Termination of closed end funds and behavior of their discounts

Based on an extensive sample of U.S. closed-end funds undergoing open-ending, we examine the behavior of discounts prior to the announcement till open-ending. Discounts are significantly reduced upon announcement of open-ending with price increase. Announcement period return is directly related to the pre-announcement discount, and other hypothesized characteristics of the fund and investor behavior. The role of investor sentiments as an explanator of discounts is weaker after announcement. We decompose the pre-announcement discount into structural and idiosyncratic parts, and report that there is a greater reduction of the idiosyncratcic part of the discount at announcement. Time series behavior of discounts lends support to investor confidence. We find that small amounts of discounts remain at the time of the open-ending.Closed-end funds, open-ending, discounts, investor sentiment.

Trading Profits in Closed-End Fund Tender Offers

Prior research has documented anomalous profits as high as 9% from participating in stock repurchase tender offers. The trading strategy is to buy shares in the market just before offer expiration and tender; it involves a trading horizon of just a few days. The large profits given a short trading horizon are puzzling, and this evidence raises serious questions about market efficiency. A possible reason inhibiting arbitragers from eliminating these profits is risk exposure. We examine whether trading profits are available in tender offer repurchases conducted by closed-end funds. Risk exposure concerns should be minimized for these offers, since the underlying assets of closed-end funds constitute a well-diversified portfolio of securities. We find significant tendering profits even in this sample, although the magnitude is much smaller at around 1%.Tender offer, closed-end fund

Toward Sustainable IoT Applications: Unique Challenges for Programming the Batteryless Edge

The advent of ultra-low-power computer systems has enabled intermittently powered, battery-free devices to operate using harvested ambient energy. We present a roadmap from today’s continuously powered Internet of Things devices to tomorrow’s battery-free devices that highlights challenges for those running intermittent programs.acceptedVersio

The behavior of discounts of closed-end funds undergoing open-ending

Based on an extensive sample of U.S. closed-end funds undergoing open-ending conversion, we examine the behavior of discounts prior to the announcement till the date of open-ending. Discounts are significantly reduced upon announcement of open-ending with price increase. Announcement period return is directly related to the pre-announcement discount, liquidity, and other characteristics of the fund. We decompose the pre-announcement discount into structural and diosyncratic parts, and report that there is a greater reduction of the idiosyncratcic part of the discount. We examine the role of distributions to the investors on the size and behavior of discounts subsequent to the open-ending announcement. We find that small amounts of discounts remain at the time of the open-ending and investigate potential explanations for such discounts.closed-end funds, discount

Maximum likelihood parameter estimation in time series models using sequential Monte Carlo

Time series models are used to characterise uncertainty in many real-world dynamical phenomena. A time series model typically contains a static variable, called parameter, which parametrizes the joint law of the random variables involved in the definition of the model. When a time series model is to be fitted to some sequentially observed data, it is essential to decide on the value of the parameter that describes the data best, a procedure generally called parameter estimation. This thesis comprises novel contributions to the methodology on parameter estimation in time series models. Our primary interest is online estimation, although batch estimation is also considered. The developed methods are based on batch and online versions of expectation-maximisation (EM) and gradient ascent, two widely popular algorithms for maximum likelihood estimation (MLE). In the last two decades, the range of statistical models where parameter estimation can be performed has been significantly extended with the development of Monte Carlo methods. We provide contribution to the field in a similar manner, namely by combining EM and gradient ascent algorithms with sequential Monte Carlo (SMC) techniques. The time series models we investigate are widely used in statistical and engineering applications. The original work of this thesis is organised in Chapters 4 to 7. Chapter 4 contains an online EM algorithm using SMC for MLE in changepoint models, which are widely used to model heterogeneity in sequential data. In Chapter 5, we present batch and online EM algorithms using SMC for MLE in linear Gaussian multiple target tracking models. Chapter 6 contains a novel methodology for implementing MLE in a hidden Markov model having intractable probability densities for its observations. Finally, in Chapter 7 we formulate the nonnegative matrix factorisation problem as MLE in a specific hidden Markov model and propose online EM algorithms using SMC to perform MLE