Quantum Monte Carlo simulations for estimating FOREX markets: A speculative attacks experience

The foreign exchange markets, renowned as the largest financial markets globally, also stand out as one of the most intricate due to their substantial volatility, nonlinearity, and irregular nature. Owing to these challenging attributes, various research endeavors have been undertaken to effectively forecast future currency prices in foreign exchange with precision. The studies performed have built models utilizing statistical methods, being the Monte Carlo algorithm the most popular. In this study, we propose to apply Auxiliary-Field Quantum Monte Carlo to increase the precision of the FOREX markets models from different sample sizes to test simulations in different stress contexts. Our findings reveal that the implementation of Auxiliary-Field Quantum Monte Carlo significantly enhances the accuracy of these models, as evidenced by the minimal error and consistent estimations achieved in the FOREX market. This research holds valuable implications for both the general public and financial institutions, empowering them to effectively anticipate significant volatility in exchange rate trends and the associated risks. These insights provide crucial guidance for future decision-making processes

Quantum Monte Carlo simulations for estimating FOREX markets: a speculative attacks experience.

The foreign exchange markets, renowned as the largest financial markets globally, also stand out as one of the most intricate due to their substantial volatility, nonlinearity, and irregular nature. Owing to these challenging attributes, various research endeavors have been undertaken to effectively forecast future currency prices in foreign exchange with precision. The studies performed have built models utilizing statistical methods, being the Monte Carlo algorithm the most popular. In this study, we propose to apply Auxiliary-Field Quantum Monte Carlo to increase the precision of the FOREX markets models from different sample sizes to test simulations in different stress contexts. Our findings reveal that the imple- mentation of Auxiliary-Field Quantum Monte Carlo significantly enhances the accuracy of these models, as evidenced by the minimal error and consistent estimations achieved in the FOREX market. This research holds valuable implications for both the general public and financial institutions, empowering them to effectively anticipate significant volatility in exchange rate trends and the associated risks. These insights provide crucial guidance for future decision-making processes.This research was funded by the Universidad de Málaga. We would also like to thank the Universitat de Barcelona, UB-AE-AS017634, for funding this research

Using Relevance Vector Machines Approach for Prediction of Total Suspended Solids and Turbidity to Sustain Water Quality and Wildlife in Mud Lake

Mud Lake is a wildlife refuge located in southeastern Idaho just north of Bear Lake that traps sediment from Bear River water flowing into Bear Lake.Very few water quality and sediment observations, if any, exist spatially in Mud Lake. Spatial patterns of sediment deposition may affect Mud Lake flows and habitat; prediction of those patterns should help refuge managers predict water quality constituents and spatial distribution of fine sediment.This will help sustain the purposes of Mud Lake as a habitat and migratory station for species. The main objective of the research is the development of Multivariate Relevant Vector Machine (MVRVM) to predict suspended fine sediment and water quality constituents, and to provide an understanding for the practical problem of determining the amount of data required for the MVRVM. MVRVM isa statistical learning algorithm that is based on Bayes theory.It has been widely used to predict patterns in hydrological systems and other fields. This research represents the first known attempt to use a MVRVM approach to predict transport of very fine sediment andwater quality constituents in a complex natural system. The results demonstrate the ability of the MVRVM to capture and predict the underlying patterns in data.Also careful construction of the experimental design for data collection can lead the Relevant Vectors (RVs is a subset of training observation which carries significant information that is used for prediction) to show locations of significant patterns. The predictions of water quality constituents will be of potential value to US Fish and Wildlife refuge managers in making decisions for operation and management in the case of Mud Lake based on their objectives, and will lead the way for scientists to expand the use of the MVRVM for modeling of suspended fine sediment and water quality in complex natural systems

The impact of macroeconomic leading indicators on inventory management

Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study

Applications of Deep Learning Models in Financial Forecasting

In financial markets, deep learning techniques sparked a revolution, reshaping conventional approaches and amplifying predictive capabilities. This thesis explored the applications of deep learning models to unravel insights and methodologies aimed at advancing financial forecasting. The crux of the research problem lies in the applications of predictive models within financial domains, characterised by high volatility and uncertainty. This thesis investigated the application of advanced deep-learning methodologies in the context of financial forecasting, addressing the challenges posed by the dynamic nature of financial markets. These challenges were tackled by exploring a range of techniques, including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), autoencoders (AEs), and variational autoencoders (VAEs), along with approaches such as encoding financial time series into images. Through analysis, methodologies such as transfer learning, convolutional neural networks, long short-term memory networks, generative modelling, and image encoding of time series data were examined. These methodologies collectively offered a comprehensive toolkit for extracting meaningful insights from financial data. The present work investigated the practicality of a deep learning CNN-LSTM model within the Directional Change framework to predict significant DC events—a task crucial for timely decisionmaking in financial markets. Furthermore, the potential of autoencoders and variational autoencoders to enhance financial forecasting accuracy and remove noise from financial time series data was explored. Leveraging their capacity within financial time series, these models offered promising avenues for improved data representation and subsequent forecasting. To further contribute to financial prediction capabilities, a deep multi-model was developed that harnessed the power of pre-trained computer vision models. This innovative approach aimed to predict the VVIX, utilising the cross-disciplinary synergy between computer vision and financial forecasting. By integrating knowledge from these domains, novel insights into the prediction of market volatility were provided

Machine learning for financial market prediction

The usage of machine learning techniques for the prediction of financial time series is investigated. Both discriminative and generative methods are considered and compared to more standard financial prediction techniques. Generative methods such as Switching Autoregressive Hidden Markov and changepoint models are found to be unsuccessful at predicting daily and minutely prices from a wide range of asset classes. Committees of discriminative techniques (Support Vector Machines (SVM), Relevance Vector Machines and Neural Networks) are found to perform well when incorporating sophisticated exogenous financial information in order to predict daily FX carry basket returns. The higher dimensionality that Electronic Communication Networks make available through order book data is transformed into simple features. These volume-based features, along with other price-based ones motivated by common trading rules, are used by Multiple Kernel Learning (MKL) to classify the direction of price movement for a currency over a range of time horizons. Outperformance relative to both individual SVM and benchmarks is found, along with an indication of which features are the most informative for financial prediction tasks. Fisher kernels based on three popular market microstructural models are added to the MKL set. Two subsets of this full set, constructed from the most frequently selected and highest performing individual kernels are also investigated. Furthermore, kernel learning is employed - optimising hyperparameter and Fisher feature parameters with the aim of improving predictive performance. Significant improvements in out-of-sample predictive accuracy relative to both individual SVM and standard MKL is found using these various novel enhancements to the MKL algorithm

Situation Awareness for Smart Distribution Systems

In recent years, the global climate has become variable due to intensification of the greenhouse effect, and natural disasters are frequently occurring, which poses challenges to the situation awareness of intelligent distribution networks. Aside from the continuous grid connection of distributed generation, energy storage and new energy generation not only reduces the power supply pressure of distribution network to a certain extent but also brings new consumption pressure and load impact. Situation awareness is a technology based on the overall dynamic insight of environment and covering perception, understanding, and prediction. Such means have been widely used in security, intelligence, justice, intelligent transportation, and other fields and gradually become the research direction of digitization and informatization in the future. We hope this Special Issue represents a useful contribution. We present 10 interesting papers that cover a wide range of topics all focused on problems and solutions related to situation awareness for smart distribution systems. We sincerely hope the papers included in this Special Issue will inspire more researchers to further develop situation awareness for smart distribution systems. We strongly believe that there is a need for more work to be carried out, and we hope this issue provides a useful open-access platform for the dissemination of new ideas