•We propose a survey of soft computing techniques applied to financial market.•We surveyed several primary studies proposed in the literature.•A framework for building an intelligent trading system ...was proposed.•Future directions of this research field are discussed.
Financial markets play an important role on the economical and social organization of modern society. In these kinds of markets, information is an invaluable asset. However, with the modernization of the financial transactions and the information systems, the large amount of information available for a trader can make prohibitive the analysis of a financial asset. In the last decades, many researchers have attempted to develop computational intelligent methods and algorithms to support the decision-making in different financial market segments. In the literature, there is a huge number of scientific papers that investigate the use of computational intelligence techniques to solve financial market problems. However, only few studies have focused on review the literature of this topic. Most of the existing review articles have a limited scope, either by focusing on a specific financial market application or by focusing on a family of machine learning algorithms. This paper presents a review of the application of several computational intelligent methods in several financial applications. This paper gives an overview of the most important primary studies published from 2009 to 2015, which cover techniques for preprocessing and clustering of financial data, for forecasting future market movements, for mining financial text information, among others. The main contributions of this paper are: (i) a comprehensive review of the literature of this field, (ii) the definition of a systematic procedure for guiding the task of building an intelligent trading system and (iii) a discussion about the main challenges and open problems in this scientific field.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Financial time series represent the stock prices over time and exhibit behavior similar to a data stream. Many works report on the use of data mining techniques to predict the future direction of ...stock prices and to discover patterns in the time series data to provide decision support for trading operations. Traditional optimization methods do not take into account the possibility that the function to be optimized, namely, the final financial balance for operations considering some stock, may have multiple peaks, i.e., be represented by multimodal functions. However, multimodality is a known feature of real-world financial time series optimization problems. To deal with this issue, this article proposes the PAA-MS-IDPSO-V approach (Piecewise Aggregate Approximation - Multi-Swarm of Improved Self-adaptive Particle Swarm Optimization with Validation). The proposed method aims to find patterns in financial time series to support investment decisions. The approach uses multi-swarms to obtain a better particle initialization for the final optimization phase since it aims to tackle multimodal problems. Furthermore, it uses a validation set with early stopping to avoid overfitting. The patterns discovered by the method are used together with investment rules to support decisions and thus help investors to maximize the profit in their operations in the stock market. The experiments reported in this paper compare the results obtained by the proposed model with the Buy-and-Hold, PAA-IDPSO approaches and another approach found in the literature. We report on experiments conducted with S&P100 index stocks and using the Friedman Non-Parametric Test with the Nemenyi post-hoc Test both with 95% confidence level. The results show that the proposed model outperformed the competing methods and was able to considerably reduce the variance for all stocks.
•A novel method for decision support in automatic stock trading is proposed.•The method aims to tackle the problem of the high variability of the results of competing methods.•The method works by discovering the best patterns of the stock for deciding on buying and selling operations.•To tackle the high variance problem, the proposed method employs multi-swarms with early stopping using a validation set.•Experiments show that our approach outperforms alternative approaches on S&P100 stocks.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Dengue, yellow fever, Zika, and chikungunya arboviruses are endemic in tropical countries and are transmitted by Aedes aegypti. Resistant populations of this mosquito against chemical insecticides ...are spreading worldwide. This study aimed to evaluate the biological effects of exposure of pesticide-sensitive Ae. aegypti larvae (Rockefeller) to conidia of the entomopathogen, Metarhizium brunneum, laboratory strains ARSEF 4556 and V275, and any synergistic activity of phenylthiourea (PTU). In addition, to investigate the nature of any cross-resistance mechanisms, these M. brunneum strains were tested against the Rockefeller larvae and two temephos- and deltamethrin-resistant wild mosquito populations from Rio de Janeiro.Treatment of Rockefeller larvae with 106 conidia/ml of ARSEF 4556 and V275 fungal strains resulted in significant decreased survival rates to 40 and 53.33%, respectively (P < 0.0001), compared with untreated controls. In contrast, exposure to 104 or 105 conidia/ml showed no such significant survival differences. However, the addition of PTU to the conidia in the bioassays significantly increased mortalities in all groups and induced a molt block. Experiments also showed no differences in Ae. aegypti mortalities between the fungal treated, wild pesticide-resistant populations and the Rockefeller sensitive strain. The results show the efficacy of M. brunneum in controlling Ae. aegypti larvae and the synergistic role of PTU in this process. Importantly, there was no indication of any cross-resistance mechanisms between Ae. aegypti sensitive or resistant to pesticides following treatment with the fungi. These results further support using M. brunneum as an alternative biological control agent against mosquito populations resistant to chemical insecticides.
Dengue, yellow fever, Zika, and chikungunya arboviruses are endemic in tropical countries and are transmitted by Aedes aegypti. Resistant populations of this mosquito against chemical insecticides ...are spreading worldwide. This study aimed to evaluate the biological effects of exposure of pesticide-sensitive Ae. aegypti larvae (Rockefeller) to conidia of the entomopathogen, Metarhizium brunneum, laboratory strains ARSEF 4556 and V275, and any synergistic activity of phenylthiourea (PTU). In addition, to investigate the nature of any cross-resistance mechanisms, these M. brunneum strains were tested against the Rockefeller larvae and two temephos- and deltamethrin-resistant wild mosquito populations from Rio de Janeiro.Treatment of Rockefeller larvae with 10.sup.6 conidia/ml of ARSEF 4556 and V275 fungal strains resulted in significant decreased survival rates to 40 and 53.33%, respectively (P < 0.0001), compared with untreated controls. In contrast, exposure to 10.sup.4 or 10.sup.5 conidia/ml showed no such significant survival differences. However, the addition of PTU to the conidia in the bioassays significantly increased mortalities in all groups and induced a molt block. Experiments also showed no differences in Ae. aegypti mortalities between the fungal treated, wild pesticide-resistant populations and the Rockefeller sensitive strain. The results show the efficacy of M. brunneum in controlling Ae. aegypti larvae and the synergistic role of PTU in this process. Importantly, there was no indication of any cross-resistance mechanisms between Ae. aegypti sensitive or resistant to pesticides following treatment with the fungi. These results further support using M. brunneum as an alternative biological control agent against mosquito populations resistant to chemical insecticides. Key words: mosquito vector, entomopathogenic fungi, phenylthiourea, larvicidal, insecticide resistance
There are many researches on forecasting time series for building trading systems for financial markets. Some of these studies have shown that it is possible to obtain satisfactory results, thereby ...contradicting the theory of Efficient Markets Hypothesis (EMH) that suggests that prices are randomly generated over time. This paper proposes an intelligent system based on historical closing prices that uses technical analysis, the Artificial Bee Colony Algorithm (ABC), a selection of past values (lags), nearest neighbor classification (k-NN) and its variation, the Adaptative Classification and Nearest Neighbor (A-k-NN). A very important step for time series prediction is the correct selection of the past observations (lags). Our method uses this strategy since it uses the k-NN and A-k-NN to decide on the buy and seIl points, combined with the ABC algorithm which is used to search for the best parameter settings of system and a good set of lags. This paper compares the results obtained by the proposed method with the buy and hold strategy and with other work that performed similar experiments with the same trading model and the same stocks. The key measure for performance comparison is the profitability in the analyzed period. The proposed method generates much larger profits compared to the other method and to the buy and hold strategy. Our method outperforms the other methods in thirteen out of the fifteen stocks tested, minimizing the risk of market ex pos ure.
Particle Swarm Optimization algorithm (PSO), when applied to problems with continuous variables, presents results with better quality at a lower computational cost when compared to the Genetic ...Algorithm (GA). Thus, the PSO becomes a very useful method to be applied with investment strategies used to optimize profit from operations made in the stock market, since investors seek quick and profitable results for their decision making. In this context, the Symbolic Aggregate Approximation (SAX) and Piecewise Aggregate Approximation (PAA) are time series representation techniques that, when used with optimization algorithms like PSO or GA, can help investors discover hidden and relevant patterns in financial time series data. The SAX uses discrete variables to represent their values, whereas PAA uses continuous values. Thus, this paper proposes the PAA-PSO technique, which combines the PAA with the optimization of PSO for discovery of patterns that will be used with a formulated investment strategy in order to maximize the profit from operations made in the stock market. Experiments that compare the proposed method to the results of the SAX-GA technique, which combines the techniques of SAX and GA in their investment strategy, are reported.
Oceanic islands can be relatively isolated from overfishing and pollution sources, but they are often extremely vulnerable to climate and anthropogenic stress due to their small size and unique ...assemblages that may rely on a limited larval supply for replenishment. Vulnerability may be especially high when these islands bear permanent human populations or are subjected to regular or intermittent fishing. Since the late 1970's, Brazil has been establishing marine protected areas (MPAs) around its four oceanic island groups, which concentrate high endemism levels and are considered peripheral outposts of the Brazilian Biogeographic Province. In 2018, the Brazilian legally marine protected area increased >10-fold, but most of the ~1,000,000 km2 of MPAs around Brazil's oceanic islands are still unknown and unprotected. Here, we provide the first detailed quantitative baseline of benthic reef assemblages, including shallow and mesophotic zones, of the Fernando de Noronha Archipelago (FNA). The archipelago is partially protected as a no-take MPA and recognized by the UNESCO as a World Heritage Site, but also represents the only Brazilian oceanic island with a large permanent human population (3,000 people), mass tourism (up to 90,000 people per year) and a permanent small-scale fishing community. The influence of depth, wave exposure, and distance from the island and shelf edge on the structure of benthic assemblages was assessed from benthic photoquadrats obtained in 12 sites distributed in the lee and windward shores of the archipelago. Unique assemblages and discriminating species were identified using Multivariate Regression Trees, and environmental drivers of dominant assemblages' components were evaluated using Boosted Regression Trees. A total of 128 benthic taxa were recorded and 5 distinct assemblages were identified. Distance to the insular slope, depth and exposure were the main drivers of assemblages' differentiation. Our results represent an important baseline for evaluating changes in benthic assemblages due to increased local and global stressors.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Rhodoliths are nodules of non-geniculate coralline algae that occur in shallow waters (<150 m depth) subjected to episodic disturbance. Rhodolith beds stand with kelp beds, seagrass meadows, and ...coralline algal reefs as one of the world's four largest macrophyte-dominated benthic communities. Geographic distribution of rhodolith beds is discontinuous, with large concentrations off Japan, Australia and the Gulf of California, as well as in the Mediterranean, North Atlantic, eastern Caribbean and Brazil. Although there are major gaps in terms of seabed habitat mapping, the largest rhodolith beds are purported to occur off Brazil, where these communities are recorded across a wide latitudinal range (2°N-27°S). To quantify their extent, we carried out an inter-reefal seabed habitat survey on the Abrolhos Shelf (16°50'-19°45'S) off eastern Brazil, and confirmed the most expansive and contiguous rhodolith bed in the world, covering about 20,900 km(2). Distribution, extent, composition and structure of this bed were assessed with side scan sonar, remotely operated vehicles, and SCUBA. The mean rate of CaCO(3) production was estimated from in situ growth assays at 1.07 kg m(-2) yr(-1), with a total production rate of 0.025 Gt yr(-1), comparable to those of the world's largest biogenic CaCO(3) deposits. These gigantic rhodolith beds, of areal extent equivalent to the Great Barrier Reef, Australia, are a critical, yet poorly understood component of the tropical South Atlantic Ocean. Based on the relatively high vulnerability of coralline algae to ocean acidification, these beds are likely to experience a profound restructuring in the coming decades.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Large rivers create major gaps in reef distribution along tropical shelves. The Amazon River represents 20% of the global riverine discharge to the ocean, generating up to a 1.3 × 10(6)-km(2) plume, ...and extensive muddy bottoms in the equatorial margin of South America. As a result, a wide area of the tropical North Atlantic is heavily affected in terms of salinity, pH, light penetration, and sedimentation. Such unfavorable conditions were thought to imprint a major gap in Western Atlantic reefs. We present an extensive carbonate system off the Amazon mouth, underneath the river plume. Significant carbonate sedimentation occurred during lowstand sea level, and still occurs in the outer shelf, resulting in complex hard-bottom topography. A permanent near-bottom wedge of ocean water, together with the seasonal nature of the plume's eastward retroflection, conditions the existence of this extensive (~9500 km(2)) hard-bottom mosaic. The Amazon reefs transition from accretive to erosional structures and encompass extensive rhodolith beds. Carbonate structures function as a connectivity corridor for wide depth-ranging reef-associated species, being heavily colonized by large sponges and other structure-forming filter feeders that dwell under low light and high levels of particulates. The oxycline between the plume and subplume is associated with chemoautotrophic and anaerobic microbial metabolisms. The system described here provides several insights about the responses of tropical reefs to suboptimal and marginal reef-building conditions, which are accelerating worldwide due to global changes.
Over the years, several studies have been performed to analyse plant-pathogen interactions. Recently, functional genomic strategies, including proteomics and transcriptomics, have contributed to the ...effort of defining gene and protein function and expression profiles. Using these 'omic' approaches, pathogenicity- and defence-related genes and proteins expressed during phytopathogen infections have been identified and enormous datasets have been accumulated. However, the understanding of molecular plant-pathogen interactions is still an intriguing area of investigation. Proteomics has dramatically evolved in the pursuit of large-scale functional assignment of candidate proteins and, by using this approach, several proteins expressed during phytopathogenic interactions have been identified. In this review, we highlight the proteins expressed during plant-virus, plant-bacterium, plant-fungus and plant-nematode interactions reported in proteomic studies, and discuss these findings considering the advantages and limitations of current proteomic tools.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, SIK, UILJ, UKNU, UL, UM, UPUK
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