Measuring flood resilience: a fuzzy logic approach

© 2017, © Emerald Publishing Limited. Purpose: Flood resilience is emerging as a major component of an integrated strategic approach to flood risk management. This approach recognizes that some flooding is inevitable and aligns with the concept of “living with water.” Resilience measurement is a key in making business case for investments in resilient retrofits/adaptations, and could potentially be used to inform the design of new developments in flood prone areas. The literature is, however, sparse on frameworks for measuring flood resilience. The purpose of this paper is to describe the development of a fuzzy logic (FL)-based resilience measuring model, drawing on a synthesis of extant flood resilience and FL literature. Design/methodology/approach: An abstraction of the flood resilience system followed by identification and characterization of systems’ variables and parameters were carried out. The resulting model was transformed into a fuzzy inference system (FIS) using three input factors: inherent resilience, supportive facilities (SF) and resident capacity. Findings: The resulting FIS generates resilience index for households with a wide range of techno-economic and socio-environmental features. Originality/value: It is concluded that the FL-based model provides a veritable tool for the measurement of flood resilience at the level of the individual property, and with the potential to be further developed for larger scale applications, i.e. at the community or regional levels

Rating firms and sensitivity analysis

This paper introduces a model for rating a firm's default risk based on fuzzy logic and expert system and an associated model of sensitivity analysis (SA) for managerial purposes. The rating model automatically replicates the evaluation process of default risk performed by human experts. It makes use of a modular approach based on rules blocks and conditional implications. The SA model investigates the change in the firm's default risk under changes in the model inputs and employs recent results in the engineering literature of Sensitivity Analysis. In particular, it (i) allows the decomposition of the historical variation of default risk, (ii) identifies the most relevant parameters for the risk variation, and (iii) suggests managerial actions to be undertaken for improving the firm's rating

How Much Do the Central Bank Announcements Matter on Financial Market? Application of the Rule-Based Trading System Approach

This paper proposes a rule-based trading system to investigate how much the central bank’s announcements matter on a financial market. We design a novel investment strategy and we simulate trades in order to quantify their profitability in the out–of–sample period using the data from a broad financial market in Poland spanning across 3 segments: stock market, foreign exchange market and bonds market. Our results show that the individual transactions delivered profits in 72.7% cases. The overall profitability across all events and all trading horizons was positive in as many as 63.6% cases. Although the financial market in Poland was only moderately sensitive to the NBP central bank’s communication, the identified types of the monetary policy announcements are economically significant and very useful for the investors, who can trade based on them and exploit them directly in the design of the rule-based trading strategies

Adaptive fuzzy system for algorithmic trading : interpolative Boolean approach

Тема овог рада je адаптивни фази систем за алгоритамско трговање. Систем је развијен коришћењем интерполативног Буловог приступа фази моделовању, анализи података и управљању. Предложени приступ укључује интерполативне логичке моделе за фази препознавање ценовних образаца на тржишту, логички ДуПонт метод за аутоматизовану анализу профитабилности предузећа, интерполативни фази контролер за управљање трговањем и генетски алгоритам за обучавање интерполативног фази контролера ради откривања стратегија. Интерполативни Булов приступ, заснован на интерполативној Буловој алгебри, превазилази проблем неконзистентности фази логике. Конструисани адаптивни фази систем може самостално, из података, да открије успешне стратегије, примени их за алгоритамско трговање и адаптира у случају пада њихових перформанси. Успешност система тестирана је на подацима са америчког тржишта акција, међународног девизног тржишта и тржишта криптовалута.The topic of this thesis is adaptive fuzzy system for algorithmic trading. The system is developed using interpolative Boolean approach for fuzzy modeling, data analysis and control. The proposed approach includes interpolative logical models for fuzzy recognition of price patterns in market data, logical DuPont method for automated analysis of company’s profitability, interpolative fuzzy controller for trading and a genetic algorithm for extracting trading strategies by training interpolative fuzzy controller. Interpolative Boolean approach, based on interpolative Boolean agebra, solves the problem of fuzzy logic’s inconsistency with Boolean axioms. The proposed system can independently discover successful trading strategies from data, apply them for algorithmic trading and adapt in the case of performance deterioration. The system was tested on historical data from US equity, foreign exchange market and cryptocurrency market