• The Korean Journal of Financial Management
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• v.23 no.2
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• pp.143-170
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• 2006

We explore how stock returns and volatility have been impacted by securities market stimulating and controlling plans during the 1980-2004 period, using return analysis, event study, and BFL tests. First, we examine effectiveness of the stimulating plans for a depressed market and the controlling plans for an overheated market with respect to different firm sizes and industries as well as the whole market. KOSPI, large-sized, finance, and manufacturing company stock prices significantly rise following stimulating plans, implying that the plans are quite effective. Controlling plans also seem effective as stock prices stop rising and tend to decline following the plans. Second, we test whether securities market plans have any further impact with respect to fun sizes and industries in addition to the impact on the entire market. Only large-sized stocks show additional response to stimulating plans, while small-sized, electrical-electronic equipment, distribution, and manufacturing industries are further impacted by controlling plans. Third, the results of BFL tests show that volatility does not change around the announcement dates of stimulating and controlling plans. It appears that securities market plans have no impact on volatility. Only stock returns respond to the plans.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.31-36
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• 2021

As the global economy stagnated due to the Corona 19 virus from Wuhan, China, most countries, including the US Federal Reserve System, introduced policies to boost the economy by increasing the amount of money. Most of the stock investors tend to invest only by listening to the recommendations of famous YouTubers or acquaintances without analyzing the financial statements of the company, so there is a high possibility of the loss of stock investments. Therefore, in this research, I have used artificial intelligence deep learning techniques developed under the existing automatic trading conditions to analyze and predict macro-indicators that affect stock prices, giving weights on individual stock price predictions through correlations that affect stock prices. In addition, since stock prices react sensitively to real-time stock market news, a more accurate stock price prediction is made by reflecting the weight to the stock price predicted by artificial intelligence through stock market news text mining, providing stock investors with the basis for deciding to make a proper stock investment.

• KDI Journal of Economic Policy
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• v.14 no.4
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• pp.63-77
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• 1992

This paper examined interactions between stock price and key macroeconomic variables over the period of 1975-1992. It has been found that more than 60% of real stock price changes can be well explained by movements in key macroeconomic variables, particularly in net exports and industrial production. On the other hand, real stock price changes were found to have a significant explanatory power for plant and equipment investments for the sample period of 1975-1985 during which the stock market was stable. In contrast, no significant linkage between stock price changes and investments emerged over the subsample period of 1986-92 despite the sharp expansion of the stock market in terms of trade volume. Based on such findings, two major policy implications were derived; (i) the government's intervention in the stock market to stabilize stock prices would be ineffective unless the stable economic growth supports the market fundamental, and (ii) the stock price stability is a precondition for the stock market to play a key role in mobilizing resources to finance the firm's long-term capital.