This paper assesses the impact of earnings quality on market responses to annual earnings announcements. Earnings quality is measured by the ratio of earnings to funds from operations. The difference in the association between forecast errors and excess returns across the high/low quality earnings subsamples is found to be statistically significant; there is a greater market response to earnings announcements of high-quality firms than to low-quality firms. Hence, earnings quality as measured by the ratio of earnings to funds from operations, is found to have pricing implications. The results are robust across two regression models: OLS on returns ordered in announcement time and SUR/GLS on returns ordered in calendar time. / Business, Sauder School of / Graduate

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This thesis focuses on binomial tree approximation for solving Backward Stochastic Differential Equations (BSDEs), particularly in the context of option pricing. The numerical method iteratively solves the discrete-time version of the BSDE backward in time. The discretization process employs constructing a binomial tree representing possible future price movements of the underlying asset. At each node of the tree, the option value is computed based on the expected payoff at that node and the discounted option values from the subsequent nodes. Furthermore, we discusses an approximation for the control process Z,, and the replicating portfolio a,. Which is expressed as the conditional expectation of the ratio of future option value increment to Brownian motion increment and demonstrate how it relates to the Black Scholes model for continuous time. An important part of the thesis is to compare theoretical expressions from the Black-Scholes model and the binomial tree. Formulas from the binomial tree are explicitly calculated and validated by numerical experiments.

The inference of causal relationships between stock markets constitutes a major research topic in the field of financial time series analysis. A successful reconstruction of the underlying causality structure represents an important step towards the overall aim of improving stock market price forecasting. In this thesis, I utilise the concept of Granger-causality for the identification of causal relationships. One major challenge is the possible presence of latent variables that affect the measured components. An instantaneous interaction can arise in the inferred network of stock market relationships either spuriously due to the existence of a latent confounder or truly as a result of hidden agreements between market players. I investigate the implications of such a scenario; proposing a new method that allows for the first time to distinguish between instantaneous interactions caused by a latent confounder and those resulting from hidden agreements. Another challenge is the implicit assumption of existing Granger-causality analysis techniques that the interactions have a time delay either equal to or a multiple of the observed data. Two sub-cases of this scenario are discussed: (i) when the collected data is simultaneously recorded, (ii) when the collected data is non-simultaneously recorded. I propose two modified approaches based on time series shifting that provide correct inferences of the complete causal interaction structure. To investigate the performance of the above mentioned method improvements in predictions, I present a modified version of the building block model for modelling stock prices allowing causality structure between stock prices to be modelled. To assess the forecasting ability of the extended model, I compare predictions resulting from network reconstruction methods developed throughout this thesis to predictions made based on standard correlation analysis using stock market data. The findings show that predictions based on the developed methods provide more accurate forecasts than predictions resulting from correlation analysis.

This paper investigates both the information contents of recommendations disseminated by foreign security firms and the interaction of foreign security firms’ trading activities with their recommendations in Taiwan’s stock market. Using event study, correlation test, and regression analysis, we find negative average abcdrmal returns(AARs) and average cumulative abcdrmal returns(CARs) for negative and neutral foreign analysts’ recommendations levels and recommendation changes in the pre-recommendation period. AARs and CARs for positive recommendations in pre-recommendation period are positive, but reverse to negative three days after the event day. Our results also show that correlation coefficients of recommendations (both in recommendation levels and recommendation changes) and holding period returns are significantly positive in the pre-recommendation period, but insignificantly negative in the post-recommendation period. In the regression analyses, we find that price momentum factor is significantly related to foreign analysts’ recommendation, but the incremental contribution of this factor to foreign analysts’ recommendations are marginal and not significant. We also find that foreign security firms respond more rigorously to stocks receiving recommendation above buy recommendations and stocks being downgraded. These results show that foreign security firms are more conservative toward trading stocks in Taiwan’s stock market. They only buy stocks above buy recommendations (in a delay pattern), but immediately sell downgraded stocks.

Three empirical studies are conducted examining the efficiency of S&P 500 futures prices and the pricing of these futures contracts. In the first study, the ability of futures prices to predict the realized spot S&P 500 index prices on the expiration date is examined for near term contracts. The futures prices are found to be unbiased predictors of the realized spot index prices for the nineteen quarterly contracts from 1982 to 1986. Previous studies report significant deviations in S&P SOO futures prices from theoretically determined Cost of Carry Model (CCM) prices. In the second study, it is found that the CCM using the federal funds rate, a proxy for the overnight repurchase rate, provides relatively better estimates of the S&P S(x) futures prices over the 1984-1986 period. The futures mispricing also reflects the weekend effect anomaly: futures prices are "over-priced" relative to CCM prices on Mondays, whereas the opposite occurs on Fridays. The futures over-pricing (under-pricing) is characterized by "bull" ("bear") financial markets and the extent of price changes are relatively greater in the futures market. The futures under-pricing is supported by strong future market volume and open-interest positions. The basis and changes in it over the futures contract period are measures of how well integrated the futures market and the underlying spot market are. In the third study, based on daily closing prices for the S&P 500 index and index futures for the 1984-1986 period, it is found that the basis decreases over the contract period but the rate of decrease is independent of the time to expiration. The change in basis on Mondays is generally positive which also reflects the weekend effect anomaly. The daily basis is negative on 107 days, which generally occurs during strong futures market trading volume and open interest positions. It is doubtful whether the negative basis can be attributed to a negative net financing cost, where the dividend yield 0.1 the spot index exceeds the cost of financing the spot index forward.

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Yang, Haiqin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 98-109). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Time Series Prediction and Its Problems --- p.1 / Chapter 1.2 --- Major Contributions --- p.2 / Chapter 1.3 --- Thesis Organization --- p.3 / Chapter 1.4 --- Notation --- p.4 / Chapter 2 --- Literature Review --- p.5 / Chapter 2.1 --- Framework --- p.6 / Chapter 2.1.1 --- Data Processing --- p.8 / Chapter 2.1.2 --- Model Building --- p.10 / Chapter 2.1.3 --- Forecasting Procedure --- p.12 / Chapter 2.2 --- Model Descriptions --- p.13 / Chapter 2.2.1 --- Linear Models --- p.15 / Chapter 2.2.2 --- Non-linear Models --- p.17 / Chapter 2.2.3 --- ARMA Models --- p.21 / Chapter 2.2.4 --- Support Vector Machines --- p.23 / Chapter 3 --- Support Vector Regression --- p.27 / Chapter 3.1 --- Regression Problem --- p.27 / Chapter 3.2 --- Loss Function --- p.29 / Chapter 3.3 --- Kernel Function --- p.34 / Chapter 3.4 --- Relation to Other Models --- p.36 / Chapter 3.4.1 --- Relation to Support Vector Classification --- p.36 / Chapter 3.4.2 --- Relation to Ridge Regression --- p.38 / Chapter 3.4.3 --- Relation to Radial Basis Function --- p.40 / Chapter 3.5 --- Implemented Algorithms --- p.40 / Chapter 4 --- Margins in Support Vector Regression --- p.46 / Chapter 4.1 --- Problem --- p.47 / Chapter 4.2 --- General ε-insensitive Loss Function --- p.48 / Chapter 4.3 --- Accuracy Metrics and Risk Measures --- p.52 / Chapter 5 --- Margin Variation --- p.55 / Chapter 5.1 --- Non-fixed Margin Cases --- p.55 / Chapter 5.1.1 --- Momentum --- p.55 / Chapter 5.1.2 --- GARCH --- p.57 / Chapter 5.2 --- Experiments --- p.58 / Chapter 5.2.1 --- Momentum --- p.58 / Chapter 5.2.2 --- GARCH --- p.65 / Chapter 5.3 --- Discussions --- p.72 / Chapter 6 --- Relation between Downside Risk and Asymmetrical Margin Settings --- p.77 / Chapter 6.1 --- Mathematical Derivation --- p.77 / Chapter 6.2 --- Algorithm --- p.81 / Chapter 6.3 --- Experiments --- p.83 / Chapter 6.4 --- Discussions --- p.86 / Chapter 7 --- Conclusion --- p.92 / Chapter A --- Basic Results for Solving SVR --- p.94 / Chapter A.1 --- Dual Theory --- p.94 / Chapter A.2 --- Standard Method to Solve SVR --- p.96 / Bibliography --- p.98

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Thesis (Ph.D.)--Southern Illinois University at Carbondale, 2000. / Major Professor: Subhash C. Sharma. Includes bibliographical references.