Financial crises and bank failures: a review of prediction methods

In this article we analyze financial and economic circumstances associated with the U.S. subprime mortgage crisis and the global financial turmoil that has led to severe crises in many countries. We suggest that the level of cross-border holdings of long-term securities between the United States and the rest of the world may indicate a direct link between the turmoil in the securitized market originated in the United States and that in other countries. We provide a summary of empirical results obtained in several Economics and Operations Research papers that attempt to explain, predict, or suggest remedies for financial crises or banking defaults; we also extensively outline the methodologies used in them. The intent of this article is to promote future empirical research for preventing financial crises.Subprime mortgage ; Financial crises

The Financial Crisis Impact on the Composition of an Optimal Portfolio in the Stock Market - Study Applied to Portuguese Index PSI 20

In order to maximize their utility function, investors select some assets over others, choosing the portfolio that will allow them to maximize their wealth. Each asset is chosen considering the relationship between the risk of that particular investment (usually measured by variance) - and the profitability it can offer, as well as the risk between this and other assets (measured by covariance). The purpose of this study consisted of constructing the minimum variance portfolio, using data from the PSI-20 (2008-2016) representative asset quotation, where investors are risk reluctant and wish to minimize risk while maintaining the same level of profitability, or on the other hand, maintaining the same level of risk but maximizing expected profit. In order to do this, a comparison of the optimal portfolio in 2004-2017 was carried out, compared to the minimum variance portfolio after the financial crisis (2008-2016). The method used to estimate each asset’s expected profitability that makes up the PSI-20 consists of extracting the obtained historical quotations. The optimal portfolio composition, in the period after the financial crisis, shows that the energy sector has an optimal portfolio weight reduction of 39.15%, that the big distribution sector (23.85%) was introduced into the portfolio and by last, the industrial sector stands its ground in the composition of the optimal portfolio.info:eu-repo/semantics/publishedVersio

A forecasting of indices and corresponding investment decision making application

Student Number : 9702018F - MSc(Eng) Dissertation - School of Electrical and Information Engineering - Faculty of Engineering and the Built EnvironmentDue to the volatile nature of the world economies, investing is crucial in ensuring an individual is prepared for future financial necessities. This research proposes an application, which employs computational intelligent methods that could assist investors in making financial decisions. This system consists of 2 components. The Forecasting Component (FC) is employed to predict the closing index price performance. Based on these predictions, the Stock Quantity Selection Component (SQSC) recommends the investor to purchase stocks, hold the current investment position or sell stocks in possession. The development of the FC module involved the creation of Multi-Layer Perceptron (MLP) as well as Radial Basis Function (RBF) neural network classifiers. TCategorizes that these networks classify are based on a profitable trading strategy that outperforms the long-term “Buy and hold” trading strategy. The Dow Jones Industrial Average, Johannesburg Stock Exchange (JSE) All Share, Nasdaq 100 and the Nikkei 225 Stock Average indices are considered. TIt has been determined that the MLP neural network architecture is particularly suited in the prediction of closing index price performance. Accuracies of 72%, 68%, 69% and 64% were obtained for the prediction of closing price performance of the Dow Jones Industrial Average, JSE All Share, Nasdaq 100 and Nikkei 225 Stock Average indices, respectively. TThree designs of the Stock Quantity Selection Component were implemented and compared in terms of their complexity as well as scalability. TComplexity is defined as the number of classifiers employed by the design. Scalability is defined as the ability of the design to accommodate the classification of additional investment recommendations. TDesigns that utilized 1, 4 and 16 classifiers, respectively, were developed. These designs were implemented using MLP neural networks, RBF neural networks, Fuzzy Inference Systems as well as Adaptive Neuro-Fuzzy Inference Systems. The design that employed 4 classifiers achieved low complexity and high scalability. As a result, this design is most appropriate for the application of concern. It has also been determined that the neural network architecture as well as the Fuzzy Inference System implementation of this design performed equally well

Analysis of Stock Selection Model Strategy in Indonesia's Capital Market

The impact of investor behavior and decision-making on investment interest in the Indonesian capital market is ultimately tied to the stock selection model used to form an optimal portfolio. Therefore, this study aims to develop an optimal stock selection model in the Indonesian capital market. And thus, the novelty of this study relates to other studies that demonstrate how to improve investment decisions in the capital market in Indonesia. Whereas previous research focused solely on the associations between variables, this study examines the variables themselves. This investigation was carried out in the Greater Jakarta of Jabodetabek (Jakarta, Bogor, Depok, Tangerang, Bekasi). This research utilizes the Fuzzy-Analytical Hierarchy Process (FAHP) methodology. FAHP is the combination of AHP and a fuzzy concept approach. The FAHP method uses qualitative and quantitative data to make decisions. The data collection method employs primary data from the Survey Questionnaire and discussions, secondary data consisting of market data, stock price movement data from the Indonesia Stock Exchange, Indonesian Central Securities Depository, Bank Indonesia, and empirical literature about stock selection models. The results of the analysis indicate that forming the optimal portfolio in the Indonesian capital market involves predicting the price or stock return using a single factor that is thought to impact security. The primary objective assumed to be realized by the optimal portfolio formation approach on the Indonesian capital market is the optimization of stock combinations. Keywords: investor behavior, investor decision-making, capital market, investment, stock

SINVLIO: using semantics and fuzzy logic to provide individual investment portfolio recommendations

Portfolio selection addresses the problem of how to diversify investments in the most efficient and profitable way possible. Portfolio selection is a field of study that has been broached from several perspectives, including, among others, recommender systems. This paper presents SINVLIO (Semantic INVestment portfoLIO), a tool based on semantic technologies and fuzzy logic techniques that recommends investments grounded in both psychological aspects of the investor and traditional financial parameters of the investments. The results are very encouraging and reveal that SINVLIO makes good recommendations, according to the high degree of agreement between SINVLIO and expert recommendationsThis work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the projects SONAR2 (TSI-020100-2008-665) and the Spanish Ministry of Science and Innovation under the project “FINANCIAL LINKED OPEN DATA REASONING AND MANAGEMENT FOR WEB SCIENCE” (TIN2011-27405).Publicad