Analysis of Stock Market Fluctuations Incidental to Internet Trends

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.