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Nature inspired computational intelligence for financial contagion modelling
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Financial contagion refers to a scenario in which small shocks, which initially affect only a few financial institutions or a particular region of the economy, spread to the rest of the financial sector and other countries whose economies were previously healthy. This resembles the âtransmissionâ of a medical disease. Financial contagion happens both at domestic level and international level. At domestic level, usually the failure of a domestic bank or financial intermediary triggers transmission by defaulting on inter-bank liabilities, selling assets in a fire sale, and undermining confidence in similar banks. An example of this phenomenon is the failure of Lehman Brothers and the subsequent turmoil in the US financial markets. International financial contagion happens in both advanced economies and developing economies, and is the transmission of financial crises across financial markets. Within the current globalise financial system, with large volumes of cash flow and cross-regional operations of large banks and hedge funds, financial contagion usually happens simultaneously among both domestic institutions and across countries. There is no conclusive definition of financial contagion, most research papers study contagion by analyzing the change in the variance-covariance matrix during the period of market turmoil. King and Wadhwani (1990) first test the correlations between the US, UK and Japan, during the US stock market crash of 1987. Boyer (1997) finds significant increases in correlation during financial crises, and reinforces a definition of financial contagion as a correlation changing during the crash period. Forbes and Rigobon (2002) give a definition of financial contagion. In their work, the term interdependence is used as the alternative to contagion. They claim that for the period they study, there is no contagion but only interdependence. Interdependence leads to common price movements during periods both of stability and turmoil. In the past two decades, many studies (e.g. Kaminsky et at., 1998; Kaminsky 1999) have developed early warning systems focused on the origins of financial crises rather than on financial contagion. Further authors (e.g. Forbes and Rigobon, 2002; Caporale et al, 2005), on the other hand, have focused on studying contagion or interdependence. In this thesis, an overall mechanism is proposed that simulates characteristics of propagating crisis through contagion. Within that scope, a new co-evolutionary market model is developed, where some of the technical traders change their behaviour during crisis to transform into herd traders making their decisions based on market sentiment rather than underlying strategies or factors. The thesis focuses on the transformation of market interdependence into contagion and on the contagion effects. The author first build a multi-national platform to allow different type of players to trade implementing their own rules and considering information from the domestic and a foreign market. Tradersâ strategies and the performance of the simulated domestic market are trained using historical prices on both markets, and optimizing artificial marketâs parameters through immune - particle swarm optimization techniques (I-PSO). The author also introduces a mechanism contributing to the transformation of technical into herd traders. A generalized auto-regressive conditional heteroscedasticity - copula (GARCH-copula) is further applied to calculate the tail dependence between the affected market and the origin of the crisis, and that parameter is used in the fitness function for selecting the best solutions within the evolving population of possible model parameters, and therefore in the optimization criteria for contagion simulation. The overall model is also applied in predictive mode, where the author optimize in the pre-crisis period using data from the domestic market and the crisis-origin foreign market, and predict in the crisis period using data from the foreign market and predicting the affected domestic market
Prediction of Stocks and Stock Price using Artificial Intelligence : A Bibliometric Study using Scopus Database
Prediction of stocks and the prices of the stock is one of the most crucial points of discussion amongst the researchers and analysts in the financial domain to date. Every stakeholder and most importantly the investor desires to earn higher profit for his investment in the market and try to use several different strategies to invest their money. There are numerous methods to predict and analyse the movement of the stock prices. They are broadly divided into â statistical and artificial intelligence-based methods. Artificial intelligence is used to predict the futuristic prices of stocks and use wide range of algorithms like â SVMs, CNNs, LSTMs, RNNs , etc. This bibliometric study focusses on the study based primarily on the Scopus database. We have considered important keywords, authors, citations along with the correlations between the co-appearing authors, source titles and keywords with the use of network diagrams for visualisation. On the basis of this paper, we conclude that there is ample opportunity for research in the domain of financial market
The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review
This literature review identifies and analyzes research topic trends, types of data sets, learning algorithm, methods improvements, and frameworks used in stock exchange prediction. A total of 81 studies were investigated, which were published regarding stock predictions in the period January 2015 to June 2020 which took into account the inclusion and exclusion criteria. The literature review methodology is carried out in three major phases: review planning, implementation, and report preparation, in nine steps from defining systematic review requirements to presentation of results. Estimation or regression, clustering, association, classification, and preprocessing analysis of data sets are the five main focuses revealed in the main study of stock prediction research. The classification method gets a share of 35.80% from related studies, the estimation method is 56.79%, data analytics is 4.94%, the rest is clustering and association is 1.23%. Furthermore, the use of the technical indicator data set is 74.07%, the rest are combinations of datasets. To develop a stock prediction model 48 different methods have been applied, 9 of the most widely applied methods were identified. The best method in terms of accuracy and also small error rate such as SVM, DNN, CNN, RNN, LSTM, bagging ensembles such as RF, boosting ensembles such as XGBoost, ensemble majority vote and the meta-learner approach is ensemble Stacking. Several techniques are proposed to improve prediction accuracy by combining several methods, using boosting algorithms, adding feature selection and using parameter and hyper-parameter optimization
Agribusiness innovation: A pathway to sustainable economic growth in Africa
The paper considers the factors that drive a strong and competitive agri-business sector with particular attention to investment in research and development (R&D) for technological innovation as well as the broader drivers and risk factors of influence. It develops a case study and in particular contrasts the very successful value chain in Thailand with the weak one in Nigeria in order to highlight the implications for Nigerian government policy if it wishes to exploit the potential for a strong cassava agri-business sector
Currency Exchange Rate Forecasting with Neural Networks
This is the published version. Copyright De GruyterThis paper presents the prediction of foreign currency exchange rates using artificial neural networks. Since neural networks can generalize from past experience, they represent a significant advancement over traditional trading systems, which require a knowledgeable expert to define trading rules to represent market dynamics. It is practically impossible to expect that one expert can devise trading rules that account for, and accurately reflect, volatile and rapidly changing market conditions. With neural networks, a trader may use the predictive information alone or with other available analytical tools to fit the trading style, risk propensity, and capitalization. Numerous factors affect the foreign exchange market, as they will be described in this paper. The neural network will help minimize these factors by simply giving an estimated exchange rate for a future day (given its previous knowledge gained from extensive training). Because the field of financial forecasting is too large, the scope in this paper is narrowed to the foreign exchange market, specifically the value of the Japanese Yen against the United States Dollar, two of the most important currencies in the foreign exchange market
RFID Technology in Intelligent Tracking Systems in Construction Waste Logistics Using Optimisation Techniques
Construction waste disposal is an urgent issue
for protecting our environment. This paper proposes a
waste management system and illustrates the work
process using plasterboard waste as an example, which
creates a hazardous gas when land filled with household
waste, and for which the recycling rate is less than 10%
in the UK. The proposed system integrates RFID
technology, Rule-Based Reasoning, Ant Colony
optimization and knowledge technology for auditing
and tracking plasterboard waste, guiding the operation
staff, arranging vehicles, schedule planning, and also
provides evidence to verify its disposal. It h relies on
RFID equipment for collecting logistical data and uses
digital imaging equipment to give further evidence; the
reasoning core in the third layer is responsible for
generating schedules and route plans and guidance, and
the last layer delivers the result to inform users. The
paper firstly introduces the current plasterboard
disposal situation and addresses the logistical problem
that is now the main barrier to a higher recycling rate,
followed by discussion of the proposed system in terms
of both system level structure and process structure.
And finally, an example scenario will be given to
illustrate the systemâs utilization
Evolutionary algorithms for financial trading
Genetic programming (GP) is increasingly popular as a research tool for applications in
finance and economics. One thread in this area is the use of GP to discover effective
technical trading rules. In a seminal article, Allen & Karjalainen (1999) used GP to find
rules that were profitable, but were nevertheless outperformed by the simple âbuy and
holdâ trading strategy. Many succeeding attempts have reported similar findings. This
represents a clear example of a significant open issue in the field of GP, namely,
generalization in GP [78]. The issue of generalisation is that GP solutions may not be
general enough, resulting in poor performance on unseen data. There are a small
handful of cases in which such work has managed to find rules that outperform buyand-
hold, but these have tended to be difficult to replicate. Among previous studies,
work by Becker & Seshadri (2003) was the most promising one, which showed
outperformance of buy-and-hold. In turn, Becker & Seshadriâs work had made several
modifications to Allen & Karjalainenâs work, including the adoption of monthly rather
than daily trading. This thesis provides a replicable account of Becker & Seshadriâs
study, and also shows how further modifications enabled fairly reliable outperformance
of buy-and-hold, including the use of a train/test/validate methodology [41] to evolve
trading rules with good properties of generalization, and the use of a dynamic form of
GP [109] to improve the performance of the algorithm in dynamic environments like
financial markets. In addition, we investigate and compare each of daily, weekly and
monthly trading; we find that outperformance of buy-and-hold can be achieved even for
daily trading, but as we move from monthly to daily trading the performance of evolved
rules becomes increasingly dependent on prevailing market conditions. This has
clarified that robust outperformance of B&H depends on, mainly, the adoption of a
relatively infrequent trading strategy (e.g. monthly), as well as a range of factors that
amount to sound engineering of the GP grammar and the validation strategy. Moreover,
v
we also add a comprehensive study of multiobjective approaches to this investigation
with assumption from that, and find that multiobjective strategies provide even more
robustness in outperforming B&H, even in the context of more frequent (e.g. weekly)
trading decisions. Last, inspired by a number of beneficial aspects of grammatical
evolution (GE) and reports on the successful performance of various kinds of its
applications, we introduce new approach for (GE) with a new suite of operators
resulting in an improvement on GE search compared with standard GE. An empirical
test of this new GE approach on various kind of test problems, including financial
trading, is provided in this thesis as well
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