This dissertation is a study of fundamental questions in macro-finance using modern tools from textual analysis. These questions include how financial constraints affect firm investment and financing decisions when they are not presently binding, and whether stock returns are predictable based on concerns revealed in conversations between firms and investors. The first chapter examines whether financial covenants are an important consideration for firm decisions when they are not presently in violation. A key empirical challenge is measuring the risk of future covenant violations, which is not directly observed. I propose a novel measure of concerns about future violations by distinguishing between discussions of covenants in earnings calls that relate to the future as opposed to the past or present. As validation, I show that the measure predicts future violations and covaries intuitively with earnings, leverage, and default risk. Importantly, I find that concerns about covenants are significantly associated with reductions in investment as well as debt and equity financing activities. These responses persist even after controlling for standard measures of investment opportunities and are economically large relative to the effects of actual violations. The second chapter empirically analyzes two explanations for how covenants concerns relate to a firm's investment decisions. One explanation is that covenant concerns coincide with a deterioration in expected profitability, which dampens firms' incentives to invest. A second explanation is that firms become concerned when they expect violations to be more costly, which indicates future difficulties with funding investments. To shed light on the relevance of these two explanations, I examine empirical patterns in analyst expectations of future earnings, loan amendments in SEC filings, and the stock returns of firms that mention covenant concerns. The evidence suggest that both explanations are relevant mechanisms driving the correlation between covenant concerns and firm activity. However, I find that the second channel is more economically significant, suggesting that covenant concerns are informative about the degree to which firms are constrained by financial covenants. In the third chapter, I investigate how covenant concerns relate to firm policies in a standard model of investments with financial frictions. In the model, the theoretical object that most naturally links to covenant concerns is the expected shadow cost of the borrowing constraint. As in the data, the shadow cost of the borrowing constraint covaries negatively with earnings as well as firm investment and financing activity. Through an analysis of impulse response functions, I show how the empirical correlations between covenant concerns and firm policy arise in the model. One channel is through negative productivity shocks, which raises covenant concerns and leads to a fall in investment, debt, and equity issuance. The second channel is through higher leverage, holding fixed productivity. In the model, firm with higher debt levels are more concerned about covenants when hit by a negative productivity shock, and also choose less investment, debt issuance, and equity issuance. In this chapter, I also discuss several shortcomings of the model and suggest avenues for modifications. The final chapter investigates a new question: are stock returns predictable based on the extent to which firms are concerned about the macroeconomy? We document that firms that pay more attention to the macroeconomy earn lower average returns relative to firms that pay less attention to the macroeconomy. Differences in returns are economically significant and are not explained by traditional asset pricing factors, such as market beta, size, value, and idiosyncratic volatility. To explain the negative macroeconomic attention premium, we propose a model of attention allocation that links analyst attention to fundamental shocks affecting firm cash flows. In the model, attention to the macroeconomy is increasing in the share of earning news explained by the macroeconomic component. Firms with a greater share of cash flow news explained by the macroeconomic component face lower cash flow risk, hence earn lower expected returns.

In chapter one, I examine the predictive content of subjective return expectations derived from price targets issued by equity analysts. Equity price targets are an ubiquitous feature of the financial information landscape, but it is not clear how informative they actually are. In this chapter, I show that the cross-section of price-target implied subjective return expectations contains rich informational content for forecasting returns. In-sample, I find that expected returns correlate strongly with average cross-sectional returns to a large panel of portfolios formed on the basis of observable firm characteristics. In out-of-sample exercises, forecasting models using subjective expectations are shown to offer more accurate predictions for portfolio returns than several other commonly employed, cross-sectional predictors, including the book-to-market and dividend-price ratios, momentum, and forward-looking cash-flow measures. Furthermore, these differences are shown to be economically relevant, with conditional portfolios formed on the basis of subjective expectations offering substantially improved risk-adjusted returns compared to many of the other predictors considered. The relative informational content, as well as the production by analysts, of subjective return expectations is found, however, to peak during recessions, with negligible predictive advantage discernible in expansions. In chapter two, my coauthors (Adam Rej, with CFM; David Thesmar, with MIT, CEPR, and NBER) and I empirically analyze a large panel of firm sales growth expectations. We find that the relationship between forecast errors and lagged revision is non-linear. Forecasters underreact to typical (positive or negative) news about future sales, but overreact to very significant news. To account for this non-linearity, we propose a simple framework, where (1) sales growth dynamics have a fat-tailed high frequency component and (2) forecasters use a simple linear rule. This framework qualitatively fits several additional features of data on sales growth dynamics, forecast errors, and stock returns. In chapter three, my coauthor (Ken Teoh, with Columbia) and I construct a novel text-based measure of firm-level attention to macroeconomic conditions and document that stocks associated with higher macroeconomic attention earn lower returns. Moving from the bottom decile to top decile of macroeconomic attention decreases a stock’s average return by 11.6\% per year. We propose a risk-based explanation in which stocks with higher macroeconomic attention contribute less idiosyncratic cash flow risk to the investor’s portfolio, hence earn lower expected returns. Decomposing the unexpected returns of macroeconomic attention-sorted portfolios into cash flow and discount rate news, we find that portfolios with higher macroeconomic attention stocks have lower cash flow risk.

by Ng Wai-Leung Anthony. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 83-87). / Abstracts in English and Chinese. / Chapter I. --- INTRODUCTION --- p.1 / Chapter II. --- LITERATURE REVIEW --- p.5 / Chapter 2.1 --- The Importance of Stock Index and Index Futures --- p.6 / Chapter 2.2 --- Importance of Index Forecasting --- p.6 / Chapter 2.3 --- Reasons for the Lead-Lag Relationship between Stock and Futures Markets --- p.9 / Chapter 2.4 --- Importance of the lead-lag relationship --- p.10 / Chapter 2.5 --- Some Empirical Findings of the Lead-Lag Relationship --- p.10 / Chapter 2.6 --- New Approach to Financial Forecasting - Artificial Neural Network --- p.12 / Chapter 2.7 --- Artificial Neural Network Architecture --- p.14 / Chapter 2.8 --- Evidence on the Employment of ANN in Financial Analysis --- p.20 / Chapter 2.9 --- Hong Kong Securities and Futures Markets --- p.25 / Chapter III. --- GENERAL GUIDELINE IN DESIGNING AN ARTIFICIAL NEURAL NETWORK FORECASTING MODEL --- p.28 / Chapter 3.1 --- Procedure for using Artificial Neural Network --- p.29 / Chapter IV. --- METHODOLOGY --- p.37 / Chapter 4.1 --- ADF Test for Unit Root --- p.38 / Chapter 4.2 --- "Error Correction Model, Error Correction Model with Short- term Dynamics, and ANN Models for Comparisons" --- p.38 / Chapter 4.3 --- Comparison Criteria of Different Models --- p.39 / Chapter 4.4 --- Data Analysis --- p.39 / Chapter 4.5 --- Data Manipulations --- p.41 / Chapter V. --- RESULTS --- p.42 / Chapter 5.1 --- The Resulting Models --- p.42 / Chapter 5.2 --- The Prediction Power among the Models --- p.45 / Chapter 5.3 --- ANN Model of Input Variable Selection Using Contribution Factor --- p.46 / Chapter VI. --- CAUSALITY ANALYSIS --- p.54 / Chapter 6.1 --- Granger Casuality Analysis --- p.55 / Chapter 6.2 --- Results Interpretation --- p.56 / Chapter VII --- CONSISTENCE VALIDATION --- p.61 / Chapter VIII --- ARTIFICIAL NEURAL NETWORK TRADING SYSTEM --- p.67 / Chapter 7.1 --- Trading System Architecture --- p.68 / Chapter 7.2 --- Simulation Runs using the Trading System --- p.77 / Chapter XI. --- CONCLUSIONS AND FUTURE WORKS --- p.79

The purpose of this research is to evaluate the performance of the emerging stock markets in three regions. The regions chosen as our testing targets are Europe, The Middle East, and Asia. Performance for 2002 to 2004 will be compared to the U.S. stock market.

Thesis (MBA (Business Management))--University of Stellenbosch, 2009. / ENGLISH ABSTRACT: The purpose of this report is to investigate whether the returns of five different trading systems applied is able to outperform the return of a Buy & Hold (B&H) strategy when applied to the Johannesburg Stock Exchange/Financial Times Stock Exchange (JSE/FTSE) Top 40 Index future contract (ALSI). The study starts with an overview of theoretical and empirical studies regarding technical trading systems as well as the application of these technical trading systems in various strategy formats. Five common trading systems were selected for the test. They include the Volatility Channel, the Bollinger Channel Breakout, the Donchian Channel, the Dual Moving Average and the Triple Moving Average systems. The trading systems were applied in three different types of strategies. In the first test the systems were employed using randomly selected parameters to generate trading signals. In the second test the systems were optimised to select the parameters that would yield the most profitable returns over the test period. Finally in the third test a stop loss was added to the systems to investigate whether it would improve returns. In virtually all tests the systems outperformed the B&H approach. This was primarily due to the collapse of world financial markets in 2008 that caused the systems, which are all trend following by nature, to generate large returns. If it had not been for this event, the trend-following systems would all have underperformed the total return generated by the B&H strategy over the duration of the test period. The tests revealed that the selection of the parameters that generate the trade signals for the trading systems can drastically influence the profitability of a trading system. Furthermore the implementation of stop-loss strategies does not necessarily improve the return or drawdown that a system displays, as several of the systems were negatively influenced by the implementation of the stop-loss strategy. / AFRIKAANSE OPSOMMING: Die doel van hierdie verslag is om te ondersoek of die opbrengs van vyf verskillende verhandelingstelsels die opbrengs van die Koop-en-Hou-strategie kan klop soos toegepas op die JSE/FTSE Top 40 Indeks termynkontrak (ALSI). Die studie begin met ’n oorsig oor teoretiese en empiriese studies oor tegniese verhandelingstelsels, asook die toepassing van hierdie tegniese stelsels in verskeie strategiese formate. Vyf algemene verhandelingstelsels is gekies vir die ondersoek, naamlik die Volatiliteitskanaal (Volatility Channel), die Bollinger Kanaal Uitbreek (Bollinger Channel Breakout), die Donchian Kanaal (Donchian Channel), die Tweeledige Bewegende Gemiddelde (Dual Moving Average) en die Drieledige Bewegende Gemiddelde (Triple Moving Average). Die stelsels is op drie verskillende tipes stategieë toegepas. In die eerste toets was die stelsels geïmplementeer deur lukraak gekose parameters te gebruik om verhandelingseine voort te bring. In die tweede toets was die stelsels geoptimaliseer deur die parameters te kies wat die mees winsgewende opbrengs oor die toetsperiode sou voortbring. In die derde toets was ’n staakverlies (stop loss) geïmplementeer om te ondersoek of dit die opbrengs sou verbeter. Feitlik al die toetse het getoon dat die verhandelingstelsels die Koop-en-Hou-benadering geklop het. Aangesien al die stelsels die algemene tendens in die mark volg, het hulle hoë opbrengste getoon hoofsaaklik as gevolg van die beermark wat die wêreld se finansiële markte in 2008 gekenmerk het. As hierdie gebeurtenis nie plaasgevind het nie, sou hierdie stelsels swakker gevaar het as die Koop-en-Hou-strategie gedurende die tydperk van die toetsperiode. Die toetse het aangedui dat die keuse van die parameters wat verhandelingseine vir die stelsels gegenereer het, die winsgewendheid van ’n verhandelingstelsel drasties kan beïnvloed. Die implementering van ’n staakverlies- (stop-loss) strategie verbeter nie noodwendig die opbrengs van ’n stelsel nie, aangesien verskeie stelsels negatief beïnvloed was deur die staakverlies-strategie.

Over the past decade of accounting and finance research, the Ohlson (1995) model has been widely adopted as a framework for stock price prediction. While using the accounting data of 391 companies from SP500 in this paper, Bayesian statistical techniques are adopted to enhance both the estimative and predictive qualities of the Ohlson model comparing to the classical approaches. Specifically, the classical methods are used for the exploratory data analysis and then the Bayesian strategies are applied using Markov chain Monte Carlo method in three stages: individual analysis for each company, grouping analysis for each group and adaptive analysis by pooling information across companies. The base data, which consist of 20 quarters' observations starting from the first quarter of 1998, are used to make inferences for the regression coefficients (or parameters), evaluate the model adequacy and predict the stock price for the first quarter of 2004, when the real observations are set as the test data to evaluate the predictive ability of the Ohlson model. The results are averaged within each specified group categorized via the general industrial classification (GIC). The empirical results show that classical models result in larger stock price prediction errors, more positively-biased predictions and have much smaller explanatory powers than Bayesian models. A few transformations of both classical and Bayesian models are also performed in this paper, however, transformations of the classical models do not outweigh the usefulness of applying Bayesian statistics.

Herdecke, Privatuniv., Diss--Witten, 2006.

published_or_final_version / Statistics / Master / Master of Philosophy

This thesis is an investigation into the relationship that exists between macroeconomic variables and the pricing of common stock under trending market conditions. By introducing a dichotomous independent variable as a way of distinguishing between periods of rising and falling thereby attaching an additional expected premium to each of five accepted sources of macroeconomic risk for participation in ‘Bear’ markets. 228 observations of the fourteen industry sub-groupings of former TSE 300 were examined separately. The ultimate results were obtained using the Arbitrage Pricing Theory (APT) as the model to obtain factor exposures. The results show that there is no significant relationship between market trend and the pricing of common stock when the APT is applied. The final recommendation is that more research is needed.

Access to abstract permanently restricted to Ball State community only. / Estimation and forecasting of time series data -- Fitting of Saudi stock price by deterministic models -- Determination and fitting of the ARIMA models for Saudi stock price data -- Evaluation of forecasts by cross validation. / Access to thesis permanently restricted to Ball State community only. / Department of Mathematical Sciences