• The Journal of Asian Finance, Economics and Business
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• 제8권1호
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• pp.767-780
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• 2021

The purpose of this research is to analyze the effect of financial elements (asset growth, liability growth, equity growth, revenue growth, and profit growth) on stock price performance and to analyze the growth of Corporate Social Responsibility (CSR) costs as a moderating effect. The technique analysis used is regression analysis. Samples in this analysis are manufacturing firms listed on the Indonesian Stock Exchange (IDX) for the period 2014-2018. The use of regression models for hypothesis testing must fulfill several applicable assumptions such as Normality Test, Heteroscedasticity Test, Multicollinearity Test, Autocorrelation Test, Model Fit Test, Determination Coefficient Test, and Hypothesis Test. Data analysis used two research models, namely model 1 and model 2. Model 1 is without the moderating variable, and model 2 is with the moderating variable, that is, CSR cost growth. Based on the result of the regression analysis, it can be inferred that the asset, revenue, and profit growth have a positive impact on stock price results. Liabilities and equity growth do not affect stock price performance. Operating expense growth has a significant effect on price performance. CSR cost growth can moderate the effect of growth in financial statement elements on stock price performance but is not significant.

• The Journal of Asian Finance, Economics and Business
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• 제8권2호
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• pp.165-173
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• 2021

This study examined the effect of Debt Equity Ratio (DER), Net Profit Margin (NPM), and Size on stock prices with company performance as measured by Return on Assets (ROA) as a mediating variable. The sample used is 136 manufacturing companies listed on the Indonesia Stock Exchange (IDX) in the 2014-2018 period. This research was tested using a Warp PLS statistical test tool to prove the proposed hypothesis. The results showed that DER has a significant negative effect on ROA and a significant positive effect on Stock Price. NPM has a significant positive effect on ROA as well as a significant positive effect on Stock Price. While Size has a significant positive effect on ROA but has no effect on Stock Price. ROA has a significant positive effect on Stock Price. ROA does not act as a mediating variable in the relationship between Size and Stock Price; however, ROA acts as a mediating variable in the DER and Stock Price relationship, as well as, in the relationship between NPM and Stock Price. The implications of the results of this study can be used by investors in making investment decisions, paying attention to the company's financial aspects, namely DER, NPM, Size, and ROA.

• The Journal of Asian Finance, Economics and Business
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• 제8권3호
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• pp.99-110
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• 2021

This study aims to examine empirically the industry and economic fundamental factors that affect the stock prices of the leading palm oil company in Indonesia. The dynamics of stock price are analyzed using the autoregressive distribution lag (ARDL) model both for symmetric and asymmetric effects. The data used in this study are monthly data for the period from 2008:01 to 2020:03. In the long run, the company stock price moves in line with the competitor company stock price at the current time. The palm oil price has a positive effect on the stock price. Meanwhile, inflation negatively affects the stock price in the short run. The estimated equilibrium correction coefficient indicates a reasonably quick correction of the distortion of the stock price equilibrium in monthly dynamics. However, fundamental factors have asymmetric effects, especially the response of stock price when these factors decrease rather than increase in the short run. Stock prices that are responsive to declines in fundamental performance should be of particular concern to both investors and management in their strategic decision making. The results of this study will contribute to the enrichment of literature related to stock prices from the viewpoint of economic analysis on firm-level data.

• The Journal of Asian Finance, Economics and Business
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• 제4권2호
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• pp.19-23
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• 2017

It is undisputable that crude oil and its price fluctuations are major components that affect most of the countries' economies. Recent studies have demonstrated that beside the impact that crude oil price fluctuations have on common macroeconomic indicators like gross domestic product (GDP), inflation rates, exchange rates, unemployment rate, etc., it also has a strong influence on stock markets and their performance. This relationship has been examined in a number of settings, but it is yet to be unraveled in the Omani context. Accordingly, the main purpose of this study is to examine the possible effect of the oil price fluctuations on stock price movements. The study applies Toda and Yamamoto's (1995) Granger non-causality test on the daily Oman stock index (Muscat Securities Market Index) and oil prices between the period of 2 January 2003 and 13 March 2016. The results indicated that the oil price fluctuations have a significant impact on stock index movements. However, the stock price movements do not have a significant impact on oil prices. These findings have significant implications not only for the Omani economy but also for the economy of similar countries, particularly in the Gulf Cooperation Council (GCC) countries. The latter should carefully consider their policies and strategies regarding crude oil production and the generated income allocation as it might potentially affect the financial markets performance in these countries.

• Asian Journal of Business Environment
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• 제10권1호
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• pp.19-28
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• 2020

Purpose: This research aims to investigate the impact of corporate integrity on stock price crash risk. Research design, data, and methodology: Taking 1419 firms listed in Shenzhen Stock Exchange in China as a sample, this paper empirically analyzed the relationship between corporate integrity and stock price crash risk. The main integrity data was hand-collected from Shenzhen Stock Exchange Website. Other financial data was collected from CSMAR Database. Results: Findings show that corporate integrity can significantly decrease stock price crash risk. After changing the selection of samples, model estimation methods and the proxy variable of stock price crash risk, the conclusion is still valid. Further research shows that the relationship between corporate integrity and stock price crash risk is only found in firms with weak internal control and firms in poor legal system areas. Conclusions: Results of the study suggest that corporate integrity has a significant influence on behaviors of managers. Business ethics reduces the likelihood of managers to overstate financial performance and hide bad news, which leads to the low likelihood of future stock price crashes. Meanwhile, corporate integrity can supplement internal control and legal system in decreasing stock price crash risks.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제23권11호
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• pp.625-631
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• 2017

과거부터 현재까지 주식시장에 대한 주가 변동 예측은 풀리지 않는 난제이다. 주가를 과학적으로 예측하기 위해 다양한 시도 및 연구들이 있어왔지만, 아직까지 정확한 미래를 예측하는 것은 불가능하다. 하지만, 주가 예측은 경제, 수학, 물리 그리고 전산학 등 여러 관련 분야에서 오랜 관심의 대상이 되어왔다. 본 논문에서는 최근 각광 받고 있는 딥러닝(Deep-Learning)을 이용하여 주가의 변동패턴을 학습하고 미래를 예측하고자한다. 본 연구에서는 오픈소스 딥러닝 프레임워크인 텐서플로우를 이용하여 총 3가지 학습 모델을 제시하였으며, 각 학습모델은 각기 다른 입력 피쳐들을 받아들여 학습을 진행한다. 입력 피쳐는 이전 연구에서 사용한 단순 가격 데이터를 확장해 입력 피쳐 개수를 증가시켜가며 실험을 하였다. 세 가지 예측 모델의 학습 성능을 측정했으며, 이를 통해 가격-기반 입력 피쳐에 따라 달라지는 예측 모델의 성능 변화 비교 분석하여 가격-기반 입력 피쳐가 주가예측에 미치는 영향을 평가하였다.

• The Journal of Asian Finance, Economics and Business
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• 제8권4호
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• pp.1-9
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• 2021

UAE has rapidly developed into one of the leading global financial hubs, with significant transformations in its stock exchanges. In its attempt at economic diversification in the last two decades, the country has also taken a lead in the GCC region in introducing extensive reforms to attract FDI to the Emirates. However, oil price volatilities have posed a significant challenge to all oil-exporting countries. The main aim of this study is to explore the impact of economic diversification and oil price on the UAE stock market. The study applies Granger Causality and Vector Autoregressive Model on monthly Abu Dhabi stock exchange index, Dubai Fateh crude oil spot price, and FDI inflows during 2001-19. The short-term interbank rate has been included as a monetary policy variable. The results show a substantial difference between the two phases of reforms. Oil price and Abu Dhabi stock index show bidirectional relationship during 2001-09 but no causality was found during 2010-19. Furthermore, the second phase was characterized by unidirectional causation from FDI to ADX index. This study highlights FDI inflows as a key driver of stock market performance during the last decade and emphasizes the success of the intense reforms in the UAE initiated for the diversification of its economy.

• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.166-171
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• 2023

The Stock price analysis is an increasing concern in a financial time series. The purpose of the study is to analyze the price parameters of date, high, low, and news feed about the stock exchange price. Long short term memory (LSTM) is a cutting-edge technology used for predicting the data based on time series. LSTM performs well in executing large sequence of data. This paper presents the Long Short Term Memory Model has used to analyze the stock price ranges of 10 days and 20 days by exponential moving average. The proposed approach gives better performance using technical indicators of stock price with an accuracy of 82.6% and cross entropy of 71%.

• International journal of advanced smart convergence
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• 제8권2호
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• pp.132-139
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• 2019

We address the problem about forecasting the direction of stock price movement in the Korea market. Recently, the deep neural network is popularly applied in this area of research. In deep neural network systems, proper parameter initialization reduces training time and improves the performance of the model. Therefore, in our study, we propose a novel parameter initialization technique and apply this technique for the stock price movement prediction model. Specifically, we design a framework which consists of two models: a base model and a main prediction model. The base model constructed with LSTM is trained by using the large data which is generated by a large amount of the stock data to achieve optimal parameters. The main prediction model with the same architecture as the base model uses the optimal parameter initialization. Thus, the main prediction model is trained by only using the data of the given stock. Moreover, the stock price movements can be affected by other related information in the stock market. For this reason, we conducted our research with two types of inputs. The first type is the stock features, and the second type is a combination of the stock features and the Korea Composite Stock Price Index (KOSPI) features. Empirical results conducted on the top five stocks in the KOSPI list in terms of market capitalization indicate that our approaches achieve better predictive accuracy and F1-score comparing to other baseline models.

• Communications for Statistical Applications and Methods
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• 제8권2호
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• pp.543-556
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• 2001

This study suggests integrated neural network modes for he stock price index forecasting using change-point detection. The basic concept of this proposed model is to obtain significant intervals occurred by change points, identify them as change-point groups, and reflect them in stock price index forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in stock price index dataset. The second phase is to forecast change-point group with various data mining classifiers. The final phase is to forecast the stock price index with backpropagation neural networks. The proposed model is applied to the stock price index forecasting. This study then examines the predictability of integrated neural network models and compares the performance of data mining classifiers.