• Management & Information Systems Review
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• v.12
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• pp.137-160
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• 2003

The association between accounting earnings and the stock price of an entity is the subject that has been most heavily researched during the past 25 years in accounting literature. Researcher's common finding is that there are positive relationships between accounting earnings and stock prices. However, the explanatory power of accounting earnings which was measured by $R^2$ of regression functions used was rather low. To be connected with these low results, The prior studies propose that there will be additional information, errors in variables. This study investigates empirically determinants of earnings response coefficients(ERCs), which measure the correlation between earnings and stock prices, using earnings level / change, as the dependent variable in the return/earnings regression. Specifically, the thesis tests whether the factors such as earnings persistence, growth, systematic risk, image, information asymmetry and firm size. specially, the determinable variables of ERC are explained in detail. The image / information asymmetry variables are selected to be connected with additional information stand point, The debt / growth variables are selected to be connected with errors in variables. In this study, The sample of firms, listed in Korean Stock Exchange was drawn from the KIS-DATA and was required to meet the following criteria: (1) Annual accounting earnings were available over the 1986-1999 period on the KIS-FAS to allow computation of variables parameter; (2) sufficient return data for estimation of market model parameters were available on the KIS-SMAT month returns: (3) each firm had a fiscal year ending in December throughout the study period. Implementation of these criteria yielded a sample of 1,141 firm-year observation over the 10-year(1990-1999) period. A conventional regression specification would use stock returns(abnormal returns) as a dependent variable and accounting earnings(unexpected earnings) changes interacted with other factors as independent variables. In this study, I examined the relation between other factors and the RRC by using reverse regression. For an empirical test, eight hypotheses(including six lower-hypotheses) were tested. The results of the performed empirical analysis can be summarized as follows; The first, The relationship between persistence of earnings and ERC have significance of each by itself, this result accord with one of the prior studies. The second, The relationship between growth and ERC have not significance. The third, The relationship between image and ERC have significance of each by itself, but a forecast code doesn't present. This fact shows that image cost does not effect on market management share, is used to prevent market occupancy decrease. The fourth, The relationship between information asymmetry variable and ERC have significance of each by. The fifth, The relationship between systematic risk$(\beta)$ and ERC have not significance. The sixth, The relationship between debt ratio and ERC have significance of each by itself, but a forecast code doesn't present. This fact is judged that it is due to the effect of financial leverage effect and a tendency of interest.

• The Journal of Information Systems
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• v.27 no.3
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• pp.123-140
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• 2018

Purpose The purpose of this study is to explore the optimal trading frequency which is useful for stock price prediction by using deep learning for charting image data. We also want to identify the appropriate time for accurate forecasting of stock price when performing pattern analysis. Design/methodology/approach In order to find the optimal trading frequency patterns and forecast timings, this study is performed as follows. First, stock price data is collected using OpenAPI provided by Daishin Securities, and candle chart images are created by data frequency and forecasting time. Second, the patterns are generated by the charting images and the learning is performed using the CNN. Finally, we find the optimal trading frequency patterns and forecasting timings. Findings According to the experiment results, this study confirmed that when the 10 minute frequency data is judged to be a decline pattern at previous 1 tick, the accuracy of predicting the market frequency pattern at which the market decreasing is 76%, which is determined by the optimal frequency pattern. In addition, we confirmed that forecasting of the sales frequency pattern at previous 1 tick shows higher accuracy than previous 2 tick and 3 tick.

• The Journal of Bigdata
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• v.4 no.2
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• pp.1-12
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• 2019

As Artificial Intelligence (AI) technology develops, it is applied to various fields such as image, voice, and text. AI has shown fine results in certain areas. Researchers have tried to predict the stock market by utilizing artificial intelligence as well. Predicting the stock market is known as one of the difficult problems since the stock market is affected by various factors such as economy and politics. In the field of AI, there are attempts to predict the ups and downs of stock price by studying stock price patterns using various machine learning techniques. This study suggest a way of predicting stock price patterns based on the Convolutional Neural Network(CNN) among machine learning techniques. CNN uses neural networks to classify images by extracting features from images through convolutional layers. Therefore, this study tries to classify candlestick images made by stock data in order to predict patterns. This study has two objectives. The first one referred as Case 1 is to predict the patterns with the images made by the same-day stock price data. The second one referred as Case 2 is to predict the next day stock price patterns with the images produced by the daily stock price data. In Case 1, data augmentation methods - random modification and Gaussian noise - are applied to generate more training data, and the generated images are put into the model to fit. Given that deep learning requires a large amount of data, this study suggests a method of data augmentation for candlestick images. Also, this study compares the accuracies of the images with Gaussian noise and different classification problems. All data in this study is collected through OpenAPI provided by DaiShin Securities. Case 1 has five different labels depending on patterns. The patterns are up with up closing, up with down closing, down with up closing, down with down closing, and staying. The images in Case 1 are created by removing the last candle(-1candle), the last two candles(-2candles), and the last three candles(-3candles) from 60 minutes, 30 minutes, 10 minutes, and 5 minutes candle charts. 60 minutes candle chart means one candle in the image has 60 minutes of information containing an open price, high price, low price, close price. Case 2 has two labels that are up and down. This study for Case 2 has generated for 60 minutes, 30 minutes, 10 minutes, and 5minutes candle charts without removing any candle. Considering the stock data, moving the candles in the images is suggested, instead of existing data augmentation techniques. How much the candles are moved is defined as the modified value. The average difference of closing prices between candles was 0.0029. Therefore, in this study, 0.003, 0.002, 0.001, 0.00025 are used for the modified value. The number of images was doubled after data augmentation. When it comes to Gaussian Noise, the mean value was 0, and the value of variance was 0.01. For both Case 1 and Case 2, the model is based on VGG-Net16 that has 16 layers. As a result, 10 minutes -1candle showed the best accuracy among 60 minutes, 30 minutes, 10 minutes, 5minutes candle charts. Thus, 10 minutes images were utilized for the rest of the experiment in Case 1. The three candles removed from the images were selected for data augmentation and application of Gaussian noise. 10 minutes -3candle resulted in 79.72% accuracy. The accuracy of the images with 0.00025 modified value and 100% changed candles was 79.92%. Applying Gaussian noise helped the accuracy to be 80.98%. According to the outcomes of Case 2, 60minutes candle charts could predict patterns of tomorrow by 82.60%. To sum up, this study is expected to contribute to further studies on the prediction of stock price patterns using images. This research provides a possible method for data augmentation of stock data.

• The Journal of Korean Academy of Prosthodontics
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• v.43 no.4
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• pp.453-465
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• 2005

Purpose: This study was performed to measure the accuracy of adjustable dental impression trays by a scanning laser three-dimensional digitizer. Materials and methods: The metal stock, individual, and adjustable stock trays were used for 60 stone casts(10 casts each) duplicated a resin master model of mandible and maxilla. The type IV dental stone was poured in a vinyl polysiloxane impressions and allowed to set for one hour. The master model and the duplicated casts were digitized using an optical digitizer. The distance between the reference points were measured and analyzed on the graphic image of 3-D graphic software(CATIA version 5.0). The statistical significance of the differences between the groups was determined by a two-way ANOVA. Results : There were no significant differences between the accuracies of the adjustable stock tray and the master model except only anterior arch width on the upper arch and the diagonal arch length and arch length on one side of the lower arch. Conclusion: The adjustable stock trays showed clinically acceptable accuracies of the study cast produced by them.

• Korean Management Science Review
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• v.30 no.2
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• pp.79-95
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• 2013

In this study, we empirically examine the impact of win-win growth effort of domestic large firms on their financial performance. Specifically, we classify the financial performance into three aspects such as profitability, stability and efficiency, select corresponding financial ratios to each aspect, and analyze the causal relationship between the firms' win-win growth effort and each of the financial ratios. In addition, we figure out the impact of the firms' win-win growth effort on their stock rate of return. From the analysis, we show that the win-win growth effort has a positive impact on the firms' profitability, stability and stock prices; however, it does not give statistically significant impact on the firms' efficiency with even negative impact on it. These results imply that the firms' win-win growth effort could bring about inefficiency in their business operations, but the effort could increase the firms' profitability and make their financial structure more stable. Furthermore, the effort could enhance the firms' image of leading CSR (corporate social responsibility), which in turn increase their stock values.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.193-200
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• 2023

This paper selects 100 IT companies listed on the Shenzhen Stock Exchange from 2016 to 2020, and the public announcement in Hwajung collects ESG integrated ratings and grades for each sector and empirically verifies the relationship between ESG ratings and stock returns. Huazheng ESG level data and QIANZHAN database Using corporate financial data, a total of 500 samples were selected through correlation analysis and linear regression analysis with SPSS23 to analyze the effect of ESG on Return. As a result of the analysis, first, the impact on stock returns was found to be a significant positive (+) value for ESG integrated ratings and ratings by E (environment), S (social), and G (governance) sectors, confirming that ESG ratings have a positive mold of corporate stock returns. Currently, the world's major economies have proposed sustainable development strategies and "carbon neutral" goals. Development strategies are very consistent with ESG concepts, and companies that agree and execute ESG concepts may have higher ratings than other companies in the same industry, resulting in certain evaluation premiums. In addition, capital market performance in recent years shows that companies with ESG concepts or "carbon neutrality" concepts are generally considered to have higher growth potential and stronger anti-risk capabilities in the market. For listed companies, they should focus on ESG investment, improve ESG performance, and actively disclose related information to investors. Improving ESG performance should deliver positive information to society, enhance corporate image, increase market confidence in the future development of listed companies, and positively improve corporate value to actively increase financial, financial, trading, and other aspects of negotiation.

• International Journal of Advanced Culture Technology
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• v.11 no.2
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• pp.389-396
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• 2023

This paper selects 100 IT companies listed on the Shenzhen Stock Exchange from 2016 to 2020, and the public announcement in Hwajung collects ESG integrated ratings and grades for each sector and empirically verifies the relationship between ESG ratings and stock returns. Huazheng ESG level data and QIANZHAN database Using corporate financial data, a total of 500 samples were selected through correlation analysis and linear regression analysis with SPSS23 to analyze the effect of ESG on Return. As a result of the analysis, first, the impact on stock returns was found to be a significant positive (+) value for ESG integrated ratings and ratings by E (environment), S (social), and G (governance) sectors, confirming that ESG ratings have a positive mold of corporate stock returns. Currently, the world's major economies have proposed sustainable development strategies and "carbon neutral" goals. Development strategies are very consistent with ESG concepts, and companies that agree and execute ESG concepts may have higher ratings than other companies in the same industry, resulting in certain evaluation premiums. In addition, capital market performance in recent years shows that companies with ESG concepts or "carbon neutrality" concepts are generally considered to have higher growth potential and stronger anti-risk capabilities in the market. For listed companies, they should focus on ESG investment, improve ESG performance, and actively disclose related information to investors. Improving ESG performance should deliver positive information to society, enhance corporate image, increase market confidence in the future development of listed companies, and positively improve corporate value to actively increase financial, financial, trading, and other aspects of negotiation.