• 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.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.559-575
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• 2014

Individual investors are increasingly flocking to message boards to seek, clarify, and exchange information. Businesses like Seekingalpha.com and business magazines like Fortune are evaluating, synthesizing, and reporting the comments made on message boards or blogs. In March of 2012, Yahoo! Finance Message Boards recorded 45 million unique visitors per month followed by AOL Money and Finance (19.8 million), and Google Finance (1.6 million) [McIntyre, 2012]. Previous studies in the finance literature suggest that online communities often provide more accurate information than analyst forecasts [Bagnoli et al., 1999; Clarkson et al., 2006]. Some studies empirically show that the volume of posts in online communities have a positive relationship with market activities (e.g., trading volumes) [Antweiler and Frank, 2004; Bagnoli et al., 1999; Das and Chen, 2007; Tumarkin and Whitelaw, 2001]. The findings indicate that information in online communities does impact investors' investment decisions and trading behaviors. However, research explicating the correlation between information on online communities and stock market activities (e.g., trading volume) is still evolving. Thus, it is important to ask whether a volume of posts on online communities influences trading volumes and whether trading volumes also influence these communities. Online stock message boards offer two different types of information, which can be explained using an economic and a psychological perspective. From a purely economic perspective, one would expect that stock message boards would have a beneficial effect, since they provide timely information at a much lower cost [Bagnoli et al., 1999; Clarkson et al., 2006; Birchler and Butler, 2007]. This indicates that information in stock message boards may provide valuable information investors can use to predict stock market activities and thus may use to make better investment decisions. On the other hand, psychological studies have shown that stock message boards may not necessarily make investors more informed. The related literature argues that confirmation bias causes investors to seek other investors with the same opinions on these stock message boards [Chen and Gu, 2009; Park et al., 2013]. For example, investors may want to share their painful investment experiences with others on stock message boards and are relieved to find they are not alone. In this case, the information on these stock message boards mainly reflects past experience or past information and not valuable and predictable information for market activities. This study thus investigates the two roles of stock message boards-providing valuable information to make future investment decisions or sharing past experiences that reflect mainly investors' painful or boastful stories. If stock message boards do provide valuable information for stock investment decisions, then investors will use this information and thereby influence stock market activities (e.g., trading volume). On the contrary, if investors made investment decisions and visit stock message boards later, they will mainly share their past experiences with others. In this case, past activities in the stock market will influence the stock message boards. These arguments indicate that there is a correlation between information posted on stock message boards and stock market activities. The previous literature has examined the impact of stock sentiments or the number of posts on stock market activities (e.g., trading volume, volatility, stock prices). However, the studies related to stock sentiments found it difficult to obtain significant results. It is not easy to identify useful information among the millions of posts, many of which can be just noise. As a result, the overall sentiments of stock message boards often carry little information for future stock movements [Das and Chen, 2001; Antweiler and Frank, 2004]. This study notes that as a dependent variable, trading volume is more reliable for capturing the effect of stock message board activities. The finance literature argues that trading volume is an indicator of stock price movements [Das et al., 2005; Das and Chen, 2007]. In this regard, this study investigates the correlation between a number of posts (information on stock message boards) and trading volume (stock market activity). We collected about 100,000 messages of 40 companies at KOSPI (Korea Composite Stock Price Index) from Paxnet, the most popular Korean online stock message board. The messages we collected were divided into in-trading and after-trading hours to examine the correlation between the numbers of posts and trading volumes in detail. Also we collected the volume of the stock of the 40 companies. The vector regression analysis and the granger causality test, 3SLS analysis were performed on our panel data sets. We found that the number of posts on online stock message boards is positively related to prior stock trade volume. Also, we found that the impact of the number of posts on stock trading volumes is not statistically significant. Also, we empirically showed the correlation between stock trading volumes and the number of posts on stock message boards. The results of this study contribute to the IS and finance literature in that we identified online stock message board's two roles. Also, this study suggests that stock trading managers should carefully monitor information on stock message boards to understand stock market activities in advance.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.745-748
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• 1996

Korea Composite Stock Price Index (KOSPI) was predicted using Cascade Correlation Network (CCN) model. CCN was suggested, by Fahlman and Lebiere [1990], to overcome the limitations of backpropagation algorithm such as step size problem and moving target problem. To test the applicability of CCN as a function approximator to the stock price movements, CCN was used as a tool for univariate time series analysis. The fitting and forecasting performance fo CCN on the KOSPI was compared with those of Multi-Layer Perceptron (MLP).

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.420-424
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• 2022

The Arab Automotive Company was the first corporation in Saudi Arabia and was founded in 1928. Since then, the number of Saudi corporations had increased. In 1985, Tadawul (The Saudi Stock Exchange ) was instituted under the supervision of the Saudi Arabian Monetary Authority (SAMA) and the base value of the index was 1000. This decision came as a response to accelerated growth in the number of Saudi corporations which had increased during the 1970s as the Saudi's economy developed.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.919-926
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• 2020

The purpose of this study is to analyze and test the effect of financial ratios and macroeconomics on Islamic stock returns listed in Jakarta Islamic Index (JII) other than to assess whether investment risk can be an intervening variable in this study. The type of research is explanatory in nature with a quantitative descriptive approach. The data used is based on secondary sources with a sample group of 29 companies listed on JII for a 5-year period ending 31 December 2018. The data obtained were analyzed by using SEM (Structural Equation Model) with AMOS (Analysis Moment of Structural) 21 program. The results of the study show that only financial ratios affect sharia stock returns and investment risk, while the mediation test found that investment risk does not act as a mediating variable between financial ratios and macroeconomics and Islamic stock return. These findings indicate that the role of the company's financial health is very important. Besides affecting the rate of return obtained, the company's financial health can also reflect the level of risk that investors will accept in the future. By improving financial performance properly, a company will have a positive impact on various interested parties and minimize the level of investor losses.

• Journal of Contemporary Eastern Asia
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• v.20 no.2
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• pp.63-84
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• 2021

The present study was conducted to understand the turmoil effects of COVID-19 pandemic on the Malaysian stock market during the different periods of the Movement Control Order (MCO). The present study was based on the secondary data extracted from the DataStream and Bloomberg from 2nd January 2020 to 29th May 2020 to evaluate the effects of COVID-19 pandemic on the Malaysian stock market. The findings suggested that during the different periods of the Movement Control Order (MCO) from the 1st January to 29th May 2020, the COVID-19 pandemic adversely affected the performance of KLCI index and all sectoral indices. The weakest performance indices were energy, property, and finance while the least affected indices were healthcare, technology, telecommunications, and media. This paper provides a review of the impacts of COVID-19 pandemic on the Malaysian stock market throughout the different periods of MCO.

• Journal of Climate Change Research
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• v.7 no.4
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• pp.485-492
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• 2016

For reporting national greenhouse gas inventory in forest sector, the forest growing stock from the National Forest Inventory (NFI) system has used as activity data sources. The National Forest Inventory system was changed from rotation system by province to annual system by 5 years across the country. The forest growing stocks based on the new inventory system produced a different trend compared to the previous estimations. This study was implemented to recalculate previous forest growing stocks for time series consistency at a national level. The recalculation of forest growing stock was conducted in an overlap approach by the IPCC guideline. In order to support the more consistency data, we used calibration factors between applied stand volumes in 1985 and 2012, respectively. As a result, the time series of recalculated forest growing stock was to be consistency using the overlap approach and the calibration factor with the lower middle/middle site index. According to the applied overlap period, however, we will recalculate activity data using more complete data from national forest inventory system.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.697-710
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• 2021

The purpose of this study is to explore the reaction of sharia stock in the Indonesian capital market to the global Covid-19 pandemic. The method used in this study is an event study with a Market Adjusted Model (MAM) approach. The population of this study is shares listed on the Indonesian Stock Exchange (IDX), with the sample chosen from the Jakarta Sharia (Islamic) Index. The result of this study found that the global Covid-19 pandemic is bad news, with the indicators as follows: a) the average expected return is negative; b) the average actual return is negative; c) the average abnormal return is negative, and d) the increase selling action of stock as a cut loss strategy. There is a negative abnormal return and significant Trading Volume Activity (TVA) before, during, and after the announcement of the global Covid-19 pandemic. However, this study found no difference in abnormal return and TVA before and after the announcement of the global Covid-19 pandemic. From these results, this study indicates that the sharia stocks in the capital market in Indonesia can respond quickly to the information that existed. Therefore, the capital market of Indonesia is a capital market with a semi-strong efficient form.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.737-745
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• 2021

This study aims to determine whether there are differences in the average abnormal return, trading volume activity, and trading frequency activity in pharmaceutical stocks before and after the announcement of the first case of the coronavirus (COVID-19) in Indonesia. The sample was selected using a purposive sampling method and collected as many as nine pharmaceutical companies listed on the Indonesia Stock Exchange during 2019-2020. The data used in this study were secondary data in the form of daily data on stock closing prices, Composite Stock Price Index (IHSG), stock volume trading, number of shares outstanding, and stock trading frequency. This study was an event study with an observation period of 14 days, namely seven days before and seven days after the announcement of the coronavirus's first positive case in Indonesia. Hypothesis testing employed the paired sample t-test method. Based on the results, it was found that there was no difference in the average abnormal return of pharmaceutical stocks before and after the announcement of the first case of COVID-19. However, there was a difference in the average trading volume activity and the average trading frequency activity in pharmaceutical stocks before and after the announcement of the first case of COVID-19.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.747-757
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• 2021

The primary purpose of the study is to investigate the volatility spillover from financial uncertainty (FU) of the United States (US) to the stock markets of SAARC member countries including India, Sri-Lanka, Pakistan, and Bangladesh. The empirical literature overlooked SAARC countries and the FU index. Based on the estimation method, the data of FU is available for three different forecast horizons including 1-month, 3-months, and 12-months. For empirical analysis, monthly data is used from February 2013 to September 2019. EGARCH model is employed to investigate the volatility spillover effects. The findings of the study show that the spillover effect of FU varies with the forecast horizon. The FU with a higher forecast horizon has a significant spillover effect on more countries. The spillover effect of US financial uncertainty is negative in most of the SAARC countries. Bangladesh stock market is influenced by FU with all three forecast horizons whereas the volatility of the Pakistan stock market is not influenced by FU with any forecast horizon. The findings are consistent with the concept of "limited trade openness" in the financial markets of emerging economies. The emerging economies avoid financial market openness to minimize the risk of spillover of other countries.