Aggregate News Sentiment and Stock Market Returns in India
Abstract
:1. Introduction
2. Theoretical Background
2.1. Efficient Market Hypothesis
2.2. Noise Trader Theory and the Behavioral Biases in Investors
2.3. News Sentiment and Related Literature
- (a)
- News sentiment during extreme movement in the stock market: Noise trader theory and other derived research from it point towards limits on arbitrage and investor sentiment as factors that give rise to large deviations in prices. However, past studies on news sentiment during extreme movement in stocks are not well documented. For instance, Chowdhury et al. (2014), Ferguson et al. (2011), Zhang and Skiena (2010), etc., explain the response of stock variables like price, returns, volatility, etc., to stock-specific news sentiment without any reference to extreme movement. A study on the influence of news sentiment during extreme movement in the stock market would be a useful contribution towards extending the scope of noise trader theory by associating news sentiment with noise traders.
- (b)
- Aggregate news sentiment and its influence on the stock market: Ferguson et al. (2011), Heston and Sinha (2017), Mo et al. (2016), and other similar studies reveal that sentiment obtained from a company’s news influences its returns. However, they do not take into the account influence of other news released on the same day. This means that these studies are conducted by considering only company-specific news, thereby undermining the influence of other news released during the same period. Every day, investors come across various types of news which may influence them to take positions in those stocks having some linkage with the news published. When various types of news are taken together, the expectation is that they will generate an aggregate news sentiment due to sentiment induced by the news having the same or differing polarity. On a given day, aggregate news sentiment may be such that it may have a higher probability of influencing investors.
- (c)
- Influence of news sentiment on a portfolio of stocks: While most of the studies like Cahan et al. (2013), Ferguson et al. (2011), Huynh and Smith (2017), Mo et al. (2016), Zhang and Skiena (2010), etc., have focused on studying the influence of company-specific news sentiment on individual stocks, there is not enough evidence available from past research in which the influence of news sentiment has been examined on a portfolio of stocks. It would be worth examining the influence of news sentiment on different portfolios of stocks and finding out if a certain type of portfolio is more strongly influenced by news sentiment than others.
- (d)
- Influence of news sentiment from other news categories: While studies mentioned above have predominantly focused on company-specific news sentiment and its influence on respective stock variables, there is not much evidence available in past research about the sentiment of other categories of news and their influence on the stock market. Event studies using quantitative data by Geetha et al. (2011), Liew and Rowland (2016), MacKinlay (1997), etc., have revealed that events related to business, politics, economy, and international categories influence stock market returns. Hence, the expectation is that news sentiment derived from news articles related to these categories may also influence the stock market.
2.4. Research Objectives and Hypotheses
- To study the relationship between aggregate news sentiment and stock market returns during extreme movement in the stock market.
- To study the relationship between news sentiment of select news categories and stock market returns during extreme movement in the stock market.
3. Data and Methodology
3.1. Data
3.2. Methodology
- (a)
- Identification of trading dates having extreme returns: One of the ways by which extreme movement in the stock market can be gauged is by examining volatility in returns. Since logarithmic returns are approximately normally distributed, with a confidence interval of 99%, approximately 1% of cases with extreme returns can be obtained. This confidence interval is equivalent to a cutoff level of 2.58σ (sigma), beyond which returns can be considered to be extreme. However, volatility is not constant in the return series, making it heteroscedastic. Because of this, using a point estimate of standard deviation for creating confidence intervals is not appropriate, and hence, we need a method to model such volatility. One of the ways to model volatility in this manner is by using the GARCH model. According to Poon and Granger (2003), empirical findings suggest that the GARCH(1,1) model is the most popular structure for modeling financial time series exhibiting volatility clustering. When employing the GARCH(1,1), model, the following assumptions must be satisfied: (a) Logarithmic stock market return series must be stationary. (b) Conditional variance and the model parameters must be non-negative. (c) Past volatility must influence the current conditional variance. (d) The ARCH parameter (α) depicting the lagged squared residuals and the GARCH parameter (β) depicting the lagged conditional variance must be positive. (e) The sum of the ARCH and GARCH parameters (α + β) must lie between 0 and 1 for model stability. (f) The residuals representing the difference between the observed data and the predicted values based on the model’s conditional mean equation must be independent and identically distributed (i.i.d.) random variables following a normal distribution with zero mean.
- (b)
- Web scraping and news categorization: After identifying trading dates with extreme returns, news articles are web-scraped from archives of the “economictimes.indiatimes.com” (accessed on 8 July 2023) portal related to four news categories: economy, international, business, and politics. Since news articles are stored in archives using a hierarchical URL (uniform resource locator) structure, identifying categories of news articles is easier. The web-scraping process starts by obtaining URLs of hyperlinks from news archives for all dates obtained above. Broader categories present in the URL include news, opinions, recommendations, interviews, etc. Only URLs having the word “news” in the URL are filtered from the rest and considered for further analysis. Others that are related to opinions, recommendations, interviews, and articles by column writers on a specific topic are filtered out and not considered relevant. URLs related to news further show that they are again categorized according to various subjects like economy, industry, politics, company, environment, sports, science, defense, international, etc. Categories like sports, science, defense, environment, etc., generally relate to articles that focus on general issues but are not necessarily related to the stock market. Therefore, all of them are brought under the general category “others” and excluded from this study. News articles from the categories “company”, “industry”, and “stocks” are brought into a single category, “business”, as news from all these categories refers to news articles related to business. A total of 42,485 news articles related to four categories economy, politics, business, and international are web-scraped and used for further processing of which 10,742 news articles belong to the business, 7976 to the economy, 3736 to the international and 20,031 to the politics category.
- (c)
- Data cleaning: Web-scraped news articles obtained in the previous subsection are available in HTML format. They cannot be used in this form in the sentiment extraction process. Therefore, they are converted into plaintext by removing HTML (hypertext markup language) tags. The content of each news article is then saved in the variable “content”. Also, the date of publishing is saved as a “date” variable.
- (d)
- Building text corpus and obtaining sentiment of each news article: News articles in plaintext are transferred to a text repository called a text corpus. Stop words like “is”, “an”, “shall”, “the”, etc., which are repeated and do not convey any sentiment, making them irrelevant, are hence removed. Also, extra white spaces and punctuation marks are removed. Words like “started”, “starting”, etc. are brought to their root word ‘start’ using a process called stemming. Also, the entire corpus text is converted to lowercase. The “Sentometrics” package from R (Ardia et al. 2021) is then used to compute the sentiment of each news article in the corpus. This requires the selection of an appropriate dictionary. Henry and Leone (2016) suggested using a domain-specific wordlist in the sentiment analysis of qualitative content of financial disclosure as it can significantly increase the power of the tests compared to other dictionaries. Compared to the dictionaries mentioned in the literature review, Henry’s finance dictionary, and the Loughran–McDonald financial sentiment dictionary are finance-domain-specific dictionaries. While Henry’s financial sentiment dictionary has a limited set of words, the Loughran–McDonald financial dictionary suffers from bias towards negative words as it has a higher ratio of negative words to positive words (2355/354 = 6.652) compared to Henry’s financial sentiment dictionary, which has a ratio of negative words to positive words close to 1 (85/105 = 0.809). Moreover, Meier et al. (2018) reported that for sentiment analysis, it does not seem to matter whether the extensive dictionary by Loughran and McDonald or the much shorter dictionary by Henry is used; the quality measures of both dictionaries seem to differ only marginally. Therefore, even though Henry’s dictionary has a lower number of positive and negative words, comparatively, it is more suitable for studies that examine text for both negative and positive sentiment. Pröllochs et al. (2015a, 2015b) also found a higher consensus classification share with human annotators and reliability in Henry’s financial sentiment dictionary compared to the Loughran–McDonald financial sentiment dictionary.
- (e)
- Examining the relationship between news sentiment and returns: Aggregate news sentiment of each trading day in the observation window is obtained by taking an average of the sentiment polarity score of all news articles published on the same trading day. In case there is a/are non-trading day(s) before a trading day in the observation window, the sentiment polarity score of news articles published on the non-trading day(s) is aggregated with the sentiment polarity score of articles published on that trading day. Linear regression is then used to examine the strength of the relationship between returns and aggregate news sentiment on day t0 and on day t−2, t−1, t+1, and t+2. The strength of the relationship on day t0 is then compared with that on t−2, t−1, t+1, and t+2 days. Similarly, news sentiment of each of the business, economy, international, and politics categories is obtained for five days in the observation window. Multiple regression is then used to find whether there is any influence created by these news categories on stock market returns on day t0. The strength of this relation is then compared with that on days t−2, t−1, t+1, and t+2.
4. Analysis and Findings
- (a)
- Influence of aggregate news sentiment on stock market returns during extreme movement in the stock market:
- (b)
- Influence of news sentiment of select news categories on market returns during extreme movement:
5. Discussion
5.1. Theoretical Contribution Theoretical Contribution
- (a)
- The results of this study lead us to derive the following theoretical contributions: Influence of aggregate news sentiment on noise traders: The results of this study found that aggregate news sentiment emanating from the news articles belonging to the business, economy, politics, and international categories influences the returns of a portfolio of stocks on the day of extreme stock market movement and a day before it. There are many news articles published every day belonging to these categories, which gives rise to noise consisting of two components—aggregate news sentiment and white noise. While aggregate news sentiment carries signals of expectation in the direction of the polarity, white noise is random and is of no use to the noise trader. This view is consistent with Black (1986), which states that noise generated because of a large number of small events is often a stronger causal factor compared to a small number of large events. When considering noise trader theory with informed investors called arbitrageurs and uninformed investors called noise traders, aggregate news sentiment is a component of noise that influences beliefs of noise traders. However, this noise may not have a sufficient amount of aggregate news sentiment to influence investors all the time because sentiment polarities of a large number of news articles may be negatively correlated giving rise to white noise. When sentiment polarities of a large number of news articles are having the same polarity, this can give rise to a net positive or negative aggregate news sentiment having greater magnitude. This can reinforce the beliefs of noise traders, encouraging them to participate in the market, causing stock prices to start deviating from their intrinsic value. Deviated prices can further bring more noise traders into the market. Also, because of their risk-seeking attitude and obtaining confidence from aggregate news sentiment complementing their beliefs, they trade large quantities. This situation is detrimental for arbitrageurs with their risk-averse attitude who find it difficult to trade due to limits on arbitrage. This inability of arbitrageurs to bet against noisy traders leads to extreme deviation in prices.
- (b)
- Short-term influence of aggregate news sentiment on stock market returns: The results indicate that aggregate news sentiment has a relatively weaker influence on the stock market on day t−1 compared to that on day t0 and no significant influence on days t−2, t+1, and t+2. This means that aggregate news sentiment has a very short-term impact that begins on the day t−1 and ends on the day t0. These results are similar to those reported by Ferguson et al. (2011), wherein a higher correlation was found between stock-specific news sentiment and returns for companies in the UK. They also reported a declining impact around the news release. This study thus contributes to the existing literature by showing that aggregate news sentiment is short-lived.
- (c)
- Categories of news that influence stock market returns: Of the four news categories examined, news sentiment from politics and business categories influence the stock market in the short term. Since news from the business category is comprised of news related to stocks, industry, and the market, this finding is similar to other studies like Cahan et al. (2013), Ferguson et al. (2011), Ranco et al. (2015), and Zhang and Skiena (2010), wherein they report similar evidence for company-specific news. Moreover, news sentiment from the politics category showing a significant contribution proves that investors get impacted by sentiment emanating from political situations. This is a significant contribution to the existing literature as prior studies had shown evidence of influence from company-specific news sentiment only. However, no significant influence on the stock market from news sentiment from the economy and international news categories was found in this study.
5.2. Limitations of the Study
6. Conclusions
6.1. Managerial Implications
- This study reveals that the influence of aggregate news sentiment is short-lived, not even lasting for a day after extreme returns. Therefore, a long-term investor need not worry too much about the stocks they hold if the market is affected by aggregate news sentiment. This implies that there are noise traders active in the market who trade on news sentiment obtained from news media, particularly from business and politics news categories, and drive the stock market to extreme levels. Therefore, arbitrageurs should be cautious when there is extreme pessimism or optimism in the market due to events related to business and politics.
- Mutual fund managers can take this opportunity by smartly balancing the portfolios of their clients soon after the day of extreme market movement by picking up stocks based on their fundamentals but affected by aggregate news sentiment. Since the sentiment impact is temporary and short-lived, fundamentally stronger stocks may be available at lower market prices which in the future will be more likely to appreciate.
- Traders in the derivative segment can design strategies in the index futures and options by studying business and political events lined up shortly and trade with caution.
- An intraday trader can take advantage of this situation and earn profit by taking positions like noise traders but needs to be cautious in doing so by applying stop losses for the positions taken.
6.2. Future Research
- The influence of aggregate news sentiment on different types of portfolios of stocks may be performed during extreme returns in the market. For instance, one may examine the influence of aggregate news sentiment on returns of sectoral indices or other portfolios based on market capitalization, beta, etc.
- One may focus news categories further to find named entities and their influence on the stock market. For instance, one may examine news sentiment related to the ruling party or opposition party and examine their influence on the stock market.
- A comparative study may be performed on the influence of positive and negative aggregate news sentiment on the stock market returns during extreme returns in the stock market.
- Instead of taking returns as a response variable, a study may be conducted to examine the influence of aggregate news sentiment and news sentiment from various news categories on other stock market variables like trading volume, liquidity, etc.
- This study has opened vistas for further research in aggregate news sentiment and its influence on stock markets in the short term. It has also brought extreme deviations in the stock market into the limelight through the lens of aggregate news sentiment following noise trader theory.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Author | Sentiment Analysis Approach | Methodology | Findings |
---|---|---|---|
Gidofalvi and Elkan (2001) | Naïve Bayesian classifier | Linear regression | Predictable stock price movement 20 min before and after news release. |
Cahan et al. (2013) | Thomson Reuter News Analytics | Linear regression | Overreaction to earnings surprises with positive media sentiment, stronger in hard-to-value firms. |
Mo et al. (2016) | SentiWordnet dictionary (lexicon-based) | Regression models, VAR model, Granger causality test | Lag-5 effect of news sentiment on market returns; lag-1 effect of market returns on news sentiment. |
Heston and Sinha (2017) | Thomson Reuters NewsScope Data (neural network-based) | Cross-sectional regression | Daily news predicts stock returns for 1–2 days; Weekly news for one quarter. Negative news reaction is delayed. |
Huynh and Smith (2017) | Thomson Reuter News Analytics | Cross-sectional regression | Market underreacts to good news, driving weekly momentum returns; similar findings in international markets. |
Shi and Ho (2021) | RavenPack Dow Jones News Analytics | MRS-FIGARCH model, discrete choice models | MRS-FIGARCH outperforms other models; news sentiment affects likelihood of intraday stock return volatility states. |
Kabbani and Usta (2022) | Not specified, uses overall sentiment score | Machine learning models (logistic regression, random forest, gradient-boosting machine) | Random forest model outperforms others with 63.58% accuracy. |
Fazlija and Harder (2022) | NLP using BERT models | Sentiment scores in random forest classifier | Sentiment scores based on news content predict stock price direction effectively. |
Dependent Variable | |||||
---|---|---|---|---|---|
Returns | |||||
(t−2) | (t−1) | (t0) | (t+1) | (t+2) | |
(1) | (2) | (3) | (4) | (5) | |
Sentiment | 0.124 | 0.288 * | 0.526 *** | 0.19 | 0.089 |
(0.801) | (1.929) | (3.963) | (1.241) | (0.574) | |
Observations | 43 | 43 | 43 | 43 | 43 |
R2 | 0.015 | 0.083 | 0.277 | 0.036 | 0.008 |
Adjusted R2 | −0.009 | 0.061 | 0.259 | 0.013 | −0.016 |
Residual Std. Error (df = 41) | 0.013 | 0.012 | 0.043 | 0.018 | 0.018 |
F Statistic (df = 1; 41) | 0.642 | 3.720 * | 15.703 *** | 1.541 | 0.329 |
Dependent Variable | |||||
---|---|---|---|---|---|
Returns | |||||
(t−2) | (t−1) | (t0) | (t+1) | (t+2) | |
0.317 | 0.210 | 0.397 *** | 0.137 | 0.033 | |
Business | (1.649) | (1.292) | (3.010) | (0.837) | (0.206) |
−0.041 | 0.118 | 0.036 | 0.170 | −0.101 | |
Economy | (−0.262) | (0.700) | (0.268) | (1.078) | (−0.580) |
0.008 | 0.018 | 0.076 | −0.067 | 0.146 | |
International | (0.051) | (0.107) | (0.587) | (−0.421) | (0.821) |
0.263 | 0.291 * | 0.543 *** | 0.146 | 0.084 | |
Politics | (1.358) | (1.849) | (4.178) | (0.902) | (0.513) |
43 | 43 | 43 | 43 | 43 | |
Observations | 0.075 | 0.114 | 0.390 | 0.060 | 0.034 |
R2 | −0.022 | 0.020 | 0.325 | −0.039 | −0.068 |
Adjusted R2 | 0.013 | 0.012 | 0.041 | 0.019 | 0.019 |
Residual Std. Error (df = 38) | 0.773 | 1.217 | 6.065 *** | 0.605 | 0.329 |
F Statistic (df = 4; 38) | 0.317 | 0.210 | 0.397 *** | 0.137 | 0.033 |
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Share and Cite
Chari, S.; Desai, P.H.; Borde, N.; George, B. Aggregate News Sentiment and Stock Market Returns in India. J. Risk Financial Manag. 2023, 16, 376. https://doi.org/10.3390/jrfm16080376
Chari S, Desai PH, Borde N, George B. Aggregate News Sentiment and Stock Market Returns in India. Journal of Risk and Financial Management. 2023; 16(8):376. https://doi.org/10.3390/jrfm16080376
Chicago/Turabian StyleChari, Sushant, Purva Hegde Desai, Nilesh Borde, and Babu George. 2023. "Aggregate News Sentiment and Stock Market Returns in India" Journal of Risk and Financial Management 16, no. 8: 376. https://doi.org/10.3390/jrfm16080376
APA StyleChari, S., Desai, P. H., Borde, N., & George, B. (2023). Aggregate News Sentiment and Stock Market Returns in India. Journal of Risk and Financial Management, 16(8), 376. https://doi.org/10.3390/jrfm16080376