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Analysis of Stock Market Fluctuations Incidental to Internet Trends

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Advances in Artificial Intelligence and Data Engineering (AIDE 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1133))

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Abstract

Time-series data pertaining to several related domains plays an important role as reinforcement for understanding the behaviour of that ecosystem. This paper intends to analyse one such system through the inspection of the rise/fall of stock prices for a duration in relation to buzzwords trending over the social media during the same time. We present a pipeline to elicit these insights and facilitate prediction of stock prices. In summary, the pooled data is subjected to time-series operations such as differencing transformations, smoothing and filtering to transform the data to a format amenable for further processing. Then, the trend corresponding to the keywords is illustrated by modelling their popularity using a counting-based algorithm customized for the application. This is followed by an attempt to forecast stock prices. The entire system is bundled into a software application that effectively delivers the results and visualizations in an intuitive fashion for a naive user. Throughout the pipeline, we have used a comparative paradigm that has helped us assess the proposed solution against alternatives.

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Author information

Authors and Affiliations

  1. Department of Computer Science, PES University, Bengaluru, India

    Vinayaka R. Kamath, Nikhil V. Revankar & Gowri Srinivasa

Authors
  1. Vinayaka R. Kamath
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  2. Nikhil V. Revankar
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  3. Gowri Srinivasa
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Corresponding author

Correspondence to Vinayaka R. Kamath .

Editor information

Editors and Affiliations

  1. NMAM Institute of Technology, Udupi, India

    Niranjan N. Chiplunkar

  2. Ritsumeikan University, Shiga, Japan

    Takanori Fukao

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Kamath, V.R., Revankar, N.V., Srinivasa, G. (2021). Analysis of Stock Market Fluctuations Incidental to Internet Trends. In: Chiplunkar, N.N., Fukao, T. (eds) Advances in Artificial Intelligence and Data Engineering. AIDE 2019. Advances in Intelligent Systems and Computing, vol 1133. Springer, Singapore. https://doi.org/10.1007/978-981-15-3514-7_58

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