Growth optimal investment with threshold rebalancing portfolios under transaction costs

We study how to invest optimally in a stock market having a finite number of assets from a signal processing perspective. In particular, we introduce a portfolio selection algorithm that maximizes the expected cumulative wealth in i.i.d. two-asset discrete-time markets where the market levies proportional transaction costs in buying and selling stocks. This is achieved by using 'threshold rebalanced portfolios', where trading occurs only if the portfolio breaches certain thresholds. Under the assumption that the relative price sequences have log-normal distribution from the Black-Scholes model, we evaluate the expected wealth under proportional transaction costs and find the threshold rebalanced portfolio that achieves the maximal expected cumulative wealth over any investment period. © 2013 IEEE

Growth optimal investment in discrete-time markets with proportional transaction costs

We investigate how and when to diversify capital over assets, i.e., the portfolio selection problem, from a signal processing perspective. To this end, we first construct portfolios that achieve the optimal expected growth in i.i.d. discrete-time two-asset markets under proportional transaction costs. We then extend our analysis to cover markets having more than two stocks. The market is modeled by a sequence of price relative vectors with arbitrary discrete distributions, which can also be used to approximate a wide class of continuous distributions. To achieve the optimal growth, we use threshold portfolios, where we introduce a recursive update to calculate the expected wealth. We then demonstrate that under the threshold rebalancing framework, the achievable set of portfolios elegantly form an irreducible Markov chain under mild technical conditions. We evaluate the corresponding stationary distribution of this Markov chain, which provides a natural and efficient method to calculate the cumulative expected wealth. Subsequently, the corresponding parameters are optimized yielding the growth optimal portfolio under proportional transaction costs in i.i.d. discrete-time two-asset markets. As a widely known financial problem, we also solve the optimal portfolio selection problem in discrete-time markets constructed by sampling continuous-time Brownian markets. For the case that the underlying discrete distributions of the price relative vectors are unknown, we provide a maximum likelihood estimator that is also incorporated in the optimization framework in our simulations

Competitive and online piecewise linear classification

In this paper, we study the binary classification problem in machine learning and introduce a novel classification algorithm based on the 'Context Tree Weighting Method'. The introduced algorithm incrementally learns a classification model through sequential updates in the course of a given data stream, i.e., each data point is processed only once and forgotten after the classifier is updated, and asymptotically achieves the performance of the best piecewise linear classifiers defined by the 'context tree'. Since the computational complexity is only linear in the depth of the context tree, our algorithm is highly scalable and appropriate for real time processing. We present experimental results on several benchmark data sets and demonstrate that our method provides significant computational improvement both in the test (5 ∼ 35×) and training phases (40 ∼ 1000×), while achieving high classification accuracy in comparison to the SVM with RBF kernel. © 2013 IEEE

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Modelling land-use change dynamics in a mediterranean coastal wetland using CA-Markov chain analysis

Rapid development in agricultural activities has resulted in a significant amount of habitat loss at ecologically sensitive areas. This is especially true in coastal regions of the Mediterranean, such as Cukurova Delta. This study aims at combining satellite remote sensing, GIS, and Cellular Automata (CA) Markov chain modeling to analyze and predict land-use/cover changes in Cukurova Delta, Turkey by 2023. Four remotely sensed images recorded in 1977 (CORONA), 1987 and 1998 (SPOT), and 2007 (ALOS) were used to derive land-use/cover information to calculate transition probability matrix. Spatial variables including distance from lagoon, distance from coastline, soil texture, soil groups and land-use/cover suitability maps were derived in a GIS environment. Maps of suitability were generated for each land-use class using the spatial variables together with multi-criteria analysis. Various combinations of image pairs were used to drive transition probability matrix. 2023 land-use/cover change map was based on the change between 1977 and 2007. The land-use/cover change matrix between 2007 (actual) and predicted 2023 maps were created, and change detection analysis was performed. Most remarkable change was an increase in agricultural areas by 10283 ha between 1977 and 2007, with 1766 ha projected to be converted to agricultural use. © by PSP Volume 21 - No 2a. 2012

Growth optimal investment in discrete-time markets with proportional transaction costs

We investigate how and when to diversify capital over assets, i.e., the portfolio selection problem, from a signal processing perspective. To this end, we first construct portfolios that achieve the optimal expected growth in i.i.d. discrete-time two-asset markets under proportional transaction costs. We then extend our analysis to cover markets having more than two stocks. The market is modeled by a sequence of price relative vectors with arbitrary discrete distributions, which can also be used to approximate a wide class of continuous distributions. To achieve the optimal growth, we use threshold portfolios, where we introduce a recursive update to calculate the expected wealth. We then demonstrate that under the threshold rebalancing framework, the achievable set of portfolios elegantly form an irreducible Markov chain under mild technical conditions. We evaluate the corresponding stationary distribution of this Markov chain, which provides a natural and efficient method to calculate the cumulative expected wealth. Subsequently, the corresponding parameters are optimized yielding the growth optimal portfolio under proportional transaction costs in i.i.d. discrete-time two-asset markets. As a widely known financial problem, we also solve the optimal portfolio selection problem in discrete-time markets constructed by sampling continuous-time Brownian markets. For the case that the underlying discrete distributions of the price relative vectors are unknown, we provide a maximum likelihood estimator that is also incorporated in the optimization framework in our simulations

A deterministic analysis of an online convex mixture of experts algorithm

We analyze an online learning algorithm that adaptively combines outputs of two constituent algorithms (or the experts) running in parallel to estimate an unknown desired signal. This online learning algorithm is shown to achieve and in some cases outperform the mean-square error (MSE) performance of the best constituent algorithm in the steady state. However, the MSE analysis of this algorithm in the literature uses approximations and relies on statistical models on the underlying signals. Hence, such an analysis may not be useful or valid for signals generated by various real-life systems that show high degrees of nonstationarity, limit cycles and that are even chaotic in many cases. In this brief, we produce results in an individual sequence manner. In particular, we relate the time-accumulated squared estimation error of this online algorithm at any time over any interval to the one of the optimal convex mixture of the constituent algorithms directly tuned to the underlying signal in a deterministic sense without any statistical assumptions. In this sense, our analysis provides the transient, steady-state, and tracking behavior of this algorithm in a strong sense without any approximations in the derivations or statistical assumptions on the underlying signals such that our results are guaranteed to hold. We illustrate the introduced results through examples. © 2012 IEEE

Anatomical landmarks for the identification of greater occipital nerve for micro-surgical decompression/excision

If pharmacological and local nerve blocking treatment fail in severe cases of occipital neuralgia surgical intervention of greater occipital nerve (GON) should be considered. In the literature two types of interventions were suggested: surgical decompression of the GON from the entrapment site, and neurotomy of the nerve trunk. In some cases, due to anatomical variations in the division of the GON trunk, typical neurotomy above the line of the trapezius muscle aponeurosis (TMA) may not result in full recovery.The anatomical landmarks for the GON have been studied comprehensively in the literature. The aim of this study is to find the emergence point of GON by making the measurements over the skin. The dissections and measurements were made on 7 formalin fixed cadaver heads. After measuring the distance between protuberentia occipitalis externa and processus mastoideus, a small incision was made to find the point in which GON pierces the m. trapezius. The measurements were made after dissecting the GON.The distance between protuberentia occipitalis externa and processus mastoideus was 10,16 cm and 10,1 cm for right and left sides, respectively. The distance between the midline and emergence point of the GON was 1,77 cm and 1,96 cm; the vertical distance from the line between protuberentia occipitalis externa and processus mastoideus to the emergence point of the GON was 2,43 cm and 2,34 cm for right and left sides, respectively.From our data, for micro-surgical decompression and/or excision we recommend exploring the emergence point of GON according to the above given mean distances