The effect of Big-data investment on the Market value of Firm (기업의 빅데이터 투자가 기업가치에 미치는 영향 연구)
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- Journal of Intelligence and Information Systems
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- v.25 no.2
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- pp.99-122
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- 2019
According to the recent IDC (International Data Corporation) report, as from 2025, the total volume of data is estimated to reach ten times higher than that of 2016, corresponding to 163 zettabytes. then the main body of generating information is moving more toward corporations than consumers. So-called "the wave of Big-data" is arriving, and the following aftermath affects entire industries and firms, respectively and collectively. Therefore, effective management of vast amounts of data is more important than ever in terms of the firm. However, there have been no previous studies that measure the effects of big data investment, even though there are number of previous studies that quantitatively the effects of IT investment. Therefore, we quantitatively analyze the Big-data investment effects, which assists firm's investment decision making. This study applied the Event Study Methodology, which is based on the efficient market hypothesis as the theoretical basis, to measure the effect of the big data investment of firms on the response of market investors. In addition, five sub-variables were set to analyze this effect in more depth: the contents are firm size classification, industry classification (finance and ICT), investment completion classification, and vendor existence classification. To measure the impact of Big data investment announcements, Data from 91 announcements from 2010 to 2017 were used as data, and the effect of investment was more empirically observed by observing changes in corporate value immediately after the disclosure. This study collected data on Big Data Investment related to Naver 's' News' category, the largest portal site in Korea. In addition, when selecting the target companies, we extracted the disclosures of listed companies in the KOSPI and KOSDAQ market. During the collection process, the search keywords were searched through the keywords 'Big data construction', 'Big data introduction', 'Big data investment', 'Big data order', and 'Big data development'. The results of the empirically proved analysis are as follows. First, we found that the market value of 91 publicly listed firms, who announced Big-data investment, increased by 0.92%. In particular, we can see that the market value of finance firms, non-ICT firms, small-cap firms are significantly increased. This result can be interpreted as the market investors perceive positively the big data investment of the enterprise, allowing market investors to better understand the company's big data investment. Second, statistical demonstration that the market value of financial firms and non - ICT firms increases after Big data investment announcement is proved statistically. Third, this study measured the effect of big data investment by dividing by company size and classified it into the top 30% and the bottom 30% of company size standard (market capitalization) without measuring the median value. To maximize the difference. The analysis showed that the investment effect of small sample companies was greater, and the difference between the two groups was also clear. Fourth, one of the most significant features of this study is that the Big Data Investment announcements are classified and structured according to vendor status. We have shown that the investment effect of a group with vendor involvement (with or without a vendor) is very large, indicating that market investors are very positive about the involvement of big data specialist vendors. Lastly but not least, it is also interesting that market investors are evaluating investment more positively at the time of the Big data Investment announcement, which is scheduled to be built rather than completed. Applying this to the industry, it would be effective for a company to make a disclosure when it decided to invest in big data in terms of increasing the market value. Our study has an academic implication, as prior research looked for the impact of Big-data investment has been nonexistent. This study also has a practical implication in that it can be a practical reference material for business decision makers considering big data investment.
Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.
These studies were made at Suwon in 1972 and at Suwon, Iri, and Kwangju in 1973 to investigate grain filling process and variation of grain quality of NB 68513 and Caprock as hard red winter wheat, Suke ＃169 as soft red winter wheat variety and Yungkwang as semi-hard winter variety, grown under-three different fertilizer levels and seeding dates. Other experiments were conducted to find the effects of temperature, humidity and light intensity on the grain filling process and grain quality of Yungkwang and NB 68513 wheat varieties. These, experiments were conducted at Suwon in 1973 and 1974. 1. Grain filling process of wheat cultivars: 1) The frequency distribution of a grain weight shows that wider distribution of grain weight was associated with large grain groups rather than small grain group. In the large grain groups, the frequency was mostly concentrated near mean value, while the frequency was dispersed over the values in the small grain group. 2) The grain weight was more affected by the grain thickness and width than by grain length. 3) The grain weight during the ripening period was rapidly increased from 14 days after flowering to 35 days in Yungkwang and from 14 days after flowering to 28 days in NB 68513. The large grain group, Yungkwang was rather slowly increased and took a longer period in increase of endosperm ratio of grain than the small grain group, NB 68513. 4) In general, the 1, 000 grain weight was reduced under high temperature, low humidity, while it was increased under low temperature and high humidity condition, and under high temperature and humidity condition. The effect of shading on grain weight was greater in high temperature than in low temperature condition and no definite tendency was found in high humidity condition. 5) The effects of temperature, humidity and shading on 1, 000 grain weight were greater in large-grain group, Yungkwang than in small grain group, NB 68513. Highly significant positive correlation was found between 1, 000 grain weight and days to ripening. 6) The 1, 000 grain weight and test weight were increased more or less as the fertilizer levels applied were increased. However, the rate of increasing 1, 000 grain weight was low when fertilizer levels were increased from standard to double. The 1, 000 grain weight was high when planted early. Such tendency was greater in Suwon than in Kwangju or Iri area. 2. Milling quality: 7) The milling rate in a same group of varieties was higher under the condition of low temperature, high humidity and early maturing culture which were responsible for increasing 1, 000 grain weight. No definite relations were found along with locations. 8) In the varieties tested, the higher milling rate was found in large grain variety, Yungkwang, and the lowest milling rate was obtained from Suke ＃ 169, the small grain variety. But the small grained hard wheat variety such as Caprock and NB 68513 showed higher milling rate compared with the soft wheat variety, Suke ＃ 169. 9) There were no great differences of ash content due to location, fertilizer level and seeding date while remarkable differences due to variety were found. The ash content was high in the hard wheat varieties such as NB 68513, Caprock and low in soft wheat varieties such as Yungkwang and Suke ＃ 169. 3. Protein content: 10) The protein content was increased under the condition of high temperature, low humidity and shading, which were responsible for reduction of 1, 000 grain weight. The varietal differences of protein content due to high temperature, low humidity and shading conditions were greater in Yungkwang than in NB 68513. 11) The high content of protein in grain within one to two weeks after flowering might be due to the high ratio of pericarp and embryo to endosperm. As grains ripen, the effects of embryo and pericarp on protein content were decreased, reducing protein content. However, the protein content was getting increased from three or four weeks after flowering, and maximized at seven weeks after flowering. The protein content of grain at three to four weeks after flowering increased as the increase of 1, 000 grain weight. But the protein content of matured grain appeared to be affected by daily temperature on calender rather than by duration of ripening period. 12) Highly significant positive correlation value was found between the grain protein content and flour protein content. 13) The protein content was increased under the high level of fertilizers and late seeding. The local differences of protein content were greater in Suwon than in Kwangju and Iri. 14) Protein content in the varieties tested were high in Yungkwang, NB 68513 and Caprock, and low in Suke ＃ 169. However, variation in protein content due to the cultural methods was low in Suke ＃ 169. 15) Protein yield per unit area was increased in accordance with increase of fertilizer levels and early maturing culture. However, nitrogen fertilizer was utilized rather effectively in early maturing culture and Yungkwang was the highest in protein yield per unit area. 4. Physio-chemical properties of wheat flour: 16) Sedimentation value was higher under the conditions of high temperature, low humidity and high levels of fertilizers than under the conditions of low temperature, high moisture and low levels of fertilizers. Such differences of sedimentation values were more apparent in NB 68513 and Caprock than Yungkwang and Suke ＃ 169. The local difference of sedimentation value was greater in Suwon than in Kwangju and Iri. Even though the sedimentation value was highly correlated with protein content of grain, the high humidity was considered one of the factors affecting sedimentation value. 17) Changes of Pelshenke values due to the differences of cultural practices and locations were generally coincident with sedimentation values. 18) The mixing time required for mixogram was four to six minutes in NB 68513, five to seven minutes in Cap rock. The great variation of mixing time for Yungkwang and Suke ＃ 169 due to location and planting conditions was found. The mixing height and area were high in hard wheat than in soft wheat. Variation of protein content due to cultural methods were inconsistent. However, the pattern of mixogram were very much same regardless the treatments applied. With this regard, it could be concluded that the mixogram is a kind of method expressing the specific character of the variety. 19) Even though the milling property of NB 68513 and Caprock was deteriorated under either high temperature and low humidity of high fertilizer levels and late seeding conditions, baking quality was better due to improved physio-chemical properties of flour. In contrast, early maturing culture deteriorated physio-chemical properties, milling property of grain and grain protein yield per unit area was increased. However, it might be concluded that the hard wheat production of NB 68513 and Caprock for baking purpose could be done better in Suwon than in Iri or Kwangju area. 5. Interrelationships between the physio-chemical characters of wheat flour: 20) Physio-chemical properties of flour didn't have direct relationship with milling rate and ash content. Low grain weight produced high protein content and better physio-chemical flour properties. 21) In hard wheat varieties like NB 68513 and Caprock, protein content was significantly correlated with sedimentation value, Pelshenke value and mixing height. However, gluten strength and baking quality were improved by the increased protein content. In Yungkwang and Suk ＃ 169, protein content was correlated with sedimentation value, but no correlations were found with Pelshenke value and mixing height. Consequently, increase of protein content didn't improve the gluten strength in soft wheat. 22) The highly significant relationships between protein content and gluten strength and sedimentation . value, and between Pelshenke value, mixogram and gluten strength indicated that the determination of mixogram and Pelshenke value are useful for de terming soft and hard type of varieties. Determination of sedimentation value is considered useful method for quality evaluation of wheat grain under different cultural practices.