Renault, T., 2017, Journal of Banking and Finance 84, 25-40
We implement a novel approach to derive investor sentiment from messages posted on social media before we explore the relation between online investor sentiment and intraday stock returns. Using an extensive dataset of messages posted on the microblogging platform StockTwits, we construct a lexicon of words used by online investors when they share opinions and ideas about the bullishness or the bearishness of the stock market. We demonstrate that a transparent and replicable approach significantly outperforms standard dictionary-based methods used in the literature while remaining competitive with more complex machine learning algorithms. Aggregating individual message sentiment at half-hour intervals, we provide empirical evidence that online investor sentiment helps forecast intraday stock index returns. After controlling for past market returns, we find that the first half-hour change in investor sentiment predicts the last half-hour S&P 500 index ETF return. Examining users’ self-reported investment approach, holding period and experience level, we find that the intraday sentiment effect is driven by the shift in the sentiment of novice traders. Overall, our results provide direct empirical evidence of sentiment-driven noise trading at the intraday level.
Picault, M., Renault, T., 2017, Journal of International Money and Finance 79, 136-156
We develop a field-specific dictionary to measure the stance of the European Central Bank monetary policy (dovish, neutral, hawkish) and the state of the Eurozone economy (positive, neutral, negative) through the content of ECB press conferences. In contrast with traditional textual analysis, we propose a novel approach using term-weighting and contiguous sequence of words (n-grams) to better capture the subtlety of central bank communication. We find that quantifying ECB communication using our field-specific weighted lexicon do help predicting future ECB monetary decision and European stock market volatility. Our indicators significantly outperform a textual classification based on the Loughran-McDonald or Apel-Blix-Grimaldi dictionaries and a media-based measure of economic policy uncertainty.
GDP statistics in France are published quarterly, with a delay of 45 days after the end of the quarter. Building on findings from other fields of research that identify that value-relevant information can be extracted from content published on traditional newspapers, we contemplate media content as an complementary source of data to improve the forecast of French GDP at different time horizon. We use a database of more than 1 million articles published in the newspaper "Le Monde" between 1990 and 2016 to create a novel indicator capturing the sentiment of the media regarding the state of the economy. Focusing our attention on short-run GDP forecasting, we compare an augmented “media model” with a benchmark auto-regressive model and an augmented "survey-based model". We find that adding a media indicator helps improving the French GDP forecast, even after controlling for changes in business tendency and confidence. In sample $R^2$ increased by 4,8 points and out-of-sample Residual Mean Squared Forecast Error (RMSFE) decreased by 5.8\%.
Erdemlioglu, D., Gillet, R., Renault, T., 2017 (Submitted)
We propose a new framework for proxying investor attention in real time by analyzing the Twitter messages of financial experts around the release of unscheduled news announcements. Using high-frequency data on large-cap U.S. stocks from January 2013 to December 2015, we find evidence that news events receiving attention on social media lead to large and persistent changes in trading activity, volatility and price jumps. When investors do not pay attention to news, however, the effects of news on such trading patterns tend to be smaller and vanish quickly. With respect to timing, we find that approximately one fourth of the news stories arrive first on Twitter before being reported by Bloomberg. This result suggests that movements prior to news releases may not be explained only by private information, but could also be related to timestamp delays. We control such potential biases with attention-adjustment and newswire-corrected timestamps, which partially eliminates the pre-announcement effect.
Social media can help investors gather and share information about stock markets. However, it also presents opportunities for fraudsters to spread false or misleading statements in the marketplace. Analyzing millions of messages sent on the social media platform Twitter about small capitalization firms, we find that an abnormally high number of messages on social media is associated with a large price increase on the event day and followed by a sharp price reversal over the next trading week. Examining users' characteristics, and controlling for lagged abnormal returns, press releases, tweets sentiment and firms' characteristics, we find that the price reversal pattern is stronger when the events are generated by the tweeting activity of stock promoters or by the tweeting activity of accounts dedicated to tracking pump-and-dump schemes. Overall, our findings are consistent with the patterns of a pump-and-dump scheme, where fraudsters/promoters use social media to temporarily inflate the price of small capitalization stocks.
We investigate the efficient market hypothesis at the intraday level by analyzing market reactions to negative tweets and reports published on the Internet by an activist short-seller. Conducting event-studies, we find that fast-moving traders can generate small, but albeit significant, abnormal profit by trading on public information published on social media. The market reaction to tweets is stronger when a company is mentioned for the first time on Twitter, showing that investors are able to disentangle new information from noise in real time. We also find that traders who manage to identify the information on the short-seller's website before the dissemination of the same news on Twitter can generate much greater abnormal returns. As acquiring information on a website is more costly and difficult than acquiring the same information on Twitter, our findings provide empirical evidence supporting the Grossman-Stiglitz paradox at the intraday level. Very short-lived market anomalies do exist in the stock market to compensate investors who spent time and money setting up bots and algorithms to trade on new information before the crowd.
This dissertation makes methodological and empirical contributions to three issues related to the informational efficiency of financial markets through the use of Big Data analytics. More precisely, it analyzes: (1) how to measure intraday investor sentiment and determine the relation between investor sentiment and aggregate market returns, (2) how to measure investor attention to news in real time, and identify the relation between investor attention and the price dynamics of large capitalization stocks, and (3) how to detect suspicious behaviors that could undermine the informational role of financial markets and determine the relation between the level of posting activity on social media and small-capitalization stock returns. In that regard, the research design of each essay involves the construction of new datasets of messages published on social media sites to create novel indicators in order to: (1) measure investor sentiment, (2) proxy investor attention to news, and (3) detect suspicious stock recommendations that could be related to market manipulation. Using textual analysis, network theories, event studies, or predictive regressions, this dissertation provides empirical evidence that textual content published on social media contains value-relevant information about asset price formation.