How novelty and narratives drive the stock market: black swans, animal spirits and scapegoats. 2021. Nicholas Mangee. Cambridge University Press.
“Where there are novelties, there is instability. Where there is instability there is uncertainty. Where there is uncertainty there are narratives, narratives are the currency of uncertainty.
Nicholas Mangee, associate professor of finance at Georgia Southern University’s Parker College of Business, begins How novelty and narratives drive the stock market with a statement encompassing the problem it addresses and the compelling reason for investor interest in the new style of thinking it addresses.
This detailed study of the stock market attempts to extend Nobel Prize winner Robert Shiller’s development of narrative economics, although Mangee’s focus is on novel information embedded in textual news narratives. Using a set of text-based indices to capture the uncertainty and ambiguity of unscheduled news, Mangee measures the impact of news narratives on equity behavior.
News reports are stories and narratives that contain unique information that cannot be easily constrained or assigned probability estimates to quantify risk. This new information confronts investors with “Knightian uncertainty” (that is, the inability to measure the probabilities of future states, as described by Chicago economist Frank Knight). Modeling advances in textual analysis and categorization into a method for measuring non-quantitative information that affects stock prices, Mangee enriches the discussion of stock price behavior by including mountains of new and unscheduled information. contained in news reports. Trying to categorize and measure the impact of news and the narrative that accompanies it is a daunting task, but this book provides a significant advance that is well worth an investor’s time.
Textual analysis using natural language processing and machine learning, which has gone beyond the normal programmatic announcement of macro and company-specific information, has become the cutting edge of financial research quantitative Mangee links this analysis to the new conception of the narrative economy as a driver of sentiment and expectations. It focuses on the measurement of uncertainty and ambiguity to improve our understanding of the drivers of actions beyond routinely scheduled and repeatable data. Stock market volatility and changes in factor behavior are shown to be related to the flow of unique information that is captured in financial news reports.
The book begins with what is called the narrative-novelty hypothesis (NNH) and links this concept to Knight’s uncertainty. The NNH states that unscheduled and unique information, which comes in the form of narratives, cannot be easily prevented, but can still affect the behavior of stocks. New, infrequent and unscheduled information is subject to interpretation through narratives or stories in our financial press because there is no good way to convert this information into measurable risks. These new narratives influence stock behavior even though they represent explanations for uncertain events.
For Mangee, the link between unique news and stock market prediction, an area that is not usually researched, can offer explanations for higher volatility, breaks in model behavior, and parameter uncertainty. Unique textual information and new data can be systematized, coded, and grouped into categories and indexes to provide meaningful information that can support our understanding of stock behavior. Using decades of data from major news organizations, the author forms what he calls Knightian Uncertainty (KU) indices for macro and micro (i.e., firm-level) data.
Mangee introduces us for the first time to textual analysis using Google trends and word cluster maps to demonstrate how investment topics change in financial news. What captures the attention of news services in a given period can vary markedly over time. Based on this high-level analysis, the author uses the RavenPack news analytics platform to categorize the textual analysis into macro and micro news categories. In addition, they are divided into uncertainty, sentiment, novelty, relevance, and aggregated event volume indices based on different characterizations to categorize news events. The resulting categorizations represent a massive effort to pull information from millions of stories by numerous news reporting services over decades to form hundreds of clusters that can be aggregated based on the importance of actions. Millions of new stories are grouped and classified into nearly 1,400 event categories to form indices as tools for measuring various forms of uncertainty.
These textual information indices are associated with fluctuations in stock market volatility. Volatility in equities is not only due to surprises in scheduled news, but also to the broad set of unscheduled and random new data that can translate into market reactions. For example, increases in KU indices, which measure novel narrative news, lead to increased stock volatility. Regime changes in equity styles and behavior, as well as changes in model parameters, can be associated with fluctuations in the unscheduled unique information that is incorporated into our news. The study of unscheduled changes in breaking news provides a window into market volatility that enhances our understanding of the complexity of the stock market. What is relevant to investors will change over time, based on sentiment and focus.
This extensive book is aimed at an academic audience and addresses several sophisticated research topics involving uncertainty and textual narratives, but its conclusions and core messages are accessible to most finance professionals. Scheduled and measurable news is important, but so is the continuous flow of commentary and interpretation of unique information entering the markets each day. Detailed and comprehensive analysis of textual data gives new meaning to market sentiment and the impact of news on stock prices.
Exploring equity reactions by converting textual narratives into measurable indices should be an area of extreme interest to many investors interested in understanding market volatility. It constitutes a new direction to potentially crack the stock market prediction code. Mangee provides a solid introduction to a new approach to explaining equity instability; however, the complexity of sorting through all the data and trying to make sense of it is still in its infancy and cannot easily be turned into investment rules. From novelty comes narrative and uncertainty, but the reader will still wonder, what next?
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All posts are the opinion of the author. Therefore, they should not be construed as investment advice, nor do the views expressed necessarily reflect the views of the CFA Institute or the author’s employer.
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