• Asian Journal of Innovation and Policy
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• 제4권1호
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• pp.35-54
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• 2015

The purpose of this study is to compare the valuation of technology firms in the KOSPI and KOSDAQ. This study analyzed 224 market reports for KOSDAQ firms and 602 reports for KOSPI firms. We compare the two markets under 3 definitions on the accuracy of stock price forecasting. Findings are as follows: Although PER multiples is the most used method of valuation, KOSDAQ valuation more heavily relies on the method than KOSPI valuation. In stock market, the period of earnings forecasting is mostly 2-3 years. Multiples of KOSDAQ is generally higher than those of KOSPI. Even for technology firms, valuation in KOSPI mostly relies on earnings of the company, but that in KOSDAQ mostly relies on relative price. In stock price forecasting, generally overestimation prevails. Moreover, forecasting of KOSPI reports is more accurate than that of KOSDAQ reports. ROE and COE of KOSDAQ firms are generally higher than those of KOSPI firms.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.185-192
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• 2022

Forecasting and time series modelling plays a vital role in the data analysis process. Time Series is widely used in analytics & data science. Forecasting stock prices is a popular and important topic in financial and academic studies. A stock market is an unregulated place for forecasting due to the absence of essential rules for estimating or predicting a stock price in the stock market. Therefore, predicting stock prices is a time-series problem and challenging. Machine learning has many methods and applications instrumental in implementing stock price forecasting, such as technical analysis, fundamental analysis, time series analysis, statistical analysis. This paper will discuss implementing the stock price, forecasting, and research using prophet and LSTM models. This process and task are very complex and involve uncertainty. Although the stock price never is predicted due to its ambiguous field, this paper aims to apply the concept of forecasting and data analysis to predict stocks.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.271-274
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• 2005

Under competitive power markets, various long-term and short-term contracts based on spot price are used by producers and consumers. So an accurate forecasting for spot price allow market participants to develop bidding strategies in order to maximize their benefit. Artificial Neural Network is a powerful method in forecasting problem. In this paper we used Radial Basis Function(RBF) network to forecast spot price. To learn ANN, in addition to price history, we used some other effective inputs such as load level, fuel price, generation and transmission facilities situation. Results indicate that this forecasting method is accurate and useful.

• Asian Journal of Innovation and Policy
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• 제3권2호
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• pp.172-192
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• 2014

This study aims to analyze the valuation of technology firms in the stock market to answer how before-market entities should be valuated. This study analyzes 230 market reports of 2012 for technology firms in the KOSDAQ under several hypotheses. The results are as follows: 90% used the 3 multiples methods consisting of PER multiples with 80%, PBR multiples 8.7% and EBITDA multiples 1.7%. The average of PER multiples was 15 with the range of 6.9 to 83. That of PBR multiples is 2.27. Forecasting for cash flow is not applied over 4 years, but mainly 2-3 years. The accuracy of forecasting was 18.8%, 34.4% and 8% according to the different definitions. No differences were found in the accuracy of forecasting between valuation methods, between the industries having more intangible assets and the industries having less, and between startups and general companies and between ages and listed ages.

• 한국농공학회논문집
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• 제50권3호
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• pp.71-79
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• 2008

Generally the price of agricultural products has much different characteristics from that of manufacturing products. If products have the limitation of long-term storage and the short period of cultivation, the price of products can be more unstable. Moreover, the price forecasting is very difficult because it doesn't follow any cycle or trend. However price can be regarded as risk instead of uncertainty if we can calculate the probability of price. Reliability analysis techniques are used for forecasting the price change of Chinese cabbage. This study aims to show the usability of reliability analysis for price forecasting. A price-forecasting model was developed based on weather data of the first 10 days of the full cultivating cycle (80 days) 70 days and the average price and standard deviation of wholesale market prices from 1996 to 2001 and applied to forecast the boom price, or the orice which is over the tolerance of market prices, of upland Chinese cabbage in 2002 and 2003. Applied results showed the possibility of boom price forecasting using reliability analysis techniques.

• The Journal of Asian Finance, Economics and Business
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• 제7권11호
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• pp.83-93
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• 2020

This empirical research aims to modeling and improving the forecasting accuracy of the volatility pattern by employing the Saudi Arabia stock market (Tadawul)by studying daily closed price index data from October 2011 to December 2019 with a number of observations being 2048. In order to achieve significant results, this study employs many mathematical functions which are non-linear spectral model Maximum overlapping Discrete Wavelet Transform (MODWT) based on the best localized function (Bl14), autoregressive integrated moving average (ARIMA) model and generalized autoregressive conditional heteroskedasticity (GARCH) models. Therefore, the major findings of this study show that all the previous events during the mentioned period of time will be explained and a new forecasting model will be suggested by combining the best MODWT function (Bl14 function) and the fitted GARCH model. Therefore, the results show that the ability of MODWT in decomposition the stock market data, highlighting the significant events which have the most highly volatile data and improving the forecasting accuracy will be showed based on some mathematical criteria such as Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE), Root Means Squared Error (RMSE), Akaike information criterion. These results will be implemented using MATLAB software and R- software.

• 기술혁신연구
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• 제13권1호
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• pp.1-24
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• 2005

This paper applies a prelaunch forecasting model to the Home-Networking (HN) market of South Korea. The HN market of Korea is categorized into two distinctive markets. One HN market consists of new apartments in which builders install HN and the other HN market consists of existing houses in which residents purchase HN Among these markets, this paper focuses on existing houses as capturing consumers' choice. To forecast sales of HN for existing houses, we use a conjoint model based on our survey data of consumer preferences. By incorporating various indicators of HN technologies into our conjoint model, we also forecast diffusion of HN system embodied in PLC or Wireless Lan. We call this model Choice-Based Diffusion Model. In addition, based on the simulation experiments, we also identify important factors that affect the demands of HN system.

• 경영과학
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• 제23권3호
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• pp.41-61
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• 2006

This study is to propose a sales forecasting framework for new products in the prelaunch phase where no saies data are available. For the purpose we first develop an extended Bass model with the dynamic market potential and then propose an estimation method based on the market survey and scenario methodology. The proposed parameter estimation method is different from previous studies in that most of them have only Proposed the management judgments or analogies. We also apply the proposed model to satellite DMB market in Korea to verify the model.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 A
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• pp.819-821
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• 2005

Since the System Marginal Price (SMP) is a vital factor to the market entities who intend to maximize the their profit, the short-term marginal price forecasting should be performed correctly. In a electricity market, the short-term trading between the market entities can be generally affected a short-term market price. Therefore, the exact forecasting of SMP can influence on the profit of market participants. This paper presents a methodology of day-ahead SMP foretasting using Autoregressive Integrated Moving Average (ARIMA). To show the efficiency and effectiveness of the proposed method, the numerical studies have been performed using historical data of SMP in 2004.

• 한국정보시스템학회지:정보시스템연구
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• 제25권2호
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• pp.153-171
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• 2016

Purpose This study suggests the development of forecasting model for local cable TV advertisement. In order to verify the expected effect of the suggestion, using the causal loop map of System Dynamics, the factors affecting the prospects of cable TV commercial market were divided into 5 groups. Then targeting 97 people involved in the cable TV commercial market in Busan, Ulsan, and Gyeongnam, a survey was conducted on their perception of the current status of local advertisement market and future prospect. Design/methodology/approach The analysis of the collected data shows that workers in advertising and advertisers perceive the influence of cable TV as an advertising media to be high, while clearly understanding the problems of cable TV commercial market. Based on this the effects on the prospects of cable TV commercial market were analyzed and a forecasting method called Weighted Moving Average was applied. In order to improve accuracy of the added value of Weighted Moving Average, the 5 factors were divided into qualitative factors and quantitative factors, and using Multi-attribute Decision Making method, all the factors were normalized and weighting factors were deduced. The result of simulating the prospects of cable TV commercial market using Weighted Moving Average, both qualitative and quantitative factors showed downward turn in the market prospect for the following 10 years. Findings The result reflects generally negative perception of advertisement viewers about the prospects of cable TV commercial market. Compared to the previous studies on domestic cable TV commercials that focused on policy suggestions and surveys on perception of current status, this study has its significance in that it used scientific method and simulation for verification.