Pub Date : 2017-02-01DOI: 10.1109/ICDMAI.2017.8073535
M. Nair, Balasubramanian, Lakshmi Yermal
Behavioral finance proposes that cognitive traits of investors impact their investment decisions which are not always rational, in contradiction to traditional finance. These cognitive traits of stock investors are influenced by their demographical profile and the financial information that they receive from various sources which in turn influences their stock investment decisions. Investors with similar demographic profile tend to follow a similar pattern with regard to their investment behavior biases. The main objective of this study is to analyze the impact of Indian stock investors' demographics and various sources of financial information on their cognitive biases. Various behavioral biases like herding, loss aversion, regret aversion, market information; mental accounting, price change, and price anchoring were studied but herding behavior has been taken into consideration for analysis in this study. A questionnaire was floated by using quota sampling. Stata software was used for analysis, by using ordered logistic regression on the conceived model. Gender, age, marital status and word of mouth are found to have significant impact on the herding behavior of stock investors.
{"title":"Factors influencing herding behavior among Indian stock investors","authors":"M. Nair, Balasubramanian, Lakshmi Yermal","doi":"10.1109/ICDMAI.2017.8073535","DOIUrl":"https://doi.org/10.1109/ICDMAI.2017.8073535","url":null,"abstract":"Behavioral finance proposes that cognitive traits of investors impact their investment decisions which are not always rational, in contradiction to traditional finance. These cognitive traits of stock investors are influenced by their demographical profile and the financial information that they receive from various sources which in turn influences their stock investment decisions. Investors with similar demographic profile tend to follow a similar pattern with regard to their investment behavior biases. The main objective of this study is to analyze the impact of Indian stock investors' demographics and various sources of financial information on their cognitive biases. Various behavioral biases like herding, loss aversion, regret aversion, market information; mental accounting, price change, and price anchoring were studied but herding behavior has been taken into consideration for analysis in this study. A questionnaire was floated by using quota sampling. Stata software was used for analysis, by using ordered logistic regression on the conceived model. Gender, age, marital status and word of mouth are found to have significant impact on the herding behavior of stock investors.","PeriodicalId":368507,"journal":{"name":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114663091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICDMAI.2017.8073529
P. Salunke, J. Kate
An interfacing device plays important role in data acquisition systems. However, different signal types of sensors in general are limited by these devices. Gradual improvement of IoT has lead to a revolution in the wireless communication; this scenario facilitates various applications from environmental monitoring to industrial management. Wired networks are mostly used to transfer data by connecting sensor. It is advantageous as it provides reliable communication system for instruments and controls. But connecting the sensors with a wired network will be costly hence low cost wireless technologies are much needed to cut down the cost. A novel approach is proposed in this paper to design smart sensor interface for water quality monitoring in IoT environment. Different sensors are available for water quality monitoring which are used to check the quality on following parameters i.e. pH, dissolved oxygen concentration, turbidity and temperature etc. IoT provide interface to monitor and operate remotely from anywhere and anytime. Intel Galileo Gen 2 board is used as interfacing device in our proposed system. The system performance is tested on water environment monitoring and improved results are gained.
接口设备在数据采集系统中起着重要的作用。然而，不同信号类型的传感器通常受到这些设备的限制。物联网的逐步完善引发了一场无线通信的革命;这种场景有利于从环境监测到工业管理的各种应用。有线网络主要通过连接传感器来传输数据。它的优点是为仪器和控制提供了可靠的通信系统。但将传感器与有线网络连接将是昂贵的，因此需要低成本的无线技术来降低成本。本文提出了一种设计物联网环境下水质监测智能传感器接口的新方法。不同的传感器可用于水质监测，用于检查以下参数的质量，即pH值，溶解氧浓度，浊度和温度等。物联网提供接口，可以随时随地远程监控和操作。本系统采用Intel Galileo Gen 2板作为接口器件。在水环境监测中对系统进行了性能测试，取得了较好的效果。
{"title":"Advanced smart sensor interface in internet of things for water quality monitoring","authors":"P. Salunke, J. Kate","doi":"10.1109/ICDMAI.2017.8073529","DOIUrl":"https://doi.org/10.1109/ICDMAI.2017.8073529","url":null,"abstract":"An interfacing device plays important role in data acquisition systems. However, different signal types of sensors in general are limited by these devices. Gradual improvement of IoT has lead to a revolution in the wireless communication; this scenario facilitates various applications from environmental monitoring to industrial management. Wired networks are mostly used to transfer data by connecting sensor. It is advantageous as it provides reliable communication system for instruments and controls. But connecting the sensors with a wired network will be costly hence low cost wireless technologies are much needed to cut down the cost. A novel approach is proposed in this paper to design smart sensor interface for water quality monitoring in IoT environment. Different sensors are available for water quality monitoring which are used to check the quality on following parameters i.e. pH, dissolved oxygen concentration, turbidity and temperature etc. IoT provide interface to monitor and operate remotely from anywhere and anytime. Intel Galileo Gen 2 board is used as interfacing device in our proposed system. The system performance is tested on water environment monitoring and improved results are gained.","PeriodicalId":368507,"journal":{"name":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124890106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICDMAI.2017.8073519
Debarati Maiti, Deepti Rajagopal, Akankshya Kar, P. B. Ramteke, S. Koolagudi
The Cathode Ray Tube (CRT) experiment performed by J. J. Thomson, is one of the most well-known physical experiments, which led to the discovery of electrons. The experiment could also describe characteristic properties, essentially, its affinity towards positive charge, and its charge to mass ratio. This paper describes the simulation of J. J. Thomson's Cathode Ray Tube experiment. The major contribution of this work is the new approach for modelling this experiment, with a great deal of accuracy and precision, using the equations of physical laws to describe the motion of the electrons. The motion of the electrons can be manipulated and recorded by the user, by assigning different values to the experimental parameters. This can be used as a good learning tool by the needy.
{"title":"Simulation of cathode ray tube","authors":"Debarati Maiti, Deepti Rajagopal, Akankshya Kar, P. B. Ramteke, S. Koolagudi","doi":"10.1109/ICDMAI.2017.8073519","DOIUrl":"https://doi.org/10.1109/ICDMAI.2017.8073519","url":null,"abstract":"The Cathode Ray Tube (CRT) experiment performed by J. J. Thomson, is one of the most well-known physical experiments, which led to the discovery of electrons. The experiment could also describe characteristic properties, essentially, its affinity towards positive charge, and its charge to mass ratio. This paper describes the simulation of J. J. Thomson's Cathode Ray Tube experiment. The major contribution of this work is the new approach for modelling this experiment, with a great deal of accuracy and precision, using the equations of physical laws to describe the motion of the electrons. The motion of the electrons can be manipulated and recorded by the user, by assigning different values to the experimental parameters. This can be used as a good learning tool by the needy.","PeriodicalId":368507,"journal":{"name":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117200164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICDMAI.2017.8073478
Shriroop C. Madiwalar, M. Wyawahare
Anthracnose and leaf spot (red rust) are the common diseases affecting the mango plant. Mango being economically important, the detection of these diseases is critical for avoiding epidemics and loss of yield. A machine vision approach has been proposed for plant disease identification using colour images of mango leaves. This approach included using YCbCr converted image and creating a feature vector of textural and colour features of the input images which are fed to the classifier during the testing phase. GLCM, colour based technique and Gabor filter were used for texture and colour feature extraction. Comparison of results obtained using a Minimum distance classifier and using Support Vector Machine (SVM) has been done. Analysis of the feature extraction techniques was performed to obtain individual results for each technique. The overall results gave a classification accuracy of 79.16% and 83.34% for Minimum distance classifier and Support Vector Machine respectively over a database of 86 images.
{"title":"Plant disease identification: A comparative study","authors":"Shriroop C. Madiwalar, M. Wyawahare","doi":"10.1109/ICDMAI.2017.8073478","DOIUrl":"https://doi.org/10.1109/ICDMAI.2017.8073478","url":null,"abstract":"Anthracnose and leaf spot (red rust) are the common diseases affecting the mango plant. Mango being economically important, the detection of these diseases is critical for avoiding epidemics and loss of yield. A machine vision approach has been proposed for plant disease identification using colour images of mango leaves. This approach included using YCbCr converted image and creating a feature vector of textural and colour features of the input images which are fed to the classifier during the testing phase. GLCM, colour based technique and Gabor filter were used for texture and colour feature extraction. Comparison of results obtained using a Minimum distance classifier and using Support Vector Machine (SVM) has been done. Analysis of the feature extraction techniques was performed to obtain individual results for each technique. The overall results gave a classification accuracy of 79.16% and 83.34% for Minimum distance classifier and Support Vector Machine respectively over a database of 86 images.","PeriodicalId":368507,"journal":{"name":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116167870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICDMAI.2017.8073536
B. Nikita, P. Balasubramanian, Lakshmi Yermal
The stock market is referred as the barometer of Indian economy; it is the indicator of the country's economic condition. Many studies have established the relationship between Indian stock returns and macro economic variables such as gold price, oil price, exchange rate, etc. This study investigates the relationship between the Indian stock returns and the Macro economic variables viz interest rate of India, interest rate of USA, inflation rate of India, inflation rate of USA, GDP growth rate of India and GDP growth rate of USA. Quarterly data was collected for a period from January, 2000 to December, 2015 for all the macro economic variables. Regression Model was used to analyze the data, the variables were tested for stationarity, serial correlation, heteroscedasticity and normality. The study found that the GDP growth rate of India and USA are the significant predictors of S&P CNX Nifty return.
{"title":"Impact of key macroeconomic variables of India and USA on movement of the Indian stock return in case of S&P CNX nifty","authors":"B. Nikita, P. Balasubramanian, Lakshmi Yermal","doi":"10.1109/ICDMAI.2017.8073536","DOIUrl":"https://doi.org/10.1109/ICDMAI.2017.8073536","url":null,"abstract":"The stock market is referred as the barometer of Indian economy; it is the indicator of the country's economic condition. Many studies have established the relationship between Indian stock returns and macro economic variables such as gold price, oil price, exchange rate, etc. This study investigates the relationship between the Indian stock returns and the Macro economic variables viz interest rate of India, interest rate of USA, inflation rate of India, inflation rate of USA, GDP growth rate of India and GDP growth rate of USA. Quarterly data was collected for a period from January, 2000 to December, 2015 for all the macro economic variables. Regression Model was used to analyze the data, the variables were tested for stationarity, serial correlation, heteroscedasticity and normality. The study found that the GDP growth rate of India and USA are the significant predictors of S&P CNX Nifty return.","PeriodicalId":368507,"journal":{"name":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","volume":"340 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116476890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICDMAI.2017.8073507
S. Kulkarni, Ajinkya B. Parit, V. B. Pulavarthi, Sagar S. Patil
The polyphase systems plays vital role in electrical systems due to its various advantages in power transmission and drives. In balanced polyphase system, due to the disturbances unbalances may occur which may depend on construction of winding and type of load. Therefore it is very essential to analyze the system components like voltages and currents. To carry out this analysis in phase components is tedious. Sequence components analysis is widely used in power sector to analyze the unbalanced systems and faults. This paper presents mathematical analysis of three phase, five phase and six phase symmetrical components and to simplify the understanding of sequence components comparative performance is analyzed using MATLAB script.
{"title":"Comparative analysis of three phase, five phase and six phase symmetrical components with MATLAB","authors":"S. Kulkarni, Ajinkya B. Parit, V. B. Pulavarthi, Sagar S. Patil","doi":"10.1109/ICDMAI.2017.8073507","DOIUrl":"https://doi.org/10.1109/ICDMAI.2017.8073507","url":null,"abstract":"The polyphase systems plays vital role in electrical systems due to its various advantages in power transmission and drives. In balanced polyphase system, due to the disturbances unbalances may occur which may depend on construction of winding and type of load. Therefore it is very essential to analyze the system components like voltages and currents. To carry out this analysis in phase components is tedious. Sequence components analysis is widely used in power sector to analyze the unbalanced systems and faults. This paper presents mathematical analysis of three phase, five phase and six phase symmetrical components and to simplify the understanding of sequence components comparative performance is analyzed using MATLAB script.","PeriodicalId":368507,"journal":{"name":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133431754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICDMAI.2017.8073494
Neha Sharma, Deepali Sawai, Ganesh G Surve
Most of the Big Data use-cases today are analyzing customer behaviour, their buying patterns, their likes and dislikes as expressed in social media. The business generates profuse data in the form of clickstreams on authentic websites and social media by all the stakeholders of the business, location information from their mobile devices and machine-generated data. The key elements are Intelligent Connected Machines with Internet and advanced sensors for data capture, Controls for automation, and software applications. We are witnessing the third revolution, following industrial revolution, Internet revolution, and now, the Internet powered by Big Data. Therefore, Big Data systems is a next frontier for any business. In this paper, three case studies related to Big Data have been presented especially for cell phone industry, e-commerce and online insurance selling. Big Data Analysing Engine has been proposed to identify, collect, store and analyse big data for the success of the business.
{"title":"Big data analytics: Impacting business in big way","authors":"Neha Sharma, Deepali Sawai, Ganesh G Surve","doi":"10.1109/ICDMAI.2017.8073494","DOIUrl":"https://doi.org/10.1109/ICDMAI.2017.8073494","url":null,"abstract":"Most of the Big Data use-cases today are analyzing customer behaviour, their buying patterns, their likes and dislikes as expressed in social media. The business generates profuse data in the form of clickstreams on authentic websites and social media by all the stakeholders of the business, location information from their mobile devices and machine-generated data. The key elements are Intelligent Connected Machines with Internet and advanced sensors for data capture, Controls for automation, and software applications. We are witnessing the third revolution, following industrial revolution, Internet revolution, and now, the Internet powered by Big Data. Therefore, Big Data systems is a next frontier for any business. In this paper, three case studies related to Big Data have been presented especially for cell phone industry, e-commerce and online insurance selling. Big Data Analysing Engine has been proposed to identify, collect, store and analyse big data for the success of the business.","PeriodicalId":368507,"journal":{"name":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129496191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICDMAI.2017.8073487
Mukund B. Maskar, A. Thorat, Iranna Korachgaon
The main motto of power system is to provide reliable power supply with most economical cost. To achieve this motto, there is need of various tools which can monitor, analyze and control the power system. Few of them are Economic Dispatch (ED) and optimal power flow (OPF). ED has aims to plan committed generating units to fulfill load demand for minimizing cost without violating equality and inequality constraints. OPF problem is tool to achieve optimal state of control variables by minimizing desired objective with satisfying all related constraints. The conventional methods are used to solve OPF but due to hybrid generation scenario the system become more complex where conventional method fails to achieve objective. So nature inspired called artificial intelligence methods inspected to solve complex OPF problem. The focus of this paper to prompt review study on various optimization techniques used to solve OPF for better understanding of all methods used in past.
{"title":"A review on optimal power flow problem and solution methodologies","authors":"Mukund B. Maskar, A. Thorat, Iranna Korachgaon","doi":"10.1109/ICDMAI.2017.8073487","DOIUrl":"https://doi.org/10.1109/ICDMAI.2017.8073487","url":null,"abstract":"The main motto of power system is to provide reliable power supply with most economical cost. To achieve this motto, there is need of various tools which can monitor, analyze and control the power system. Few of them are Economic Dispatch (ED) and optimal power flow (OPF). ED has aims to plan committed generating units to fulfill load demand for minimizing cost without violating equality and inequality constraints. OPF problem is tool to achieve optimal state of control variables by minimizing desired objective with satisfying all related constraints. The conventional methods are used to solve OPF but due to hybrid generation scenario the system become more complex where conventional method fails to achieve objective. So nature inspired called artificial intelligence methods inspected to solve complex OPF problem. The focus of this paper to prompt review study on various optimization techniques used to solve OPF for better understanding of all methods used in past.","PeriodicalId":368507,"journal":{"name":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132435093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICDMAI.2017.8073524
P. Kanawade, S. Gundal
In this paper, the Tree-Structured Vector Quantization (TSVQ) method for proficient speech compression is presented. Efficient utilization of memory is always needed when analog-encoded or digitized data such as image, audio, videos, portable files are need to store and/or convey to digital channels. Compression offers betterments with storage requirements while transmitting the encoded signals with lossy and lossless techniques. Lossy compression is always intended for compression of high volume data with Scalar Quantization (SQ) and Vector Quantization (VQ). The Tree based VQ method is used with hieratically organized binary sequences of codeword of data (speech) for compression with reduced and minimized arithmetic calculation requirements. Speech compression has been gained by compressed-codebook coefficients and structured in binary fashion. The quantization noise ratio with signal power is obtained efficiently around less than 1.082 dB. Shared codebook method described in this TSVQ algorithm achieves 3.6 reduced storage requirements of factor 5 to 3.
{"title":"Tree structured vector quantization based technique for speech compression","authors":"P. Kanawade, S. Gundal","doi":"10.1109/ICDMAI.2017.8073524","DOIUrl":"https://doi.org/10.1109/ICDMAI.2017.8073524","url":null,"abstract":"In this paper, the Tree-Structured Vector Quantization (TSVQ) method for proficient speech compression is presented. Efficient utilization of memory is always needed when analog-encoded or digitized data such as image, audio, videos, portable files are need to store and/or convey to digital channels. Compression offers betterments with storage requirements while transmitting the encoded signals with lossy and lossless techniques. Lossy compression is always intended for compression of high volume data with Scalar Quantization (SQ) and Vector Quantization (VQ). The Tree based VQ method is used with hieratically organized binary sequences of codeword of data (speech) for compression with reduced and minimized arithmetic calculation requirements. Speech compression has been gained by compressed-codebook coefficients and structured in binary fashion. The quantization noise ratio with signal power is obtained efficiently around less than 1.082 dB. Shared codebook method described in this TSVQ algorithm achieves 3.6 reduced storage requirements of factor 5 to 3.","PeriodicalId":368507,"journal":{"name":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123300472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICDMAI.2017.8073532
K. Pranesh, P. Balasubramanian, Deepti Mohan
This study examines the determinants of India's implied volatility index (VIX). The factors considered are Purchasing Managers Index (PMI), Business Confidence Index (BCI), Net activity of Foreign Institutional Investors (FII) and Net activity of Domestic Institutional Investors (DII). In this study Granger causality is used to find whether these factors cause IndiaVIX. This study confirms that only BCI has significant and positive impact with IndiaVIX and other factors such as PMI, FII and DII do not have any significant impact on India VIX. The results show that FII has a significant and negative impact on DII and hence these two factors do not have a significant impact on IndiaVIX.
{"title":"The determinants of India's implied volatility index","authors":"K. Pranesh, P. Balasubramanian, Deepti Mohan","doi":"10.1109/ICDMAI.2017.8073532","DOIUrl":"https://doi.org/10.1109/ICDMAI.2017.8073532","url":null,"abstract":"This study examines the determinants of India's implied volatility index (VIX). The factors considered are Purchasing Managers Index (PMI), Business Confidence Index (BCI), Net activity of Foreign Institutional Investors (FII) and Net activity of Domestic Institutional Investors (DII). In this study Granger causality is used to find whether these factors cause IndiaVIX. This study confirms that only BCI has significant and positive impact with IndiaVIX and other factors such as PMI, FII and DII do not have any significant impact on India VIX. The results show that FII has a significant and negative impact on DII and hence these two factors do not have a significant impact on IndiaVIX.","PeriodicalId":368507,"journal":{"name":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","volume":"595 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123360762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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