• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.19-24
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• 2020

Artificial intelligence technology, which is the core of the 4th industrial revolution, is making intelligent judgments through deep learning techniques and machine learning that it is impossible to predict if it is applied to stock prediction beyond human capabilities. In US fund management companies, artificial intelligence is replacing the role of stock market analyst, and research in this field is actively underway. In this study, we use BLSTM to reduce errors that occur in unidirectional prediction of the existing LSTM method, reduce errors in predictions by predicting in both directions, and macroscopic indicators that affect stock prices, namely, economic growth rate, economic indicators, interest rate, analyze the trade balance, exchange rate, and volume of currency. To help stock investment by accurately predicting the target price of stocks by analyzing the PBR, BPS, and ROE of individual stocks after analyzing macro-indicators, and by analyzing the purchase and sale quantities of foreigners, institutions, pension funds, etc., which have the most influence on stock prices.

• East Asian Journal of Business Economics (EAJBE)
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• v.4 no.4
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• pp.1-7
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• 2016

This paper has studied the impacts of the exchange rate, government debt as a percent of GDP and other relevant macroeconomic variables on aggregate output in the Philippines. A simultaneous-equation model consisting of aggregate demand and short-run aggregate supply is applied. The dummy variable technique is employed to detect whether the slope and intercept of the real effective exchange rate may have changed. Real depreciation during 1998.Q1 - 2006.Q3, real appreciation during 2006.Q4 - 2016.Q1, a lower domestic debt as a percent of GDP, a lower real interest rate, a higher stock price or a higher lagged real oil price would raise aggregate output. Recent trends of real peso appreciation, declining domestic debt as a percent of GDP, lower real interest rates, and rising stock prices are in line with the empirical results and would promote economic growth. The authorities may need to continue to pursue fiscal prudence and maintain a stronger peso as the positive effect of real appreciation dominates its negative effect in recent years.

• Journal of Service Research and Studies
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• v.6 no.4
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• pp.59-74
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• 2016

The purpose of this paper is to see what the problem is and what the direction of the Investment of ELB is after this study has analyzed an achievable possibility for a suggested yield of ELB. It analyzes estimated yields from January in 2010 to June in 2016 for ELB Structures issued during 2015~2016. It carries correlation analysis and regression analysis between ELB yield and minimum guarantee yield, maximum stock price growth limit, participation rate. As the study result, a probability of achievement over 2% yield was below 20% as stock price growth had been inside maximum limit. An estimated average yield of ELB was 1.49% and it was lowed than 1.72% of Bank Deposit in 2015. So a realized yield was not satisfied the expected yield. As the correlation coefficient between ELB yield and minimum guarantee yield was 0.843, the correlation coefficient between ELB yield and maximum limit yield was 0.279, the correlation of minimum guarantee yield was high. The suggestion is that the a realized yield of ELB is lower than Bank Deposit interest and that the probability of stock growth inside maximum limit is low.

• The Journal of Asian Finance, Economics and Business
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• v.5 no.1
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• pp.5-9
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• 2018

Many developing countries have attempted to depreciate their currencies in order to make their products cheaper, stimulate exports, shift aggregate demand to the right, and increase aggregate output. However, currency depreciation tends to increase import prices, raise domestic inflation, reduce capital inflows, and shift aggregate supply to the left. The net impact is unclear. The paper incorporates the monetary policy function in the model, which is determined by the inflation gap, the output gap, the real effective exchange rate, and the world real interest rate. Applying an extended IS-MP-AS model (Romer, 2000), the paper finds that real depreciation raised real GDP during 1997.Q1-2005.Q3 whereas real appreciation increased real GDP during 2005.Q4-2017.Q2. In addition, a higher government debt-to-GDP ratio, a lower U.S. real federal funds rate, a higher real stock price, a lower real oil price or a lower expected inflation rate would help increase real GDP. Hence, real depreciation or real appreciation may increase or reduce aggregate output, depending upon the level of economic development. Although expansionary fiscal policy is effective in stimulating the economy, caution needs to be exercised as there may be a debt threshold beyond which a further increase in the debt-to-GDO ratio would hurt economic growth.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.195-220
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• 2018

In this study, we conducted an empirical analysis of the factors that affect the change of Bitcoin Closing Price. Previous studies have focused on the security of the block chain system, the economic ripple effects caused by the cryptocurrency, legal implications and the acceptance to consumer about cryptocurrency. In various area, cryptocurrency was studied and many researcher and people including government, regardless of country, try to utilize cryptocurrency and applicate to its technology. Despite of rapid and dramatic change of cryptocurrencies' price and growth of its effects, empirical study of the factors affecting the price change of cryptocurrency was lack. There were only a few limited studies, business reports and short working paper. Therefore, it is necessary to determine what factors effect on the change of closing Bitcoin price. For analysis, hypotheses were constructed from three dimensions of consumer, industry, and macroeconomics for analysis, and time series data were collected for variables of each dimension. Consumer variables consist of search traffic of Bitcoin, search traffic of bitcoin ban, search traffic of ransomware and search traffic of war. Industry variables were composed GPU vendors' stock price and memory vendors' stock price. Macro-economy variables were contemplated such as U.S. dollar index futures, FOMC policy interest rates, WTI crude oil price. Using above variables, we did times series regression analysis to find relationship between those variables and change of Bitcoin Closing Price. Before the regression analysis to confirm the relationship between change of Bitcoin Closing Price and the other variables, we performed the Unit-root test to verifying the stationary of time series data to avoid spurious regression. Then, using a stationary data, we did the regression analysis. As a result of the analysis, we found that the change of Bitcoin Closing Price has negative effects with search traffic of 'Bitcoin Ban' and US dollar index futures, while change of GPU vendors' stock price and change of WTI crude oil price showed positive effects. In case of 'Bitcoin Ban', it is directly determining the maintenance or abolition of Bitcoin trade, that's why consumer reacted sensitively and effected on change of Bitcoin Closing Price. GPU is raw material of Bitcoin mining. Generally, increasing of companies' stock price means the growth of the sales of those companies' products and services. GPU's demands increases are indirectly reflected to the GPU vendors' stock price. Making an interpretation, a rise in prices of GPU has put a crimp on the mining of Bitcoin. Consequently, GPU vendors' stock price effects on change of Bitcoin Closing Price. And we confirmed U.S. dollar index futures moved in the opposite direction with change of Bitcoin Closing Price. It moved like Gold. Gold was considered as a safe asset to consumers and it means consumer think that Bitcoin is a safe asset. On the other hand, WTI oil price went Bitcoin Closing Price's way. It implies that Bitcoin are regarded to investment asset like raw materials market's product. The variables that were not significant in the analysis were search traffic of bitcoin, search traffic of ransomware, search traffic of war, memory vendor's stock price, FOMC policy interest rates. In search traffic of bitcoin, we judged that interest in Bitcoin did not lead to purchase of Bitcoin. It means search traffic of Bitcoin didn't reflect all of Bitcoin's demand. So, it implies there are some factors that regulate and mediate the Bitcoin purchase. In search traffic of ransomware, it is hard to say concern of ransomware determined the whole Bitcoin demand. Because only a few people damaged by ransomware and the percentage of hackers requiring Bitcoins was low. Also, its information security problem is events not continuous issues. Search traffic of war was not significant. Like stock market, generally it has negative in relation to war, but exceptional case like Gulf war, it moves stakeholders' profits and environment. We think that this is the same case. In memory vendor stock price, this is because memory vendors' flagship products were not VRAM which is essential for Bitcoin supply. In FOMC policy interest rates, when the interest rate is low, the surplus capital is invested in securities such as stocks. But Bitcoin' price fluctuation was large so it is not recognized as an attractive commodity to the consumers. In addition, unlike the stock market, Bitcoin doesn't have any safety policy such as Circuit breakers and Sidecar. Through this study, we verified what factors effect on change of Bitcoin Closing Price, and interpreted why such change happened. In addition, establishing the characteristics of Bitcoin as a safe asset and investment asset, we provide a guide how consumer, financial institution and government organization approach to the cryptocurrency. Moreover, corroborating the factors affecting change of Bitcoin Closing Price, researcher will get some clue and qualification which factors have to be considered in hereafter cryptocurrency study.

• Ocean and Polar Research
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• v.40 no.4
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• pp.237-247
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• 2018

This study aims to estimate optimal harvests, fishing efforts, and stock levels of hairtail harvested by the large pair bottom trawl, the large otter trawl, the large purse seine, the offshore long line, and the offshore angling fisheries by using the surplus production models and the current value Hamiltonian method. Processes of this study are as follows. First of all, this study estimates the standardized fishing efforts regarding the harvesting of the hairtail by the above five fishing gears based on the general linear model developed by Gavaris. Secondly, this study estimates environmental carrying capacity (k), intrinsic growth rate (r), and catchability coefficient (q) by applying the Clarke Yoshimoto Pooley (CY&P) model among various surplus production models. Thirdly, this study estimates the optimal harvests, fishing efforts, and stock levels regarding the hairtail by the current value Hamiltonian method, including the average landing price, the average unit cost, and the social discount rate. Finally, this study attempts a sensitivity analysis to figure out changes in optimal harvests, fishing efforts, and stock levels due to changes in the average landing price and the average unit cost. As results induced by the current value Hamiltonian method, the optimal harvests, fishing efforts, and stock levels regarding the hairtail caught by several fishing gears were estimated as 33,133 tons, 901,080 horse power, and 79,877 tons, respectively. In addition, from the results of the sensitivity analysis, first of all, if the average landing price of the hairtail constantly increases, the optimal harvests of it increase at a decreasing rate, and then harvests finally slightly decrease as a result of decreases in stock levels. Secondly, if the average unit cost of fishing efforts continuously increases, the optimal fishing efforts decreases, but optimal stock levels increase. Optimal harvests start climbing and then decrease continuously due to increases in the average unit cost. In summary, this study suggests that the optimal harvests (33,133 tons) were larger than actual harvests (25,133 tons), but the optimal fishing efforts (901,080 horse power) were much less than estimated standardized fishing efforts (1,277,284 horse power), corresponding to the average of the recent three years (2014-2016). This result implies that the hairtail has been inefficiently harvested and recently overfished due to excessive fishing efforts. Efficient management and conservation policies on stock levels need to be urgently implemented. Some appropriate strategies would be to include the hairtail in the Korean TAC species or to extend the closed fishing season for this species.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.5
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• pp.779-803
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• 2023

The growth of digital news media and the stock price index has resulted in economic fluctuations in the fashion industry. This study examines the impact of fashion industry news and macroeconomic changes on the Textile & Wearing Apparel KOSPI over the past five years. An auto-regressive integrated moving average exogenous time series model was conducted using the fashion industry stock market index, the news topic index, and macro-economic indicators. The results indicated the topics of "Cosmetic business expansion" and "Digital innovation" impacted the Textile & Wearing Apparel KOSPI after one week, and the topics of "Pop-up store," "Entry into the Chinese fashion market," and "Fashion week and trade show" affected it after two weeks. Moreover, the topics of "Cosmetic business expansion" and "Entry into the Chinese fashion market" were statistically significant in the macroeconomic environment. Regarding the effect relation of Textile & Wearing Apparel KOSPI, "Cosmetic business expansion," "Entry into the Chinese fashion market," and consumer price fluctuation showed negative effects, while the private consumption change rate, producer price fluctuation, and unemployment change rate had positive effects. This study analyzes the impact of media framing on fashion industry business cycles and provides practical insights into managing stock market risk for fashion companies.

• Korea Trade Review
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• v.45 no.2
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• pp.229-262
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• 2020

The price-to-earnings ratio (PER) is an important indicator to measure the stock price and profitability of a firm; it is also the most used valuation indicator among investors. When using the PER to compare the investment values of different stocks, these stocks must come from the same sector. This study mainly focuses on the China's listed manufacturing firms. By learning from previous research results and analyzing the current situation, we studied the correlation between the manufacturing sector's PER and its influencing factors from both macro and micro perspectives, the combination of which eventually sheds light on such correlation. Analyzing GDP growth rate data, Manufacturing Purchasing Managers' Index, and other macroeconomic variables from 2008 to 2018, we conclude that these variables jointly have a certain impact on the average PER of the manufacturing sector. We then form panel data based on relevant (2014-2018) data gathered from 317 of China's A-listed manufacturing firms to study the impact of micro-variables on PER. By using Stata and other software to analyze the panel data, we reach the conclusion that the Debt to Asset Ratio, Return on Equity, EPS growth rate, Operating Profit Ratio, Dividend Payout Ratio, and firm size have a significant impact on PER. The Current Ratio, Treasury Stock ratio and Ownership Concentration have no distinct effect on PER. Based on our empirical findings, we design a theoretical model that affects the PER.

• The Journal of Fisheries Business Administration
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• v.33 no.2
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• pp.75-98
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• 2002

As a contribution to developing fishery stock assessment, optimum sustainable yield and its international standards such as MSY, MEY, and dynamic MEY for six recommended fisheries are developed using bio-economic models. For selecting the appropriate model, five models - Schaefer, Schnute, Walters and Hilborn, Fox, and CY&P models are tested in effort and catch data of six species. Surprisingly all the models except the CY&P model failed to satisfy statistical standards such as goodness-of-fitness and reliability. Generally, the CY&P model holds good fitness and statistically significant level for all of six fisheries. However, the CY&P model for squid, where the intrinsic growth rate is high, could not explain MSY, MEY, and dynamic MEY appropriately. This study makes a contribution to develop the modified model for the intrinsic growth rate of 1. The reformulated model represents the results reasonably even though the estimated equation has not good fitness. Although most of the CY&P models appear to have good fits and validated results for some cases, these models also seem to be quite sensitive to parameters which means a more stable model should be developed and data should carefully be handled. In particular biological and technical interactions such as multispecies, predator prey relationship, age structure and mortality should be taken into account. In addition, economic factors and fishing efforts such as price, cost, technical change and a reasonable function of fishing input should simultaneously be considered.

• Journal of Digital Convergence
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• v.19 no.6
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• pp.133-141
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• 2021

Governments of each country are actively implementing fiscal expansion policies to recover the real economy after Corona 19. In Korea, the stock market and housing market are greatly affected as liquidity in the market increases due to the implementation of disaster subsidies and welfare policies. The purpose of this study is to analyze the relationship between stock market and housing market trends and liquidity. Data were collected by the Bank of Korea and Kookmin Bank. The analysis period is from January 2000 to December 2020, and monthly data are used. For empirical analysis, the rate of change from the same month of the previous year was calculated for each variable, and numerical analysis, index analysis, and model analysis were performed. As a result of the analysis, it was found that the stock index showed a positive(+) relationship with the house price, while a negative(-) relationship with M2. Previous studies have suggested that, in general, an increase in liquidity affects the stock market and the housing market, and inflation also rises. In this study, it was found that the stock market and the housing market had an effect on each other. However, it was investigated that liquidity showed an inverse relationship with the stock market and had no relationship with the housing market. Through this, this study estimated that there is a time difference in the relationship between liquidity and the stock market & housing market.