This chapter applies a manual textual analysis of scapegoat effects in the stock market under the Novelty–Narrative Hypothesis. Information contained in Bloomberg News daily market wrap reports proxy for the story-weights investors place on observable and unobservable micro and macro fundamentals. Findings of dramatic variation in the narrative-based attention investors place across particular fundamentals suggests instability, in both frequency and magnitude, characterizing their forecasting strategies of future stock market outcomes. Time-varying properties of the narratives are found to be connected to shifts in the underlying variables particularly level- and gap-effects. Evidence suggests that adding narrative dynamics as proxied by scapegoat effects to benchmark macro-finance models improves fundamentals' ability to explain stock price fluctuations under uncertainty. Measures of model fit and forecasting accuracy provide the most empirical support for varying-coefficient scapegoat models based on micro and macro novel events and narrative effects, or attention-weights, attached to fundamentals.

Chapter 1 sets the stage for the Novelty-Narrative Hypothesis as applied to stock market outcomes throughout the book. The importance of nonrepetitive events, narrative dynamics, and investor emotion for understanding market instability is introduced through insights from great early thinkers of modern-day financial markets – think Knight and Keynes – but also through extant evidence from other disciplines such as cognitive psychology and sociology. Benefits of textual news analytics in assessing the role of novelty and narratives under uncertainty are introduced with brief descriptions of the Bloomberg and RavenPack data employed throughout. The main analytical features of Chapters 5 through 12 are previewed emphasizing the methodological benefits of big data textual analysis applied to millions of financial news reports. The chapter highlights key findings from empirical investigations connecting Knightian uncertainty indices derived from novel corporate events and narrative proxies to periods of temporal instability in stock returns, volatility, trading volume, and fund flow relationships. The chapter closes with foreshadowing a Kuhnian paradigm shift in macro-finance.

Chapter 2 is about time-varying relationships driving stock price fluctuations and volatility and how novel events and narrative dynamics may be at play. It provides a survey of the relevant literature on structural change, popular forms, such as regime switching and parameter nonconstancy, and the potential sources of narrative dynamics related to instability. Emphasis is placed on whether transition probabilities are better understood as time-varying and how rare events fit in. Historic events that are good candidates for having catalyzed periods of change between stock prices and fundamentals over the last three decades will be identified. Narrative anecdotes from financial news will be provided in support of the view that much of the instability in the stock market is unforeseeable ex ante. Therefore, probabilistic, and other quantitative rules modeling change in stock market relationships are inappropriate when Knightian uncertainty events are unfolding in real time. Finally, the role of investor sentiment underpinning instability will be discussed through the lens of dictionary-based notions of individual rationality.