Browsing by Author "Jha, R.A."

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Gaining Actionable Insights in COVID-19 Dataset Using Word Embeddings
(Springer Science and Business Media Deutschland GmbH, 2022) Jha, R.A.; Ananthanarayana, V.S.
The field of unsupervised natural language processing (NLP) is gradually growing in prominence and popularity due to the overwhelming amount of scientific and medical data available as text, such as published journals and papers. To make use of this data, several techniques are used to extract information from these texts. Here, in this paper, we have made use of COVID-19 corpus (https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge ) related to the deadly corona virus, SARS-CoV-2, to extract useful information which can be invaluable in finding the cure of the disease. We make use of two word-embeddings model, Word2Vec and global vector for word representation (GloVe), to efficiently encode all the information available in the corpus. We then follow some simple steps to find the possible cures of the disease. We got useful results using these word-embeddings models, and also, we observed that Word2Vec model performed better than GloVe model on the used dataset. Another point highlighted by this work is that latent information about potential future discoveries are significantly contained in past papers and publications. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
GARCH Model Identification for Stock Crises Events
(Elsevier B.V., 2020) Naik, N.; Mohan, B.R.; Jha, R.A.
The stock market crash is a sudden dramatic decline of stock price due to uncertainty in the stock market. The stock prices are the influence of many factors, such as global trends, local trends, and economic conditions. The identification stock crisis is a challenging task for stock traders and investors. The goal of this paper is to forecast stock crisis events. The experiment is carried out in two steps. First is the least square (LS) method, and the least absolute deviation (LAD) is considered to identify a correlation between mean and median. Based on the correlation between mean and median, the GARCH (General autoregression conditional heteroskedasticity) model proposed to calculate the error distribution in stock returns. To identify the appropriate error distribution, we have varied the degree of t distribution parameters. In the second step, the volatility of stock prices is given as input to the GARCH model to forecast the future crisis events. To carry out the proposed experiment, we have considered Infosys and sbi stock. Experiment results reduce the error in predicting stock crises events. Â© 2020 The Authors. Published by Elsevier B.V.
GARCH-Model Identification based on Performance of Information Criteria
(Elsevier B.V., 2020) Naik, N.; Mohan, B.R.; Jha, R.A.
The stock market prices are volatile due to influence by many factors such as global trends, local trends, and economic conditions. Identification of Generalized autoregressive conditional heteroscedasticity(GARCH) order for stock data is a challenging task due to more fluctuation in stock prices and high variance in data. GARCH is considered to model the conditional volatility of a stock time series. Stock markets data often exhibit volatility clustering. Though many models which belong to autoregressive conditional heteroscedasticity (ARCH) family has proposed, but all the previous studies gave their affirmative consent on the performance of GARCH (1,1), which is considered the standard model, maybe because of the belief held by many researchers that the first lag of conditional variance can capture all the volatility clustering. This can be highly misguiding, especially when the stock market data has high order variance. The focus of this work is to make use of existing, well-known Information Criteria (IC) to identify the stock indices data-generating-process whenever the GARCH effect is present. Akaike Informations Criteria (AIC), Bayesian Information Criteria(BIC), and Hannan-Quinn information(HQ) criteria have used for this experiment. We studied different models with different parameter values and observed the abilities of information criterion in choosing the correct model from a given pool of models. For higher-order GARCH models and high sample sizes, AIC was able to correctly predict the model with high probability, while BIC and HQ performed well for smaller order models. Â© 2020 The Authors. Published by Elsevier B.V.