Behavior Analysis Herding of Indonesian Stock Exchange (Case Study of Stocks Listed in LQ45 Index)

Decision making in the capital market is not always based on rational considerations. Investors' actions are also influenced by psychological characteristics that emerge as human innate nature. These psychological characteristics will encourage different investor reactions. This study aims to test the indications of behavior herding on the Indonesia Stock Exchange 2006 to 2010. This study uses a sample of companies listed in the LQ45 index of 62 companies. The herding detection method is the CSAD (Cross-Sectional Absolute Deviation) method from Chang et al (2000). The variables used were dispersion value, returns absolute market and returns market squares. The data return used is derived from returns weekly for 260 weeks. The results in this study are no discovery of behavior herding on the Indonesia Stock Exchange either overall (5 years) or every year.

Degradation Behaviors of Two Epoxy Coatings After Dry/Wet-Accelerated Fog Exposure of Different Solutions

Applying organic coating is an important and effective approach for the protection of metal from corrosion. Weathering degradation and under-film corrosion are the two major important factors that cause the failure of organic coatings. In this work, the degradation investigation of two epoxy coatings (clear and pigmented coatings) was carried out under the dry–wet circulation of three different water fog solutions (deionized water, 0.5 wt% NaCl, 0.05 wt% NaCl + 0.35 wt% (NH4)2SO4) in 35 days. The apparent performance (pull-off adhesion and surface potentials) and electrochemical features [electrochemical noise and electrochemical impedance spectroscopy (EIS)] of the coating samples were monitored after dry–wet fog exposure. In our three accelerating systems of the fog atmosphere, the time that the detectable defects appeared on the surface of coating samples was far ahead in the mixed salt solution than that in the deionized water or 0.5 wt% NaCl solution. For a defective or damaged coating surface, the derived results by using the standard deviation method (SDM) or Fourier power spectrum (FPS) were rather higher than those obtained from EIS as a whole, while for the same coating, the degradation trend with time derived from EIS, SDM, FPS, and scanning Kelvin probe was consistent with each other.

Behavior Analysis Herding of Indonesian Stock Exchange (Case Study of Stocks Listed in LQ45 Index)

Decision making in the capital market is not always based on rational considerations. Investors' actions are also influenced by psychological characteristics that emerge as human innate nature. These psychological characteristics will encourage different investor reactions. This study aims to test the indications of behavior herding on the Indonesia Stock Exchange 2006 to 2010. This study uses a sample of companies listed in the LQ45 index of 62 companies. The herding detection method is the CSAD (Cross-Sectional Absolute Deviation) method from Chang et al (2000). The variables used were dispersion value, returns absolute market and returns market squares. The data return used is derived from returns weekly for 260 weeks. The results in this study are no discovery of behavior herding on the Indonesia Stock Exchange either overall (5 years) or every year.

Interval TOPSIS with a novel interval number comprehensive weight for threat evaluation on uncertain information

Threat evaluation (TE) is essential in battlefield situation awareness and military decision-making. The current processing methods for uncertain information are not effective enough for their excessive subjectivity and difficulty to obtain detailed information about enemy weapons. In order to optimize TE on uncertain information, an approach based on interval Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and the interval SD-G1 (SD standard deviation) method is proposed in this article. By interval SD-G1 method, interval number comprehensive weights can be calculated by combining subjective and objective weights. Specifically, the subjective weight is calculated by interval G1 method, which is an extension of G1 method into interval numbers. And the objective weight is calculated by interval SD method, which is an extension of SD method with the mean and SD of the interval array defined in this paper. Sample evaluation results show that with the interval SD-G1 method, weights of target threat attributes can be better calculated, and the approach combining interval TOPSIS and interval SD-G1 can lead to more reasonable results. Additionally, the mean and SD of interval arrays can provide a reference for other fields such as interval analysis and decision-making.

Occupational Categorization of female Work Force in Kashmir Himalayas, India

Occupational classification has been a subject of many experiments. From the definition of a worker to their attribution to a particular category of occupation. Multiple attempts have been made in understanding and explaining the same. The present paper attempts to take the case of female workforce and delves into the subject of categorizing their prevalence across the Kashmir valley (geographic unit), which by and large corresponds to Kashmir Division (administrative unit) in a comprehensible manner. The researcher adopts the Standard deviation method to do so. Using the standard deviations observed for each occupational category, the researcher proposes a rank table visualizing the target area employing a standard deviation-based occupational coding. Based on the rank table it is observed that amongst the female workforce the primary sector is dominant in Ganderbal and Kulgam districts, secondary sector in Shopian, Pulwama and Kulgam, tertiary sector in Srinagar and Budgam and the Quaternary/Quinary sectors in Srinagar, Anantnag and Pulwama districts. The standard deviation method adopted in this paper has led to a satisfactory representation of the occupational distribution observed in the sample area. The occupational coding of the target districts using this method not only makes it easier to visualize the occupational trends but also gives us a clear sense of variation (positive and negative) amongst these categories across the districts.

Transform and statistical parameter-based image noise level prediction

Noise level estimation in an image is important and useful in many image processing algorithms such as image denoising, image segmentation, and image compression. Accurately estimating the noise level without prior knowledge of the image is the major challenge of today’s research. We present an improved patch-based fast noise level estimation using DCT and standard deviation method for fast and reliable noise level estimation and the result is compared with the available state-of-art methods. Experimental result shows the proposed method provides greater accuracy, the stability and also the proposed method is an average of six times faster than that of the state- of – art methods for noise level estimation.

Study on the Mixed Materials Proportion of Stratum Based on the Modelling Experiment

It is highly significant to select similar materials as well as the proportion of mixed materials in the model test. The mixed materials are used to simulate the stratum of the model test, including the iron ore powder, natural sands, gypsum, and lime. The stratum contains silty clay and silt soil. First of all, the symmetry coefficient of model mechanics parameters are calculated by the equation, and the symmetry ratio is 16 : 1. Second, calculate the proportion of compositions in mixed materials by the orthogonal test. The deviation method is used to analyze the mixed materials and how to influence the elastic modulus, cohesion, and friction angle. Finally, get the mixed materials which meet the symmetry theory and control factors.

PERHITUNGAN PORTOFOLIO OPTIMAL DENGAN METODE MEAN-SEMIVARIANCE DAN MEAN ABSOLUTE DEVIATION

Knowing and managing investment portfolio risk is the most important factor in growing and preserving capital. The purpose of this study is to determine the optimal portfolio using Mean-Semivariance and Mean Absolute Deviation methods. The Mean-Semivariance method is a method that uses semivariance-semicovariance as a measure of risk while the Mean Absolute Deviation method uses the absolute deviation between realized return and expected return as a measure of risk. This study uses stock index data of LQ45 period February 2017-July 2019. The results of this study are that the Mean Absolute Deviation method gives higher return and risk than the Mean-Semivariance method.