Deeper Hedging: A New Agent-based Model for Effective Deep Hedging

We propose the Chiarella-Heston model, a new agent-based model for improving the effectiveness of deep hedging strategies. This model includes momentum traders, fundamental traders, and volatility traders. The volatility traders participate in the market by innovatively following a Heston-style volatility signal. The proposed model generalises both the extended Chiarella model and the Heston stochastic volatility model, and is calibrated to reproduce as many empirical stylized facts as possible. According to the stylised facts distance metric, the proposed model is able to reproduce more realistic financial time series than three baseline models: the extended Chiarella model, the Heston model, and the Geometric Brownian Motion. The proposed model is further validated by the Generalized Subtracted L-divergence metric. With the proposed Chiarella-Heston model, we generate a training dataset to train a deep hedging agent for optimal hedging strategies under various transaction cost levels. The deep hedging agent employs the Deep Deterministic Policy Gradient algorithm and is trained to maximize profits and minimize risks. Our testing results reveal that the deep hedging agent, trained with data generated by our proposed model, outperforms the baseline in most transaction cost levels. Furthermore, the testing process, which is conducted using empirical data, demonstrates the effective performance of the trained deep hedging agent in a realistic trading environment.Comment: Accepted in the 4th ACM International Conference on AI in Finance (ICAIF'23

Random qubit-states and how best to measure them

We consider the problem of measuring a single qubit, known to have been prepared in either a randomly selected pure state or a randomly selected real pure state. We seek the measurements that provide either the best estimate of the state prepared or maximise the accessible information. Surprisingly, any sensible measurement turns out to be optimal. We discuss the application of these ideas to multiple qubits and higher-dimensional systems

Hybrid MM/SVM structural sensors for stochastic sequential data

In this paper we present preliminary results stemming from a novel application of Markov Models and Support Vector Machines to splice site classification of Intron-Exon and Exon-Intron (5' and 3') splice sites. We present the use of Markov based statistical methods, in a log likelihood discriminator framework, to create a non-summed, fixed-length, feature vector for SVM-based classification. We also explore the use of Shannon-entropy based analysis for automated identification of minimal-size models (where smaller models have known information loss according to the specified Shannon entropy representation). We evaluate a variety of kernels and kernel parameters in the classification effort. We present results of the algorithms for splice-site datasets consisting of sequences from a variety of species for comparison

Synthesis and characterization of hybrid organic-inorganic materials based on sulphonated polyamideimide and silica

The preparation of hybrid organic–inorganic membrane materials based on a sulphonated polyamideimide resin and silica filler has been studied. The method allows the sol–gel process to proceed in the presence of a high molecular weight polyamideimide, resulting in well dispersed silica nanoparticles (<50 nm) within the polymer matrix with chemical bonding between the organic and inorganic phases. Tetraethoxysilane (TEOS) was used as the silica precursor and the organosilicate networks were bonded to the polymer matrix via a coupling agent aminopropyltriethoxysilane (APTrEOS). The structure and properties of these hybrid materials were characterized via a range of techniques including FTIR, TGA, DSC, SEM and contact angle analysis. It was found that the compatibility between organic and inorganic phases has been greatly enhanced by the incorporation of APTrEOS. The thermal stability and hydrophilic properties of hybrid materials have also been significantly improved

The role of primary healthcare professionals in oral cancer prevention and detection

AIM: To investigate current knowledge, examination habits and preventive practices of primary healthcare professionals in Scotland, with respect to oral cancer, and to determine any relevant training needs. SETTING: Primary care. METHOD: Questionnaires were sent to a random sample of 357 general medical practitioners (GMPs) and 331 dental practitioners throughout Scotland. Additionally, focus group research and interviews were conducted amongst primary healthcare team members. RESULTS: Whilst 58% of dental respondents reported examining regularly for signs of oral cancer, GMPs examined patients' mouths usually in response to a complaint of soreness. The majority of GMPs (85%) and dentists (63%) indicated that they felt less than confident in detecting oral cancer, with over 70% of GMPs identifying lack of training as an important barrier. Many practitioners were unclear concerning the relative importance of the presence of potentially malignant lesions in the oral cavity. A high proportion of the GMPs indicated that they should have a major role to play in oral cancer detection (66%) but many felt strongly that this should be primarily the remit of the dental team. CONCLUSION: The study revealed a need for continuing education programmes for primary care practitioners in oral cancer-related activities. This should aim to improve diagnostic skills and seek to increase practitioners' participation in preventive activities

Identification of furfural resistant strains of Saccharomyces cerevisiae and Saccharomyces paradoxus from a collection of environmental and industrial isolates

Background Fermentation of bioethanol using lignocellulosic biomass as a raw material provides a sustainable alternative to current biofuel production methods by utilising waste food streams as raw material. Before lignocellulose can be fermented it requires physical, chemical and enzymatic treatment in order to release monosaccharides, a process that causes the chemical transformation of glucose and xylose into the cyclic aldehydes furfural and hydroxyfurfural. These furan compounds are potent inhibitors of Saccharomyces fermentation, and consequently furfural tolerant strains of Saccharomyces are required for lignocellulosic fermentation. Results This study investigated yeast tolerance to furfural and hydroxyfurfural using a collection of 71 environmental and industrial isolates of the baker’s yeast Saccharomyces cerevisiae and its closest relative Saccharomyces paradoxus. The Saccharomyces strains were initially screened for growth on media containing 100 mM glucose and 1.5 mg ml-1 furfural. Five strains were identified that showed a significant tolerance to growth in the presence of furfural and these were then screened for growth and ethanol production in the presence of increasing amounts (0.1-4 mg ml-1) of furfural. Conclusions Of the five furfural tolerant strains S. cerevisiae NCYC 3451 displayed the greatest furfural resistance, and was able to grow in the presence of up to 3.0 mg ml-1 furfural. Furthermore, ethanol production in this strain did not appear to be inhibited by furfural, with the highest ethanol yield observed at 3.0 mg ml-1 furfural. Although furfural resistance was not found to be a trait specific to any one particular lineage or population, three of the strains were isolated from environments where they might be continually exposed to low levels of furfural through the on-going natural degradation of lignocelluloses, and would therefore develop elevated levels of resistance to these furan compounds. Thus these strains represent good candidates for future studies of genetic variation relevant to understanding and manipulating furfural resistance and in the development of tolerant ethanologenic yeast strains for use in bioethanol production from lignocellulose processing

The modern pollen-vegetation relationship of a tropical forest-savannah mosaic landscape, Ghana, West Africa

Transitions between forest and savannah vegetation types in fossil pollen records are often poorly understood due to over-production by taxa such as Poaceae and a lack of modern pollen-vegetation studies. Here, modern pollen assemblages from within a forest-savannah transition in West Africa are presented and compared, their characteristic taxa discussed, and implications for the fossil record considered. Fifteen artificial pollen traps were deployed for 1 year, to collect pollen rain from three vegetation plots within the forest-savannah transition in Ghana. High percentages of Poaceae and Melastomataceae/Combretaceae were recorded in all three plots. Erythrophleum suaveolens characterised the forest plot, Manilkara obovata the transition plot and Terminalia the savannah plot. The results indicate that Poaceae pollen influx rates provide the best representation of the forest-savannah gradient, and that a Poaceae abundance of >40% should be considered as indicative of savannah-type vegetation in the fossil record