"General Conclusions: From Crisis to A Global Political Economy of Freedom"

In this chapter I sum up the basic problems for a new theory of 21st century financial crises in light of the Asian and other subsequent crises. My conclusion is that there are indeed deep structural causes at work in the global markets that affect the political economy of countries and regions. Methodologically, new concepts, models and theories are constructed, at ;least partially, to conduct further meaningful empirical work leading to relevant policy conclusions. This book belongs to the beginning of intellectual efforts in this direction. Political economic analyses at the country level, CGE modeling within a new theoretical framework, and neural network approach to learning in a bounded rationality framework point to a role for reforms at the state, firm and regional level. A new type of institutional analysis called the 'extended panda's thumb approach' leads to the recommendation that path dependent hybrid structures need to be constructed at the local, national, regional and global level to lead to a new global financial architecture for the prevention--- and if prevention fails--- management of financial crises.

High-low Strategy of Portfolio Composition using Evolino RNN Ensembles

trategy of investment is important tool enabling better investor's decisions in uncertain finance market. Rules of portfolio selection help investors balance accepting some risk for the expectation of higher returns. The aim of the research is to propose strategy of constructing investment portfolios based on the composition of distributions obtained by using high–low data. The ensemble of 176 Evolino recurrent neural networks (RNN) trained in parallel investigated as an artificial intelligence solution, which applied in forecasting of financial markets. Predictions made by this tool twice a day with different historical data give two distributions of expected values, which reflect future dynamic exchange rates. Constructing the portfolio, according to the shape, parameters of distribution and the current value of the exchange rate allows the optimization of trading in daily exchange-rate fluctuations. Comparison of a high-low portfolio with a close-to-close portfolio shows the efficiency of the new forecasting tool and new proposed trading strategy

ROBUST DECISION SUPPORT SYSTEMS WITH MATRIX FORECASTS AND SHARED LAYER PERCEPTRONS FOR FINANCE AND OTHER APPLICATIONS

The recent financial crisis showed the need for more robust decision support systems. In this paper, we introduce a novel type of recurrent artificial neural network, the shared layer perceptron, which allows forecasts that are robust by design. This is achieved by not over-fitting to a specific variable. An entire market is forecast. By training not one, but many networks, we obtain a distribution of outcomes. Further, multi-step forecasts are possible. Our system uses hidden states to model internal dynamics. This allows the network to build a memory and hardens it against external shocks. Using a single shared weight matrix offers the possibility of interpreting system output. An often cited disadvantage of neural networks, the black box character, is not an issue with our approach. We focus on two case studies: determining value at risk and transaction decision support. We also present other applications, including load forecast in electricity networks

Prediction of Banks Financial Distress

In this research we conduct a comprehensive review on the existing literature of prediction techniques that have been used to assist on prediction of the bank distress. We categorized the review results on the groups depending on the prediction techniques method, our categorization started by firstly using time factors of the founded literature, so we mark the literature founded in the period (1990-2010) as history of prediction techniques, and after this period until 2013 as recent prediction techniques and then presented the strengths and weaknesses of both. We came out by the fact that there was no specific type fit with all bank distress issue although we found that intelligent hybrid techniques considered the most candidates methods in term of accuracy and reputatio

Using ANFIS in joint dynamics of monetization, financial development, public debt and unemployment analysis

Purpose: The modern concepts of contemplating joint dynamics of monetary policy effects on economic growth and its indicators require an indirect approach based on empirical research of mainly financial infrastructure, competitiveness of the financial markets and current economic conditions. Meanwhile, the problems of unemployment and the structure of employment within these concepts are most frequently linked with the polarization of the labor market and two important factors, that is, the effects of growth on unemployment and the fact that technological changes affect the changes in salary ranges. Methodology: By using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the set of data from 1995 to 2016, this paper analyzes these issues through a prism of established balances between the labor and financial markets, i.e., the monetization of economy (M1/GDP), financial development (Loans/GDP) and the share of gross government debt in GDP (government gross debt/GDP). Results: The proposed model suggests that the rate of unemployment is conditioned by the financial cycle and monetary policy (M1/GDP, Loans/GDP), as well as the business cycle and fiscal policy (gross d/BDP) and that a controlled and properly directed level of monetization of the economy (M1/BDP) and financial development measured as Loans/GDP can be “sufficient” for economic growth. Conclusion: Waiting in the “monetary union lobby”, i.e., waiting for the ERM II exchange mechanism can last longer than the set deadlines, leading to the need for Croatian economic policy to optimize monetary and fiscal policy measures in order to increase economic growth and reduce unemployment

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 “transmission” 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. Traders’ strategies and the performance of the simulated domestic market are trained using historical prices on both markets, and optimizing artificial market’s 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

An academic review: applications of data mining techniques in finance industry

With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance

Using Text Mining to Predicate Exchange Rates with Sentiment Indicators

Recent innovations in text mining facilitate the use of novel data for sentiment analysis related to financial markets, and promise new approaches to the field of behavioral finance. Traditionally, text mining has allowed a near-real time analysis of available news feeds. The recent dissemination of web 2.0 has seen a drastic increase of user participation, providing comments on websites, social networks and blogs, creating a novel source of rich and personal sentiment data potentially of value to behavioral finance. This study explores the efficacy of using novel sentiment indicators from Market Psych, which analyses social media in addition to newsfeeds to quantify various levels of individual’s emotions, as a predictor for financial time series returns of the Australian Dollar (AUD)-US Dollar (USD) exchange rate. As one of the first studies evaluating both news and social media sentiment indicators as explanatory variables for linear and nonlinear regression algorithms, our study aims to make an original contribution to behavioral finance, combining technical and behavioral aspects of model building. An empirical out-of-sample evaluation with multiple input structures compares Multivariate Linear Regression models (MLR) with multilayer perceptron (MLP) neural networks for descriptive modelling. The results indicate that sentiment indicators are explanatory for market movements of exchange rate returns, with nonlinear MLPs showing superior accuracy over linear regression models with a directional out-of-sample accuracy of 60.26% using cross validation

Utjecaj volatilnosti tečaja na međunarodne trgovinske tijekove

The aim of this paper is to analyze the effects of exchange rate volatility on international trade fl ows by using two different approaches, the panel data analysis and fuzzy logic, and to compare the results. To a panel with the crosssection dimension of 91 pairs of EU15 countries and with time ranging from 1964 to 2003, an extended gravity model of trade is applied in order to determine the effects of exchange rate volatility on bilateral trade fl ows of EU15 countries. The estimated impact is clearly negative, which indicates that exchange rate volatility has a negative infl uence on bilateral trade fl ows. Then, this traditional panel approach is contrasted with an alternative investigation based on fuzzy logic. The key elements of the fuzzy approach are to set fuzzy decision rules and to assign membership functions to the fuzzy sets intuitively based on experience. Both approaches yield very similar results and fuzzy approach is recommended to be used as a complement to statistical methods.Cilj ovog rada je analizirati utjecaj volatilnosti tečaja na međunarodne trgovinske tijekove pomoću dva različita pristupa i to panel analize podataka i fuzzy logike, te potom usporediti rezultate. Prema platformi presjeka dimenzija 91 par EU15 zemlje s vremenskim rasponom 1964–2003. godine primjenjuje se prošireni gravitacijski model trgovine kako bi se utvrdili utjecaji volatilnosti tečaja na bilateralne trgovinske tijekove u zemljama EU15. Procijenjeni utjecaj je jasno negativan, što znači da volatilnost tečaja ima negativan utjecaj na bilateralne trgovinske tijekove. Potom, ovaj tradicionalni platformni pristup je u suprotnosti s alternativnom istragom temeljenom na fuzzy pristupu. Ključni elementi fuzzy pristupa su intuitivno postaviti fuzzy pravila odlučivanja i dodijeliti funkcije članstva fuzzy skupovima temeljem iskustva. Vidljivo je da oba pristupa daju vrlo slične rezultate, te se fuzzy pristup preporuča kao dopuna postojećih statističkih metoda