affect pension replacement rates at retirement. The study made use of both simulated and historical asset return distributions using mean-variance, regression analysis and ruin-risk analyses, the study found that the current equity investment restriction policy in Nigeria reduces replacement rates at retirement.

Keywords: equity investment, replacement rates, restrictions, ruin-risk

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Authors: Farheen Akram

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Research and Development (R&D) is the most crucial element of the firm’s survival in a competitive business environment. R&D is a long-term investment; therefore, executives having the power to make the investment decisions may be pessimistic when their compensation is closely linked with short-term firm performance. Thus, the current study investigates the impact of board composition and executives’ compensation (cash or short-term benefits and LTIs) on R&D spending using a sample of 85 S&P/100 firms listed on the Australian Stock Exchange (ASX) in 2017. SmartPLS (v.3.2.7) was used to evaluate the proposed model of current research. The empirical findings of this study indicate that board composition has a significant and positive effect on R&D spending. While, as expected, executive cash compensation has negative and Long-Term-Incentives (LTIs) has a positive impact on R&D spending. Based on current findings, the study suggested that myopic behavior of CEOs and top management towards long-term value creation investment like R&D can be controlled by using long-term compensation rewards.

Keywords: cash compensation, LTIs, board composition, R&D spending

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Authors: Ugbajah Maryjane

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The study examined the production of grass cutter and the constraints in Anambra state, Nigeria. Specifically, it described socio-economic characteristics of the respondents, determinants of net farm income and constraints to grass cutter production. Multistage and random sampling methods were used to select 50 respondents for this study. Primary data were collected by means of structured questionnaire. Non-parametric and parametric statistical tools including frequency percentage mean ranking counts, cost and returns and returns and multiple regression were deployed for data analysis. Majority 84% produce on small scale, 64 % had formal education 68% had 3-4 years of farming experience hence small scaled production were common. The income (returns) on investment was used as index of profitability, gross margin (#5,972,280), net farm income (#5,327,055.2) net return on investment (2.5) and return on investment 3.1. Net farm income was significantly influence by stock size and years of farming experience. Grass cutter farmers production problem would be ameliorated by the expression of extension education awareness campaigns to discourage unhealthy practices such as indiscriminant bush burning, use of toxic chemicals as baits, and provision of credits to the farmers.

Keywords: socio-economic factors, profitability, awareness, toxic chemicals, credits

Procedia PDF Downloads 387

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

Procedia PDF Downloads 465

Authors: Ali Akber Jaffri, Asad Naseem, Javeria Khan, Zubair Hamza, Ishtiaq

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The purpose of this study is to determine safety stock, maximum inventory level, reordering point, and reordering quantity by rearranging lot sizes for supplier and customer in MRO (maintenance repair operations) warehouse of Engro Polymers & Chemicals. To achieve the aim, physical analysis method and excel commands were carried out to elicit the customer and supplier data provided by the company. Initially, we rearranged the current lot sizes and MOUs (measure of units) in SAP software. Due to change in lot sizes, we have to determine the new quantities for safety stock, maximum inventory, reordering point and reordering quantity as per company's demand. By proposed system, we saved extra cost in terms of reducing time of receiving from vendor and in issuance to customer, ease of material handling in MRO warehouse and also reduce human efforts.

Keywords: maintenance repair operation, maximum inventory, reorder quantity, safety stock

Procedia PDF Downloads 254

Authors: H. Florianová, J. Nešleha

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Financial derivatives are considered to be risky investment instruments which could possibly bring another financial crisis. As prevention, European Union and its member states have released new legal acts adjusting this area of law in recent years. There have been several cases in history of capital markets worldwide where it was shown that legislature may affect behavior of subjects on capital markets. In our paper we analyze main events on selected European stock exchanges in order to apply them on three chosen markets - Czech capital market represented by Prague Stock Exchange, German capital market represented by Deutsche Börse and Polish capital market represented by Warsaw Stock Exchange. We follow time series of development of the sum of listed derivatives on these three stock exchanges in order to evaluate popularity of those exchanges. Afterwards we compare newly listed derivatives in relation to the speed of development of these exchanges. We also make a comparison between trends in derivatives and shares development. We explain how a legal regulation may affect situation on capital markets. If the regulation is too strict, potential investors or traders are not willing to undertake it and move to other markets. On the other hand, if the regulation is too vague, trading scandals occur and the market is not reliable from the prospect of potential investors or issuers. We see that making the regulation stricter usually discourages subjects to stay on the market immediately although making the regulation vaguer to interest more subjects is usually much slower process.

Keywords: capital markets, financial derivatives, investors' behavior, regulation

Procedia PDF Downloads 246

Authors: Anita Setianingrum, Oki S. Jaya, Zuherman Rustam

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Forecasting stock price is a challenging task due to the complex time series of the data. The complexity arises from many variables that affect the stock market. Many time series models have been proposed before, but those previous models still have some problems: 1) put the subjectivity of choosing the technical indicators, and 2) rely upon some assumptions about the variables, so it is limited to be applied to all datasets. Therefore, this paper studied a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) time series model based on Support Vector Regression (SVR) for forecasting the stock market. In order to evaluate the performance of proposed models, stock market transaction data of TAIEX and HIS from January to December 2015 is collected as experimental datasets. As a result, the method has outperformed its counterparts in terms of accuracy.

Keywords: ANFIS, fuzzy time series, stock forecasting, SVR

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Authors: Cristian Pauna

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With few steps away from the 2020, investments in financial markets is a common activity nowadays. In the electronic trading environment, the automated investment software has become a major part in the business intelligence system of any modern financial company. The investment decisions are assisted and/or made automatically by computers using mathematical algorithms today. The complexity of these algorithms requires computer assistance in the investment process. This paper will present several investment strategies that can be automated with algorithmic trading for Deutscher Aktienindex DAX30. It was found that, based on several price action mathematical models used for high-frequency trading some investment strategies can be optimized and improved for automated investments with good results. This paper will present the way to automate these investment decisions. Automated signals will be built using all of these strategies. Three major types of investment strategies were found in this study. The types are separated by the target length and by the exit strategy used. The exit decisions will be also automated and the paper will present the specificity for each investment type. A comparative study will be also included in this paper in order to reveal the differences between strategies. Based on these results, the profit and the capital exposure will be compared and analyzed in order to qualify the investment methodologies presented and to compare them with any other investment system. As conclusion, some major investment strategies will be revealed and compared in order to be considered for inclusion in any automated investment system.

Keywords: Algorithmic trading, automated investment systems, limit conditions, trading principles, trading strategies

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

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

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

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Authors: Arkaprabha Bhattacharyya, Makarand Hastak

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The COVID-19 pandemic is unprecedented in terms of its global reach and economic impacts. Historically, investment in infrastructure development projects has been touted to boost the economic growth of a nation. The State and Local governments responsible for delivering infrastructure assets work under tight budgets. Therefore, it is important to understand which infrastructure projects have the highest potential of boosting economic growth in the post-pandemic era. This paper presents relationships between infrastructure projects and economic growth. Statistical relationships between investment in different types of infrastructure projects (transit, water and wastewater, highways, power, manufacturing etc.) and indicators of economic growth are presented using historic data between 2002 and 2020 from the U.S. Census Bureau and U.S. Bureau of Economic Analysis (BEA). The outcome of the paper is the comparison of statistical correlations between investment in different types of infrastructure projects and indicators of economic growth. The comparison of the statistical correlations is useful in ranking the types of infrastructure projects based on their ability to influence economic prosperity. Therefore, investment in the infrastructures with the higher rank will have a better chance of boosting the economic growth. Once, the ranks are derived, they can be used by the decision-makers in infrastructure investment related decision-making process.

Keywords: economic growth, infrastructure development, infrastructure projects, strategic investment

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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: Bo Sun, Ni Zhong

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Bilateral investment treaties have played a role in the construction of the Belt and Road, providing institutional protection for Chinese companies' overseas investments. However, such treaties between China and countries along the Belt and Road were signed in the 1980s and 1990s, and their provisions are outdated and insufficiently detailed to provide adequate legal protection for Chinese investors when they initiate investment arbitration against host countries. By studying cases involving China in international investment arbitration, this paper suggests that China should pay attention to further clarifying the identity of "investors", the scope of disputes that can be submitted to arbitration, and the concept of "indirect expropriation" when updating bilateral investment treaties in the future, in order to reduce the risk of losing cases for Chinese investors.

Keywords: belt and road, bilateral investment agreement, investment arbitration, indirect expropriation

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Authors: Serpil Türkyılmaz, Mesut Balıbey

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

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

Procedia PDF Downloads 273

Authors: Seyed Habib A. Rahmati, Parsa Fallah Sheikhlari, Morteza Musakhani

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This paper studies the structure of financial investment and efficiency on the subway would be created between Hashtgerd and Qazvin in Iran. Regarding ascending rate of transportation between Tehran and Qazvin which directly air pollution, it clearly implies to public transportation requirement between these two cities near Tehran. The railway transportation like subway can help each country to terminate traffic jam which has some advantages such as speed, security, non-pollution, low cost of public transport, etc. This type of transportation needs national infrastructures which require enormous investment. It couldn’t implement without leading and managing funds and investments properly. In order to response 'needs', clear norms or normative targets have to be agreed and obviously it is important to distinguish costs from investment requirements critically. Implementation phase affects investment requirements and financing needs. So recognizing barrier related to investment and the quality of investment (what technologies and services are invested in) is as important as the amounts of investment. Different investment methods have mentioned as follows loan, leasing, equity participation, Line of financing, finance, usance, bay back. Alternatives survey before initiation and analyzing of risk management is one of the most important parts in this project. Observation of similar project cities each country has the own specification to choose investment method.

Keywords: subway project, project investment, project contract, project management

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Authors: Muhammad Atif, M. Samsul Alam

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The purpose of this study is to investigate the effects of board room gender diversity on firm sustainable investment. We test the extent to which sustainable investment is affected by the presence of female directors on U.S. corporate boards. Using data of S&P 1500 indexed firms collected from Bloomberg covering the period 2004-2016, we estimate the baseline model to investigate the effects of board room gender diversity on firm sustainable investment. We find a positive relationship between board gender diversity and sustainable investment. We also find that boards with two or more women have a pronounced impact on sustainable investment, consistent with the critical mass theory. Female independent directors have a stronger impact on sustainable investment than female executive directors. Our findings are robust to different identification and estimation techniques. The study offers another perspective of the ongoing debate in the social responsibility literature about the accountability relationships between business and society.

Keywords: sustainable investment, gender diversity, environmental proctection, social responsibility

Procedia PDF Downloads 131

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: Mahdi Karazmodeh, Pooyan Jafari

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

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

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Authors: Rihab Belguith, Fathi Abid

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The paper develops a system-based approach to investigate the dynamic adjustment of debt structure and investment policies of the Dow-Jones index. This approach enables the assessment of relations among sales, debt, and investment opportunities by considering the simultaneous effect of the market environmental change and future growth opportunities. We integrate the firm-specific sales variance to capture the industries' conditions in the model. Empirical results were obtained through a panel data set of firms with different sectors. The analysis support that environmental change does not affect equally the different industry since operating leverage differs among industries and so the sensitivity to sales variance. Including adjusted-specific variance, we find that there is no monotonic relation between leverage, sales, and investment. The firm may choose a low debt level in response to high sales variance but high leverage to attenuate the negative relation between sales variance and the current level of investment. We further find that while the overall effect of debt maturity on leverage is unaffected by the level of growth opportunities, the shorter the maturity of debt is, the smaller the direct effect of sales variance on investment.

Keywords: dynamic panel, investment, leverage decision, sales uncertainty

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Authors: Michelo Obrey

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Traditionally, mutual funds have long been esteemed as stand-alone entities in the U.S. However, the prevalence of the fund families’ affiliation to financial conglomerates is eroding this striking feature. Mutual fund families' affiliation with financial conglomerates can potentially be an important source of superior performance or cost to the affiliated mutual fund investors. On the one hand, financial conglomerates affiliation offers the mutual funds access to abundant resources, better research quality, private material information, and business connections within the financial group. On the other hand, conflict of interest is bound to arise between the financial conglomerate relationship and fund management. Using a sample of U.S. domestic equity mutual funds from 1994 to 2017, this paper examines whether fund family affiliation to an investment bank help the affiliated mutual funds deliver superior performance through private material information advantage possessed by the investment banks or it costs affiliated mutual fund shareholders due to the conflict of interest. Robust to alternative risk adjustments and cross-section regression methodologies, this paper finds that the investment bank-affiliated mutual funds significantly outperform those of the mutual funds that are not affiliated with an investment bank. Interestingly the paper finds that the outperformance is confined to holding return, a return measure that captures the investment talent that is uninfluenced by transaction costs, fees, and other expenses. Further analysis shows that the investment bank-affiliated mutual funds specialize in hard-to-value stocks, which are not more likely to be held by unaffiliated funds. Consistent with the information advantage hypothesis, the paper finds that affiliated funds holding covered stocks outperform affiliated funds without covered stocks lending no support to the hypothesis that affiliated mutual funds attract superior stock-picking talent. Overall, the paper findings are consistent with the idea that investment banks maximize fee income by monopolistically exploiting their private information, thus strategically transferring performance to their affiliated mutual funds. This paper contributes to the extant literature on the agency problem in mutual fund families. It adds to this stream of research by showing that the agency problem is not only prevalent in fund families but also in financial organizations such as investment banks that have affiliated mutual fund families. The results show evidence of exploitation of synergies such as private material information sharing that benefit mutual fund investors due to affiliation with a financial conglomerate. However, this research has a normative dimension, allowing such incestuous behavior of insider trading and exploitation of superior information not only negatively affect the unaffiliated fund investors but also led to an unfair and unleveled playing field in the financial market.

Keywords: mutual fund performance, conflicts of interest, informational advantage, investment bank

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Authors: Sayed Kifayat Shah, Tang Zhongjun, Arfa Tanveer

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This study explores the significant heatwave effect on stock return and volatility. Using an ARCH/GARCH approach, it examines the relationship between the heatwave of Karachi, Islamabad, and Lahore on the KSE-100 index. It also explores the impact of heatwave on returns of the pharmaceutical and electronics industries. The empirical results confirm that that stock return is positively related to the heat waves of Karachi, negatively related to that of Islamabad, and is not affected by the heatwave of Lahore. Similarly, pharmaceutical and electronics indices are also positively related to heatwaves. These differences in results can be ascribed to the change in the behavior of the residents of that city. The outcomes are useful for understanding an investor's behavior reacting to weather and fluxes in stock price related to heatwave severity levels. The results can support investors in fixing biases in behavior.

Keywords: ARCH/GARCH model, heat wave, KSE-100 index, stock market return

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Authors: Sadam Alwadi

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Outlier values become a problem that frequently occurs in the data observation or recording process. Thus, the need for data imputation has become an essential matter. In this work, it will make use of the methods described in the prior work to detect the outlier values based on a collection of stock market data. In order to implement the detection and find some solutions that maybe helpful for investors, real closed price data were obtained from the Amman Stock Exchange (ASE). Tukey and Maximum Overlapping Discrete Wavelet Transform (MODWT) methods will be used to impute the detect the outlier values.

Keywords: outlier values, imputation, stock market data, detecting, estimation

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Authors: K. Y. Chen, H. Chua, C. W. Kan

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Japan is a well-developed country but the environmental issues are still a hot issue. In this study, we will analyse how the environmental investment affects the sustainable development in Japan. This paper will first describe the environmental policy of Japan and the effort input by the Japan government. Then, we will collect the yearly environmental data and also information about the environmental investment. Based on the data collected, we try to figure out the relationship between environmental investment and sustainable development in Japan. In addition, we will analyse the SWOT of environmental investment in Japan. Based on the economic information collected, Japan established a sound material-cycle society through changes in business and life styles. A comprehensive legal system for this kind of society was established in Japan. In addition, other supporting measures, such as financial measures, utilization of economic instruments, implementation of research and promotion of education and science and technology, help Japan to cope with the recent environmental challenges. Japan’s excellent environmental technologies changed its socioeconomic system. They are at the highest global standards. This can be reflected by the number of patents registered in Japan which has been on the steady growth. Country by country comparison in the application for patents on environmental technologies also indicates that Japan ranks high in such areas as atmospheric pollution and water quality management, solid waste management and renewable energy. This is a result of the large expenditure invested on research and development.

Keywords: Japan, environmental investment, sustainable development, analysis

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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: Ese Urhie, Bosede Olopade, Muyiwa Oladosun, Henry Okodua

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In the past three and half decades, aside from the fact that the contribution of the industrial sector to gross domestic product in Nigeria has nose-dived, its performance has also been highly unstable. Investment funds needed to develop the industrial sector usually come from both internal and external sources. The internal sources include surplus generated within the industrial sector and surplus diverted from other sectors of the economy. It has been observed that due to the small size of the industrial sector in developing countries, very limited funds could be raised for further investment. External sources of funds which many currently industrialized and some ‘newly industrializing countries’ have benefited from including direct and indirect investment by foreign capitalists; foreign aid and loans; and investments by nationals living abroad. Foreign direct investment inflow in Nigeria has been declining since 2009 in both absolute and relative terms. High level of human capital has been identified as one of the crucial factors that explain the miraculous growth of the ‘Asian Tigers’. Its low level has also been identified as the major cause for the low level of FDI flow to Nigeria in particular and Africa in general. There has been positive, but slow improvement in human capital indicators in Nigeria in the past three decades. In spite of this, foreign direct investment inflow has not only been low; it has declined drastically in recent years. i) Why has the improvement in human capital in Nigeria failed to attract more FDI inflow? ii) To what extent does the level of human capital influence FDI inflow in Nigeria? iii) Is there a threshold of human capital stock that guarantees sustained inflow of FDI? iv) Does the quality of human capital matter? v) Does the influence of other (negative) factors outweigh the benefits of human capital? Using time series secondary data, a system of equations is employed to evaluate the effect of human capital on FDI inflow in Nigeria on one hand and the effect of FDI on the level of industrialization on the other. A weak relationship between human capital and FDI is expected, while a strong relationship between FDI and industrial growth is expected from the result.

Keywords: human capital, foreign direct investment, industrialization, gross domestic product

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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: Daniil Karzanov

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This paper studies the effect of quarterly earnings reports on the stock price. The profitability of the stock is modeled by geometric Brownian diffusion and the Constant Elasticity of Variance model. We fit several variations of stochastic differential equations to the pre-and after-report period using the Maximum Likelihood Estimation and Grid Search of parameters method. By examining the change in the model parameters after reports’ publication, the study reveals that the reports have enough evidence to be a structural breakpoint, meaning that all the forecast models exploited are not applicable for forecasting and should be refitted shortly.

Keywords: stock market, earnings reports, financial time series, structural breaks, stochastic differential equations

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Authors: Ashneel Ajay Singh, Kazumi Sakuramoto, Naoki Suzuki, Kalla Alok, Nath Paras

Abstract:

Bluefin (Thunnus orientalis) tuna is one of the most economically valuable tuna species in the world. In recent years the stock has been observed to decline. It is suspected that the stock-recruitment relationship and population structure is influenced by environmental and climatic variables. This study was aimed at investigating the influence of environmental and climatic conditions on the trajectory of the different life stages of the North Pacific bluefin tuna. Exploratory analysis was performed for the North Pacific sea surface temperature (SST) and Pacific Decadal Oscillation (PDO) on the time series of the bluefin tuna cohorts (age-0, 1, 2,…,9, 10+). General Additive Modeling (GAM) was used to reconstruct the recruitment (R) trajectory. The spatial movement of the SST was also monitored from 1953 to 2012 in the distribution area of the bluefin tuna. Exploratory analysis showed significance influence of the North Pacific Sea Surface temperature (SST) and Pacific Decadal Oscillation (PDO) on the time series of the age-0 group. Other age group (1, 2,…,9, 10+) time series did not exhibit any significant correlations. PDO showed most significant relationship in the months of October to December. Although the stock-recruitment relationship is of biological significance, the recruits (age-0) showed poor correlation with the Spawning Stock Biomass (SSB). Indeed the most significant model incorporated the SSB, SST and PDO. The results show that the stock-recruitment relationship of the North Pacific bluefin tuna is multi-dimensional and cannot be adequately explained by the SSB alone. SST and PDO forcing of the population structure is of significant importance and needs to be accounted for when making harvesting plans for bluefin tuna in the North Pacific.

Keywords: pacific bluefin tuna, Thunnus orientalis, cohorts, recruitment, spawning stock biomass, sea surface temperature, pacific decadal oscillation, general additive model

Procedia PDF Downloads 217

Authors: Thapthep Paprach, Benya Lertsuwan

Abstract:

Investment fraud is not a new criminal, but there are still more victims during the Internet of Things era. This kind of criminal has been classified as a national and transnational financial crime problem all over the world. In Thailand, the country has also been attacked by this kind of crime. This research concerns whether the mass media that is supposed to cover news about online investment scams realized and warned Thais about this crime. Thus, this study explores the value of news about investment fraud in terms of frequency. The methodology uses web crawling from the top 5 news agency websites that have the most access. We pull out all information reporting about investment fraud. The findings revealed that the ‘Khaosod’ news agency was the first rank in reporting on investment crime. On the other hand, ‘Matichon’ was the least reported. Thairat news agencies frequently reported such criminals from midnight to very early in the morning, while other news agencies reported during the daytime. The results between the frequency of news reporting about investment fraud and the monthly number of victim reports are not correlated. Although the most cases reported to Thai police were in February 2023, but the most news reported was in January 2023. In conclusion, there might be a negative correlation between the amount of investment fraud news reported and the number of victims.

Keywords: investment fraud, news value, online news report, Ponzi schemes, Romance scam

Procedia PDF Downloads 51

Authors: Vish Putcha, Chandrasekhar Putcha, Siva Hari

Abstract:

In the current digital era of free trading and pandemic-driven remote work culture, markets worldwide gained momentum for retail investors to trade from anywhere easily. The number of retail traders rose to 24% of the market from 15% at the pre-pandemic level. Most of them are young retail traders with high-risk tolerance compared to the previous generation of retail traders. This trend boosted the growth of subscription-based market predictors and market data vendors. Young traders are betting on these predictors, assuming one of them is correct. However, 90% of retail traders are on the losing end. This paper presents multiple indicators and attempts to derive behavioral patterns from the underlying stocks. The two major indicators that traders and investors follow are technical and fundamental. The famous investor, Warren Buffett, adheres to the “Value Investing” method that is based on a stock’s fundamental Analysis. In this paper, we present multiple indicators from various methods to understand the behavior patterns of stocks. For this research, we picked five stocks with a market capitalization of more than $200M, listed on the exchange for more than 20 years, and from different industry sectors. To study the behavioral pattern over time for these five stocks, a total of 8 indicators are chosen from fundamental, technical, and financial indicators, such as Price to Earning (P/E), Price to Book Value (P/B), Debt to Equity (D/E), Beta, Volatility, Relative Strength Index (RSI), Moving Averages and Dividend yields, followed by detailed mathematical Analysis. This is an interdisciplinary paper between various disciplines of Engineering, Accounting, and Finance. The research takes a new approach to identify clear indicators affecting stocks. Statistical Analysis of the data will be performed in terms of the probabilistic distribution, then follow and then determine the probability of the stock price going over a specific target value. The Chi-square test will be used to determine the validity of the assumed distribution. Preliminary results indicate that this approach is working well. When the complete results are presented in the final paper, they will be beneficial to the community.

Keywords: stock pattern, stock market analysis, stock predictions, trading, investing, fundamental analysis, technical analysis, quantitative trading, financial analysis, behavioral analysis

Procedia PDF Downloads 57