Three Essays on Forecasting and Information Acquisition in Finance PDF Download
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Author: Maria Putintseva
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Category :
Languages : en
Pages : 123
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Author: Maria Putintseva
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ISBN:
Category :
Languages : en
Pages : 123
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Author: Yan Liu
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Category :
Languages : en
Pages : 0
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Author: Yan Liu (Emory University Graduate Student.)
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Category :
Languages : en
Pages : 0
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Author: Farshid Balaneji
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Category :
Languages : en
Pages : 0
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Author: Dingqian Liu
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Category : Economics
Languages : en
Pages : 0
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This thesis has three essays that study the intersections of macroeconomics, finance, and text analysis. The topics include executives' attention and financial decisions, economic policy uncertainty and stock market forecasting, and the stock market performance in the time of the Covid-19 pandemic. The essays hope to provide unique measurements of attention and uncertainty, empirical evidence, and theories to understand the connections and differences between classic theories and agents' behavior in actual economic activities. The first essay is my job market paper. I examine the attention of executive managers and their financing behavior, focusing on the information acquisition process. Corporations are sensitive to both macroeconomic and firm-specific challenges. Executives must choose overall attention capacity and divide finite attention between these topics. By using natural language processing and quarterly earnings call transcripts, I assess the information content of this dialog. The attention capacity quantifies the effective information used to make borrowing decisions, consisting of information processing macro and firm-specific issues. The attention allocation measures the ratio of attention paid to macroeconomics. Executives make two critical decisions during the information acquiring process. First, executives decide the overall attention capacity, determined by the general uncertainty. Second, executives decide the optimal attention allocated between macro and firm-specific topics. In the rise of uncertainty from either subject, executives' attention capacity increases (scale effect) and assign greater awareness to this topic (substitution effect). I show that the substitution effect is higher than the scale effect. Using an optimal static capital structure model with endogenous information choice, I demonstrate that an executive can tolerate a higher leverage rate when actively acquiring information. Thus, the information decision process is crucial to understanding the recent rising leverage phenomenon.The second essay examines the relationship between the stock market performance and the economic activities in the time of Covid-19. Stock prices and workplace mobility trace out striking clockwise paths in daily data from mid-February to late May 2020. Global stock prices fell 30 percent from February 17 to March 12, before mobility declined. Over the next 11 days, stocks fell another 10 percentage points as mobility dropped 40 percent. From March 23 to April 9, stocks recovered half their losses, and mobility decreased further. From April 9 to late May, both stocks and mobility rose modestly. This dynamic plays out across the 35 countries in our sample, with notable departures in China, South Korea, and Taiwan. The size of the global stock market crash in reaction to the pandemic is many times larger than a standard asset-pricing model implies. Looking more closely at the world's two largest economies, the pandemic had greater effects on stock market levels and volatilities in the U.S. than in China, even before it became evident that early U.S. containment efforts would flounder. Newspaper-based narrative evidence confirms the dominant - and historically unprecedented - the role of pandemic-related developments in the stock market behavior of both countries. The third essay tests the prediction power of the mainland China Economic Policy Uncertainty in forecasting the Chinese stock market. Rational asset pricing theory indicates that the fluctuations of the real economy have a significant impact on the stock market. The Chinese stock market is highly regulated and sensitive to regulations and market policies uncertainty. Using an efficient Dynamic Model Averaging (eDMA) model, this paper investigates how well the newspaper-based Economic Policy Uncertainty (EPU) index can predict the returns of the Chinese Shanghai Stock Exchange Index. Empirical evidence shows that EPU mutes the impact of monetary policy as a predictor. Also, eDMA significantly improves the forecasting performance compared to other forecasting methodologies.
Author: Wenjuan Xie
Publisher:
ISBN:
Category :
Languages : en
Pages : 138
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Author: David F. Andrade
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Category :
Languages : en
Pages : 236
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Author: Marius del Giudice Rodriguez
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Category : Corporate profits
Languages : en
Pages : 132
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This dissertation contains three self-contained chapters dealing with specific aspects of financial analysts' earnings forecasts. After recent accounting scandals, much attention has turned to the incentives present in the career of professional financial analysts. The literature points to several reasons why financial analysts behave overoptimistically when providing their predictions. In particular, analysts may wish to maintain good relations with firm management, to please the underwriters and brokerage houses at which they are employed, and to broaden career choice. While the literature has focused more on analysts' strategic behavior in these situations, less attention has been paid to the implications these factors have on financial analysts' loss functions. The loss function dictates the criteria that analysts use in order to build their forecasts. Using a simple compensation scheme in which the sign of prediction errors affect their incomes differently, in the first chapter we examine the implications this has on their loss function. We show that depending on the contract offered, analysts have a strict preference for under-prediction or over-prediction and the size of this asymmetric behavior depends on the parameter that governs the financial analyst's preferences over wealth. This is turn affects the bias in their forecasts. Recent developments in the forecasting literature allow for the estimation of asymmetry parameters after observing data on forecasts. Moreover, they allow for a more general test of rationality once asymmetries are present. We make use of forecast data from financial analysts, provided by I/B/E/S, and present evidence of asymmetries and weak evidence against rationality. In the second chapter we study the evolution over time in the revisions to financial analysts' earnings estimates for the 30 Dow Jones firms over a 20 year period. If analysts' forecasts used information efficiently, earnings revisions should not be predictable. However, we find strong evidence that earnings revisions can in fact be predicted by means of the sign of the last revision or by using publicly available information such as short interest rates and past revisions. We propose a three-state model that accounts for the very different magnitude and persistence of positive, negative and `no change' revisions and find that this model forecasts earnings revisions significantly better than an autoregressive model. We also find that our forecasts of earnings revisions predict the actual earnings figure beyond the information contained in analysts' earnings estimates. Finally, the empirical literature on financial analysts' forecast revisions of corporate earnings has focused on past stock returns as the key determinant. The effects of macroeconomic information on forecast revisions is widely discussed, yet rarely tested in the literature. In the third chapter, we use dynamic factor analysis for large data sets to summarize a large cross-section of macroeconomic variables. The estimated factors are used as predictors of the average analyst's forecast revisions for different sectors of the economy. Our analysis suggests that factors extracted from macroeconomic variables do, indeed, improve on the current model with only past stock returns. In trying to explain what drives financial analysts' forecast revisions, the factors representing the macroeconomic environment must be considered to avoid a potential omitted variable problem. Moreover, the explanatory power and direction of such factors strongly depend on the industry in question.
Author: Brian Gibbons
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Category :
Languages : en
Pages :
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This dissertation contains three essays. In the first essay, I document that disclosure of financially immaterial environmental and social (E&S) information has material effects on firms' investment and financing decisions using the staggered introduction of 87 country-level regulations that mandate firms report such information. Firms domiciled in countries that mandate E&S transparency increase R&D expenditures and patenting activity after disclosing. Transparent non-financial disclosure reduces financing frictions, resulting in more innovation for equity-dependent firms and increased reliance on external equity. It also improves shareholders' contracting and monitoring abilities, incentivizing managers to invest in innovation. Fixed capital investment, which is less sensitive to information frictions, does not change following E&S disclosure. Additionally, I only observe changes to investment and financing decisions when E&S disclosure is mandatory--highlighting the unique value of consistent and comparable disclosure. In the second essay, I study venture capital firms (VCs) use of public market information and how attention to this information relates to their private market investment outcomes. I link web traffic to public disclosure filings hosted on the Security and Exchange Commission's (SEC's) EDGAR server to individual VCs. VCs analyze public information before most deals. An increase in EDGAR filing views relates positively to the probability of an exit through acquisition, suggesting that public information helps identify paths to acquisition. The effect is stronger when the VC has less access to private information. I conclude that policymakers should consider spillover effects on private markets when setting public disclosure requirements. In the third essay, we identify analysts' information acquisition patterns by linking EDGAR server activity to analysts' brokerage houses. Analysts rely on EDGAR in 24% of their estimate updates, with an average of eight filings viewed. We document that analysts' attention to public disclosure is driven by the demand for information and the analysts' incentives and career concerns. We find that information acquisition via EDGAR is associated with a significant reduction in analysts' forecasting error relative to their peers. This relationship is likewise present when we focus on the intensity of analyst research. Attention to public information further enables analysts to provide forecasts for more time periods and more financial metrics. Informed recommendation updates are associated with substantial and persistent abnormal returns, even when the analyst accesses historical filings. Analysts' use of EDGAR is associated with longer and more informative analyses within recommendation reports.
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Author: Duncan Lyall Burn
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ISBN:
Category : Economic forecasting
Languages : en
Pages : 62
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