Social sentiment and impact in US equity market: an automated approach

A recent research topic is defined through the impact of the textual analysis on the prices of stock prices of companies which receive comments in social media. Twitters provoke an important influence on stock prices given that many persons are participating with different opinions and all participants are informed of the rest of the opinions. The analysis of text and sentiment has been applied in different contexts, health in Corti et al. (2022), presidential elections Ali et al. (2022), forecasting short-term prices of stock in Shen and Shafiq (2020), event detection in Kolajo et al. (2022) and consumer sentiment in Kaur and Sharma (2023). In the financial field, the efficient market hypothesis established by Fama in 1965 recognizes the influence on prices of the information accessible to investors (Fama 1965). In this sense the mood of the investor has an impact of time series of prices and so we can say the psychological factor affect the investments of financial agents (Shiller 2003). The impact of news dissemination has two components: the size and the direction. In general, positive news has a positive impact on returns and negative news has a negative impact on returns, see (Shiller 2003; DeGennaro and Shrieves 1997; Atkins et al. 2018; Audrino et al. 2020; Ren et al. 2020; Li et al. 2014). Sentiment not only has this impact on prices, but there also exists an asymmetric effect, i.e., negative news has a stronger impact than positive news (Mendoza-Urdiales et al. 2022). Moreover, an important issue is the prediction of prices using social sentiment.

A challenge emerges to investors and risk managers given that the information available and necessary to include in decision making process has been increasing exponentially in volume and the decision time reduced In particular, Twitter for many of the financial markets’ actors is one of the most popular and accepted methods to deliver and gather relevant information to the general public.

There are documented cases in which a fake news affects the stock price of companies. One recent relevant example is from last November 12, 2022, in which a fake Eli Lilly (Major insulin provider) Twitter account claimed that insulin was free. The announcement was not well received by investors; consequently, the stock price of the company fell 4.37%.¹ Going further, we can observe that even when a “positive” announcement was given, with initially mostly positive responses and fewer negative comments, the negative impact in the stock price supports the hypothesis in which the negative effects are greater than positive ones, an effect we have analyzed and presented in this study.

Additionally, the impact on the volatility of stock returns can be considered through the family of generalized autoregressive conditional heteroskedasticity (GARCH) models. As a contribution of this paper, we analyzed several models of this family with different lags for 2,557 companies in stocks exchanges of the US economy. Most of these companies have showed that the returns are affected in an asymmetric way using models like EGARCH and APARCH. Over 50 million tweets were extracted, analyzed, and categorized for a period of 4 months (850 hourly observations) to create negative and positive sentiment factors, which were used as independent variables in the mean equation of the GARCH models.

The objective of this study is to use all public available information mentioning the companies’ tickers and measure the sentiment. With it, calculate the total exposure of each company performance to its corresponding sentiment factors (positive and negative) independently from the rest of the companies. Finally, the questions to answer are:

The rest of this paper is structured as follows. “Related work” section introduces related works. “Problem definition” and “Method” sections represent the proposed methodology. “Results and discussion” section shows experimental and evaluation of the proposed method. Finally, “Conclusion” section concludes the paper and gives some future research directions.

Before the emergence of social networks, the question of the effect of news on the stock prices has been studied in the financial literature. For example, in Chan (2003) bad news is followed by a strong drift return using headlines about individual companies. Antweiler et al. (2004) studied the influence of messages in Yahoo! Finance and Raging Bull on 45 Dow Jones Industrial Average stock prices. The main finding is the existence of influence on market return and volatility but without predictive capability. In Dougal et al. (2012) it is shown that returns of Dow Jones Industrial Average (DJIA) can be predicted using a column from Wall Street Journal ( Abreast of the Market).

Tetlock (2007) developed an analysis of the relationship between stock market and media (Wall Street Journal news) using the vector autoregressive technique. Similarly, consequences of negative words in Wall Street Journal and Dow Jones News Service on S&P500 earnings and returns are studied in Tetlock et al. (2008) using quantification of language.

More recently has been recognized the influence of social media on the prices. For example, a sentiment analysis by topic is applied in Nguyen et al. (2015) for prediction of 18 stocks using support vector machine. In Daniel et al. (2017) the impact of popular events tweets on thirty companies of the Dow Jones Average is developed using sentiment analysis and four algorithms, after filtering the noisy tweets.

In a theoretical model, Yang and Li (2013) showed that investors sentiment has impact on the asset prices and in Bollen et al. (2011) the mood in large scale set of twitters is used for prediction of the Dow Jones Industrial Average (DJIA) and some dimensions of sentiment cause in the Granger sense the DJIA. Applying a textual analysis which works in a phrase level, Boudoukh et al. (2012) showed a strong relation of this information with the change of prices.

Heston and Sinha (2016) used 900 thousand news stories to demonstrate that returns are predicted by this news and the predictability depends on the aggregation level of news. Daily news is useful for one or two days ahead. In a study of 181 altcoins prediction, Steinert et al. (2018) used sentiment analysis to show the prediction of such altcoins prices.

In Derakhshan and Beigy (2019) support vector machine algorithm is used to demonstrate the level of prediction of stocks using sentiment and in Yang and Wu (2021) an asset pricing model is developed showing that public information has an impact on the sentiment of investors and the sentiment investors has an impact on asset price.

For the period of COVID Outbreak, Das et al. (2022) showed for the case of Nifty-50 data that the level of accuracy is better when mood is combined with news headlines together stock data. Using snapshots of information and sentiment time series Anbaee Farimani et al. (2022) studied the impact on price prediction for EUR/USD, GBP/USD, USD/JPY32 and BTC/USD.

In Figà-Talamanca and Patacca (2022) a study of the impact of textual content of newspapers or social media postings on the return and variance of components of S&P500 using a GARCH family approach is studied. In Jiang et al. (2021) a GARCH model including Baidu index is used to forecast the returns of Chinese stock during the COVID-19 period. The Baidu index affects the sentiment of the investors and therefore has an influence on the stocks returns.

In our paper we applied GARCH-type models to take into account the variance equation together the inclusion of the positive and negative indexes in the equation of the mean similar to the sentiment impact on variance developed in Olson and Nowak (2020) for Dow Jones Average using a GARCH (1,1) model and Mendoza et al. (2022) for a sample of 24 companies over a 10-year period.

Of the 2,557 analyzed companies and calculated sentiment factors paired with the hourly closing stock price, the companies with less than 30% of missing observations—independently of the reason for this—were selected for the GARCH modeling. Finally keeping 556 companies divided in three clusters presented in Table 3:

Of the 556 companies selected for the GARCH modeling, there were feasible solutions for 539 cases, in which 93.32% of the cases the asymmetric condition in which the negative factor coefficient (with negative sign) absolute value was greater than the positive factor coefficient (with positive sign). An interesting characteristic of the results is that the success rate is higher for the clusters with fewer missing observations. In Fig. 7, the R squared for the 503 companies is presented as a distribution chart; it can be appreciated that the solutions fitted models ranges up to 50%, but the majority are concentrated between 10 and 30%.

In Table 3, we present the solution for each company; it can be observed that the models orders and distributions were included, in which the solutions volatility equations varied between GARCH, EGARCH and APARCH. An Important remark is that in previous works, the sentiment factors were calculated using daily observations. In this case, we have been able to measure the asymmetric influence in hourly observations, meaning that the velocity in which the sentiment travels from the social network to the stock market occurs under an hour.

Another remark is that in all our studies, the sample data from social networks the volume of positive comments is greater than the volume of positive comments. And even with this disbalance between the data, the models have captured the asymmetric effect. In Fig. 8, there is presented the percentage of positive tweets versus negative tweets for Tesla (Ticker TSLA) in which the mean equation for an EGARCH model that satisfied the sentiment factor asymmetry resulted:

In which the negative factor has an impact nearly 10 times as greater than positive factor. This effect has been present in our two previous studies. Let us recall that we are working with hourly observations in this study and that the volume of tweets is somewhat large at this point.

There is still debate in academia regarding how to measure the signal from social networks to stock market performance. In this study, consistent with our two previous works, we have been able once again to measure the impact of negative and positive factor sentiment in stock market for a wide number of companies. Compared to Olson and Nowak (2020), in which the authors measure the negative and positive impact based on negative and positive word count for panel data for the Dow Jones average performance. In this case we have been able to breakdown the effect to each company analyzed and create an individual sentiment factor.

In this study it is presented a methodology to measure the level of overreaction to negative news through the Negative sentiment Factor and that the signal travels as fast as an hour. The econometric model used is around GARCH family showing the asymmetric impact of news on the returns of stock prices. These effects have been recognized to exist showing that negative news tend to have a greater impact on the dependent variable than positive news. For the case of the firms reported in our paper, this stylized fact is found in the modeling of the variable of sentiment divided into positive and negative mood related to the prices (returns) of the stocks.

The present study is useful for the investors and regulators given the insights for the impact on portfolios which involve stocks and want to investigate for possible sources of returns and risk in the financial markets. As limitation here we used a member of the GARCH family. However, other models are more general like Markov switching GARCH, i.e., change of regime models to study the changes in the dynamic of princes/returns.

In this paper we have gone through the full framework that we have developed. To clarify, having coded the full process, it was a matter of automatizing the routines to have the extraction, transformation, analysis, and summarization on a constant basis without major intervention.

Starting from the premise that there exists influence of the social networks in the stock markets, the natural next step would be to measure negative and positive impact aiming to capture the asymmetric effect. This research concludes that there is influence from the social network into the stock market and reaffirms our proposed factor calculation methodology in a previous work (Mendoza et al. 2022, 2021), the data sampling from top tweets to all available was increased and the stock companies from 24 to 2,557. Additionally, the frequency was increased from daily to hourly observations.

Another remark is that even when the volume of positive comments is on average 4 times as larger as the volume of negative comments, the impact of negative comments is as greater as 10 times than the positive comments. Additionally, if there exists persistence of negative comments, the affectation in the stock price keeps constant. Meaning that a single volume of very negative comments won’t hurt a company price more than a constant not so negative flow of comments over a longer period of time.

Finally, we conclude with the success rate increase from 83% to 93% versus our previous work, meaning that for 503 of the 539 companies, it was possible to capture the asymmetric effect of negative and positive factor. At last, but not least, the speed at which the sentiment travels from the social network toward the stock market can be measured in under an hour.

Limitations of this work are the complication to extract and structure the enormous amount of information in time to make managerial decisions. Additionally, the access to the data given the acknowledgement of its importance by the managers of social media has been restricted over the last years. Policy implications are multiple, starting from including the analysis of a company popularity to make informed decisions, as to how can managers of the companies being mentioned in social media control their image to the general population. Future studies can focus in optimizing the sentiment and text trends extraction to capture further hidden data important for decision makers. Additionally, optimizing the data processing allowing managers to make decisions faster with more accurate information.