Financial bubbles represent a severe problem for investors. In particular, the cryptocurrency market has witnessed the bursting of different bubbles in the last decade, which in turn have had spillovers on all the markets and real economies of countries. These kinds of markets and their unique characteristics are of great interest to researchers. Generally, investors and financial operators study market trends to understand when bubbles might occur using technical analysis tools. Such tools, which have been historically used, resulted in being precious allies at the basis of more advanced systems. In this regard, different autonomous, adaptive and automated trading agents have been introduced in the literature to study several kinds of markets. Among these, we can distinguish between agents with Zero/Minimal Intelligence (ZI/MI) and Computational Intelligence (CI)-based agents. The first ones typically trade on the market without resorting to complex learning strategies; the second ones usually use (deep) reinforcement learning mechanisms. However, these trading agents have never been tested on the cryptocurrencies market and related financial bubbles, which are still mostly overlooked in the literature. It is unclear how these agents can make profits/losses before, during, and after a bubble to adjust their strategy and avoid critical situations. This paper compares a broad set of trading agents (between ZI/MI and CI ones) and evaluates them with well-known financial indicators (e.g., volatility, returns Sharpe ratio, drawdown, Sortino and Omega ratio). Among the experiment’s outcomes, ZI/MI agents were more explainable than CI ones. Based on the results obtained above, we introduce GGSMZ, a trading agent relying on a neuro-fuzzy mechanism. The neuro-fuzzy system is able to learn from the trades performed by the agents adopted in the previous stage. GGSMZ’s performances overcome those of other tested agents. We argue that GGSMZ could be used by investors as a decision support tool.
Patients with stages 4 and 5 chronic kidney disease (CKD), and particularly chronic dialysis patients, commonly are found to have substantially reduced daily physical activity in comparison to age- and sex-matched normal adults. This reduction in physical activity is associated with a major decrease in physical exercise capacity and physical performance. The CKD patients are often physically deconditioned, and protein energy wasting (PEW) and frailty are commonly present. These disorders are of major concern because physical dysfunction, muscle atrophy, and reduced muscle strength are associated with poor quality of life and increased morbidity and mortality in CKD and chronic dialysis patients. Many randomized controlled clinical trials indicate that when CKD and chronic dialysis are provided nutritional supplements or undergo exercise training their skeletal muscle mass and exercise capacity often increase. It is not known whether the rise in skeletal muscle mass and exercise capacity associated with nutritional support or exercise training will reduce morbidity or mortality rates. A limitation of these clinical trials is that the sample sizes of the different treatment groups were small. The aim of this review is to discuss the effects of nutrition and exercise on body composition, exercise capacity, and physical functioning in advanced CKD patients.
Protoplanetary disks orbiting intermediate-mass stars, Herbig Ae/Be stars, that have formed in a metal-poor environment may evolve differently than their Galactic cousins. A study of the planet-formation process in such an environment requires identification and characterization of a sample of candidates. We have observed several stars in the Small Magellanic Cloud, a nearby metal-poor dwarf galaxy, that have optical spectral properties of Herbig Ae/Be stars, including strong Hα emission, blue continuum excess, and spectral types ranging from early G to B. Infrared spectra of these sources from the Spitzer Space Telescope show strong excess emission indicating the presence of silicate dust, molecular and atomic gas, and polycyclic aromatic hydrocarbons. We present an analysis of the likelihood that these candidates are Herbig Ae/Be stars. This identification is the necessary first step to future investigations that will examine the role of metallicity in the evolution of protoplanetary disks.
In financial markets, sentiment analysis on natural language sentences can improve forecasting. Many investors rely on information extracted from newspapers or their feelings. Therefore, this information is expressed in their language. Sentiment analysis models classify sentences (or entire texts) with their polarity (positive, negative, or neutral) and derive a sentiment score. In this paper, we use this sentiment (polarity) score to improve the forecasting of stocks and use it as a new “view” in the Black and Litterman model. This score is related to various events (both positive and negative) that have affected some stocks. The sentences used to determine the scores are taken from articles published in Financial Times (an international financial newspaper). To improve the forecast using this average sentiment score, we use a Monte Carlo method to generate a series of possible paths for several trading hours after the article was published to discretize (or approximate) the Wiener measure, which is applied to the paths and returning an exact price as results. Finally, we use the price determined in this way to calculate a yield to be used as views in a new type of “dynamic” portfolio optimization, based on hourly prices. We compare the results by applying the views obtained, disregarding the sentiment and leaving the initial portfolio unchanged.
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