Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    Measuring the influence of moods on stock market using Twitter analysis
    (Springer Verlag service@springer.de, 2019) Cowlessur, S.K.; Annappa, B.; Sree, B.K.; Gupta, S.; Velaga, C.
    It is a well-known fact that sentiments play a vital role and is an incredibly influential tool in several aspects of human life. Sentiments also drive proactive business solutions. Studies have shown that the more appropriate data is gathered and analyzed at the right time, the higher the success of sentiment analysis. This paper analyses the correlation between the public mood and the variation in stock prices towards companies in different domains. For each tweet, scores are assigned to eight predefined moods namely “Joy†, “Sadness†, “Fear†, “Anger†, “Trust†, “Disgust†, “Surprise†and “Anticipation†. A regression model is applied to the mood scores and the stock prices dataset to obtain the R-squared score, which is a metric used to evaluate the model. The paper aims to find the moods that best reflect the stock values of the respective companies. From the results, it is observed that there is a definite correlation between public mood and stock market. © Springer Nature Singapore Pte Ltd. 2019.
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    Log Periodic Power Law Fitting on Indian Stock Market
    (Springer, 2020) Naik, N.; Mohan, B.R.
    Stock price prediction is one of the challenging tasks for researchers and academics due to frequent changes in stock prices. The stock prices are speculation, and it purely depends on the demand and supply of the market during the trading session. Most of the existing work approach is foresting stock prices using machine learning methods. There has been a limited number of studies on stock crisis identification. Log periodic power law (LPPL) is one of the approaches to identify bubbles in the stock market before crises happened. By looking at existing work, we found that LPPL has not applied in the Indian stock market. In this paper, we have considered LPPL to identify a bubble in the Indian stock market. Due to fluctuation in the market, stock price follows the nonlinearity behavior, hence LPPL is considered to fit the equations. The experiment is carried out R Studio platform. © 2020, Springer Nature Singapore Pte Ltd.
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    Deep Learning for Stock Index Tracking: Bank Sector Case
    (Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2021) Arjun, R.; Suprabha, K.R.; Majhi, R.
    The current study explores the efficacy of deep learning models in stock market prediction specific to banking sector. The secondary data of major fundamental indicators and technical variables during 2004–2019 periods of two banking indices, BSE BANKEX and NIFTY Bank of Bombay stock exchange and National stock exchange, respectively, are collected. The factors impacting market index prices were analyzed using nonlinear autoregressive neural network. Preliminary findings contradict the general random walk hypothesis theory and model improvement over previous studies. The implications from practical and theoretical perspective for stakeholders are discussed. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.