Trust region algorithms and neural networks for financial forecasting

Master'sMASTER OF SCIENC

Unveiling commodities-financial markets intersections from a bibliometric perspective

The prominence of commodity markets within the domains of empirical finance and energy economics is well established, largely due to oil's importance and its relationship with other commodities and financial markets. In this study, we present a bibliometric examination of 437 journal articles addressing the phenomenon of commodity connectedness, spanning the period from 1994 to 2022. The research methods include a blend of qualitative and quantitative approaches, incorporating bibliometrics and content analysis. Based on the findings of the analysis, four primary research streams have been identified within the literature concerning commodity connectedness, namely (1) commodity interconnectivity, (2) the relationship between traditional commodities, renewable energy, and cryptocurrencies, (3) the relationship between oil and stock markets, and (4) studies utilizing copula methods to examine the interconnectivity between oil and financial markets. We proposed 15 future research questions for further investigation in the domain of commodity connectedness, including topics such as the impact of the post-COVID era and global uncertainties on commodity markets, how commodities can address the issue of climate change, the exponential growth of cryptocurrencies as a new financial asset, and the impact of the ongoing Russia-Ukraine conflict on commodity and financial markets

Analysing Financial Distress in Malaysian Islamic Banks: Exploring Integrative Predictive Methods

Against the background of global financial crisis, some argue in favour of the ‘resilience’ of Islamic finance, while others suggest that Islamic financial institutions are not more prone to distress and crisis than their conventional counterparts. However, there have been a number of cases of Islamic finance and banking distress in recent years, including instances in Malaysia. These cases, hence, motivated this study in terms of emphasising the importance of employing financial distress prediction models for analysing Islamic banks. This study aims at empirically exploring, examining and analysing the financial distress of the Malaysian Islamic banks. In doing so, the effectiveness of the existing early warning statistical insolvency prediction models that have been used in previous studies, and a particular model adapted by Islamic banks in Malaysia were critically evaluated. This study, hence, employed a number of models to predict the financial distress faced by Islamic banks in Malaysia. In addition, an attempt was made at the modification of the existing early warning insolvency prediction models in evaluating and analysing the financial distress of Malaysian Islamic banks. This research is constructed within four empirical chapters by employing three prediction models in assessing the financial distress of Islamic banks. The first empirical chapter analyses the secondary data collected from a sample of Islamic banks, based on selected ratios developed in the literature, whereby a comprehensive description of these selected financial ratios in terms of descriptive statistical analysis for the selected Islamic banks in Malaysia is provided. The second empirical chapter investigates the performance of the ‘emerging market Z-score’, introduced by Altman in predicting the performance of Islamic banks and conventional banks in Malaysia. The study aimed to introduce the EM Z-score as a valuable analytical tool in monitoring the deterioration of the performance of banks as well as looking at the impact of the global financial crisis on the performance of Islamic and conventional banks. This chapter examines thirteen Islamic banks and ten conventional banks during the period of 2005-2010. The results show that the EM Z-score for all banks is well above the cut-off point of 2.6, although for Islamic banks the EM Z-score showed a declining trend whilst for conventional banks it showed an increasing trend. This empirical evidence is important for the banks since it provides a warning signal to the banks’ management as well as the related parties involved in the planning, controlling and decision making process. The third empirical chapter presents the newly constructed integrated predictive model designed to evaluate and analyse the financial distress of Islamic banks in Malaysia, which can be used as an alternative model for regulators in monitoring the performance of Islamic banks that are experiencing any serious financial problems. This paper develops a preliminary model for the prediction of the performance level of Islamic financial institutions for the period of December 2005 to September 2010 by using quarterly data for ten selected Islamic banks in Malaysia. For this, factor analysis and three parametric models (discriminant analysis, logit analysis and probit analysis) are used. The results depict that the first few quarters before the benchmark quarter are the most important period for making a correct prediction and crucial decisions on the survival of Islamic banks. Thus, the results demonstrate the predictive ability of the integrated model to differentiate between the healthy and non-healthy Islamic banks, therefore reducing the expected cost of bank failure. The fourth empirical chapter conducts further exploration in predicting the financial distress position of Islamic banks by introducing new variables such as the funding structure, deposit composition, and macroeconomic variables. Using the same sample and data set for Islamic banks as in the previous chapter, this study shows the relationship between the banks’ funding profiles and other alternative variables, and the Islamic banks’ performance in Malaysia. For this, the logit model is used. Based on the results of all models, this study recommended two final models, which showed an excellent fit for predicting the Islamic banks’ performance. The results indicate that none of the macroeconomic variables included were significant, thus suggesting that the performance of Islamic banks in Malaysia was not affected by the economic conditions throughout the study period. This can perhaps be attributed to efficient regulation and supervision by the relevant authorities in the country

Applications in computational finance with a focus on approximation of financial time series by neurocomputing

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Every crypto breath in the world : the current global position of the cryptocurrency market and future prediction

This study was motivated by the breakthrough of cryptocurrencies in 2018. The other main reasons behind the motivation are the total market capitalisation of one trillion-dollar diversification possibilities and the lack of preceding scientific research to identify the portfolio diversification possibilities of cryptocurrencies from many angles. Four empirical studies were conducted to provide a holistic view of cryptocurrency as an investment tool. The first study investigated the portfolio diversification possibilities between cryptocurrencies and traditional financial markets. A quantitative method was employed with Cointegration, ARDL bound testing approach, causality, and co-movement testing. Applying Modern portfolio theory to identify the diversification possibilities between the aforementioned markets enabled the study to highlight how investors can reap the benefits of cryptocurrencies. The second study extended the investigation of the portfolio diversification possibilities of cryptocurrency by including precious metals and cryptocurrencies in the same investment basket. Investors switch from traditional investment assets, such as equity and debt market instruments, to precious metal markets to reap benefits. Therefore, this study investigates how cryptocurrency can be an alternative source of investment to include in an investment portfolio. The daily precious metal and cryptocurrency data from 2017 to 2022 was utilised through an ARDL framework to obtain the Cointegration between cryptocurrency, precious metal and across cryptocurrencies. Modern portfolio theory is used to identify the diversification possibilities in this study with different portfolio diversification strategies. The third study clarified the cryptocurrency stakeholders to identify the global perception of cryptocurrency investments. A qualitative method was employed with sentiment analysis, followed by data extractions from the global databases using machine learning algorithms. The study identified the percentage of stakeholder groups' positive, negative, and neutral perceptions of cryptocurrency. The main obstacles hindering cryptocurrency investment growth are the fear of current scams, lack of definitional issues and the absence of a legal framework in some countries. The fourth study included the findings from the first, second and third studies to develop a cryptocurrency predictive model by factoring in macroeconomic variables. Panel data regression with fixed and dynamic effects was employed to analyse the data from 2017 to 2002. The findings suggest the impact of each macroeconomic variable selected in the study for the cryptocurrency price changes while adding more significance to technological variables. The overall findings provide strong support for the portfolio diversification possibilities of cryptocurrencies. Inclusions of the wide range of investment classes, exploring stakeholder perception and highlighting the macroeconomic variables' influence on the cryptocurrency price prediction generate new insights and valuable comparisons about cryptocurrency markets for academia, crypto issuers, investors, government, policymakers, and fund managers to use as an investment and decision-support tools. Keywords: Cryptocurrency, ARDL, Financial Markets, Cointegration, Causality, Portfolio diversification, Precious Metals, Predictive model.Doctor of Philosoph

Non Linear Modelling of Financial Data Using Topologically Evolved Neural Network Committees

Most of artificial neural network modelling methods are difficult to use as maximising or minimising an objective function in a non-linear context involves complex optimisation algorithms. Problems related to the efficiency of these algorithms are often mixed with the difficulty of the a priori estimation of a network's fixed topology for a specific problem making it even harder to appreciate the real power of neural networks. In this thesis, we propose a method that overcomes these issues by using genetic algorithms to optimise a network's weights and topology, simultaneously. The proposed method searches for virtually any kind of network whether it is a simple feed forward, recurrent, or even an adaptive network. When the data is high dimensional, modelling its often sophisticated behaviour is a very complex task that requires the optimisation of thousands of parameters. To enable optimisation techniques to overpass their limitations or failure, practitioners use methods to reduce the dimensionality of the data space. However, some of these methods are forced to make unrealistic assumptions when applied to non-linear data while others are very complex and require a priori knowledge of the intrinsic dimension of the system which is usually unknown and very difficult to estimate. The proposed method is non-linear and reduces the dimensionality of the input space without any information on the system's intrinsic dimension. This is achieved by first searching in a low dimensional space of simple networks, and gradually making them more complex as the search progresses by elaborating on existing solutions. The high dimensional space of the final solution is only encountered at the very end of the search. This increases the system's efficiency by guaranteeing that the network becomes no more complex than necessary. The modelling performance of the system is further improved by searching not only for one network as the ideal solution to a specific problem, but a combination of networks. These committces of networks are formed by combining a diverse selection of network species from a population of networks derived by the proposed method. This approach automatically exploits the strengths and weaknesses of each member of the committee while avoiding having all members giving the same bad judgements at the same time. In this thesis, the proposed method is used in the context of non-linear modelling of high-dimensional financial data. Experimental results are'encouraging as both robustness and complexity are concerned.Imperial Users onl

Application of neural network to study share price volatility.

by Lam King Wan.Thesis (M.B.A.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 72-73).ABSTRACT --- p.ii.TABLE OF CONTENTS --- p.iv.SectionChapter I. --- OBJECTIVE --- p.1Chapter II. --- INTRODUCTION --- p.3The principal investment risk --- p.3Effect of risk on investment --- p.4Investors' concern for investment risk --- p.6Chapter III. --- THE INPUT PARAMETERS --- p.9Chapter IV. --- LITERATURE REVIEW --- p.15What is an artificial neural network? --- p.15What is a neuron? --- p.16Biological versus artificial neuron --- p.16Operation of a neural network --- p.17Neural network paradigm --- p.20Feedforward as the most suitable form of neural network --- p.22Capability of neural network --- p.23The learning process --- p.25Testing the network --- p.29Neural network computing --- p.29Neural network versus conventional computer --- p.30Neural network versus a knowledge based system --- p.32Strength of neural network --- p.34Weaknesses of neural network --- p.35Chapter V. --- NEURAL NETWORK AS A TOOL FOR INVESTMENT ANALYSIS --- p.38Neural network in financial applications --- p.38Trading in the stock market --- p.41Why neural network could outperform in the stock market? --- p.43Applications of neural network --- p.45Chapter VI. --- BUILDING THE NEURAL NETWORK MODEL --- p.47Implementation process --- p.48Step 1´ؤ Problem specification --- p.49Step 2 ´ؤ Data collection --- p.51Step 3 ´ؤ Data analysis and transformation --- p.55Step 4 ´ؤ Training data set extraction --- p.58Step 5 ´ؤ Selection of network architecture --- p.60Step 6 ´ؤ Selection of training algorithm --- p.62Step 7 ´ؤ Training the network --- p.64Step 8 ´ؤ Model deployment --- p.65Chapter 7 --- RESULT AND CONCLUSION --- p.67Result --- p.67Conclusion --- p.69BIBLIOGRAPHY --- p.7