Sentiment Analysis of Indian Stock Market Volatility

Paramanik, Rajendra Narayan; Singhal, Vatsal

doi:10.1016/j.procs.2020.08.035

Cited by 17 publications

(4 citation statements)

References 22 publications

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“…Table6indicates the presence of ARCH effect in the model. The p-values stated in the table are above 0.05, which indicates that there is no ARCH effect in the model(Paramanik & Singhal, 2020).…”

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confidence: 88%

Repercussion of Covid 19 Upsurge: An Analysis on the Efficaciousness of Autoregressive Models in Volatility and Return Estimation of Bitcoin

M P

2021

CBR

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Cryptocurrencies, especially Bitcoin, is a hot commodity today. High volatility is a common feature of almost all the cryptocurrencies in the world. A systematic exploration and examination of the volatility of cryptos enables the investor to earn more on their investments. After the outbreak of the COVID-19 pandemic, the crypto market witnessed a highly volatile situation with a huge increase in price. The pandemic also affected the volatility and return of Bitcoin. This research aims to analyse and compare the risk and volatility characteristics of Bitcoin after the outbreak of the COVID-19 pandemic. The study further tests the capacity of several autoregressive models, such as ARMA, GARCH, EGARCH, and TARCH in estimating and evaluating the return and volatility associated with Bitcoin. Identified models were tested and compared with the help of Akaike information criteria (AIC) and Schwarz information criteria (SIC). For this article, the data of daily adjusted closing price of Bitcoin INR (BTC-INR) were collected from Yahoo Finance during the period January, 2017 to December, 2021. We witnessed a huge change in the daily average return of Bitcoin after the COVID-19 outbreak. Also, we identified TARCH (1, 1) as the best model in the ARCH family for evaluating and estimating volatility and ARMA (10, 10) as the best model for predicting the return of Bitcoin.

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confidence: 88%

Repercussion of Covid 19 Upsurge: An Analysis on the Efficaciousness of Autoregressive Models in Volatility and Return Estimation of Bitcoin

M P

2021

CBR

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“…We have considered stock market indices from some developed and mature economies like the USA and Japan. We have also considered indices from a developing economy like India having high volatility and noise [22]. Description of all the datasets used in this paper is summarised in Table 1 Stock market data is usually noisy [23], and this can lead to inaccurate forecasting results.…”

Section: Datasets Usedmentioning

confidence: 99%

Fusion of Wavelet Decomposition and N-BEATS for improved Stock Market Forecasting

Singhal

Neeraj

Mathew

et al. 2022

Preprint

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Stock market forecasting is one of the most exciting areas of time series forecasting both for the industry and academia. Stock market is a complex, non-linear and non-stationary system with many governing factors and noise. Some of these factors can be quantified and modeled, whereas some factors possess a random walk behavior making the process of forecasting challenging. Various statistical methods, machine learning, and deep learning techniques are prevalent in stock market forecasting. Recently, there has been a paradigm shift towards hybrid models, showing some promising results. In this paper, we propose a technique that combines a recently proposed deep learning architecture N-BEATS with wavelet transformation for improved forecasting of future prices of stock market indices. This work uses daily time series data from five stock market indices, namely NIFTY 50, Dow Jones Industrial Average (DJIA), Nikkei 225, BSE SENSEX, and Hang Seng Index (HSI), for the experimental studies to compare the proposed technique with some traditional deep learning techniques. The empirical findings suggest that the proposed architecture has high accuracy as compared to some traditional time series forecasting methods and can improve the forecasting of non-linear and non-stationary stock market time series.2010 MSC: 00-01, 99-00

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“…What is relatively easy for people to measure subjectively is face-to-face communication. For example, whether a person is happy or sad, smiley or angry with the subject matter at hand, must be transformed into objective, quantifiable scores that account for the numerous nuances in the human language in a debate [5,6]. Here in this study, SA is the practice of evaluating the reviews of online product datasets from Amazon provided by customers to identify whether they are good, negative, or neutral.…”

Section: Introductionmentioning

confidence: 99%

Affirmative Ant Colony Optimization Based Support Vector Machine for Sentiment Classification

Hamdi

2022

Electronics

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Sentiment analysis is part of contextual text mining, which detects, extracts and supports an organization in understanding their brand or service in social sentiment while monitoring the reviews provided by customers in online shops. The rise of online shopping and digitalization is practically achieved, and the quality of products is tough for users to judge. There is no model to find out about the same or unlike a set of people with similar sentiment analysis concerning online product evaluations. In this paper optimization-based classification algorithm is proposed namely, Affirmative Ant Colony Optimization Based Support Vector Machine (AACOSVM) to classify sentiments provided by customers in online shopping. This paper provides a new Ant Colony Optimization method via providing a novel pheromone model for support vector machine optimization parameters in two steps. The first one is statute of state transition, and the second step is statute of state updates. They aim to allow the ants to use the fake pheromone path to pick parameters and to motivate ants to create subsets having the least classification mistakes. The proposed work includes product review datasets from Amazon to assess the performance of the AACOSVM against existing classifiers, namely, Entropy-Based Classifier (EBC) and Enhanced Feature Attention Network (EFAN). Various review datasets are accessible at Amazon for various items. This research effort has identified a dataset from DVDs, books, kitchen appliances and electronics from the many multiple available review datasets. It utilizes the natural foraging behavior of ants towards searching for food to identify and classify the sentiments present in the product reviews. AACOSVM is evaluated using two standard data mining performance metrics, namely F-Measure and Classification Accuracy. Results indicate that the proposed classification algorithm AACOSVM achieves better F-Measure and Classification Accuracy than the EBC and EFAN classifiers.

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Sentiment Analysis of Indian Stock Market Volatility

Cited by 17 publications

References 22 publications

Repercussion of Covid 19 Upsurge: An Analysis on the Efficaciousness of Autoregressive Models in Volatility and Return Estimation of Bitcoin

Repercussion of Covid 19 Upsurge: An Analysis on the Efficaciousness of Autoregressive Models in Volatility and Return Estimation of Bitcoin

Fusion of Wavelet Decomposition and N-BEATS for improved Stock Market Forecasting

Affirmative Ant Colony Optimization Based Support Vector Machine for Sentiment Classification

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