• 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

• Tuberculosis and Respiratory Diseases
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• v.42 no.6
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• pp.871-877
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• 1995

Background: The index which could predict the prognosis of critically ill patients is needed to find out high risk patients and to individualize their treatment. The APACHE III scoring system was established in 1991, but there has been only a few studies concerning its prognostic value. We wanted to know whether the APACHE III scores have prognostic value in discriminating survivors from nonsurvivors in sepsis. Methods: In 48 patients meeting the Bones criteria for sepsis, we retrospectively surveyed the day 1(D1), day 2(D2) and day 3(D3) scores of patients who were admitted to intensive care unit. The scores of the sepsis survivors and nonsurvivors were compared in respect to the D1 score, and also in respect to the changes of the updated D2 and D3 scores. Results: 1) Of the 48 sepsis patients, 21(43.5%) survived and 27(56.5%) died. The nonsurvivors were older($62.7{\pm}12.6$ vs $51.1{\pm}18.1$ yrs), presented with lower mean arterial pressure($56.9{\pm}26.2$ vs $67.7{\pm}14.2\;mmHg$) and showed greater number of multisystem organ failure($1.2{\pm}0.8$ vs $0.2{\pm}0.4$) than the survivors(p<0.05, respectively). There were no significant differences in sex and initial body temperature between the two groups. 2) The D1 score was lower in the survivors (n=21) than in the nonsurvivors ($44.1{\pm}14.6$, $78.5{\pm}18.6$, p=0.0001). The D2 and D3 scores significantly decreased in the survivors (D1 vs D2, $44.1{\pm}14.6$ : $37.9{\pm}15.0$, p=0.035; D2 vs D3, $37.9{\pm}15.0$ : $30.1{\pm}9.3$, p=0.0001) but showed a tendency to increase in the nonsurvivors (D1 vs D2 (n=21), $78.5{\pm}18.6$ : $81.3{\pm}23.0$, p=0.1337; D2 vs D3 (n=11), $68.2{\pm}19.3$ : $75.3{\pm}18.8$, p=0.0078). 3) The D1 scores of 12 survivors and 6 nonsurvivors were in the same range of 42~67 (mean D1 score, $53.8{\pm}10.0$ in the survivors, $55.3{\pm}10.3$ in the nonsurvivors). The age, sex, initial body temperature, and mean arterial pressure were not different between the two groups. In this group, however, D2 and D3 was significantly decreased in the survivors(D1 vs D2, $53.3{\pm}10.0$ : $43.6{\pm}16.4$, p=0.0278; D2 vs D3, $43.6{\pm}16.4$ : $31.2{\pm}10.3$, p=0.0005), but showed a tendency to increase in the nonsurvivors(D1 vs D2 (n=6), $55.3{\pm}10.3:66.7{\pm}13.9$, p=0.1562; D2 vs D3 (n=4), $64.0{\pm}16.4:74.3{\pm}18.6$, p=0.1250). Among the individual items of the first day APACHE III score, only the score of respiratory rate was capable of discriminating the nonsurvivors from the survivors ($5.5{\pm}2.9$ vs $1.9{\pm}3.7$, p=0.046) in this group. Conclusion: In sepsis, nonsurvivors had higher first day APACHE III score and their updated scores on the following days failed to decline but showed a tendency to increase. Survivors, on the other hand, had lower first day score and showed decline in the updated APACHE scores. These results suggest that the first day and daily updated APACHE III scores are useful in predicting the outcome and assessing the response to management in patients with sepsis.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

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.