Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Fundamental frequency estimation in speech signals with variable rate particle filters

Fundamental frequency estimation, known as pitch estimation in speech signals is of interest both to the research community and to industry. Meanwhile, the particle filter is known to be a powerful Bayesian inference method to track dynamic parameters in nonlinear state-space models. In this paper, we propose a speech model under a time-varying source-filter speech model, and use variable rate particle filters (VRPF) to develop methods for estimation of pitch periods in speech signals. A Rao–Blackwellised variable rate particle filter (RBVRPF) is also implemented. The proposed VRPF and RBVRPF are compared with a state-of-the-art pitch estimation algorithm, the YIN algorithm. Simulation results show that more accurate estimation of pitch can be obtained by VRPF and RBVRPF even under strong background noise conditions.The authors would like to thank CSC Cambridge International Scholarship and Natural Science Foundation of China (No.61463035) for providing financial support.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TASLP.2016.253128

Jump Particle Filtering Framework for Joint Target Tracking and Intent Recognition

This paper presents a Bayesian framework for inferring the posterior of the extended state of a target, incorporating its underlying goal or intent, such as any intermediate waypoints and/or final destination. The methodology is thus for joint tracking and intent recognition. Several novel latent intent models are proposed here within a virtual leader formulation. They capture the influence of the target's hidden goal on its instantaneous behaviour. In this context, various motion models, including for highly maneuvering objects, are also considered. The a priori unknown target intent (e.g. destination) can dynamically change over time and take any value within the state space (e.g. a location or spatial region). A sequential Monte Carlo (particle filtering) approach is introduced for the simultaneous estimation of the target's (kinematic) state and its intent. Rao-Blackwellisation is employed to enhance the statistical performance of the inference routine. Simulated data and real radar measurements are used to demonstrate the efficacy of the proposed techniques.Comment: Submitted to IEEE Transactions on Aerospace and Electronic Systems (T-AES

Integrated Environmental Modelling Framework for Cumulative Effects Assessment

Global warming and population growth have resulted in an increase in the intensity of natural and anthropogenic stressors. Investigating the complex nature of environmental problems requires the integration of different environmental processes across major components of the environment, including water, climate, ecology, air, and land. Cumulative effects assessment (CEA) not only includes analyzing and modeling environmental changes, but also supports planning alternatives that promote environmental monitoring and management. Disjointed and narrowly focused environmental management approaches have proved dissatisfactory. The adoption of integrated modelling approaches has sparked interests in the development of frameworks which may be used to investigate the processes of individual environmental component and the ways they interact with each other. Integrated modelling systems and frameworks are often the only way to take into account the important environmental processes and interactions, relevant spatial and temporal scales, and feedback mechanisms of complex systems for CEA. This book examines the ways in which interactions and relationships between environmental components are understood, paying special attention to climate, land, water quantity and quality, and both anthropogenic and natural stressors. It reviews modelling approaches for each component and reviews existing integrated modelling systems for CEA. Finally, it proposes an integrated modelling framework and provides perspectives on future research avenues for cumulative effects assessment

Integration of Technical Trading Behaviour in Asset Pricing

This thesis investigates methods applied to technical analysis based on particle filtering for detecting the presence of technical trading on foreign exchange and futures markets. The objective is to measure the intensity of that trading and its influence on short term price formation of the traded securities. Technical trading is a type of trading that is based on technical analysis. This is a method to form a view on the future development of the price of a traded security based on the currently observed pattern of the price itself. Technical analysis and trading strategies based on price patterns are not isolated phenomena. They are extremely popular among a very large group of financial markets professionals. The experiments of this research rely on extensive amount of data. We use prices for a wide range of securities representing different markets, countries and liquidity profiles. More than ten years of data at different level of aggregation are processed in order to test the ideas during different economic cycles. The research comprises an experimental software environment and three experiments: 1. Experimental software environment The experiments carried out in this project require a robust software environment and a significant amount of coding. An intuitive choice for this purpose is R, a language and environment for statistical computing. Most of the programming is done in R utilising the flexibility of the language and the environment. The code has been consolidated into an R package - a library module for the environment. Wherever better speed or interfacing to other systems are needed, additional modules have been developed. 2. Detection and tracking of technical trading We developed a method based on particle filtering for detecting and measuring the intensity of technical trading. We tested the methodology on a simulation framework created for this purpose. The technique has been used to test whether a set of technical price patterns are actively traded on the market. 3. Option pricing in the presence of technical trading This experiment demonstrates that the intraday security price is not a Markov process when it is actively traded by technical traders. It then proposes a model for pricing intraday options on securities that are subject of technical trading. This is achieved by including the technical patterns parameters in the state space of the random process. The experiment is exemplified with price patterns that have been identified as actively traded. 4. Technical trading, prominence and liquidity This experiment introduces a new method for automation of technical patterns detection based on topographic prominence. It measures the prominence of the fluctuations on the price series and uses the result to linearize the series and detect technical patterns such as ‘Head-and- shoulders’ and support and resistance levels. The model from the first experiment combined with the method of this experiment is applied on a range of securities. The experiment extends the analysis by exploring the effect of different liquidity conditions on the intensity of technical trading. The first contribution of this thesis is developing an environment for trading simulation and technical strategies identification and backtesting. The second main contribution of the thesis is developing a methodology, based on particle filtering, for identifying the presence of technical trading on the futures and foreign exchange markets. We applied the technique on a number of securities to measure the intensity of technical trading. We also investigated the effect of liquidity on the presence of technical trading. The next contribution is developing a new approach for technical pattern automation based on price prominence. The next contribution of the research is the application of the newly developed technical pattern automation method to identify and test the performance of four different technical price patterns on a range of futures and foreign exchange securities. Finally, we demonstrated how the information on specific technical patterns and their trading intensities can be used for pricing intraday options on the securities exposed to active intraday technical trading

Bernoulli Race Particle Filters

When the weights in a particle filter are not available analytically, standard resampling methods cannot be employed. To circumvent this problem state-of-the-art algorithms replace the true weights with non-negative unbiased estimates. This algorithm is still valid but at the cost of higher variance of the resulting filtering estimates in comparison to a particle filter using the true weights. We propose here a novel algorithm that allows for resampling according to the true intractable weights when only an unbiased estimator of the weights is available. We demonstrate our algorithm on several examples.Comment: 19 page

Supervised Learning Models to Predict Stock Direction Within Different Sectors in a Bull and Bear Market

Forecasting stock market price movement is a well researched and an alluring topic within the machine learning and financial realm. Supervised machine learning algorithms such as Random Forest (RF) and Support Vector Machines (SVM) have been used independently to gain insight on the market. With such volatility in the market the scope of this study will utilized the RF and SVM in a very volatility market to determine if these models will perform at a high level or outperform each other in both markets. This relative study is performed on 16 stocks in 4 different sectors over the bear market ”housing crash” of 2008 . The model utilized technical indicators as the respective parameters to assist in predicting the stock price movement when determining the performance of each model. Despite the No Free Lunch Theorem stating one model can not out perform another model, the study displayed higher accuracy for the RF model. Each model was evaluated using the confusion metrics to calculate the precision, recall, and F1 score