• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Journal of Families and Better Life
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• v.7 no.1
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• pp.119-138
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• 1989

This study attempts to apply its result to decision whether housewives are in the labor market or not by investigating the level of reservation wage of them and the influencing factors on it. The purpose of this study is to analyse the effects by identifying , on the basis of theoretical models, the factors that influence reservation wage, and to predict future state of female employment. 1) The level of reservation wage of housewives showed significant differences by husband's income, household asset, housewife's education level, housewife's age, number of children division of husband in household labor, and by three marketization of housework. 2) The variables which affected reservation wage of housewife independently had influence on it in the following order: Husband's income, education level, age affect positively reservation wage of housewife, and a negative relation has been found between division of husband, level of commodity substitution and reservation wage of housewife. 3) Husband's income, housewife's education level, housewife's age, division of husband in household labor, level of commodity substitution give direct effects on reservation wage of housewife. Education level, age, number of children and family type influenced reservation wage of housewife through level of commodity substitution indirectly.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.601-611
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• 2021

There are many asset prices which are interlinked and have a bearing on the stock market index. Studies have shown that the interrelationship among these asset prices vary and are inconsistent. The ultimate aim of this study is to examine the dynamic relationship between gold price, oil price, exchange rate and stock index. Monthly time series data has been utilized by the researcher to examine the interrelationship between four variables. The relationship among stock exchange rate index, oil price and gold price have been undertaken using regression and granger causality test. The results indicate that the exchange rate and oil price have an indirect influence on NIFTY; whereas gold price had a direct impact on NIFTY. It is evident from the results that volatility in the price of gold is mainly dependent on the exchange rate and vice versa. All the variables affect NIFTY in some way or the other. However, gold has a direct and vital relationship. From the study findings, it can be concluded that macroeconomic variables like commodity prices and foreign exchange rate, gold and oil, have a strong relationship on the return on securities at the national stock exchange of India.

• KIEE International Transactions on Power Engineering
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• v.5A no.4
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• pp.311-321
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• 2005

A competitive generating company (GENCO) could maximize its payoff by optimizing its generation assets. This paper considers the GENCO's arbitrage problem using price-based unit commitment (PBUC). The GENCO could consider arbitrage opportunities in purchases from qualifying facilities (QFs) as well as simultaneous trades with spots markets for energy, ancillary services, emission, and fuel. Given forecasted hourly market prices for each market, the GENCO's generating asset arbitrage problem is formulated as a mixed integer program (MIP) and solved by a branch-and-cut algorithm. A GENCO with 54 thermal and 12 combined-cycle units is considered for analyzing the proposed formulation. The proposed case studies illustrate the significance of simultaneous arbitrage by applying PBUC to multi-commodity markets.

• The Journal of Society for e-Business Studies
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• v.22 no.4
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• pp.117-128
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• 2017

The increased national utilization of Bitcoin results in multiple complications. Therefore, there are continuous debates on the subject, the main point being how to characterize Bitcoin's asset nature. The following study bases, focusing on the function value, justifies Bitcoin's asset characterization. Using regression analysis to construct relations between gold and indexes such as CPI, DXY, and S&P500 as well as the relation between Bitcoin and the previously mentioned indexes, the question of whether gold and Bitcoin reacted in a similar fashion to the same indicators was examined. The results conclude that Bitcoin has similarities with gold, showing that it is risk averse and an investable commodity in lieu to profitability when it comes to inflation and currency value. When considered with price volatility, the main force behind the function of investment asset, categorizing Bitcoin as a high-risk financial investment asset rather than as a currency within the system would be more effective for management.

• The Journal of Society for e-Business Studies
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• v.24 no.4
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• pp.109-118
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• 2019

As a result of the United States (U.S) -China trade conflict, the recent instability of the stock market has led many people to invest in Bitcoin, a commodity that many previous studies have interpreted as a safe asset. However, recent Bitcoin market price fluctuations suggest that the asset's stability stems from speculative purchasing trends. Therefore, classifying the characteristics of Bitcoin assets can be an important reference point in analyzing relevant accounting information. To determine whether Bitcoin is a safe asset, this study analyzed the correlation between Bitcoin and economic indicators to verify whether gold and Bitcoin responded similarly in time series analyses. These show that the regression explanatory power between the price of gold and bitcoin is low, thus no relation between the two assets could be drawn. Additionally, the Granger causality analyses of six individual economic variables and Bitcoin did not establish any notable causality. This can be interpreted that short-term price fluctuations have a significant impact on the nature of Bitcoin as an asset.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.111-113
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• 2015

Floats are used by the parties involved in a construction project. The owner may use float by changing order(s) or by executing risk avoidance plan; the contractor may use it for leveling resources or substituting activities' construction methods to reduce costs. Floats are accepted either just as by-product obtained by critical path method(CPM) scheduling or as asset having significant value. Succinctly, existing studies involved in float value does not consider its' changes on project time domain. It is important to identify float ownership and to quantify its' corresponding values. This paper presents a method that quantifies float value of money that changes over project execution. The method which accurately computes the monetary value of float may contributes to resolve conflicts relative to float ownership and/or delay issues among project participants. It compares the difference between the monetary value of total float - on non-critical path in each and every schedule update. It makes use of critical path method (CPM) and commercial software with which practitioners are already familiar.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.10
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• pp.147-158
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• 2021

Due to COVID-19, the risk of price volatility in commodity and equity markets increases. The research and application of hedging is the most effective way to reduce the market risk. Hedging is a risk management strategy employed to offset losses in investments by taking an opposite position in a related asset. We use K-means and hierarchical clustering methods to cluster companies and futures products respectively, and analyze the relationship between the number of hedging firms, regional distribution, nature of firms, capital distribution, company size, profitability, number of local Futures Commission Merchants (FCMs), regional location, and listing time. The study shows that listed companies with large scale and good profitability invest more money in hedging, while state-owned enterprises' participation in hedging is more likely to be affected by the company size and the number of local futures commission merchants, and private enterprises are more likely to be affected by the company profitability and the regional location. Listed companies are more willing to choose long-listed and mature futures products for hedging. We also provide policy advice based on our conclusion. So far, there is no study on the characteristics of hedging. This paper fills the gap. The results provide a basis and guidance for people's investment and risk management. Using clustering analysis in hedging study is another innovation of this paper.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.459-466
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• 2013

Risk exists in all construction projects and resides among the collection of subcontractors and their array of individual activities. Wherever risk resides, the interrelation of participants to one another becomes paramount for the way in which risk is measured. Inherent risk becomes recognizable and quantifiable within network schedules in the form of consuming float - the flexibility to absorb delays. Allocating, owning, valuing, and expending such float in network schedules has been debated since the inception of the critical path method itself. This research investigates the foundational element of a three-part approach that examines how float can be traded as a commodity, a concept whose promise remains unfulfilled for lack of a holistic approach. The Capital Asset Pricing Model (CAPM) of financial portfolio theory, which describes the relationship between risk and expected return of individual stocks, is explored as an analogy to quantify the inherent risk of the participants in construction projects. The inherent relationship between them and their impact on overall schedule performance, defined as schedule risk -the likelihood of failing to meet schedule plans and the effect of such failure, is matched with the use of CAPM's beta component - the risk correlation measure of an individual stock to that of the entire market - to determine parallels with respect to the inner workings and risks represented by each entity or activity within a schedule. This correlation is the initial theoretical extension that is required to identify where risk resides within construction projects, allocate and commoditize it, and achieve actual tradability.

• Journal of the Economic Geographical Society of Korea
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• v.17 no.2
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• pp.280-295
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• 2014

This paper analyzes the topological structure of housing market networks with an application of minimal spanning tree method into apartment sales markets in the Capital Region over the period 2003.7-2014.3. The characteristics of topological network structure gained from this application to some extent share with those found in equity markets, although there are some differences in their intensities and degrees, involving a hierarchical structure in networks, an existence of communities or modules in networks, a contagious diffusion of log-return rate across nodes over time, an existence of correlation breakdown due to the time-dependent structure of networks and so on. These findings could be partially attributed to the facts that apartments as a quasi-financial asset have been strongly overwhelmed by speculative motives over the period investigated and they can be regarded as a housing commodity with the highest level of liquidity in Korea.