Cash holdings and bond returns around takeovers

Using a large sample of 2712 unique U.S. domestic takeovers over the period 1993 to 2014, we show a negative relation between the level of cash holdings and post-announcement corporate bond returns. Our findings support the agency cost of cash holdings view and show that bondholders and shareholders share the same interests with respect to cash policy around takeovers. We further find that cash holdings are viewed less negatively by bondholders in firms with strong shareholders. This paper is the first to document the role of cash holdings on bondholder wealth around takeover announcements.

Returns to scale, market power, and the nature of price rigidity in new keynesian models with self-fulfilling expectations

Increasing returns to scale and firms' market power are two potential sources of sunspot expectations in neoclassical models. We show that in New Keynesian models, returns to scale and market power can have fundamentally different implications for broad macroeconomic issues, including self-fulfilling expectations, depending on the nature of price rigidity. Our findings suggest that the design of stabilization monetary policy can depend on precise knowledge about the economy's real and nominal features. Therefore, a clear understanding of the specific economic environment and its relevance to monetary policymaking for ensuring macroeconomic stability can be an integrated part of monetary policy practice.

Online mining abnormal period patterns from multiple medical sensor data streams

With the advanced technology of medical devices and sensors, an abundance of medical data streams are available. However, data analysis techniques are very limited, especially for processing massive multiple physiological streams that may only be understood by medical experts. The state-of-the-art techniques only allow multiple medical devices to independently monitor different physiological parameters for the patient's status, thus they signal too many false alarms, creating unnecessary noise, especially in the Intensive Care Unit (ICU). An effective solution which has been recently studied is to integrate information from multiple physiologic parameters to reduce alarms. But it is a challenge to detect abnormalities from high frequently changed physiological streams data, since abnormalities occur gradually due to the complex situation of patients. An analysis of ICU physiological data streams shows that many vital physiological parameters are changed periodically (such as heart rate, arterial pressure, and respiratory impedance) and thus abnormalities are generally abnormal period patterns. In this paper, we develop a Mining Abnormal Period Patterns from Multiple Physiological Streams (MAPPMPS) method to detect and rank abnormalities in medical sensor streams. The efficiency and effectiveness of the MAPPMPS method is demonstrated by a real-world massive database of multiple physiological streams sampled in ICU, comprising 250 patients' streams (each stream involving over 1.3 million data points) with a total size of 28 GB data.

Does financial news predict stock returns? New evidence from Islamic and Non-Islamic stocks

The paper extends the time-series financial news data set constructed by Garcia (2013) and uses it to examine whether financial news predicts returns of Islamic stocks differently compared to non-Islamic (conventional) stocks. We find that they do. First, while both positive and negative worded news predict most Islamic and conventional stock returns, positive words have a larger impact on both types of stock returns. Second, shock to returns from financial news reverses only in part for some stocks. Third, for a mean-variance investor, investing in Islamic stocks is relatively more profitable than investing in the corresponding conventional stocks. Fourth, we show that profits are robust to a range of time-series risk factors, namely, market risk, size-based risk, and momentum-induced risk.

Financial constraints and stock returns — evidence from Australia

Using multiple discriminant analysis, we construct an index thatmeasures firms' external financial constraints in an Australian setting.We form portfolios of firms based on our financial constraints index andfind that financially constrained firms earn lower return than theirunconstrained counterparts. Moreover, stock returns of financiallyconstrained firms are found to move together, indicating the potentialexistence of a financial constraints factor. Neither the variation nor themean return of the constraints factor are well explained by existing assetpricing models, suggesting an independent role for our financialconstraints factor in affecting stock returns.

Can consumer price index predict gold price returns?

In this paper using data for 54 countries we test whether consumer price index (CPI) predicts gold price returns. Our test for predictability is based on a recently developed flexible generalised least squares estimator, which most importantly accommodates the endogeneity of CPI, its persistency and any heteroskedasticity in the model. We find limited evidence that CPI predicts gold price returns in in-sample tests; however, out-of-sample tests reveal relatively strong evidence that CPI predicts gold returns. These results are robust to different forecasting horizons. On the whole, we discover reasonable evidence that consumer prices predict gold price returns.

Are Islamic stock returns predictable? : a global perspective

Using the sharia-compliant measures, we compile a data set that spans January 1981 to December 2014 and contains 2577 Islamic stocks. Using as many as 12 financial and macroeconomic predictors, we discover strong evidence of both in-sample and out-of-sample return predictability. There is robust evidence of predictability only when U.S. stock returns are used as a predictor. We find that investing in regional (industry) portfolios offers on average, across the 12 predictors, meaningful profits of 6.16% (6.03%) per annum. Investing in a portfolio of Islamic stocks belonging to emerging markets (9.89% per annum) and a portfolio of Islamic stocks belonging to the consumer goods sector (6.37% per annum) offers the most returns amongst regions and industries, respectively.

Exploring the effectiveness of true abnormal data elimination in context-aware web services recommendation

Recent years have witnessed a growing interest in context-aware recommender system (CARS), which explores the impact of context factors on personalized Web services recommendation. Basically, the general idea of CARS methods is to mine historical service invocation records through the process of context-aware similarity computation. It is observed that traditional similarity mining process would very likely generate relatively big deviations of QoS values, due to the dynamic change of contexts. As a consequence, including a considerable amount of deviated QoS values in the similarity calculation would probably result in a poor accuracy for predicting unknown QoS values. In allusion to this problem, this paper first distinguishes two definitions of Abnormal Data and True Abnormal Data, the latter of which should be eliminated. Second, we propose a novel CASR-TADE method by incorporating the effectiveness of True Abnormal Data Elimination into context-aware Web services recommendation. Finally, the experimental evaluations on a real-world Web services dataset show that the proposed CASR-TADE method significantly outperforms other existing approaches.