A novel stock recommendation system using Guba sentiment analysis

Sun, Yunchuan; Fang, Mengting; Wang, Xinyu

doi:10.1007/s00779-018-1121-x

Cited by 38 publications

(16 citation statements)

References 25 publications

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“…In the prior work of our team (see [ 20 ]), we use the method introduced by Antweiler and Frank [ 29 ] to synthesize sentiment index of a single stock as follows: where N p and N n represent the number of positive posts and negative posts, respectively.…”

Section: Data and Approachmentioning

confidence: 99%

“…For example, Garćıa [ 18 ] used the proportion of positive and negative words in the two-column financial news of the New York Times to measure investor sentiment. (3) Text-based data from social media and online financial forums , such as Twitter [ 19 , 29 , 30 ], Facebook [ 31 ], StockTwits [ 32 ], Sina Guba 1 [ 20 , 21 ], and Eastmoney Guba 2 [ 20 , 21 ]. Compared with survey-based and market-based sentiment proxies, text-based measures are available with much higher frequency and with larger data volume.…”

Section: Introductionmentioning

confidence: 99%

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What investors say is what the market says: measuring China’s real investor sentiment

Sun

Zeng

Zhou

et al. 2021

Pers Ubiquit Comput

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This paper describes a novel approach to measure individual investor sentiment using text-based analysis of millions of posts extracted from Chinese financial online forums. We describe how we built a database of more than 200 million stock posts from online financial forums, created GubaLex , a sentiment dictionary consisting of 48,878 words to allow sentiment analysis, and how we developed GubaSenti , an individual investor sentiment index for the stock market in China. This allowed (1) the first systemic measurement of individual investor sentiment in China; (2) an approach to text-based analysis that reflects investor sentiment about millions of posts about stocks listed in Guba ; (3) a way to flexibly measure investor sentiment of a single stock, a sector or an industry and the whole market; and (4) made this possible for daily, weekly, monthly, quarterly, and yearly time periods. We also examine the relationship of the sentiment proxy and stock returns and compare it with two typical BW metrics in China. Empirical results show that GubaSenti correlates better with market performance than BW metrics in China and can be used to predict market changes in the short term.

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Section: Data and Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

What investors say is what the market says: measuring China’s real investor sentiment

Sun

Zeng

Zhou

et al. 2021

Pers Ubiquit Comput

Self Cite

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“…Sun et al propose a novel stock recommendation system using Guba sentiment analysis [10]. Investment recommendation has been one of the hottest topics in the finance area which can help investors to get more profits and to avoid loss.…”

Section: Review Of the Contributionsmentioning

confidence: 99%

Advancing researches on IoT systems and intelligent applications

Sun

Zhang

Bie

et al. 2018

Pers Ubiquit Comput

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“…Given the widespread access to market data, the purely quantitative nature of the problem and its financial appeal, there has been a large amount of literature devoted to the stock market. Most efforts have been focused on identifying strategies for picking stocks [4,5,11,12,14,17] or portfolios [2,9,10] which are likely to be profitable in the future. Although these approaches are more akin to trading, they fall within the scope of non-personalized recommendation.…”

Section: Related Workmentioning

confidence: 99%

Large-scale recommendation for portfolio optimization

Swezey

Charron

2018

Proceedings of the 12th ACM Conference on Recommender Systems

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Individual investors are now massively using online brokers to trade stocks with convenient interfaces and low fees, albeit losing the advice and personalization traditionally provided by fullservice brokers. We frame the problem faced by online brokers of replicating this level of service in a low-cost and automated manner for a very large number of users. Because of the care required in recommending financial products, we focus on a riskmanagement approach tailored to each user's portfolio and risk profile. We show that our hybrid approach, based on Modern Portfolio Theory and Collaborative Filtering, provides a sound and effective solution. The method is applicable to stocks as well as other financial assets, and can be easily combined with various financial forecasting models. We validate our proposal by comparing it with several baselines in a domain expert-based study. CCS CONCEPTS• Information systems Ñ Recommender systems; • Theory of computation Ñ Convex optimization; • Applied computing Ñ Economics.

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A novel stock recommendation system using Guba sentiment analysis

Cited by 38 publications

References 25 publications

What investors say is what the market says: measuring China’s real investor sentiment

What investors say is what the market says: measuring China’s real investor sentiment

Advancing researches on IoT systems and intelligent applications

Large-scale recommendation for portfolio optimization

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