Does Online Chatter Really Matter? Dynamics of User-Generated Content and Stock Performance

User-Generated Content in online platforms or chatter for short provides a valuable source of consumer feedback on market performance of firms. This study examines whether chatter can predict stock market performance, which metric of chatter has the strongest relationship, and what the dynamics of the relationship are. The authors aggregate chatter (in the form of product reviews) from multiple websites over a four year period across six markets and fifteen firms. They derive multiple metrics of chatter (volume, positive chatter, negative chatter, and 5-start ratings) and use multivariate time series models to assess the short and long term relationship between chatter and stock market performance. They use three measures of stock market performance: abnormal returns, risk, and trading volume. The findings reveal that two metrics of chatter can predict abnormal returns with a lead of a few days. Of four metrics of chatter, volume shows the strongest relationship with returns and trading volume, followed by negative chatter. Whereas negative chatter has a strong effect on returns and trading volume with a short “wearin” and long “wearout,” positive chatter has no effect on these metrics. Negative chatter also increases volatility (risk) in returns. A portfolio analysis of trading stocks based on their chatter provides a return of 8% over and above normal market returns. In addition to the investing opportunities, the results show managers that chatter is an important metric to follow to gauge the performance of their brands and products. Because chatter is available daily and hourly, it 2 can provide an immediate pulse of performance that is not possible with infrequent sales and earnings reports. The fact that negative chatter is more important than positive, indicates that negatives are more diagnostic than positives. The negatives suggest what aspects of the products managers should focus on

How well do financial experts perform? A review of empirical research on performance of analysts, day-traders, forecasters, fund managers, investors, and stockbrokers

In this manuscript, empirical research on performance of various types of financial experts is reviewed. Financial experts are used as the umbrella term for financial analysts, stockbrokers, money managers, investors, and day-traders etc. The goal of the review is to find out about the abilities of financial experts to produce accurate forecasts, to issue profitable stock recommendations, as well as to make successful investments and trades. On the whole, the reviewed studies show discouraging tendencies of the alleged excellence of financial experts.Behavioral finance; Expert judgment; Financial psychology; Forecasting; Investment; Trading; Performance

Stock Prediction Analyzing Investor Sentiments

We are going through a phase of data evolution where a major portion of the data from our daily lives is now been stored on social media platforms. In recent years, social media has become ubiquitous and important for social networking and content sharing. Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. In the financial sector, sentiments are also of paramount importance, and this dissertation mainly focuses on the effect of sentiments from investors [3] on the behavior of stocks. The dissertation work leverages social data from Twitter and seeks the sentiment of certain investors. Once the sentiment of the tweets is calculated using an advanced sentiment analyzer, it is used as an additional attribute to the other fundamental properties of the stock. This dissertation demonstrates how incorporating the sentiments improves forecasting accuracy of predicting stock valuation. In addition, various experimental analysis on regression based statistical models are considered which show statistical measures to consider for effectively predicting the closing price of the stock. The Efficient Market Hypothesis (EMH) states that stock market prices are largely driven by additional information and follow a random walk pattern [7, 8, 37, 39, 41]. Though this hypothesis is widely accepted by the research community as a central paradigm governing the markets in general, several people have attempted to extract patterns in the way stock markets behave and respond to external stimuli. We test a hypothesis based on the premise of behavioral economics, that the emotions and moods of individuals basically the sentiments affect their decision-making process, thus, leading to a direct correlation between ?public sentiment? and ?market sentiment? [42, 43, 44, 45]. We first select key investors from Twitter [27, 28] whose sentiments matter and do sentiment analysis on the tweets pertaining to stock related information. Once we retrieve the sentiment for every stock, we combine this information with the other fundamental information about stocks and build different regression-based prediction models to predict their closing price

Statistical Inferences for Polarity Identification in Natural Language

Information forms the basis for all human behavior, including the ubiquitous decision-making that people constantly perform in their every day lives. It is thus the mission of researchers to understand how humans process information to reach decisions. In order to facilitate this task, this work proposes a novel method of studying the reception of granular expressions in natural language. The approach utilizes LASSO regularization as a statistical tool to extract decisive words from textual content and draw statistical inferences based on the correspondence between the occurrences of words and an exogenous response variable. Accordingly, the method immediately suggests significant implications for social sciences and Information Systems research: everyone can now identify text segments and word choices that are statistically relevant to authors or readers and, based on this knowledge, test hypotheses from behavioral research. We demonstrate the contribution of our method by examining how authors communicate subjective information through narrative materials. This allows us to answer the question of which words to choose when communicating negative information. On the other hand, we show that investors trade not only upon facts in financial disclosures but are distracted by filler words and non-informative language. Practitioners - for example those in the fields of investor communications or marketing - can exploit our insights to enhance their writings based on the true perception of word choice

Using Twitter to Predict the Stock Market - Where is the Mood Effect?

Behavioral finance researchers have shown that the stock market can be driven by emotions of market participants. In a number of recent studies mood levels have been extracted from Social Media applications in order to predict stock returns. The paper tries to replicate these findings by measuring the mood states on Twitter. The sample consists of roughly 100 million tweets that were published in Germany between January, 2011 and November, 2013. In a first analysis, a significant relationship between aggregate Twitter mood states and the stock market is not found. However, further analyses also consider mood contagion by integrating the number of Twitter followers into the analysis. The results show that it is necessary to take into account the spread of mood states among Internet users. Based on the results in the training period, a trading strategy for the German stock market is created. The portfolio increases by up to 36 % within a six-month period after the consideration of transaction costs

The Impact of Warrant Introduction Australian Experience

The impact that derivative trading has on the underlying security is essential to our understanding of security market behaviour, and important in the fields of market efficiency and pricing of such derivatives. This paper examines the impact that the introduction of exchange traded derivative warrants has on the underlying securities’ price, volume and volatility in the Australian market. The major findings of significant negative abnormal returns, reduction in skewness, no change in beta and small changes in variance are consistent with recent research findings in the US, UK and Hong Kong. However findings of derivative warrant listing resulting in decreased trading volume in contrast with most prior research in the field.Derivatives, Warrants, Market Efficiency, Event Study.

Attention, search, and information diffusion : study of stock network dynamics and returns

textThere is growing literature on search behavior and using search for prediction of market share or macroeconomic indicators. This research explores investors' stock search behaviors and investigates whether there are patterns in stock returns using those for return prediction. Stock search behaviors may reveal common interest among investors. In the first study, we use graph theory to find investment habitats (or search clusters) formed by users who search common set of stocks frequently. We study stock returns of stocks within the clusters and across the clusters to provide theoretical arguments that drive returns among search clusters. In the second study, we analyze return comovement and cross-predictability among economically related stocks searched frequently by investors. As search requires a considerable amount of cognitive resources of investors, they only search a few stocks and pay high attention to them. According to attention theory, the speed of information diffusion is associated with the level of attention. Quick information diffusion allows investors to receive relevant information immediately and take instantaneous trading action. This immediate action may lead to correlated return comovement. Slow information diffusion creates latency between the occurrence of an event and the action of investors. The slower response may lead to cross-predictability. Making use of the discrepancy in information diffusion, we implement a trading strategy to establish arbitrage opportunities among stocks due to difference in user attention. This research enriches the growing IS literature on information search by (1) identifying new investment habitats based on user search behaviors, (2) showing that varying degrees of co-attention and economic linkages may lead to different speed of information diffusion (3) developing a stock forecasting model based on real-time co-attention intensity of a group economically linked stocks and (4) embarking a new research area on search attention in stock market. The methods in handling complex search data may also contribute to big data research.Information, Risk, and Operations Management (IROM