Financial volatility trading using a self-organising neural-fuzzy semantic network and option straddle-based approach

Financial volatility refers to the intensity of the fluctuations in the expected return on an investment or the pricing of a financial asset due to market uncertainties. Hence, volatility modeling and forecasting is imperative to financial market investors, as such projections allow the investors to adjust their trading strategies in anticipation of the impending financial market movements. Following this, financial volatility trading is the capitalization of the uncertainties of the financial markets to realize investment profits in times of rising, falling and side-way market conditions. In this paper, an intelligent straddle trading system (framework) that consists of a volatility projection module (VPM) and a trade decision module (TDM) is proposed for financial volatility trading via the buying and selling of option straddles to help a human trader capitalizes on the underlying uncertainties of the Hong Kong stock market. Three different measures, namely: (1) the historical volatility (HV), (2) implied volatility (IV) and (3) model-based volatility (MV) of the Hang Seng Index (HSI) are employed to quantify the implicit volatility of the Hong Kong stock market. The TDM of the proposed straddle trading system combines the respective volatility measures with the well-established moving-averages convergence/divergence (MACD) principle to recommend trading actions to a human trader dealing in HSI straddles. However, the inherent limitation of the MACD trading rule is that it generates time-delayed trading signals due to the use of moving averages, which are essentially lagging trend indicators. This drawback is intuitively addressed in the proposed straddle trading system by applying the VPM to compute future projections of the volatility measures of the HSI prior to the activation of the TDM. The VPM is realized by a self-organising neural-fuzzy semantic network named the evolving fuzzy semantic memory (eFSM) model. As compared to existing statistical and computational intelligence based modeling techniques currently employed for financial volatility modeling and forecasting, eFSM possesses several desirable attributes such as: (1) an evolvable knowledge base to continuously address the non-stationary characteristics of the Hong Kong stock market; (2) highly formalized human-like information computations; and (3) a transparent structure that can be interpreted via a set of linguistic IF–THEN semantic fuzzy rules. These qualities provide added credence to the computed HSI volatility projections. The volatility modeling and forecasting performances of the eFSM, when benchmarked to several established modeling techniques, as well as the observed trading returns of the proposed straddle trading system, are encouraging.     

A multi-strategy knowledge interoperability framework for heterogeneous learning objects

This paper presents a knowledge exchange framework that can leverage the interoperability among semantically heterogeneous learning objects. With the release of various e-Learning standards, learning contents and digital courses are easy to achieve cross-platform sharing, exchanging, and even reorganizing. However, knowledge sharing in semantic level is still a challenge due to that the learning materials can be presented in any form, such as audios, videos, web pages, and even flash files. The proposed knowledge exchange framework allows users to share their learning materials (also called “learning objects”) in semantic level automatically. This framework contains two methodologies: the first is a semantic mapping between knowledge bases (i.e. ontologies) which have essentially similar concepts, and the second is an ontology-based classification algorithm for sharable learning objects. The proposed algorithm adopts the IMS DRI standard and classifies the sharable learning objects from heterogeneous repositories into a local knowledge base by their inner meaning instead of keyword matching. Significance of this research lies in the semantic inferring rules for ontology mapping and learning objects classification as well as the full automatic processing and self-optimizing capability. Focused on digital learning materials and contrasted to other traditional technologies, the proposed approach has experimentally demonstrated significantly improvement in performance.     

Data hiding in AMBTC images using quantization level modification and perturbation technique

Multimedia Tools and Applications - A novel data hiding method for Absolute Moment Block Truncation Coding (AMBTC) compressed image based on quantization level modification is proposed. Blocks of...     

SoHyFIS-Yager: A self-organizing Yager based Hybrid neural Fuzzy Inference System

The Hybrid neural Fuzzy Inference System (HyFIS) is a multilayer adaptive neural fuzzy system for building and optimizing fuzzy models using neural networks. In this paper, the fuzzy Yager inference scheme, which is able to emulate the human deductive reasoning logic, is integrated into the HyFIS model to provide it with a firm and intuitive logical reasoning and decision-making framework. In addition, a self-organizing gaussian Discrete Incremental Clustering (gDIC) technique is implemented in the network to automatically form fuzzy sets in the fuzzification phase. This clustering technique is no longer limited by the need to have prior knowledge about the number of clusters present in each input and output dimensions. The proposed self-organizing Yager based Hybrid neural Fuzzy Inference System (SoHyFIS-Yager) introduces the learning power of neural networks to fuzzy logic systems, while providing linguistic explanations of the fuzzy logic systems to the connectionist networks. Extensive simulations were conducted using the proposed model and its performance demonstrates its superiority as an effective neuro-fuzzy modeling technique.     

Analysis of the Optimum Configuration of Roadside Units and Onboard Units in Dedicated Short-Range Communication Systems

With the aid of a simple relation, which is analogous to the radar equation, the uplink signal strength received by the receiving module of a roadside unit (RSU) and emitted from the radiation module of an onboard unit (OBU) can be described. Setting the scale of this relation arbitrarily and determining the signal strength threshold from experimental measurements, and combined with the simulation of the radiation and the receiving pattern by cosineⁿ function, the relative signal strength emitted from the OBU and received by the RSU can be calculated successfully. From this computed relative signal strength and the threshold, the influence of the RSU and OBU mounting parameters, such as the mounting angles and mounting height, on the available communication region is analyzed. The effect of windshield fading is also considered. With the help of the analysis results, an optimum RSU and OBU mounting configuration can be easily obtained. This method can be used conveniently and successfully for very short wavelengths. This includes visible light, infrared, and even submillimeter-wave ranges. For millimeter-wave and microwave systems, this method can, in some cases, also provide a rudimentary estimation     

Efficient mining of intertransaction association rules

Most of the previous studies on mining association rules are on mining intratransaction associations, i.e., the associations among items within the same transaction. We extend the scope to include multidimensional, intertransaction associations. In a database of stock price information, an example of such an association is "if (company) A's stock goes up on day one, B's stock will go down on day two but go up on day four:" whether we treat company or day as the unit of transaction, the items belong to different transactions. Moreover, such an intertransaction association can be extended to associate multiple properties in the same rule, so that multidimensional intertransaction associations can also be defined and discovered. Mining intertransaction associations pose more challenges on efficient processing than mining intratransaction associations because the number of potential association rules is extremely large. We introduce the notion of intertransaction association rule and develop an efficient algorithm, FITI (first intra then inter), for mining intertransaction associations, which adopts two major ideas: 1) an intertransaction frequent itemset contains only the frequent itemsets of its corresponding intratransaction counterpart; and 2) a special data structure is built among intratransaction frequent itemsets for efficient mining of intertransaction frequent itemsets.     

Dieldrin in fish and shellfish from the Mersey Estuary and Liverpool Bay

OBJECT: The authors studied the reliability of a new method for noninvasive assessment of cerebral perfusion pressure (CPP) in head-injured patients in which mean arterial blood pressure (ABP) and transcranial Doppler middle cerebral artery mean and diastolic flow velocities are measured. METHODS: Cerebral perfusion pressure was estimated (eCPP) over periods of continuous monitoring (20 minutes-2 hours, 421 daily examinations) in 96 head-injured patients (Glasgow Coma Scale score < 13) who were admitted to the intensive care unit. All patients were sedated, paralyzed, and ventilated. The eCPP and the measured CPP (ABP minus intracranial pressure, measured using an intraparenchymal microsensor) were compared. The correlation between eCPP and measured CPP was r=0.73; p < 10(-6). In 71% of the examinations, the estimation error was less than 10 mm Hg and in 84% of the examinations, the error was less than 15 mm Hg. The method had a high positive predictive power (94%) for detecting low CPP (< 60 mm Hg). The eCPP also accurately reflected changes in measured CPP over time (r > 0.8; p < 0.001) in situations such as plateau and B waves of intracranial pressure, arterial hypotension, and refractory intracranial hypertension. A good correlation was found between the average measured CPP and eCPP when day-by-day variability was assessed in a group of 41 patients (r=0.71). CONCLUSIONS: Noninvasive estimation of CPP by using transcranial Doppler ultrasonography may be of value in situations in which monitoring relative changes in CPP is required without invasive measurement of intracranial pressure.