• Title/Summary/Keyword: Investment Propensity Index

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A Study on Influence of Economic Preparation for Later Life after Retirement

  • KIM, Jong-Jin
    • The Journal of Asian Finance, Economics and Business
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    • v.7 no.5
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    • pp.279-290
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    • 2020
  • This study examines how economic preparation for later life directly influences life after retirement. As people's life cycle is gradually getting longer, preparation for the later time with less economic activity after retirement is becoming more important. Thus, this study analyzes the factors influencing life after retirement. Data comes from the Korean Retirement and Income Study (KReIS) surveyed carried out by the National Pension Research Institute in 2015. The analysis includes Cronbach's alpha, Pearson Product Moment Correlation Coefficient and Sobel Test. This study confirms that voluntary retirement has a positive influence on life satisfaction. Results are in line with previous research about the relationship between voluntary retirement and retired life. When a person retires voluntarily, financial preparation can be made in advance for retirement. In case of involuntary retirement, people may experience a sense of loss in personal standing and financial difficulties due to the unexpected situation. Especially, early retirement from the main workplace leads to unstable later life. The study's policy recommendation, in particular, calls on government and businesses to agree on social responsibility for helping employees to retire in the predictable retirement time and, thus, enabling the retiree to decide all aspects of the path after retirement.

Asset-Liability Analysis of Baby-Boomer Households: Comparison of year 2006 and 2011 (베이비붐세대 가계의 자산.부채상태 분석: 2006년과 2011년 비교)

  • Cha, Kyung-Wook
    • Journal of Family Resource Management and Policy Review
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    • v.16 no.3
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    • pp.153-176
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    • 2012
  • This study gives an account of the state of baby-boomer households in regard to assets and liabilities utilizing the 2006 Household Asset Survey and the 2011 Survey of Household Finances. Using the data gathered from each year, this study examined the proportion of households who had each type of asset and liability, and the amount of them. This study also compared the amount of assets and liabilities of baby-boomer households with those of non baby-boomer households in 2006 and 2011 respectively. Finally, this study examined the amount of change and composition ratio of assets and liabilities of baby-boomer households between 2006 and 2011. Selected financial ratios were also presented for both years. Major findings are as follows. The average asset amount for baby-boomer households was approximately 296 million in 2006 and 392 million in 2011. Of total assets, 78% and 76.5% were real assets in 2006 and 2011 respectively. The average financial assets of 2006 baby-boomer households were approximately 66 thousand and the average amount of debt was 42 thousand. For 2011 baby-boomer households, the average amount of financial assets was 92 thousand and the average amount of debt was 73 thousand. Results from the 2011 survey showed that baby-boomer households had a significantly higher proportion of total assets, total debt, and net worth than non baby-boomer households. The proportion of savings, saving insurance, stocks, and mutual funds were significantly higher for baby-boomer households than non baby-boomer households in 2011. In regard to financial ratios, the emergency fund index and debt burden index were appropriate to the guidelines of asset quality, although the propensity to investment indexes were not.

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VKOSPI Forecasting and Option Trading Application Using SVM (SVM을 이용한 VKOSPI 일 중 변화 예측과 실제 옵션 매매에의 적용)

  • Ra, Yun Seon;Choi, Heung Sik;Kim, Sun Woong
    • Journal of Intelligence and Information Systems
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    • v.22 no.4
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    • pp.177-192
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    • 2016
  • Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

A Study on Industries's Leading at the Stock Market in Korea - Gradual Diffusion of Information and Cross-Asset Return Predictability- (산업의 주식시장 선행성에 관한 실증분석 - 자산간 수익률 예측 가능성 -)

  • Kim Jong-Kwon
    • Proceedings of the Safety Management and Science Conference
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    • 2004.11a
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    • pp.355-380
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    • 2004
  • I test the hypothesis that the gradual diffusion of information across asset markets leads to cross-asset return predictability in Korea. Using thirty-six industry portfolios and the broad market index as our test assets, I establish several key results. First, a number of industries such as semiconductor, electronics, metal, and petroleum lead the stock market by up to one month. In contrast, the market, which is widely followed, only leads a few industries. Importantly, an industry's ability to lead the market is correlated with its propensity to forecast various indicators of economic activity such as industrial production growth. Consistent with our hypothesis, these findings indicate that the market reacts with a delay to information in industry returns about its fundamentals because information diffuses only gradually across asset markets. Traditional theories of asset pricing assume that investors have unlimited information-processing capacity. However, this assumption does not hold for many traders, even the most sophisticated ones. Many economists recognize that investors are better characterized as being only boundedly rational(see Shiller(2000), Sims(2201)). Even from casual observation, few traders can pay attention to all sources of information much less understand their impact on the prices of assets that they trade. Indeed, a large literature in psychology documents the extent to which even attention is a precious cognitive resource(see, eg., Kahneman(1973), Nisbett and Ross(1980), Fiske and Taylor(1991)). A number of papers have explored the implications of limited information- processing capacity for asset prices. I will review this literature in Section II. For instance, Merton(1987) develops a static model of multiple stocks in which investors only have information about a limited number of stocks and only trade those that they have information about. Related models of limited market participation include brennan(1975) and Allen and Gale(1994). As a result, stocks that are less recognized by investors have a smaller investor base(neglected stocks) and trade at a greater discount because of limited risk sharing. More recently, Hong and Stein(1999) develop a dynamic model of a single asset in which information gradually diffuses across the investment public and investors are unable to perform the rational expectations trick of extracting information from prices. Hong and Stein(1999). My hypothesis is that the gradual diffusion of information across asset markets leads to cross-asset return predictability. This hypothesis relies on two key assumptions. The first is that valuable information that originates in one asset reaches investors in other markets only with a lag, i.e. news travels slowly across markets. The second assumption is that because of limited information-processing capacity, many (though not necessarily all) investors may not pay attention or be able to extract the information from the asset prices of markets that they do not participate in. These two assumptions taken together leads to cross-asset return predictability. My hypothesis would appear to be a very plausible one for a few reasons. To begin with, as pointed out by Merton(1987) and the subsequent literature on segmented markets and limited market participation, few investors trade all assets. Put another way, limited participation is a pervasive feature of financial markets. Indeed, even among equity money managers, there is specialization along industries such as sector or market timing funds. Some reasons for this limited market participation include tax, regulatory or liquidity constraints. More plausibly, investors have to specialize because they have their hands full trying to understand the markets that they do participate in

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