• Title/Summary/Keyword: Stock prices

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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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Deep Learning-Based Short-Term Time Series Forecasting Modeling for Palm Oil Price Prediction (팜유 가격 예측을 위한 딥러닝 기반 단기 시계열 예측 모델링)

  • Sungho Bae;Myungsun Kim;Woo-Hyuk Jung;Jihwan Woo
    • Information Systems Review
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    • v.26 no.2
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    • pp.45-57
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    • 2024
  • This study develops a deep learning-based methodology for predicting Crude Palm Oil (CPO) prices. Palm oil is an essential resource across various industries due to its yield and economic efficiency, leading to increased industrial interest in its price volatility. While numerous studies have been conducted on palm oil price prediction, most rely on time series forecasting, which has inherent accuracy limitations. To address the main limitation of traditional methods-the absence of stationarity-this research introduces a novel model that uses the ratio of future prices to current prices as the dependent variable. This approach, inspired by return modeling in stock price predictions, demonstrates superior performance over simple price prediction. Additionally, the methodology incorporates the consideration of lag values of independent variables, a critical factor in multivariate time series forecasting, to eliminate unnecessary noise and enhance the stability of the prediction model. This research not only significantly improves the accuracy of palm oil price prediction but also offers an applicable approach for other economic forecasting issues where time series data is crucial, providing substantial value to the industry.

A Study on Developing a VKOSPI Forecasting Model via GARCH Class Models for Intelligent Volatility Trading Systems (지능형 변동성트레이딩시스템개발을 위한 GARCH 모형을 통한 VKOSPI 예측모형 개발에 관한 연구)

  • Kim, Sun-Woong
    • Journal of Intelligence and Information Systems
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    • v.16 no.2
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    • pp.19-32
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    • 2010
  • Volatility plays a central role in both academic and practical applications, especially in pricing financial derivative products and trading volatility strategies. This study presents a novel mechanism based on generalized autoregressive conditional heteroskedasticity (GARCH) models that is able to enhance the performance of intelligent volatility trading systems by predicting Korean stock market volatility more accurately. In particular, we embedded the concept of the volatility asymmetry documented widely in the literature into our model. The newly developed Korean stock market volatility index of KOSPI 200, VKOSPI, is used as a volatility proxy. It is the price of a linear portfolio of the KOSPI 200 index options and measures the effect of the expectations of dealers and option traders on stock market volatility for 30 calendar days. The KOSPI 200 index options market started in 1997 and has become the most actively traded market in the world. Its trading volume is more than 10 million contracts a day and records the highest of all the stock index option markets. Therefore, analyzing the VKOSPI has great importance in understanding volatility inherent in option prices and can afford some trading ideas for futures and option dealers. Use of the VKOSPI as volatility proxy avoids statistical estimation problems associated with other measures of volatility since the VKOSPI is model-free expected volatility of market participants calculated directly from the transacted option prices. This study estimates the symmetric and asymmetric GARCH models for the KOSPI 200 index from January 2003 to December 2006 by the maximum likelihood procedure. Asymmetric GARCH models include GJR-GARCH model of Glosten, Jagannathan and Runke, exponential GARCH model of Nelson and power autoregressive conditional heteroskedasticity (ARCH) of Ding, Granger and Engle. Symmetric GARCH model indicates basic GARCH (1, 1). Tomorrow's forecasted value and change direction of stock market volatility are obtained by recursive GARCH specifications from January 2007 to December 2009 and are compared with the VKOSPI. Empirical results indicate that negative unanticipated returns increase volatility more than positive return shocks of equal magnitude decrease volatility, indicating the existence of volatility asymmetry in the Korean stock market. The point value and change direction of tomorrow VKOSPI are estimated and forecasted by GARCH models. Volatility trading system is developed using the forecasted change direction of the VKOSPI, that is, if tomorrow VKOSPI is expected to rise, a long straddle or strangle position is established. A short straddle or strangle position is taken if VKOSPI is expected to fall tomorrow. Total profit is calculated as the cumulative sum of the VKOSPI percentage change. If forecasted direction is correct, the absolute value of the VKOSPI percentage changes is added to trading profit. It is subtracted from the trading profit if forecasted direction is not correct. For the in-sample period, the power ARCH model best fits in a statistical metric, Mean Squared Prediction Error (MSPE), and the exponential GARCH model shows the highest Mean Correct Prediction (MCP). The power ARCH model best fits also for the out-of-sample period and provides the highest probability for the VKOSPI change direction tomorrow. Generally, the power ARCH model shows the best fit for the VKOSPI. All the GARCH models provide trading profits for volatility trading system and the exponential GARCH model shows the best performance, annual profit of 197.56%, during the in-sample period. The GARCH models present trading profits during the out-of-sample period except for the exponential GARCH model. During the out-of-sample period, the power ARCH model shows the largest annual trading profit of 38%. The volatility clustering and asymmetry found in this research are the reflection of volatility non-linearity. This further suggests that combining the asymmetric GARCH models and artificial neural networks can significantly enhance the performance of the suggested volatility trading system, since artificial neural networks have been shown to effectively model nonlinear relationships.

The Informativeness of Cash Flows and Earnings (현금흐름과 이익의 정보성)

  • Pyo, Young-In
    • Korean Business Review
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    • v.11
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    • pp.241-253
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    • 1998
  • One form of the anomalies of stock price changes as reaction to earnings information is believed to be caused by the so-called earnings fixation, which is the overreaction of stock prices to earnings. According to the Sloan (1996) study, stock price changes are positively associated with earnings at the time of earnings releases, but the association becomes negative after that, as the early overreaction is corrected. However, the problem in his study is to use cash flows computed by adjusting earnings with appropriate income statement and balance sheet items. As Bahnson et al. (1996) show, these cash flows substantially deviate from SFAS No. 95 cash flows and the sample used in this study is found to be subject to this substantial measurement error. Therefore, the result of Sloan might be driven by this error and the reexamination of earnings fixation is warranted. The results are generally consistent with those in Sloan. First, earnings is positively associated with stock price changes at the time of earnings releases, but the association becomes negative after that. Second, cash flows show a weak association with stock price changes at the time of earnings releases, but the association become stronger thereafter. Third, when seperated from cash flows, accruals have an incremental explanation about stock price changes beyond that of cash flows, accruals have a negative association later on. This finding is consistent with stock price overreaction to accruals, even when more cleaner cash flow data are used.

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Optimal LNG Procurement Policy in a Spot Market Using Dynamic Programming (동적 계획법을 이용한 LNG 현물시장에서의 포트폴리오 구성방법)

  • Ryu, Jong-Hyun
    • Journal of Korean Institute of Industrial Engineers
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    • v.41 no.3
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    • pp.259-266
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    • 2015
  • Among many energy resources, natural gas has recently received a remarkable amount of attention, particularly from the electrical generation industry. This is in part due to increasing shale gas production, providing an environment-friendly fossil fuel, and high risk of nuclear power. Because South Korea, the world's second largest LNG importing nation after Japan, has no international natural gas pipelines and relies on imports in the form of LNG, the natural gas has been traditionally procured by long term LNG contracts at relatively high price. Thus, there is a need of developing an Asian LNG trading hub, where LNG can be traded at more competitive spot prices. In a natural gas spot market, the amount of natural gas to be bought should be carefully determined considering a limited storage capacity and future pricing dynamics. In this work, the problem to find the optimal amount of natural gas in a spot market is formulated as a Markov decision process (MDP) in risk neutral environment and the optimal base stock policy which depends on a stage and price is established. Taking into account price and demand uncertainties, the basestock target levels are simply approximated from dynamic programming. The simulation results show that the basestock policy can be one of effective ways for procurement of LNG in a spot market.

Volatility Computations for Financial Time Series: High Frequency and Hybrid Method (금융시계열 변동성 측정 방법의 비교 분석: 고빈도 자료 및 융합 방법)

  • Yoon, J.E.;Hwang, S.Y.
    • The Korean Journal of Applied Statistics
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    • v.28 no.6
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    • pp.1163-1170
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    • 2015
  • Various computational methods for obtaining volatilities for financial time series are reviewed and compared with each other. We reviewed model based GARCH approach as well as the data based method which can essentially be regarded as a smoothing technique applied to the squared data. The method for high frequency data is focused to obtain the realized volatility. A hybrid method is suggested by combining the model based GARCH and the historical volatility which is a data based method. Korea stock prices are analysed to illustrate various computational methods for volatilities.

The Effect of Regenerative Energy Storage System on Stabilization of Electro-Pneumatic Braking Blending (회생에너지 저장시스템이 제동 브랜딩 안정화에 미치는 영향)

  • Kim, Kyu-Joong;Lee, Keun-Oh
    • Journal of the Korean Society of Safety
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    • v.25 no.5
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    • pp.15-21
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    • 2010
  • Regenerative Energy Storage System(ESS) is a system that saves regenerative energy which generated instantly in the regenerative braking of Electric Multiple Unit(EMU) and disappear, and reuse the stored energy when EMU is in powering. Such system related to a research field of renewable energy which emerged concerning climate change and high oil prices. In the case of existing domestic rolling stock, about 25% to 30% of generated regenerative energy is restored to power source and is regarded as direct factor of raising catenary voltage. Such rapid change of catenary voltage is a cause of the failure of EMU's electronic equipment and lowering its reliability and is also a cause of train's fault occurred by tripping circuit breaker. In this paper, we intend to investigate the effect on blending characteristics of electric-braking and pneumatic-braking whether the regenerative energy storage system is used or not in urban transit DC 1,500V feeding system, while trains run. And we also intend to investigate its effect on stabilization of the blending, fluctuation of catenary voltage and various electric equipments.

Extensions of Histogram Construction Algorithms for Interval Data (구간 데이타에 대한 히스토그램 구축 알고리즘의 확장)

  • Lee, Ho-Seok;Shim, Kyu-Seok;Yi, Byoung-Kee
    • Journal of KIISE:Databases
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    • v.34 no.4
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    • pp.369-377
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    • 2007
  • Histogram is one of tools that efficiently summarize data, and it is widely used for selectivity estimation and approximate query answering. Existing histogram construction algorithms are applicable to point data represented by a set of values. As often as point data, we can meet interval data such as daily temperature and daily stock prices. In this paper, we thus propose the histogram construction algorithms for interval data by extending several methods used in existing histogram construction algorithms. Our experiment results, using synthetic data, show our algorithms outperform naive extension of existing algorithms.

Comparison Study of Time Series Clustering Methods (시계열자료 눈집방법의 비교연구)

  • Hong, Han-Woom;Park, Min-Jeong;Cho, Sin-Sup
    • The Korean Journal of Applied Statistics
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    • v.22 no.6
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    • pp.1203-1214
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    • 2009
  • In this paper we introduce the time series clustering methods in the time and frequency domains and discuss the merits or demerits of each method. We analyze 15 daily stock prices of KOSPI 200, and the nonparametric method using the wavelet shows the best clustering results. For the clustering of nonstationary time series using the spectral density, the EMD method remove the trend more effectively than the differencing.

δ-approximate Periods and γ-approximate Periods of Strings over Integer Alphabets (정수문자집합에 대한 문자열의 δ-근사주기와 γ-근사주기)

  • Kim, Youngho;Sim, Jeong Seop
    • Journal of KIISE
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    • v.43 no.10
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    • pp.1073-1078
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    • 2016
  • (${\delta}$, ${\gamma}$)-matching for strings over integer alphabets can be applied to such fields as musical melody and share prices on stock markets. In this paper, we define ${\delta}$-approximate periods and ${\gamma}$-approximate periods of strings over integer alphabets. We also present two $O(n^2)$-time algorithms, each of which finds minimum ${\delta}$-approximate periods and minimum ${\gamma}$-approximate periods, respectively. Then, we provide the experimental results of execution times of both algorithms.