How are the prices of financial assets determined? In this dissertation, I test various theories empirically, focusing on several classes of bonds. In the first chapter, I test whether asset prices reflect the risk-exposures of financial intermediaries in a setting that is well suited to tackling concerns about omitted risk factors. I analyze catastrophe bonds whose cash flows are linked to the occurrence of natural disasters and find that 71% of the variation in their expected returns can be explained by a theoretically-motivated measure of financial intermediaries’ marginal rate of substitution. Assuming that natural disasters are independent of aggregate wealth, this pricing result is inconsistent with any explanation based on macroeconomic risk factors. However, the result is consistent with intermediary asset pricing models that suggest that financial intermediaries are marginal investors in capital markets. I also show that the premium on natural disaster risk has decreased significantly in recent years and has become less responsive to the occurrence of disasters, suggesting that intermediaries’ access to outside capital has improved over time. In the second chapter, which is coauthored with Robert J. Hodrick, we examine the statistical term structure model of Cochrane and Piazzesi (2005) and its affine counterpart, developed in Cochrane and Piazzesi (2008), in several out-of-sample analyzes. The model’s one-factor forecasting structure across bonds with two, three, four, and five years to maturity characterizes the term structures of additional major currencies in samples ending in 2003. In post-2003 data such one-factor structures again characterize each currency’s term structure, but we reject equality of the coefficients across the two samples. We derive currency return forecasting implications from the Cochrane and Piazzesi (2008) affine model showing that the term structure forecasting variables in each currency should predict cross-currency investments, but we find no support for these predictions in either pre-2004 or post-2003 data, whereas the interest differentials do predict currency returns. Here too, though, we find strong evidence of parameter instability as the parameter estimates on the interest differentials change sign. In recursive out-of-sample forecasts of excess rates of return on bonds in each currency, the Cochrane and Piazzesi (2008) term structure forecasting models fail to beat forecasts from the historical average excess rates of return. Graphical analysis indicates that the instability in the forecasting models’ parameters begins in the global financial crisis.

This dissertation studies topics in Passive Investing and Asset Pricing. The first chapter, "When do flows matter for asset prices: Evidence from adoption of ETF creation in Israel," focuses on the effect of capital allocation to passive investments on asset prices. We study a 2012 reform in Israel where all exchange traded products listed on the Tel Aviv Stock Exchange (TASE) adopted the Exchange Traded Fund (ETF) creation mechanism wherein designated market makers arbitrage between the index price and the net asset value of its benchmark. The reform greatly decreased the cost of this arbitrage activity and translated into a significant increase in demand for passive investments. The effect was stronger for illiquid indices containing smaller stocks. We show that the price effects of the reform were dramatically higher for stocks located at the top of indices composed of smaller stocks relative to stocks at the bottom of indices composed of bigger stocks. A 1 p.p. increase in passive ownership as a percent of market capitalization leads to an 11.7% increase in the price of stocks. Our findings provide new evidence on how passive inflows can change the distribution of capital across indices, and in turn impact price efficiency. In the second chapter, "Passive Investing and Algorithmic Trading," I examine the trading behavior of market participants against the growing demand for passive investing. I show that the growth of passive investing and algorithmic trading is complementary and mutually reinforcing. Algorithmic traders respond to a spike in demand for passive investments listed on the TASE following a major reform in the Israeli index market in 2012 by front-running the index inflows. Algorithmic traders accumulate stocks when index inflows are low and sell stocks when they are high. Based on this strategy, algorithmic traders earn a 12.7% annualized return in realized gains over passive strategies in the same period. Instead, Mutual Funds load on the index when inflows are high.

Asset prices are forward looking. Therefore, expectations play a central role in shaping asset prices. In this dissertation, I challenge the rational expectation assumption that has been influential in the field of asset pricing over the past few decades. Different from previous approaches, which typically build on behavioral theories originated from psychology literature, my approach takes data on subjective beliefs seriously and proposes empirically grounded models of subjective beliefs to evaluate the merits of the rational expectation assumption. Specifically, this dissertation research: 1). collects and analyzes data on investors' actual subjective return expectations; 2). builds models of subjective expectation formation; 3). derives and tests the models' implications for asset prices. I document the results of the research in two chapters. In summary, the dissertation shows that investors do not hold full-information rational expectations. On the other hand, their subjective expectations are not necessarily irrational. Rather, they are bounded by the information environment investors face and reflect investors' personal experiences and preferences. The deviation from fully-rational expectations can explain asset pricing anomalies such as cross-sectional anomalies in the U.S. stock market. In the first chapter, I provide a framework to rationalize the evidence of extrapolative return expectations, which is often interpreted as investors being irrational. I first document that subjective return expectations of Wall Street (sell-side, buy-side) analysts are contrarian and counter-cyclical. I then highlight the identification problem investors face when theyform return expectations using imperfect predictors through Kalman Filters. Investors differ in how they impose subjective priors, the same way rational agents differ in different macro-finance models. Estimating the priors using surveys, I find Wall Street and Main Street (CFOs, pension funds) both believe persistent cash flows drive asset prices but disagree on how fundamental news relates to future returns. These results support models featuring heterogeneous agents with persistent subjective growth expectations. In the second chapter, I propose and test a unifying hypothesis to explain both cross-sectional return anomalies and subjective return expectation errors: some investors falsely ignore the dynamics of discount rates when forming return expectations. Consistent with the hypothesis: 1) stocks' expected cash flow growth and idiosyncratic volatility explain significant cross-sectional variation of analysts' return forecast errors; 2). a measure of mispricing at the firm level strongly predicts stock returns, even among stocks in the S&P500 and at long horizon; 3). a tradable mispricing factor explains the CAPM alphas of 12 leading anomalies including investment, profitability, beta, idiosyncratic volatility and cash flow duration.

A thesis submitted to the School of Economic and Business Sciences, Faculty of Commerce, Law and Management, University of the Witwatersrand in fulfilment of the requirements for the degree of Master of Commerce (M.Com) in Finance, Johannesburg June 2017 / Traditional finance theory surrounding the risk-return relationship is underpinned by the CAPM which posits that a single risk factor, specifically market risk, is priced into asset returns. Even though it is a popular asset pricing model, the CAPM has been widely criticised due to its unrealistic assumptions and the APT was developed to address the CAPM’s weaknesses. The APT framework allows for a multitude of risk factors to be priced into asset returns; implying that it can be used to model returns using either macroeconomic or microeconomic factors. As such, the APT allows for non-traditional factors, such as investor sentiment, to be included. A macroeconomic APT framework was developed for nine countries using the variables outlined by Chen, Roll, and Ross (1986) and investor sentiment was measured by the FEARS index (Da, Engelberg, & Gao, 2015). Regression testing was used to determine whether FEARS is a statistically significant explanatory variable in the APT model for each country. The results show that investor sentiment is a statistically significant explanatory variable for market returns in five out of the nine countries examined. These results add to the existing APT literature as they show that investor sentiment has a significant explanatory role in explaining asset prices and their associated returns. The international nature of this study allows it to be extended by considering the role that volatility spill-over or the contagion effect would have on each model. / XL2018

Thesis (M.M. (Finance & Investment)--University of the Witwatersrand, Faculty of Commerce, Law and Management, Wits Business School, 2016 / This study investigates how interest rates impact the South African Stock market. We investigate how the selected interest rates proxies predict the level of the FTSE/JSE All Share Index returns. The vector auto-regression (VAR) model was estimated and interpreted, based on the monthly data from June 1995 to September 2014. Using tools such as Granger causality, impulse response function and variance decomposition, we found that the selected variables did not significantly influence the FTSE/JSE All Share Index returns. Consequently, these variables are not useful as predictive tools for the South African stock market returns. / MT2017

Previous research has shown that the asset pricing model of Sharpe, Litner and Black fails to capture the relationship between market β and average return. This previous work showed that the relationship between β and average return was flat. Subsequently it was shown that a strong relationship between book-to-market equity and stock price returns existed. It has also been shown that book to market equity has strong roots in economic fundamentals. Utilities have historically used betas to justify rate increases I developing rate structures that meet the rate of return demands for investors given the risk profiles that the company betas suggest. Realizing that low betas argue against large rate increases l utilities have turned to other avenues to justify higher returns. The suggested relationship of book-to-market equity and average stock returns would provide utilities with a new argument. This thesis will show that the search for a risk proxy in the rate of return relationship for the electric utility is not resolved. The relationship reported between book-to-market equity and stock price returns does not appear to be statistically significant in the electric utility sector and extreme caution is advised in using this empirical model to predict or explain stock price returns. / Master of Arts

No description available.

Nie Xiaofeng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 52-56). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- A Minimax Model and Its CAPM --- p.9 / Chapter 2.1 --- Model Formulation --- p.9 / Chapter 2.2 --- Efficient Frontier --- p.11 / Chapter 2.3 --- Market Portfolio --- p.15 / Chapter 2.4 --- CAPM of Minimax Model --- p.22 / Chapter 3 --- "A Revised Minimax Model with Downside Risk, and Its CAPM" --- p.28 / Chapter 3.1 --- Model Formulation --- p.28 / Chapter 3.2 --- Efficient Frontier --- p.30 / Chapter 3.3 --- Market Portfolio and CAPM --- p.38 / Chapter 4 --- Numerical Analysis --- p.43 / Chapter 4.1 --- Efficient Frontiers --- p.43 / Chapter 4.1.1 --- Input Data --- p.45 / Chapter 4.1.2 --- Efficient Frontiers --- p.45 / Chapter 4.2 --- Monthly Rate of Return Comparison --- p.47 / Chapter 5 --- Summary --- p.50 / Bibliography --- p.52

published_or_final_version / Economics / Master / Master of Social Sciences

Practitioners and academics in emerging markets are yet to agree on how best they can value companies in emerging markets. In contrast, academics and practitioners in developed markets seem to agree on mainstream valuation practices (Bruner, Eades, Harris and Haggins, 1998; Graham and Harvey, 2001). This study was therefore aimed at achieving such consensus with particular attention being paid to the emerging market banks. Emerging market banks are by no means small and are growing fast. Furthermore, these banks are currently involved in lots of cutting age economic activities such as mergers and acquisitions (M&A), joint ventures and strategic alliances which require sound valuation practices that are based on empirical evidence. The primary purpose of this research was to establish consensus of opinion among experts with regard to the valuation of banks in emerging markets. To achieve the purpose of this study the Delphi technique, which is a structured survey method that relies on a panel of experts to answer questionnaires in two or more Delphi rounds, was used to gather data and develop consensus among experts (Kalaian and Kasim, 2012). The main findings in this study pertain to aspects concerning the type of analysis considered by experts when analysing the performance of banks, how experts compare the discounted cash flow (DCF) approach to multiples valuation approach, the challenges encountered by experts when valuing banks in emerging markets, and how experts compute the cost of capital for banks in emerging markets. The main findings of this study can be summarised as follows: ∙ When analyzing the performance of banks, it is essential to conduct a bank-specific, industry and macroeconomic analysis; ∙ When estimating the future performance of banks, the time series analysis and an explicit forecast period of between 4-10 years may be used; ∙ When estimating the terminal value for banks in emerging markets, the perpetuity with growth is used; ∙ When computing the value for banks, the DCF valuation approach (equity DCF and DDM valuation models) are used as primary valuation methods and the relative valuation approach (P/E and P/BV ratio) are used as secondary valuation methods; ∙ The DCF valuation approach is considered as more accurate and popular when valuing banks in emerging markets; and ∙ When estimating the cost of equity, the capital asset pricing model (CAPM) is used.