• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.5 no.1
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• pp.45-67
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• 2010

On July 21st in 2005, People's Bank of China (PBOC) turned the currency peg against the U.S. dollar into managed currency system based on a basket of unnamed currencies under China's exchanged rate regime. This change means that China's enterprises are not free from currency fluctuations. The purpose of this study is to analyze the relations between Renminbi fluctuations in the floating exchange rate and financial results of venture companies. The process and outcomes of this study are as follows, First, in order to measure the financial results of venture companies, I choose venture companies in Shandong Province listed on the Shanghai Stock Exchange (SSE) at random and several quarter financial sheets according to safety ratios, profitability ratios, growth ratios, activity ratios. Second, I arrange the daily Renminbi exchange rate data announced from July 21st, 2005 to December 31st, 2008 by PBOC into the quarterly data. Third, in order to confirm the relations between Renminbi fluctuations and financial results of venture companies, I carry out Pearson's correlation analysis. As a result, the revaluation of the Chinese Renminbi has weakly negative effects on debt ratio, total assets turnover ratio and equity turnover ratio in statistics. But the revaluation of the Chinese Renminbi is not related to other financial index in statistics. The result of this study is that the revaluation of the Chinese Renminbi has little influence on the export and import of Chinese venture companies and certifies the fact that Chinese venture companies have much foreign currency assets. In addition to avoid the currency exposure risk, this study shows the effective method about currency exposure risk which adjusts proportion of Renminbi to foreign currency.

• 한국벤처창업학회:학술대회논문집
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• 2010.08a
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• pp.139-160
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• 2010

On July 21st in 2005, People's Bank of China (PBOC) turned the currency peg against the U.S. dollar into managed currency system based on a basket of unnamed currencies under China's exchanged rate regime. This change means that China's enterprises are not free from currency fluctuations. The purpose of this study is to analyze the relations between Renminbi fluctuations in the floating exchange rate and financial results of venture companies. The process and outcomes of this study are as follows, First, in order to measure the financial results of venture companies, I choose venture companies in Shandong Province listed on the Shanghai Stock Exchange (SSE) at random and several quarter financial sheets according to safety ratios, profitability ratios, growth ratios, activity ratios. Second, I arrange the daily Renminbi exchange rate data announced from July 21st, 2005 to December 31st, 2008 by PBOC into the quarterly data. Third, in order to confirm the relations between Renminbi fluctuations and financial results of venture companies, I carry out Pearson's correlation analysis. As a result, the revaluation of the Chinese Renminbi has weakly negative effects on debt ratio, total assets turnover ratio and equity turnover ratio in statistics. But the revaluation of the Chinese Renminbi is not related to other financial index in statistics. The result of this study is that the revaluation of the Chinese Renminbi has little influence on the export and import of Chinese venture companies and certifies the fact that Chinese venture companies have much foreign currency assets. In addition to avoid the currency exposure risk, this study shows the effective method about currency exposure risk which adjusts proportion of Renminbi to foreign currency.

• Journal of Intelligence and Information Systems
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• v.5 no.1
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• pp.103-123
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• 1999

Input filtering as a preprocessing method is so much crucial to get good performance in time series forecasting. There are a few preprocessing methods (i.e. ARMA outputs as time domain filters, and Fourier transform or wavelet transform as time-frequency domain filters) for handling time series. Specially, the time-frequency domain filters describe the fractal structure of financial markets better than the time domain filters due to theoretically additional frequency information. Therefore, we, first of all, try to describe and analyze specially some issues on the effectiveness of different filtering methods from viewpoint of the performance of a neural network based forecasting. And then we discuss about neural network model architecture issues, for example, what type of neural network learning architecture is selected for our time series forecasting, and what input size should be applied to a model. In this study an input selection problem is limited to a size selection of the lagged input variables. To solve this problem, we simulate on analyzing and comparing a few neural networks having different model architecture and also use an embedding dimension measure as chaotic time series analysis or nonlinear dynamic analysis to reduce the dimensionality (i.e. the size of time delayed input variables) of the models. Throughout our study, experiments for integration methods of joint time-frequency analysis and neural network techniques are applied to a case study of daily Korean won / U. S dollar exchange returns and finally we suggest an integration framework for future research from our experimental results.

• International Area Studies Review
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• v.12 no.2
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• pp.3-26
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• 2008

This article investigates the usefulness of the skewed Student-t distribution in modeling the long memory volatility property that might be present in the daily returns of two Australian financial series; the ASX200 stock index and AUD/USD exchange rate. For this purpose we assess the performance of FIGARCH and FIAPARCH Value-at-Risk (VaR) models based on the normal, Student-t, and skewed Student-t distribution innovations. Our results support the argument that the skewed Student-t distribution models produce more accurate VaR estimates of Australian financial markets than the normal and Student-t distribution models. Thus, consideration of skewness and excess kurtosis in asset return distributions provides appropriate criteria for model selection in the context of long memory volatility models in Australian stock and foreign exchange markets.

• The Korean Journal of Financial Management
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• v.23 no.1
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• pp.193-225
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• 2006

The purpose of this paper is to analyze contagion in Asian foreign exchange markets using Extreme Value Theory and Copula. Our application deals with asymptotic dependence of daily exchange rate return for a sample of eight countries over period 1997.1.1-2005.4.13. The empirical results are summarized as follows. Firstly, Gumbel Copula is a good model to our data according to the value of AIC. Secondly, the extremal dependence between East Asian crisis countries became lower in the post crisis period than the crisis period. Thirdly, It seemed that high extremal dependence exists between East Asian countries with Singapore. Fourthly, the tail dependence between Indonesia, Malaysia, Thailand, Philippine became higher in the crisis period than the total period and post crisis period. Fifthly, the fact that the extremal dependence between Korea and Indonesia, Malaysia, Thailand, Philippine did not increase during the Asian Financial Crisis showed that the contagion effect was not the reason of the Korea's Fiancial Crisis. Sixthly, the extremal dependence between Asian exchange markets was not very high while comparing with the European exchange markets.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• International Area Studies Review
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• v.22 no.3
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• pp.3-27
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• 2018

This paper analyzes Cuba's market-oriented reforms to alleviate essential problems with socialist countries such as soft budget constraints and incentive problems. It also discuss about effectiveness of industrial policy as a development strategy. The soft budget constraints and incentive problems resulted in the collapse of Soviet bloc and COMECON in early 1990s. After the collapse, Cuban economy suffered a steep dive, and national income tumbling down rapidly. Cuban faced serious shortages of food, gasoline, and other basic necessities of life. To halt and partially reverse economic downturn and dire austerity in the 1990's, the Cuban government made some partial reforms to the inherited Soviet system of cental planningand faced severe shortage in food, energy, and daily necessities. In response to the economic crisis. Cuba introduced economic reforms and implemented industrial policy as a development strategy as long as Cuba maintained a strong socialist country. Cuban government established the economic free zone law and attempted to induce foreign direct investment by implementing export-led industrial policy. Fiedel Castro approved the Law No. 165 "Free Zones and Industrial Parks", in 1996. However, Cuba's ESZ strategy seems to have failed because of the U.S. sanctions, but also because of Cuba's own policies, which do not allow foreign investors to hire workers directly and impose a high implicit tax on wages. By limiting advanced techniques of personnel and organization management, indirect employment can result in lowering work efforts and productivity of workers, and aggravating production efficiency in the ESZs. Another reason to fail comes from the double wage structure due to the double monetary-exchange rate system. Most of the high non-wage costs result from the double exchange rate system. Due to Cuba's imbalanced industry and production structures, concentrated labor force, and urbanization and centralization of agriculture production, the industrial transformation development model suggested by Lewis has not been successful unlike other Asian agriculture-led development model. Cuba has to overcome many difficulties in implementing industrial policy as a development strategy.

• The Korean Journal of Financial Studies
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• v.3 no.1
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• pp.233-265
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• 1996

We examine the impact of public and private information on price movements using the thirty DJIA stocks and twenty-one NASDAQ stocks. We find that the standard deviation of daily returns on information days (dividend announcement, earnings announcement, insider purchase, or insider sale) is much higher than on no-information days. Both public information matters at the NYSE, probably due to masked identification of insiders. Earnings announcement has the greatest impact for both DJIA and NASDAQ stocks, and there is some evidence of positive impact of insider asle on return volatility of NASDAQ stocks. There has been considerable debate, e.g., French and Roll (1986), over whether market volatility is due to public information or private information-the latter gathered through costly search and only revealed through trading. Public information is composed of (1) marketwide public information such as regularly scheduled federal economic announcements (e.g., employment, GNP, leading indicators) and (2) company-specific public information such as dividend and earnings announcements. Policy makers and corporate insiders have a better access to marketwide private information (e.g., a new monetary policy decision made in the Federal Reserve Board meeting) and company-specific private information, respectively, compated to the general public. Ederington and Lee (1993) show that marketwide public information accounts for most of the observed volatility patterns in interest rate and foreign exchange futures markets. Company-specific public information is explored by Patell and Wolfson (1984) and Jennings and Starks (1985). They show that dividend and earnings announcements induce higher than normal volatility in equity prices. Kyle (1985), Admati and Pfleiderer (1988), Barclay, Litzenberger and Warner (1990), Foster and Viswanathan (1990), Back (1992), and Barclay and Warner (1993) show that the private information help by informed traders and revealed through trading influences market volatility. Cornell and Sirri (1992)' and Meulbroek (1992) investigate the actual insider trading activities in a tender offer case and the prosecuted illegal trading cased, respectively. This paper examines the aggregate and individual impact of marketwide information, company-specific public information, and company-specific private information on equity prices. Specifically, we use the thirty common stocks in the Dow Jones Industrial Average (DJIA) and twenty one National Association of Securities Dealers Automated Quotations (NASDAQ) common stocks to examine how their prices react to information. Marketwide information (public and private) is estimated by the movement in the Standard and Poors (S & P) 500 Index price for the DJIA stocks and the movement in the NASDAQ Composite Index price for the NASDAQ stocks. Divedend and earnings announcements are used as a subset of company-specific public information. The trading activity of corporate insiders (major corporate officers, members of the board of directors, and owners of at least 10 percent of any equity class) with an access to private information can be cannot legally trade on private information. Therefore, most insider transactions are not necessarily based on private information. Nevertheless, we hypothesize that market participants observe how insiders trade in order to infer any information that they cannot possess because insiders tend to buy (sell) when they have good (bad) information about their company. For example, Damodaran and Liu (1993) show that insiders of real estate investment trusts buy (sell) after they receive favorable (unfavorable) appraisal news before the information in these appraisals is released to the public. Price discovery in a competitive multiple-dealership market (NASDAQ) would be different from that in a monopolistic specialist system (NYSE). Consequently, we hypothesize that NASDAQ stocks are affected more by private information (or more precisely, insider trading) than the DJIA stocks. In the next section, we describe our choices of the fifty-one stocks and the public and private information set. We also discuss institutional differences between the NYSE and the NASDAQ market. In Section II, we examine the implications of public and private information for the volatility of daily returns of each stock. In Section III, we turn to the question of the relative importance of individual elements of our information set. Further analysis of the five DJIA stocks and the four NASDAQ stocks that are most sensitive to earnings announcements is given in Section IV, and our results are summarized in Section V.