Abstract
In this work we show that the sentiment of the broader stock market, namely the S&P 500, is related to the activity of individual stocks intraday. We introduce a concept we term as embedded context which is an approach to improving unigram language models for restricted use cases. We use a Gaussian Mixture Model to create different sentiment regimes (i.e. distributions) of the broader market over our training period and perform an analysis of the return and volatility characteristics of each stock per each regime. We create an intraday momentum trading strategy using a moving average and Relative Strength Index (RSI) over our testing period with no consideration to our prior sentiment regime analysis which serves as our baseline model. We then create an updated version of our intraday trading strategy which considers the sentiment regime of the broader market. Our results show an improvement in each stock’s intraday strategy performance as a result of considering the broader market’s sentiment regime.
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Coleman, L., Milanova, M. (2019). Human Computer Interaction with Multivariate Sentiment Distributions of Stocks Intraday. In: Stephanidis, C. (eds) HCI International 2019 - Posters. HCII 2019. Communications in Computer and Information Science, vol 1034. Springer, Cham. https://doi.org/10.1007/978-3-030-23525-3_8
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DOI: https://doi.org/10.1007/978-3-030-23525-3_8
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