The puzzle of long swings in equity markets: Which way forward?

The main purpose of this dissertation is to determine which class of models -- bubble or Imperfect Knowledge Economics (IKE) -- provides the better account of short-term stock price fluctuations -- and thus long-swings -- on the basis of empirical evidence. However, it is not clear how to test the bubble models\u27 implication that pure psychological and technical momentum-related factors are the primary driver of stock price movements. Moreover, IKE models\u27 implication that fundamentals are the primary drivers of stock price movements -- but that changes in this relation are non-routine -- is also problematic. This thesis addresses these difficulties in two main ways. One is to construct a novel dataset based on Bloomberg News\u27 end-of-the-day equity market wrap stories. The textual data provides unambiguous support for IKE models over the bubble models. They indicate that fundamental factors are the primary driver of price fluctuations and that this relation changes at times and in ways that would be difficult to adequately capture with any overarching rule. Psychological considerations are also found to be quite important, but their impact is almost always tethered to a fundamental factor. The bubble models\u27 implication that pure psychological and technical momentum-related considerations are the main drivers of stock prices receives little support. The thesis also relies on formal econometric analysis to reexamine the connection between stock prices and fundamental factors. It employs recursive structural change tests and cointegration and out-of-sample fit analyses. The results support those obtained with the Bloomberg data: short-term stock price fluctuations are related to fundamentals but the relationship between prices and fundamentals is temporally unstable at times and in ways that cannot be fully foreseen. Beyond shedding new light on the empirical validity of bubble and IKE models, the thesis examines the question of what circumstances cause market participants to pay attention to certain fundamentals over others when forecasting market outcomes. Analyses combining both the Bloomberg data and formal econometrics suggest that the frequency with which certain fundamentals merit the attention of market participants is a function of the recent variation of such factors as well as deviations of fundamentals away from estimates of common benchmark levels

When and why does it pay to be green?

Environmental policy; innovation; Porter hypothesis; environmental regulation; pollution; capital market; green products.

Participation in online activation (#) campaigns: A look at the drivers in an African setting

Online brand activation is in vogue and has been used by several brand giants (e.g. Apple, Adidas, etc.) to create viral campaigns that invite consumers to join brand-initiated conversations. The opportunities therein for brand visibility and customer engagement are immense. However, to leverage it, brand communicators must first understand the characteristics of persons most likely to participate in it and target them, a question that is yet to be addressed in the budding literature on the subject. For brand communicators, particularly in developing economies such as Ghana where low internet penetration levels and high associated costs may hinder participation, this is a critical gap in their quest to justify the relevance of online activations to boost brand visibility and customer engagement. This study sought to address this gap by testing a model of the drivers of participation in online activations using a consumer survey (N = 278), set in a recent online activation campaign in Ghana. The findings suggest that persons who trust the activated brand would be inclined to participate in it, suggesting the need for brand communicators to first work on building trust. The study also finds that individuals who are susceptible to interpersonal influence are less likely to participate in such activations. Possible explanations for this are explored in the study along with their implications

Multinomial Inverse Regression for Text Analysis

Text data, including speeches, stories, and other document forms, are often connected to sentiment variables that are of interest for research in marketing, economics, and elsewhere. It is also very high dimensional and difficult to incorporate into statistical analyses. This article introduces a straightforward framework of sentiment-preserving dimension reduction for text data. Multinomial inverse regression is introduced as a general tool for simplifying predictor sets that can be represented as draws from a multinomial distribution, and we show that logistic regression of phrase counts onto document annotations can be used to obtain low dimension document representations that are rich in sentiment information. To facilitate this modeling, a novel estimation technique is developed for multinomial logistic regression with very high-dimension response. In particular, independent Laplace priors with unknown variance are assigned to each regression coefficient, and we detail an efficient routine for maximization of the joint posterior over coefficients and their prior scale. This "gamma-lasso" scheme yields stable and effective estimation for general high-dimension logistic regression, and we argue that it will be superior to current methods in many settings. Guidelines for prior specification are provided, algorithm convergence is detailed, and estimator properties are outlined from the perspective of the literature on non-concave likelihood penalization. Related work on sentiment analysis from statistics, econometrics, and machine learning is surveyed and connected. Finally, the methods are applied in two detailed examples and we provide out-of-sample prediction studies to illustrate their effectiveness.Comment: Published in the Journal of the American Statistical Association 108, 2013, with discussion (rejoinder is here: http://arxiv.org/abs/1304.4200). Software is available in the textir package for

Is media just noise? The link between media factors and stock performance

PURPOSE OF THE STUDY Interest towards media analytics has increased significantly by both practitioners and academia alike. The hot topic is whether or not qualitative texts contain information relevant to stock financials, and if they do, whether the impact can be used to earn abnormal returns. In order to answer this, we study the impact media factors have on financial metrics in a novel specification that combines all the major media factors in a holistic media model. To transform qualitative texts information into a "sentiment score", we develop a new methodology to estimate sentiment more accurately than currently prevailing methods. DATA AND METHODOLOGY Our study focuses on the S&P 100 constituents between the time period of 2006 and 2011. As a source of qualitative texts, we use major news publications and earnings announcements retrieved from LexisNexis -database using a web scraper program developed for the purpose of this study. We retrieve the financials data for our study using Thomson Reuters Datastream -database. In order to estimate investor sentiment, we employ both the customary word count, as well as our novel Linearized Phrase-Structure -methodology. For word count, we test the Harvard Psychological -dictionary and a finance-specific dictionary by Loughran and McDonald (2011). As our data is panel in nature, we analyze the correlations in our error terms in line with Petersen (2009), first without clustering and then clustering by firm and by time. We find time-effect in our error terms, and therefore employ a Fama-Macbeth (1973) methodology with clustering done in quarters. To mitigate a methodological choice driving our results, we run our specifications with a multitude of alternative specifications. RESULTS We find that Linearized Phrase-Structure (LPS) outperforms the predominant naïve word count methodology. Also, we find that if employing word counts, researchers should employ context dependent dictionaries, such as Loughran and McDonald's (2011). In terms of our main variables, we find that the existing media factors are not mutually exclusive, and impact financial metrics in chorus. Alas, we do not find statistically significant relationship between sentiment and abnormal returns. However, we find a relationship between aggregate market news volume and abnormal returns, and also between sentiment and abnormal volatility. We infer that our findings support limited attention -theory, and provide evidence against market efficiency