Trading on online social mood: A machine learning strategy based on Twitter sentiment

Chengying He, Mason Lin and Ning Wang
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Chengying He: Sino-UK Blockchain Industry Research Institute, Guangxi University, Nanning, P. R. China
Mason Lin: Davidson College Davidson, NC 28035, USA
Ning Wang: Oxford Nie Financial Big Data Laboratory, Mathematical Institute, University of Oxford, Oxford, UK

International Journal of Financial Engineering (IJFE), 2021, vol. 08, issue 04, 1-16

Abstract: This paper examines the potential of using online social sentiment data in algorithmic trading strategies. Several machine learning models are tested to produce a trading signal from the sentiment data to forecast the trend of a stock’s price. The algorithms are trained on the features extracted from PsychSignal data (containing bullish and bearish sentiment from Twitter). One most popular model, Random Forest (RF) classifier, is selected to generate a signal for the trading strategy. After backtesting on 1386 stocks listed in both NYSE and NASDAQ, the results show that the proposed model outperforms the baseline model, a simple moving average (SMA) strategy. We use the GridSearchCV to fine-tune the parameters of the classifier and compare the performance with the SMA baseline and the SPY benchmark, showing that our model generates 114.5% return on investment from January 2013 through October 2015. Additionally, the portfolios constructed by the RF classifier appear to produce a higher return than portfolios constructed by an SMA strategy. The results show that Twitter sentiment data is a valuable technical trading indicator for specific sectors.

Keywords: Machine learning; online sentiment; social media (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S2424786321410115

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