• Information Systems Review
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• v.18 no.2
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• pp.23-38
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• 2016

Previous studies on information systems (IS) and finance suggest that information on stock message boards influence the investment decisions of individual investors. However, how information on online stock message boards influences an individual investor's buy or sell decisions is unclear. To address this research question, we investigate the relationship between a number of posts on stock message boards and order imbalance in stock markets. Order imbalance is defined as the difference between the daily sum of buy-side shares traded and the daily sum of sell-side shares traded. Therefore, order imbalance can suggest the direction of trades and the strength of the direction with trading volumes. In this regard, this study examines how the number of posts (information on stock message boards) influences order imbalance (stock trading behavior). We collected about 46,077 messages of 40 companies on the Korea Composite Stock Price Index from Paxnet, the most popular Korean online stock message board. The messages we collected were divided based on in-trading and after-trading hours to examine the relationship between the numbers of posts and trading volumes. We also collected order imbalance data on individual investors. We then integrated the balanced panel data sets and analyzed them through vector regression. We found that the number of posts on online stock message boards is positively related to prior order imbalance. We believe that our findings contribute to knowledge in IS and finance. Furthermore, this study suggests that investors should carefully monitor information on stock message boards to understand stock market sentiments.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.157-177
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• 2022

Investors are trading stocks by keeping a close watch on the order information submitted by domestic and foreign investors in real time through Limit Order Book information, so-called price current provided by securities firms. Will order information released in the Limit Order Book be useful in stock price prediction? This study analyzes whether it is significant as a predictor of future stock price up or down when order imbalances appear as investors' buying and selling orders are concentrated to one side during intra-day trading time. Using classification algorithms, this study improved the prediction accuracy of the order imbalance information on the short-term price up and down trend, that is the closing price up and down of the day. Day trading strategies are proposed using the predicted price trends of the classification algorithms and the trading performances are analyzed through empirical analysis. The 5-minute KOSPI200 Index Futures data were analyzed for 4,564 days from January 19, 2004 to June 30, 2022. The results of the empirical analysis are as follows. First, order imbalance information has a significant impact on the current stock prices. Second, the order imbalance information observed in the early morning has a significant forecasting power on the price trends from the early morning to the market closing time. Third, the Support Vector Machines algorithm showed the highest prediction accuracy on the day's closing price trends using the order imbalance information at 54.1%. Fourth, the order imbalance information measured at an early time of day had higher prediction accuracy than the order imbalance information measured at a later time of day. Fifth, the trading performances of the day trading strategies using the prediction results of the classification algorithms on the price up and down trends were higher than that of the benchmark trading strategy. Sixth, except for the K-Nearest Neighbor algorithm, all investment performances using the classification algorithms showed average higher total profits than that of the benchmark strategy. Seventh, the trading performances using the predictive results of the Logical Regression, Random Forest, Support Vector Machines, and XGBoost algorithms showed higher results than the benchmark strategy in the Sharpe Ratio, which evaluates both profitability and risk. This study has an academic difference from existing studies in that it documented the economic value of the total buy & sell order volume information among the Limit Order Book information. The empirical results of this study are also valuable to the market participants from a trading perspective. In future studies, it is necessary to improve the performance of the trading strategy using more accurate price prediction results by expanding to deep learning models which are actively being studied for predicting stock prices recently.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.5
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• pp.401-406
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• 2015

In this study, the gait imbalance evaluation algorithm based on two axes angle using encoder is proposed. This experiment was carried out to experiment with a healthy adult male to 10 people. The device is attached to the hip and knee joint in order to measure the angle during the gait. Normal and imbalance gait angle data were measured using an encoder attached to the hip and knee joints. Also, in order to verify the reliability of estimation of asymmetrical gait using hip and knee angle, it was compared with the result of asymmetrical gait estimation using foot pressure. SI (Symmetry Index) was used as an index for determining the gait imbalance. As a result, normal gait and 1.5cm imbalance gait were evaluation as normal gait through SI using an encoder. And imbalance gait of 3cm, 4cm, and 6cm were judge by imbalance gait. Whereas all gait experiments except normal gait were evaluation as imbalance gait through SI using the pressure. It was possible to determine both the normal gait and imbalance gait through measurement for the angle and the pressure.

• Journal of Information Processing Systems
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• v.10 no.4
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• pp.628-645
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• 2014

In past few years, distributed hash table (DHT)-based P2P systems have been proven to be a promising way to manage decentralized index information and provide efficient lookup services. However, the skewness of users' preferences regarding keywords contained in a multi-keyword query causes a query load imbalance that combines both routing and response load. This imbalance means long file retrieval latency that negatively influences the overall system performance. Although index replication has a great potential for alleviating this problem, existing schemes did not explicitly address it or incurred high cost. To overcome this issue, we propose, in this paper, an integrated solution that consists of three replication schemes to alleviate query load imbalance while minimizing the cost. The first scheme is an active index replication that is used in order to decrease routing load in the system and to distribute response load of an index among peers that store replicas of the index. The second scheme is a proactive pointer replication that places location information of each index to a predetermined number of peers for reducing maintenance cost between the index and its replicas. The third scheme is a passive index replication that guarantees the maximum query load of peers. The result of simulations indicates that the proposed schemes can help alleviate the query load imbalance of peers. Moreover, it was found by comparison that our schemes are more cost-effective on placing replicas than PCache and EAD.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.309-320
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• 2007

The data imbalance problem which can be uncounted in data mining classification problems typically means that there are more or less instances in a class than those in other classes. It causes low prediction accuracy of the minority class because classifiers tend to assign instances to major classes and ignore the minor class to reduce overall misclassification rate. In order to solve the data imbalance problem, there has been proposed a number of techniques based on resampling with replacement, adjusting decision thresholds, and adjusting the cost of the different classes. In this paper, we study the feasibility of the combination usage of the techniques previously proposed to deal with the data imbalance problem, and suggest a combination method using genetic algorithm to find the optimal combination ratio of the techniques. To improve the prediction accuracy of a minority class, we determine the combination ratio based on the F-value of the minority class as the fitness function of genetic algorithm. To compare the performance with those of single techniques and the matrix-style combination of random percentage, we performed experiments using four public datasets which has been generally used to compare the performance of methods for the data imbalance problem. From the results of experiments, we can find the usefulness of the proposed method.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.2
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• pp.18-26
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• 2015

In this paper, we studied the method for analyzing and estimating the parameters that induce I/Q imbalance in the repeater using direct conversion RF. In repeater, amplitude, phase, and filter mismatch are generated in the receiving-end which converts RF signal to baseband signal. And amplitude and phase mismatch are generated in the transmitting-end which converts baseband signal to RF signal. Accordingly, we modeled the parameters that cause I/Q imbalance in the structure of the repeater in order, and proposed a feedback test structure from the transmitting-end to the receiving-end for estimating the corresponding parameters. By comparing the test transmitting signal and received signal, it is possible to estimate the I/Q imbalance parameters which occurred from mixed components of real and imaginary part. And it was confirmed that I/Q imbalance phenomenon has been properly compensated with estimated parameters at the direct conversion RF repeater.

• Journal of Biomedical Engineering Research
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• v.35 no.1
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• pp.8-13
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• 2014

In this paper, the gait imbalance evaluation algorithm based on temporal symmetry ratio using encoder is proposed. The device is attached to the hip joint in order to measure the angle during the normal gait. Using an angle data, the stance phase and swing phase was determined. And the value of TSR(temporal symmetry ratio) was calculated by stance phase and swing phase of gait cycle. For the comparative experiment, the conventional method of the foot pressure was measured at the same conditions. The results of statistical analysis, there was a significant difference (p < 0.05) when using encoder. The gait imbalance analysis using encoder is effective in determining the imbalance than using the existing method of pressure.

• Journal of Intelligence and Information Systems
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• v.14 no.3
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• pp.133-154
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• 2008

The data imbalance problem which can be uncounted in data mining classification problems typically means that there are more or less instances in a class than those in other classes. In order to solve the data imbalance problem, there has been proposed a number of techniques based on re-sampling with replacement, adjusting decision thresholds, and adjusting the cost of the different classes. In this paper, we study the feasibility of the combination usage of the techniques previously proposed to deal with the data imbalance problem, and suggest a combination method using genetic algorithm to find the optimal combination ratio of the techniques. To improve the prediction accuracy of a minority class, we determine the combination ratio based on the F-value of the minority class as the fitness function of genetic algorithm. To compare the performance with those of single techniques and the matrix-style combination of random percentage, we performed experiments using four public datasets which has been generally used to compare the performance of methods for the data imbalance problem. From the results of experiments, we can find the usefulness of the proposed method.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.179-190
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• 2022

Data imbalance refers to a phenomenon in which the number of data in one category is too large or too small compared to another category. Due to this, it has been raised as a major factor that deteriorates performance in machine learning that utilizes classification algorithms. In order to solve the data imbalance problem, various ovrsampling methods for amplifying prime number distribution data have been proposed. Among them, SMOTE is the most representative method. In order to maximize the amplification effect of minority distribution data, various methods have emerged that remove noise included in data (SMOTE-IPF) or enhance only border lines (Borderline SMOTE). This paper proposes a method to ultimately improve classification performance by improving the processing method for anomaly data in the traditional SMOTE method that amplifies minority classification data. The proposed method consistently presented relatively high classification performance compared to the existing methods through experiments.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1310-1319
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• 2013

Resolvers are transducers that are used to sense the angular position of rotational machines. The analog resolver is necessary to use resolver to digital converter. Among the RDC software method, angle tracking observer (ATO) is the most popular method. In an actual resolver-based position sensing system, amplitude imbalance dominantly distorts the estimate position information of ATO. Minority papers have reported position error compensation of resolver's output signal with amplitude imbalance. This paper proposes new ATO algorithm in order to compensate position errors caused by the amplitude imbalance. There is no need premeasured off line data. This is easy, simple, cost-effective, and able to work on line compensation. To verify feasibility of the proposed algorithm, simulation and experiments are carried out.