• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.525-532
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• 2021

This study examines the association between a firm's business strategy and audit report lags. This study employs 5,072 firm-year observations from 2015 to 2019. Our sample comprises all of the firms listed on the Korea Composite Stock Price Index (KOSPI) market and Korea Securities Dealers Automated Quotation (KOSDAQ). We perform OLS regression analysis to test our hypothesis. The OLS regression analysis was conducted through the SAS and STATA programs. We find that business strategy is positively associated with audit report lags. Especially, we find that defender firms are negatively associated with audit report lags. The findings of this study suggest that prospector-like firms would increase their performance uncertainty as well as audit risk. Therefore, prospector-like firms interfere with the efficient audit procedures of auditors. On the other hand, our findings indicate that defender-like firms would decrease their performance uncertainty as well as an audit risk because they focus on simple product lines and cost-efficiency. For this reason, auditors will be able to carry out the audit procedures much more easily. Our results present that a prospector-like business strategy degrades audit effectiveness as it exacerbates a company's financial risk, willingness to accept uncertainty, and the complexity of organizational structure.

• East Asian Economic Review
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• v.27 no.3
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• pp.213-242
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• 2023

This paper investigates whether the price of cryptocurrency is determined by the US dollar index, the price of investment assets such gold and oil, and the implied volatility of the KOSPI. Overall, the returns on cryptocurrencies are best predicted by the trading volume of the cryptocurrency both in-sample and out-of-sample. The estimates of gold and the dollar index are negative in the return prediction, though they are not significant. The dollar index, gold, and the cryptocurrencies seem to share characteristics which hedging instruments have in common. When investors take notice of the imminent market risks, they increase the demand for one of these assets and thereby increase the returns on the asset. The most notable result in the out-of-sample predictability is the predictability of the returns on value-weighted portfolio by gold. The empirical results show that the restricted model fails to encompass the unrestricted model. Therefore, the unrestricted model is significant in improving out-of-sample predictability of the portfolio returns using gold. From the empirical analyses, we can conclude that in-sample predictability cannot guarantee out-of-sample predictability and vice versa. This may shed light on the disparate results between in-sample and out-of-sample predictability in a large body of previous literature.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.213-234
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• 2024

In recent decades, increasing research attention has been directed toward predicting the price of stocks in financial markets using deep learning methods. For instance, recurrent neural network (RNN) is known to be competitive for datasets with time-series data. Long short term memory (LSTM) further improves RNN by providing an alternative approach to the gradient loss problem. LSTM has its own advantage in predictive accuracy by retaining memory for a longer time. In this paper, we combine both supervised and unsupervised dimension reduction methods with LSTM to enhance the forecasting performance and refer to this as a dimension reduction based LSTM (DR-LSTM) approach. For a supervised dimension reduction method, we use methods such as sliced inverse regression (SIR), sparse SIR, and kernel SIR. Furthermore, principal component analysis (PCA), sparse PCA, and kernel PCA are used as unsupervised dimension reduction methods. Using datasets of real stock market index (S&P 500, STOXX Europe 600, and KOSPI), we present a comparative study on predictive accuracy between six DR-LSTM methods and time series modeling.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.2
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• pp.75-93
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• 2013

In applying Markowitz's portfolio selection model to the stock market, we developed a comprehensive investment decision-making framework including key inputs for portfolio theory (i.e., individual stocks' expected rate of return and covariance) and minimum required expected return. For estimating the key inputs of our decision-making framework, we utilized an exponentially weighted moving average (EWMA) which places more emphasis on recent data than the conventional simple moving average (SMA). We empirically analyzed the investment results of the decision-making framework with the same 15 stocks in Samsung Group Funds found in the Korean stock market between 2007 and 2011. This five-year investment horizon is marked by global financial crises including the U.S. subprime mortgage crisis, the collapse of Lehman Brothers, and the European sovereign-debt crisis. We measure portfolio performance in terms of rate of return, standard deviation of returns, and Sharpe ratio. Results are compared with the following benchmarks : 1) KOSPI, 2) Samsung Group Funds, 3) Talmudic portfolio based on the na$\ddot{i}$ve 1/N rule, and 4) Markowitz's model with SMA. We performed sensitivity analyses on all the input parameters that are necessary for designing an investment decision-making framework : smoothing constant for EWMA, minimum required expected return for the portfolio, and portfolio rebalancing period. In conclusion, appropriate use of the comprehensive investment decision-making framework based on the Markowitz's model integrated with EWMA proves to achieve outstanding performance compared to the benchmarks.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.4
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• pp.53-63
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• 2013

Together with soaring interest on Big Data, now there are vigorous reports that unearth various social values lying underneath those data from a number of application areas. Among those reports many are using such data as Internet search histories from Google site, social relationships from Facebook, and transactional or locational traces collected from various ubiquitous devices. Many of those researches, however, are conducted based on the data sets that are accumulated over the North American and European areas, which means that direct interpretation and application of social values exhibited by those researches to the other areas like Korea can be a disturbing task. This research has started from a validation study against Korean environment of the former paper which says an investment strategy that exploits up and down of Google search volume on a carefully selected set of terms shows high market performance. A huge difference between North American and Korean environment can be eye witnessed via the distinction in profit rates that are exhibited by the corresponding set of search terms. Two sets of search terms actually presented low correlation in their profit rates over two financial markets. Even in an experiment which compares the profit rates with two different investment periods with the same set of search terms showed no such meaningful result that outperforms the market average. With all these results, we cautiously conclude that establishing an investment strategy that exploits Internet search volume over a specified word set needs more conscious approach.

• The Journal of Industrial Distribution & Business
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• v.10 no.6
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• pp.51-58
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• 2019

Purpose - Prior studies reported that the opacity of information caused stock price crash. If managers fail to disclose unfavorable information about the firm over a long period of time, the stock price is overvalued compared to its original value. If the accumulated information reaches a critical point and spreads quickly to the market, the stock price plunges. Information management by management's disclosure policy can cause information uncertainty, which will lead to a plunge in stock prices in the future. Thus, this study aims at examining the impact of disclosure quality on crash risk by focusing on the unfaithful disclosure firms. Research design, data, and methodology - This study covers firms listed on KOSPI and KOSDAQ from 2004 to 2013. Firms excluded from the sample are non-December firms, capital-eroding firms, and financial firms. The financial data used in the research was extracted from the KIS-Value and TS2000 database. Unfaithful disclosure firm designation data was collected from the Korea Exchange's electronic disclosure system (kind.krx.co.kr). Stock crash is measured as a dummy variable that equals one if a firm experiences at least one crash week over the fiscal year, and zero otherwise. Results - Empirical results as to the relation between unfaithful disclosure corporation designation and stock price crashes are as follows: There was a significant positive association between unfaithful disclosure corporation designation and stock price crash. This result supports the hypothesis that firms that have previously exhibited unfaithful disclosure behavior are more likely to suffer stock price plunges due to information asymmetry. Second, stock price crashes due to unfaithful disclosures are more likely to occur in Chaebol firms. Conclusions - While previous studies used estimates as a proxy for information opacity, this study used an objective measure such as unfaithful disclosure corporation designation. The designation by Korea Exchange is an objective evidence that the firm attempted to conceal and distort information in the previous year. The results of this study suggest that capital market investors need to investigate firms' disclosure behaviors.

• The Journal of Industrial Distribution & Business
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• v.11 no.4
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• pp.39-46
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• 2020

Purpose: This study looks at the relevance between discretionary revenue and book-tax differences (hereafter BTDs). While the study of earnings management, which focused on discretionary accruals and real earnings management, has largely made, it has not yet been actively researched on discretionary revenues. Therefore, it was believed that discretionary revenue would expand the preceding study by looking at its relevance to BTD, known as financial reporting quality and measures of tax avoidance. In general, prior research suggested that earnings management make BTDs larger. Thus, the relationship between discretionary revenue and the amount of BTD is predicted positive. Research design, data and methodology: To this end, the method of discretionary revenues was used and BTDs measured in four ways. First, Earnings before income tax - estimated taxable income divided by total asset (BTD). Second is fractional rank variable of BTDs (FBTD). Third is Indicator variable equals 1 if the firm-year has a positive BTD, 0 otherwise (PBTD). Fourth is that Indicator variable equals 1 if the firm-year has a BTDs in top(bottom) quartile, 0 otherwise (LPBTD, LNBTD). 4,251 samples were analyzed in the Korean Security market (KOSPI) from 2003 to 2014. Results Empirical analysis shows that BTDs increases as discretionary revenue increases. These results were equally observed when BTDs was measured as a ranking variable or as a indicating variable. These results indicate that earnings management through the revenue of managers exacerbate the quality of financial reporting. Conclusions: In sum, discretionary revenues can be used as an indicator of making BTDs larger and meaningful as the first study of the Korean capital market where discretionary revenues affect accounting information quality. Investors need to increase interest in discretionary revenues because intervention in financial reporting through revenue accounts by managers can increase information asymmetry and agency costs. This means that studies on discretionary revenues that have been relatively small should be expanded. The results also provide important implications for the relevant authorities and investors. Despite these benefits, however, measurement error problems with estimates still appear as limited points, and prudent interpretations are required, and additional follow-up studies are needed in that variables that are not yet considered in this study may affect our findings.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1263-1274
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• 2013

VaR (value at risk), which represents the expectation of the worst loss that may occur over a period of time within a given level of confidence, is currently used by various financial institutions for the purpose of risk management. In the majority of previous studies, the probability of return has been modeled with normal distribution. Recently Chen et al. (2010) measured VaR with asymmetric Laplacian distribution. However, it is difficult to estimate the mode, the skewness, and the degree of variance that determine the shape of an asymmetric Laplacian distribution with limited data in the real-world market. In this paper, we show that the VaR estimated with (symmetric) Laplacian distribution model provides more accuracy than those with normal distribution model or asymmetric Laplacian distribution model with real world stock market data and with various statistical measures.

• The Journal of Information Systems
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• v.26 no.3
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• pp.63-90
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• 2017

Purpose This study proposes a novel system trading model using case-based reasoning (CBR) based on absolute similarity threshold. The proposed model is designed to optimize the absolute similarity threshold, feature selection, and instance selection of CBR by using genetic algorithm (GA). With these mechanisms, it enables us to yield higher returns from stock market trading. Design/Methodology/Approach The proposed CBR model uses the absolute similarity threshold varying from 0 to 1, which serves as a criterion for selecting appropriate neighbors in the nearest neighbor (NN) algorithm. Since it determines the nearest neighbors on an absolute basis, it fails to select the appropriate neighbors from time to time. In system trading, it is interpreted as the signal of 'hold'. That is, the system trading model proposed in this study makes trading decisions such as 'buy' or 'sell' only if the model produces a clear signal for stock market prediction. Also, in order to improve the prediction accuracy and the rate of return, the proposed model adopts optimal feature selection and instance selection, which are known to be very effective in enhancing the performance of CBR. To validate the usefulness of the proposed model, we applied it to the index trading of KOSPI200 from 2009 to 2016. Findings Experimental results showed that the proposed model with optimal feature or instance selection could yield higher returns compared to the benchmark as well as the various comparison models (including logistic regression, multiple discriminant analysis, artificial neural network, support vector machine, and traditional CBR). In particular, the proposed model with optimal instance selection showed the best rate of return among all the models. This implies that the application of CBR with the absolute similarity threshold as well as the optimal instance selection may be effective in system trading from the perspective of returns.

• The Korean Journal of Financial Management
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• v.22 no.2
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• pp.135-164
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• 2005

We analyze trade and balance records of 10,000 stock investment accounts of individual investors for the period of 1998 to 2003. Individual investors em an annual gross return of 12.3% while the KOSPI and the value weighted composite including KOSDAQ stocks yield 13.6% and 9.7% respectively during the same period. Net return performance is 8.3%, a drop of 5.3% mainly due to heavy trading. Individual investors' annual turnover amounts to over 270 percent. In an analysis of groups formed on the month's end position value, the performance of the top quintile is found comparable to the market while the rest yield significantly lower risk-adjusted returns than the market. We also find evidence rejecting the rational expectation model while supporting the overconfidence hypothesis which states overconfidence leads to a higher level of trading, resulting in poor performance. Individuals tilt their stock investment toward high-beta, small, and value stocks.