The Impact of Earnings Announcement Surprise on Stock Prices

The 2008 financial crisis and recent volatility in global markets have provided important motivations to better understand the functioning of equity markets, since stock market crashes can trigger severe recessions through wealth and balance-sheet effects. Against efficient market theory, the literature has found much evidence that stock price movements seem unrelated to expected future movements in corporate fundamentals. This thesis investigates the impact of earnings announcement surprise on stock prices and contributes to the existing literature by examining the impact’s dependency on various factors (the P/E ratio, the output gap, whether the forecast error is positive or negative, and the distribution of forecasts). The thesis measures earnings surprise with Bloomberg quarterly forecasts for three companies (Hewlett Packard, IBM, and Walt Disney) from 1984 to 2015. Regression results indicate that positive surprise tends to raise stock prices around announcement days, with the exception of IBM. Other factors affect each company with different significances and magnitudes. Positive surprise has a smaller impact than negative surprise under a lower P/E ratio or a decreasing output gap. When the standard deviation of forecasts is high, investors may respond more or less to earnings surprise

Do Exports lead Economic Output in Five Asian Countries? A Cointegration and Granger Causality Analysis

This paper examines the relationship between exports and economic output for five major Asian economies using annual data in an expanded data set and employing unit root and cointegration analysis. It employs a Vector Error Correction Model (VECM) that treats all variables in the modified production function as potentially endogenous and then determines via weak exogeneity tests whether some of the key variables can be treated as exogenous (omitted from the system). Johansen cointegration tests find a positive long-run relationship between exports and economic output for the Philippines, Singapore, and Thailand. Cointegration tests find a negative long-run relationship between exports and economic output for India. The Block Granger causality tests and impulse response functions for the Philippines and Singapore find stronger causality from exports to economic output rather than the reverse. Granger causality tests in level form also find significant causality from exports to economic output. No causality exists between exports and economic output in the case of India. Exports seem to promote economic growth in three of the four countries that have cointegrated data, which supports the exports-led growth hypothesis found in some of the extant literature. The paper does not find cointegration for China because the variables are integrated of different orders from I(0) to I(2)

Calibrating Bayesian Learning via Regularization, Confidence Minimization, and Selective Inference

The application of artificial intelligence (AI) models in fields such as engineering is limited by the known difficulty of quantifying the reliability of an AI's decision. A well-calibrated AI model must correctly report its accuracy on in-distribution (ID) inputs, while also enabling the detection of out-of-distribution (OOD) inputs. A conventional approach to improve calibration is the application of Bayesian ensembling. However, owing to computational limitations and model misspecification, practical ensembling strategies do not necessarily enhance calibration. This paper proposes an extension of variational inference (VI)-based Bayesian learning that integrates calibration regularization for improved ID performance, confidence minimization for OOD detection, and selective calibration to ensure a synergistic use of calibration regularization and confidence minimization. The scheme is constructed successively by first introducing calibration-regularized Bayesian learning (CBNN), then incorporating out-of-distribution confidence minimization (OCM) to yield CBNN-OCM, and finally integrating also selective calibration to produce selective CBNN-OCM (SCBNN-OCM). Selective calibration rejects inputs for which the calibration performance is expected to be insufficient. Numerical results illustrate the trade-offs between ID accuracy, ID calibration, and OOD calibration attained by both frequentist and Bayesian learning methods. Among the main conclusions, SCBNN-OCM is seen to achieve best ID and OOD performance as compared to existing state-of-the-art approaches at the cost of rejecting a sufficiently large number of inputs.Comment: Under revie

What causes Chinese listed firms to switch bank loan provider? Evidence from a survival analysis

This paper analyses the duration of firm-bank relationships and examines what drives firms in China to change from one bank loan provider to another. Matched data of firm-loan-duration to bank provides a unique panel data set of relationship between China's listed firms and their lending banks consisting of 2102 firms listed on both the Shanghai Stock Exchange and Shenzhen Stock Exchange in the period of 1996–2016. The Cox proportional hazard model is used to allow for a semiparametric hazard function after parametrically controlling for firm-specific financial factors, industry factors, ownership characteristics, internal management changes, and external macroeconomic changes. In addition, we explore the impact of the 2008 financial crisis, bank-financial and ownership characteristics. The main finding of this study is that in an environment of growing commercialisation of relationships the firm-bank relationship between state-owned enterprises (SOEs) and state-owned banks (SOBs) in China remains super-stable. However, a change in the CEO of a firm even of a SOE increases the probability of the loan-provider being changed

Radiotherapy for elderly patients with glioblastoma: An assessment of hypofractionation and modern treatment techniques

Glioblastoma (GBM) is a disease with a poor prognosis. For decades, radiotherapy has played a critical role in the management of GBM. The standard of care radiation prescription is 60 Gy in 30 fractions, but landmark trials have historically excluded patients older than 70 years. Currently, there is considerable variation in the management of elderly patients with GBM. Shortened radiation treatment (hypofractionated) regimens have been explored since conventional treatment schedules are lengthy and many elderly patients have functional, cognitive, and social limitations. Clinical trials have demonstrated the effectiveness of hypofractionated radiotherapy (40 Gy in 15 fractions) to treat elderly or frail patients with GBM. Although previous studies have suggested these unique hypofractionation prescriptions effectively treat these patients, there are many avenues for improvement in this patient population. Herein, we describe the unique tumor biology of glioblastoma, key hypofractionated radiotherapy studies, and health-related quality of life (HRQOL) studies for elderly patients with GBM. Hypofractionated radiation has emerged as a shortened alternative and retrospective studies have suggested survival outcomes are similar for elderly patients with GBM. Prospective studies comparing hypofractionation with conventional treatment regiments are warranted. In addition to evaluating survival outcomes, HRQOL endpoints should be incorporated into future studies