Sentiment analysis and prediction of Indian stock market amid Covid-19 pandemic

Gondaliya, Chetan; Patel, Ajay; Shah, Tirthank

doi:10.1088/1757-899x/1020/1/012023

Cited by 17 publications

(5 citation statements)

References 18 publications

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“…To be specific, Long relied exclusively on historical stock prices to forecast future values, while Singh used several technical indicators to augment the historical data [7]. Besides, various methods, such as Random Forest, Naïve Bayes, Decision Trees, and Support Vector Machines, are used to evaluate the accuracy of classifying stock market fluctuations [8]. Also, Bharadwaj articulated the importance of sentiment analysis to obtain an exact forecast of stock price [9].…”

Section: Literature Reviewmentioning

confidence: 99%

The prediction of Apple stock price based on linear regression model and random forest model

Gao

2024

TNS

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In the financial market, due to various factors, the stock price fluctuation is universal. Therefore, the directional prediction of stock market price based on technical analysis is very important in stock investment. This paper conducted a regression analysis and forecasted the future trends in the close price of Apple stock through recent five years between 2018 and 2023. For the purpose of this specific study, this paper did descriptive statistical analysis of the dataset, and made graphs and analyses of regression and predictions relied on the techniques of the Linear Regression Model and Random Forest Model. Based on the three indices: MSE, RMSE, and MAE, the paper compared the advantages and disadvantages of the two machine learning methods. The result of the experiments indicated that the regression generated through employment of the Linear Regression Model outperforms the result of the Random Forest Model, leading to the conclusion that Linear Regression Model is a more effective method to forecast in this dataset.

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Section: Literature Reviewmentioning

confidence: 99%

The prediction of Apple stock price based on linear regression model and random forest model

Gao

2024

TNS

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“…A sentiment analyzer that clusters the tweets based on their sentiments and uses it for price movement prediction via machine learning models is presented in [14]. A study on the perception of people regarding the stock markets during the time of the COVID pandemic for the prediction of stock prices has also been undertaken in [15]. The paper makes use of six machine learning algorithms, namely, Decision Tree method, Random Forest method, Logistic Regression method, Naïve Bayes method, Support Vector Machine method, and the KNN method.…”

Section: Related Workmentioning

confidence: 99%

A Blended Approach to Analyze Indian Stock Market during COVID-19

Kochhar¹,

Sharma²,

Jain³

et al. 2023

IJCDS

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The coronavirus (COVID-19) outbreak has proven to be one of the most defining health and socio-economic crisis of this century. The effect of the coronavirus pandemic on the Indian economy has been particularly disturbing. This work blends statistical and semantic approaches to analyze the effect of the COVID-19 pandemic on the Indian stock market. It empirically investigates the impact of the significant events such as lockdown, unlock, and vaccination announcements on eight key NIFTY-50 sectors of the Indian stock market, using event-based statistical analysis. The micro blogging platform Twitter is deployed to extract COVID-19 related tweets, pertaining to the Indian stock market, and perform sentiment analysis to analyze and understand the general sentiment of consumers towards the stock market. Results and detailed Inferences are drawn from these two completely different facets viz. the objective results drawn from the stock market and the subjective perspectives drawn from the social media in order to holistically analyze the impact of the COVID 19 pandemic on the Indian stock market.

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“…On the other hand, Singh et al [37], Patel et al [33] have added various technical indicators to the historical data as well. Gonadliya et al [15] have scraped data from sources like -twitter, rss feeds and news portals and then used Bag of Words (BoW), Tf-idf, N-grams to create different feature sets. They finally used different algorithms like -Naive Bayes, Decision Trees, Logistic Regression, Random Forest and Support Vectors Machines (SVM) to compare the classification accuracy of the stock market movement.…”

Section: Related Workmentioning

confidence: 99%

Stock price prediction using BERT and GAN

Sonkiya¹,

Bajpai²,

Bansal³

2021

Preprint

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The stock market has been a popular topic of interest in the recent past. The growth in the inflation rate has compelled people to invest in the stock and commodity markets and other areas rather than saving. Further, the ability of Deep Learning models to make predictions on the time series data has been proven time and again. Technical analysis on the stock market with the help of technical indicators has been the most common practice among traders and investors. One more aspect is the sentiment analysis -the emotion of the investors that shows the willingness to invest. A variety of techniques have been used by people around the globe involving basic Machine Learning and Neural Networks. Ranging from the basic linear regression to the advanced neural networks people have experimented with all possible techniques to predict the stock market. It's evident from recent events how news and headlines affect the stock markets and cryptocurrencies. This paper proposes an ensemble of state-of-the-art methods for predicting stock prices. Firstly sentiment analysis of the news and the headlines for the company Apple Inc, listed on the NASDAQ is performed using a version of BERT, which is a pre-trained transformer model by Google for Natural Language Processing (NLP). Afterward, a Generative Adversarial Network (GAN) predicts the stock price for Apple Inc using the technical indicators, stock indexes of various countries, some commodities, and historical prices along with the sentiment scores. Comparison is done with baseline models like -Long Short Term Memory (LSTM), Gated Recurrent Units (GRU), vanilla GAN, and Auto-Regressive Integrated Moving Average (ARIMA) model.

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Sentiment analysis and prediction of Indian stock market amid Covid-19 pandemic

Cited by 17 publications

References 18 publications

The prediction of Apple stock price based on linear regression model and random forest model

The prediction of Apple stock price based on linear regression model and random forest model

A Blended Approach to Analyze Indian Stock Market during COVID-19

Stock price prediction using BERT and GAN

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