• The Journal of Fisheries Business Administration
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• v.43 no.1
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• pp.1-9
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• 2012

Based on Clark and Munro's theory of dynamic optimization between fishery resources and production, this study is aimed to take an empirical analysis of optimal production level to the Danish Seine fishery under the sandfish stock rebuilding plan. For empirical analysis, it examined the optimal fish stock size, production and fishing effort levels and it also made an additional evaluation of optimal production changes on main variables by sensitivity analyses. When a 4% of the discount rate is assumed, the optimal sandfish production of Danish Seine fishery would be 3,049 t, and the sandfish optimal stock size is evaluated to be 19,016 t. In addition, the optimal fishing effort is estimated to be 4,368 days. Accordingly, to achieve the optimal production level, current fishing efforts should be reduced while the fish stock size should be increased up to the optimal level.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.559-575
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• 2014

Individual investors are increasingly flocking to message boards to seek, clarify, and exchange information. Businesses like Seekingalpha.com and business magazines like Fortune are evaluating, synthesizing, and reporting the comments made on message boards or blogs. In March of 2012, Yahoo! Finance Message Boards recorded 45 million unique visitors per month followed by AOL Money and Finance (19.8 million), and Google Finance (1.6 million) [McIntyre, 2012]. Previous studies in the finance literature suggest that online communities often provide more accurate information than analyst forecasts [Bagnoli et al., 1999; Clarkson et al., 2006]. Some studies empirically show that the volume of posts in online communities have a positive relationship with market activities (e.g., trading volumes) [Antweiler and Frank, 2004; Bagnoli et al., 1999; Das and Chen, 2007; Tumarkin and Whitelaw, 2001]. The findings indicate that information in online communities does impact investors' investment decisions and trading behaviors. However, research explicating the correlation between information on online communities and stock market activities (e.g., trading volume) is still evolving. Thus, it is important to ask whether a volume of posts on online communities influences trading volumes and whether trading volumes also influence these communities. Online stock message boards offer two different types of information, which can be explained using an economic and a psychological perspective. From a purely economic perspective, one would expect that stock message boards would have a beneficial effect, since they provide timely information at a much lower cost [Bagnoli et al., 1999; Clarkson et al., 2006; Birchler and Butler, 2007]. This indicates that information in stock message boards may provide valuable information investors can use to predict stock market activities and thus may use to make better investment decisions. On the other hand, psychological studies have shown that stock message boards may not necessarily make investors more informed. The related literature argues that confirmation bias causes investors to seek other investors with the same opinions on these stock message boards [Chen and Gu, 2009; Park et al., 2013]. For example, investors may want to share their painful investment experiences with others on stock message boards and are relieved to find they are not alone. In this case, the information on these stock message boards mainly reflects past experience or past information and not valuable and predictable information for market activities. This study thus investigates the two roles of stock message boards-providing valuable information to make future investment decisions or sharing past experiences that reflect mainly investors' painful or boastful stories. If stock message boards do provide valuable information for stock investment decisions, then investors will use this information and thereby influence stock market activities (e.g., trading volume). On the contrary, if investors made investment decisions and visit stock message boards later, they will mainly share their past experiences with others. In this case, past activities in the stock market will influence the stock message boards. These arguments indicate that there is a correlation between information posted on stock message boards and stock market activities. The previous literature has examined the impact of stock sentiments or the number of posts on stock market activities (e.g., trading volume, volatility, stock prices). However, the studies related to stock sentiments found it difficult to obtain significant results. It is not easy to identify useful information among the millions of posts, many of which can be just noise. As a result, the overall sentiments of stock message boards often carry little information for future stock movements [Das and Chen, 2001; Antweiler and Frank, 2004]. This study notes that as a dependent variable, trading volume is more reliable for capturing the effect of stock message board activities. The finance literature argues that trading volume is an indicator of stock price movements [Das et al., 2005; Das and Chen, 2007]. In this regard, this study investigates the correlation between a number of posts (information on stock message boards) and trading volume (stock market activity). We collected about 100,000 messages of 40 companies at KOSPI (Korea Composite Stock Price Index) from Paxnet, the most popular Korean online stock message board. The messages we collected were divided into in-trading and after-trading hours to examine the correlation between the numbers of posts and trading volumes in detail. Also we collected the volume of the stock of the 40 companies. The vector regression analysis and the granger causality test, 3SLS analysis were performed on our panel data sets. We found that the number of posts on online stock message boards is positively related to prior stock trade volume. Also, we found that the impact of the number of posts on stock trading volumes is not statistically significant. Also, we empirically showed the correlation between stock trading volumes and the number of posts on stock message boards. The results of this study contribute to the IS and finance literature in that we identified online stock message board's two roles. Also, this study suggests that stock trading managers should carefully monitor information on stock message boards to understand stock market activities in advance.

• Journal of the Korean Society for Railway
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• v.13 no.5
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• pp.528-534
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• 2010

In the new growth theory, R&D stock is the third factor of production excluding a labor and capital. In this point, a R&D stock is located in a capital which is accumulated by money like existing capital and this is a knowledge capital. The effort for escalating this knowledge capital is R&D investment and R&D stock is an accumulation of this. A contribution degree of the economic growth and a return of R&D investments are analyzed by an estimation of relation R&D stock and a total factor of productivity. This study analyzed R&D stock of railroad R&D investments and compared R&D stock with a technical level. So, a technical level is proportionally escalated following escalation of R&D stock. and compared railroad industry weight on the GDP with a railroad R&D stock weight on whole industries R&D stock. According to a relatively small railroad R&D stock weight against the railroad industry weight, a continuous railroad R&D investment is needed.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.35 no.4
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• pp.23-33
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• 2003

The possibility of real time analysis about hot melt resins and pressure sensitive adhesives in newsprint stock was investigated by performing comparative tests using conventional image analysis method and real time contaminants analyzer. Based on the test results, the performance of real time contaminants analyzer in terms of detecting primary stickies in newsprint stock could be verified. Real time stickies analysis showed good precision and over-estimation of hot melt resins and under-estimation of pressure sensitive adhesives could be corrected by adapting new method. Real time analysis of primary stickies in the actual newsprint stock also showed good correlation with conventional image analysis and the performance of real time contaminants analyzer could be verified again. Adjustment of the contrast sensitivity of real time contaminants analyzer was enough to set the proper monitoring conditions for primary stickies in newsprint stock.

• Korean Management Science Review
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• v.30 no.1
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• pp.43-58
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• 2013

Although analysis of charts, which used in stock trading by distinguishing standardized patterns in the movements of stock prices, is simple and easy to use, there can be problems stemming from specific patterns being distinguished as a result of the subjective perspectives of analysts. In accordance with such problems, through the method of template pattern matching, 4 trend reversal patterns were designed and the fitness of the patterns were quantitatively measured. In cases when a stock is purchased when the template pattern fitness value is within a certain range and held for at least 20-days, the average return ratio was analyzed to be higher-with the difference being statistically significant-than the average return ratio attained from trading a stock according to the same method per the Efficient Market Hypothesis. From the results of stock trades of 2 domestic corporations to which the values of the 4 patterns had been applied based on the 4 strategies, it was possible to ascertain differences in the strategy- and pattern-dependent return ratios. Through this study, along with presenting the exceptions for the Efficient Market Hypothesis in stock trading, the fitness level of quantitative chart patterns was measured and the theoretical basis for application of such fitness level was proposed.

• Asian Journal of Business Environment
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• v.11 no.4
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• pp.17-29
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• 2021

Purpose: The aim of this study is to explore the dynamic linkage between conditional stock market volatility and macroeconomic uncertainty of Bangladesh. Research design, data, and methodology: This study uses monthly data covering the time period from January 2005 to December 2018. A comprehensive set of macroeconomic variables, namely industrial production index (IP), consumer price index (CPI), broad money supply (M2), 91-day treasury bill rate (TB), treasury bond yield (GB), exchange rate (EX), inflow of foreign remittance (RT) and stock market index of DSEX are used for analysis. Symmetric and asymmetric univariate GARCH family of models and multivariate VAR model, along with block exogeneity and impulse response functions, are implemented on conditional volatility series to discover the possible interactions and causal relations between macroeconomic forces and stock return. Results: The analysis of the study exhibits time-varying volatility and volatility persistence in all the variables of interest. Moreover, the asymmetric effect is found significant in the stock return and most of the growth series of macroeconomic fundamentals. Results from the multivariate VAR model indicate that only short-term interest rate significantly influence the stock market volatility, while conditional stock return volatility is significant in explaining the volatility of industrial production, inflation, and treasury bill rate. Conclusion: The findings suggest an increasing interdependence between the money market and equity market as well as the macroeconomic fundamentals of Bangladesh.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.1-9
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• 2021

This paper investigates the impact of the domestic and global outbreak of the coronavirus (COVID-19) pandemic on the trading size of the Malaysian stock (MS) market. The theoretical model posits that stock markets are affected by their response to disasters and events that arise in the international or local environments, as well as to several financial factors such as stock volatility and spread bid-ask prices. Using daily time-series data from 27 January to 12 May 2020, this paper utilizes the traditional Augmented Dickey and Fuller (ADF) technique and Zivot and Andrews with structural break' procedures for a stationarity test analysis, while the autoregressive distributed lag (ARDL) method is applied according to the trading size of the MS market model. The analysis considered almost all 789 listed companies investing in the main stock market of Malaysia. The results confirmed our hypotheses that both the daily growth in the active domestic and global cases of coronavirus (COVID-19) has significant negative effects on the daily trading size of the stock market in Malaysia. Although the COVID-19 has a negative effect on the Malaysian stock market, the findings of this study suggest that the COVID-19 pandemic may have an asymmetric effect on the market.

• Journal of Information Technology Services
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• v.13 no.3
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• pp.221-233
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• 2014

Because people's interest of the stock market has been increased with the development of economy, a lot of studies have been going to predict fluctuation of stock prices. Latterly many studies have been made using scientific and technological method among the various forecasting method, and also data using for study are becoming diverse. So, in this paper we propose stock prices prediction models using sentiment analysis and machine learning based on news articles and SNS data to improve the accuracy of prediction of stock prices. Stock prices prediction models that we propose are generated through the four-step process that contain data collection, sentiment dictionary construction, sentiment analysis, and machine learning. The data have been collected to target newspapers related to economy in the case of news article and to target twitter in the case of SNS data. Sentiment dictionary was built using news articles among the collected data, and we utilize it to process sentiment analysis. In machine learning phase, we generate prediction models using various techniques of classification and the data that was made through sentiment analysis. After generating prediction models, we conducted 10-fold cross-validation to measure the performance of they. The experimental result showed that accuracy is over 80% in a number of ways and F1 score is closer to 0.8. The result can be seen as significantly enhanced result compared with conventional researches utilizing opinion mining or data mining techniques.

• Journal of Labour Economics
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• v.34 no.1
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• pp.33-69
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• 2011

This study analyses a productivity effect by E&T and R&D activities via estimation of E&T stock, R&D stock, and patent stock in a corporate level. Particularly, the analysis reflects the effects of skilled training after estimating E&T stock from E&T flow. When a spillover effect of E&T is analyzed, a methodology using technical proximity concept becomes a new experiment. Also classifying long and short term effects from the usage of Dynamic Panel Data Analysis becomes a new trial, too. The results of study appear that the productivity effects from E&T investments are relatively lager than R&D investments. Through spillover effects and long-term effects E&T and R&D activities have a strong influence on the corporate's productivity.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.31-39
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• 2022

Stock prices are influenced by a number of external factors, such as laws and trends, as well as number-based internal factors such as trading volume and closing prices. Since many factors affect stock prices, it is very difficult to accurately predict stock prices using only fragmentary stock data. In particular, since the value of a company is greatly affected by the perception of people who actually trade stocks, emotional information about a specific company is considered an important factor. In this paper, we propose a deep learning-based stock price prediction model using sentiment analysis with news data considering temporal characteristics. Stock and news data, two heterogeneous data with different characteristics, are integrated according to time scale and used as input to the model, and the effect of time scale and sentiment index on stock price prediction is finally compared and analyzed. Also, we verify that the accuracy of the proposed model is improved through comparative experiments with existing models.