• The Korean Journal of Financial Management
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• v.22 no.2
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• pp.43-69
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• 2005

This study provides empirical evidence that the stochastic beta model based on Bayesian analysis outperforms the existing conditional beta model and GARCH model in terms of the estimation accuracy and the explanatory power in the cross-section of stock returns in Korea. Betas estimated by the stochastic beta model explain $30{\sim}50%$ of the cross-sectional variation in stock-returns, whereas other time-varying beta models account for less than 3%. Such a difference in explanatory power across models turns out to come from the fact that the stochastic beta model absorbs the variation due to the market anomalies such as size, BE/ME, and idiosyncratic volatility. These results support the rational asset pricing model in that market anomalies are closely related to the variation of expected returns generated by time-varying betas.

• Asia-Pacific Journal of Business
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• v.11 no.3
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• pp.199-215
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• 2020

Purpose - This article tries to test if the conditional consumption capital asset pricing model (CCAPM) with bank credit for household as a conditional variable can explain the cross-sectional variation of stock returns in Korea. The performance of conditional CCAPM is compared to that of multifactor asset pricing models based on Arbitrage Pricing Theory. Design/methodology/approach - This paper extends the simple CCAPM to the conditional version of CCAPM by using bank credit for household as conditioning information. By employing KOSPI and KOSDAQ stocks as test assets from the second quarter of 2003 to the first quarter of 2018, this paper estimates risk premiums of conditional CCAPM and a variety of multifactor linear models such as Fama-French three and five-factor models. The significance of risk factors and the adjusted coefficient of determination are the basis for the comparison in models' performances. Findings - First, the paper finds that conditional CCAPM with bank credit performs as well as the multifactor linear models from Arbitrage Pricing theory on 25 test assets sorted by size and book-to-market. When using long-term consumption growth, the conditional CCAPM explains the cross-sectional variation of stock returns far better than multifactor models. Not only that, although the performances of multifactor models decrease on 75 test assets, conditional CCAPM's performance is well maintained. Research implications or Originality - This paper proposes bank credit for household as a conditional variable for CCAPM. This enables CCAPM, one of the most famous economic asset pricing models, to conform with the empirical data. In light of this, we can now explain the cross-sectional variation of stock returns from an economic perspective: Asset's riskiness is determined by its correlation with consumption growth conditional on bank credit for household.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.4
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• pp.191-196
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• 2022

From several socioeconomic perspectives, the present health crisis can be connected to the 2008 financial and economic catastrophe. Governments worldwide are working hard to keep the markets in check, as evidence suggests that the health crisis may soon become an economic crisis. This paper aims to analyze the effect of COVID-19 on the selected stock market. Using a panel of daily COVID-19 confirmed cases and deaths and the stock market from 22 developing countries, we exploit an oil price as a shock to the stock market and examine the effect of COVID-19 on the slowdown of the stock market. We find a negative and significant impact of COVID-19 on the stock market in the first stage till April. However, there is no net influence on the stock market downturn when we extend the period. However, further study suggests that the outbreak's negative influence on the selected stock market has diminished and has begun to decline as of mid-April. As a result of the COVID-19 effect on the chosen stock, our findings imply that the government in the chosen market should consider a regulatory mechanism to reduce the stock market slowdown induced by the pandemic COVID-19.

• Journal of Information Technology Applications and Management
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• v.19 no.1
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• pp.1-12
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• 2012

Data mining is the process of searching and analyzing large quantities of data for finding out meaningful patterns and rules. Artificial Neural Network (ANN) is one of the tools of data mining which is becoming very popular in forecasting the future values. Some of the areas where it is used are banking, medicine, retailing and fraud detection. In finance, artificial neural network is used in various disciplines including stock market forecasting. In the stock market time series, due to high volatility, it is very important to choose a model which reads volatility and forecasts the future values considering volatility as one of the major attributes for forecasting. In this paper, an attempt is made to develop two models - one using feed forward back propagation Artificial Neural Network and the other using Autoregressive Conditional Heteroskedasticity (ARCH) technique for forecasting stock market returns. Various parameters which are considered for the design of optimal ANN model development are input and output data normalization, transfer function and neuron/s at input, hidden and output layers, number of hidden layers, values with respect to momentum, learning rate and error tolerance. Simulations have been done using prices of daily close of Sensex. Stock market returns are chosen as input data and output is the forecasted return. Simulations of the Model have been done using MATLAB$^{(R)}$ 6.1.0.450 and EViews 4.1. Convergence and performance of models have been evaluated on the basis of the simulation results. Performance evaluation is done on the basis of the errors calculated between the actual and predicted values.

• The Korean Journal of Applied Statistics
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• v.36 no.5
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• pp.489-499
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• 2023

As sharing of analyst reports became widely available, reports generated by analysts have become a useful tool to reduce difference in financial information between market participants. The quantitative information of analyst reports has been used in many ways to predict stock returns. However, there are relatively few domestic studies on the prediction power of text information in analyst reports to predict stock returns. We test stock return predictability of text in analyst reports by creating variables representing the TONE from the text. To overcome the limitation of the linear-model-assumption-based approach, we use the random-forest-based F-test.

• Industrial Engineering and Management Systems
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• v.12 no.4
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• pp.406-415
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• 2013

The present study examines a set of financial ratios in predicting the up or down movements of stock prices in the context of a securities law, the Sarbanes-Oxley Act of 2002 (SOA), controlling for macroeconomic variables. Using the logistic regression with proxy betas to alleviate the incompatibility problem between the firm-specific financial ratios and macroeconomic indicators, we report evidence that financial ratios are meaningful predictors of stock price changes, which subdue the influence of macroeconomic indicators on stock returns, and more importantly that the SOA truly improves the stock price predictability of financial ratios for the markup sample. The empirical results further suggest that industry and time effects exist and that for the markdown sample the SOA actually deteriorates the predictive power of financial ratios.

• East Asian Economic Review
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• v.21 no.2
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• pp.147-165
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• 2017

This study quantifies the dynamic interrelationship between the KOSPI index return and search query data derived from the Naver DataLab. The empirical estimation using a bivariate GARCH model reveals that negative contemporaneous correlations between the stock return and the search frequency prevail during the sample period. Meanwhile, the search frequency has a negative association with the one-week- ahead stock return but not vice versa. In addition to identifying dynamic correlations, the paper also aims to serve as a test bed in which the existence of profitable trading strategies based on big data is explored. Specifically, the strategy interpreting the heightened investor attention as a negative signal for future returns appears to have been superior to the benchmark strategy in terms of the expected utility over wealth. This paper also demonstrates that the big data-based option trading strategy might be able to beat the market under certain conditions. These results highlight the possibility of big data as a potential source-which has been left largely untapped-for establishing profitable trading strategies as well as developing insights on stock market dynamics.

• Asia-Pacific Journal of Business
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• v.10 no.3
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• pp.37-51
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• 2019

In this paper, we investigate the performance of anomaly factors in Asia-Pacific Stock market and show the higher Sharpe ratio of the volatility managed smart beta portfolio. The smart beta portfolio combines the benefit of passive strategy and active strategy. However, the smart beta portfolios are seems to be exposed to the risk of anomaly factors from the perspective of traditional financial equilibrium model. Therefore, the smart beta strategy may generate negatively skewed returns unappealing to investors having lower risk tolerance. Our empirical investigations find that the return of the Asia-Pacific region stock market is more volatile than other regions with the lower efficiency ratio. However, the value factor and the momentum factor of Asia-Pacific region both show good performances. More interestingly, we also find that managing the volatility of the momentum factor in Asia-Pacific stock market almost doubles the efficiency ratio.

• The Korean Journal of Financial Management
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• v.24 no.1
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• pp.149-182
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• 2007

This study examines the determinants of the long-run stock price performance of the firms that granted stock options between 1997 and 2002. We divide the sample into the firms run by the owner and those run by the professional manager. If the primary reason for granting stock options is reduction of the agency costs between the manager and shareholders, the effect of stock options is likely to be more pronounced in the firms run by the professional manager. We find that the long-run abnormal returns of the firms run by the professional manager are negatively associated with the shareholdings by the manager and the book-to-market value and are positively associated with the earnings growth and the size of the outstanding stock options. In contrast, the long-run abnormal returns of the firms run by the owner are negatively associated with the cash flows rate and the sales growth rate and are positively associated with the firm size. This is consistent with the argument that the agency costs arising from the conflicts between the manager and shareholders are an important determinant of the post-stock option granting long-run stock price performance only in the firms run by the professional manager. The results also suggest that stock options in the firms run by the owner are likely to be used for the purposes such as additional compensation, a signaling device, a means that reduce the agency costs within firms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.12
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• pp.5671-5676
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• 2011

Volatility Index is the index that represents future volatility of underlying asset implied in option price and expected value of market that measures the possibility of stock price's change expected by investors. The Korea Exchange announces a volatility Index, VKOSPI, since April, 13, 2009. This paper used daily data from January, 2002 through December, 2008 and tested power of Volatility index for future returns of portfolios sorted by size, book-to-market equity and beta. As a result, VKOSPI has the predictive power to future returns and then VKOSPI may be determinants of returns. Also if beta is included when sorting portfolio, the predictive power of VKOSPI is stronger for future portfolio returns.