The Forecasting Technique Using SSA-SVM Applied to Foreign Tourist Arrivals to Bali

In order to achieve a targeted number of foreign tourist arrivals set by the Indonesian government in 2017, we need to predict the number of foreign tourist arrivals. As a major tourist destination in Indonesia, Bali plays an important role in determining the target. According to the characteristic of the tourist arrivals data, one shows that we need a more flexible forecasting technique. In this case we propose to use a Support Vector Machine (SVM) technique. Furthermore, the effects of noise components have to be filtered. Singular Spectrum Analysis (SSA) plays an important role in filtering such noise. Therefore, the combination of these two methods (SSA-SVM) will be used to predict the number of foreign tourist arrivals to Bali in 2017. The performance of SSA-SVM is evaluated via simulation studies and applied to tourist arrivals data in Bali. As the results, SSA-SVM shows better performances compare to other methods

Forecasting bitcoin volatility: Exploring the potential of deep learning

This study aims to evaluate forecasting properties of classic methodologies (ARCH and GARCH models) in comparison with deep learning methodologies (MLP, RNN, and LSTM architectures) for predicting Bitcoin's volatility. As a new asset class with unique characteristics, Bitcoin's high volatility and structural breaks make forecasting challenging. Based on 2753 observations from 08-09-2014 to 01-05-2022, this study focuses on Bitcoin logarithmic returns. Results show that deep learning methodologies have advantages in terms of forecast quality, although significant computational costs are required. Although both MLP and RNN models produce smoother forecasts with less fluctuation, they fail to capture large spikes. The LSTM architecture, on the other hand, reacts strongly to such movements and tries to adjust its forecast accordingly. To compare forecasting accuracy at different horizons MAPE, MAE metrics are used. Diebold-Mariano tests were conducted to compare the forecast, confirming the superiority of deep learning methodologies. Overall, this study suggests that deep learning methodologies could provide a promising tool for forecasting Bitcoin returns (and therefore volatility), especially for short-term horizons.info:eu-repo/semantics/publishedVersio

Artificial Intelligence & Machine Learning in Finance: A literature review

In the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical ResearchIn the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical Researc

Supply chain management based on volatility clustering: The effect of CBDC volatility

A Central Bank Digital Currency (CBDC) launched by the Bank of England could enable businesses to directly make electronic payments. It can be argued that digital payment is helpful in supply chain management applications. However, the adoption of CBDC in the supply chain could bring new turbulence since the CBDC value may fluctuate. Therefore, this paper intends to optimize the production plan of manufacturing supply chain based on a volatility clustering model by reducing CBDC value uncertainty. We apply both GARCH model and machine learning model to depict the CBDC volatility clustering. Empirically, we employed Baltic Dry Index, Bitcoin and exchange rate as main variables with sample period from 2015 to 2021 to evaluate the performance of the two models. On this basis, we reveal that our machine learning model overwhelmingly outperforms the GARCH model. Consequently, our result implies that manufacturing companies’ performance can be strengthened through CBDC uncertainty reduction

Synergy of Physics-based Reasoning and Machine Learning in Biomedical Applications: Towards Unlimited Deep Learning with Limited Data

Technological advancements enable collecting vast data, i.e., Big Data, in science and industry including biomedical field. Increased computational power allows expedient analysis of collected data using statistical and machine-learning approaches. Historical data incompleteness problem and curse of dimensionality diminish practical value of pure data-driven approaches, especially in biomedicine. Advancements in deep learning (DL) frameworks based on deep neural networks (DNN) improved accuracy in image recognition, natural language processing, and other applications yet severe data limitations and/or absence of transfer-learning-relevant problems drastically reduce advantages of DNN-based DL. Our earlier works demonstrate that hierarchical data representation can be alternatively implemented without NN, using boosting-like algorithms for utilization of existing domain knowledge, tolerating significant data incompleteness, and boosting accuracy of low-complexity models within the classifier ensemble, as illustrated in physiological-data analysis. Beyond obvious use in initial-factor selection, existing simplified models are effectively employed for generation of realistic synthetic data for later DNN pre-training. We review existing machine learning approaches, focusing on limitations caused by training-data incompleteness. We outline our hybrid framework that leverages existing domain-expert models/knowledge, boosting-like model combination, DNN-based DL and other machine learning algorithms for drastic reduction of training-data requirements. Applying this framework is illustrated in context of analyzing physiological data

Supervised Classification and Mathematical Optimization

Data Mining techniques often ask for the resolution of optimization problems. Supervised Classification, and, in particular, Support Vector Machines, can be seen as a paradigmatic instance. In this paper, some links between Mathematical Optimization methods and Supervised Classification are emphasized. It is shown that many different areas of Mathematical Optimization play a central role in off-the-shelf Supervised Classification methods. Moreover, Mathematical Optimization turns out to be extremely useful to address important issues in Classification, such as identifying relevant variables, improving the interpretability of classifiers or dealing with vagueness/noise in the data

Mixture of Poisson distributions to model discrete stock price changes.

An application of a mixture of Poisson distributions is proposed to model the discrete changes in stock price based on the minimum price movement known as `tick-size\u27. The parameters are estimated using the Expectation-Maximization (EM) algorithm with a constant mixing probability as well as mixing probabilities which depend on order size. The model is evaluated using simulations and real data. Both the simulated and real data show reasonable estimates. Several adjustments are made to the model implementation to improve the efficiency with user written codes for the Newton Raphson algorithm and also implementing one of the most recent versions of the EM algorithm (PEM). Both the improvements show an exponentially increasing efficiency to the implementation. Further a Clustered Signed model is proposed to use summarized data to reduce the amount of data to be used in the model implementation using the discrete order sizes and the signs of the discrete stock price changes. The clustered model provided a significant time efficiency. A parametric bootstrap procedure is also considered to assess the significance of the order size on the mixing probabilities. The results show that the use of a variable mixture probability, which depends on the order size, is more appropriate for the model. The methods are illustrated with data from simulations and real data from Federal Express

Utilização de modelos de optimização para obter soluções em técnicas de classificação e agrupamento

Orientador: Washington Alves de OliveiraDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Ciências AplicadasResumo: Esta dissertação tem como objetivo estudar algumas abordagens de manipulação de bancos de dados em larga escala com o objetivo de extrair informações representativas a partir do uso de programação matemática. Os padrões estruturais dos dados fornecem informações que podem ser usadas para classificá-los e agrupá-los por meio da solução ótima de problemas específicos de otimização. As técnicas utilizadas podem ser confrontadas com abordagens de aprendizado de máquina para fornecer novas possibilidades numéricas de resolução. Testes computacionais conduzidos em dois estudos de caso (dados oriundos de experimentos práticos) validam esta pesquisa. As análises são conduzidas sobre um conjunto de dados relacionados com a identificação de tumores de câncer de mama, com diagnóstico maligno ou benigno, e um banco de dados de animais bovinos que fornecem características físicas e de raça de cada animal, porém sem um padrão previamente conhecido. Uma classificação binária com base em um modelo matemático de programação de metas é usado para o primeiro estudo de caso. No estudo conduzido sobre as características dos animais bovinos, o interesse é identificar padrões entre os diversos animais ao agrupá-los por meio da análise das soluções de um modelo de otimização linear com variáveis inteiras. Os resultados computacionais são estudados a partir de um conjunto de procedimentos estatístico descritivo para validar o estudo propostoAbstract: This dissertation aims to study some techniques for handling large scale datasets to extract representative information from the use of mathematical programming. The structural patterns of data provide pieces of information that can be used to classify and cluster them through the optimal solution of specific optimization problems. The techniques used could be confronted with machine learning approaches to supply new numerical possibilities of resolution. Computational tests conducted on two case studies with real data (practical experiments) validate this research. The analyzes are done for the well-known database on the identification of breast cancer tumors, which either have a malignant or have a benign diagnosis, and also for a bovine animal database containing physical and breed characteristics of each animal but with unknown patterns. A binary classification based on a goal programming formulation is suggested for the first case study. In the study conducted on the characteristics of bovine animals, the interest is to identify patterns among the different animals by grouping them from the solutions of an integer linear optimization model. The computational results are studied from a set of descriptive statistical procedures to validate this researchMestradoPesquisa Operacional e Gestão de ProcessosMestra em Engenharia de Produção e de Manufatura019/2012Funcam