• Title/Summary/Keyword: 거시경제변수

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Influence of Liquidity on the Housing Market before and after Macroeconomic Fluctuations (거시경제변동 전후 유동성이 주택시장에 미치는 영향 분석)

  • Lee, Young-Hoon;Kim, Jae-Jun
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.17 no.5
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    • pp.116-124
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    • 2016
  • In the past, once apartments were built by housing construction companies, their presale went smoothly. Therefore, the developer and construction companies in Korea were extremely competitive in the housing market. However, when the 1997 foreign exchange crisis and 2008 global financial crisis occurred, the quantity of unsold new housing stocks rapidly increased, which caused construction companies to experience a serious liquidity crisis. This paper aims at analyzing the influence of Liquidity on the Housing Market before and after Macroeconomic Fluctuations using VECM. The periods from September 2001 to September 2008 and from October 2008 to October 2015, which were before and after the Subprime financial crisis, were set as Models 1 and 2, respectively. The results are as follows. First, it is important to develop a long-term policy for the housing transaction market to improve household incomes. Second, due to the shortage in the supply of jeonse housing, structural changes in the housing market have appeared. Thus, it is necessary to seek political measures to minimize the impact of transitional changes on the market.

The Effect of Data Size on the k-NN Predictability: Application to Samsung Electronics Stock Market Prediction (데이터 크기에 따른 k-NN의 예측력 연구: 삼성전자주가를 사례로)

  • Chun, Se-Hak
    • Journal of Intelligence and Information Systems
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    • v.25 no.3
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    • pp.239-251
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    • 2019
  • Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

The Effect of Interest Rate Variability on Housing Prices (이자율 변동이 주택가격에 미치는 영향)

  • Han, Myung-hoon
    • Journal of Venture Innovation
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    • v.5 no.3
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    • pp.71-80
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    • 2022
  • The real estate market is an important part of a country's economy and plays a major role in economic growth through the growth of many related industries. Changes in interest rates affect asset prices and have a significant impact on housing prices. This study analyzed housing prices by dividing them into nationwide, local, and Seoul housing prices in order to analyze whether the effect of changes in interest rates on housing prices shows regional differences. The analysis was conducted from the first quarter of 2011 to the fourth quarter of 2021, and was analyzed using the DOLS model. The main analysis results are as follows. First, interest rates were found to have a significant negative effect on national housing prices, and a drop in interest rates significantly increased national housing prices and an increase in interest rates significantly lowered national housing prices. The consumer price index and loan growth rate also had a positive effect on housing prices nationwide, but statistical significance was not high. Second, interest rates had a negative effect on local housing prices, unlike national housing prices, but were not statistically significant. On the other hand, it was found that the consumer price index and loan growth rate had a larger and significant positive effect on local housing prices compared to national housing prices. Finally, it was found that the interest rate had the only significant negative effect on housing prices in Seoul. And this effect was greater and more significant than the effect on national and local housing prices. In the end, it was found that the effect of interest rates on Korean housing prices differs locally. Interest rates have a significant negative effect on national housing prices, and local housing prices, but they are not statistically significant. In addition, the interest rate was found to have the largest and most significant negative effect on housing prices in Seoul. In addition, it was found that there was a difference in the effect of macroeconomic variables on housing prices. This means that there are differences between regions with different factors influencing local and Seoul housing prices, and this point should be considered when drafting and implementing real estate policies.

The Impact of BIS Regulation on Bank Behavior in Asset Management (신 BIS 자기자본규제가 은행자산운용행태에 미치는 영향)

  • Oh, Hyun-Tak;Choi, Seok-Gyu
    • The Korean Journal of Financial Management
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    • v.26 no.3
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    • pp.171-198
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    • 2009
  • The primary purpose of this study is to examine the impact of new BIS regulation, which is the preparations to incorporate not only credit risk but also market and operation risk, on the bank behaviors. As methodology, SUR(seemingly unrelated regression) and pool unit test are used in the empirical analysis of banks survived in Korea. It is employed that quarterly data of BIS capital ratio, ratio of standard and below loans to total loans, ratio of liquid assets to liquid liabilities, allowances for credit losses, real GDP, yields of corporate bonds(3years, AA) covering the period of 2000Q1~2009Q1. As a result, it could be indicated that effectiveness and promoting improvements of BIS capital regulation policy as follows; First, it is explicitly seen that weight of lending had decreased and specific gravity of international investment had increased until before BIS regulation is built up a step for revised agreement in late 2001. Second, after more strengthening of BIS standard in late 2002, banks had a tendency to decrease the adjustment of assets weighted risk through issuing of national loan that is comparatively low profitability. Also, it is implicitly sought that BIS regulation is a bit of a factor to bring about credit crunch and then has become a bit of a factor of economic stagnation. Third, as the BIS regulation became hard, it let have a effort to raise the soundness of a credit loan because of selecting good debtor based on its credit ratings. Fourth, it should be arranged that the market disciplines, the effective superintendence system and the sound environment to be able to raise enormous bank capital easily, against the credit stringency and reinforce the soundness of banks etc. in Korea capital market.

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