Economic regimes identification using machine learning technics

43 páginas.Trabajo de Máster en Economía, Finanzas y Computación. Director: Dr. José Manuel Bravo Caro. Economic conditions over long time periods can be distinguished by regimes. Regime identification has been object of numerous investigations in economics and financial modeling for years. Recently, new machine learning technics such as decision trees, support vector machines and neural networks, among others, followed by alternative datasets and cheap computational processing power became available, allowing for alternative ways to model complex economic relationships. In the present work, we develop a supervised machine learning classifier using Random Forest technic to identify economic regimes using the S&P 500 stock market index series.Las condiciones económicas durante largos períodos de tiempo pueden distinguirse por regímenes. La identificación del régimen ha sido objeto de numerosas investigaciones en economía y modelos financieros durante años. Recientemente, se pusieron a disposición nuevas técnicas de aprendizaje automático, como árboles de decisión, máquinas de suporte vectorial y redes neuronales, entre otras, seguidas de conjuntos de datos alternativos y una capacidad de procesamiento computacional barata, que permite formas alternativas de modelar relaciones económicas complejas. En el presente trabajo, desarrollamos un clasificador de aprendizaje automático supervisado utilizando la técnica de Random Forest para identificar regímenes económicos utilizando la serie del índices de mercado S&P 500

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

Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction

Most existing event extraction (EE) methods merely extract event arguments within the sentence scope. However, such sentence-level EE methods struggle to handle soaring amounts of documents from emerging applications, such as finance, legislation, health, etc., where event arguments always scatter across different sentences, and even multiple such event mentions frequently co-exist in the same document. To address these challenges, we propose a novel end-to-end model, Doc2EDAG, which can generate an entity-based directed acyclic graph to fulfill the document-level EE (DEE) effectively. Moreover, we reformalize a DEE task with the no-trigger-words design to ease the document-level event labeling. To demonstrate the effectiveness of Doc2EDAG, we build a large-scale real-world dataset consisting of Chinese financial announcements with the challenges mentioned above. Extensive experiments with comprehensive analyses illustrate the superiority of Doc2EDAG over state-of-the-art methods. Data and codes can be found at https://github.com/dolphin-zs/Doc2EDAG.Comment: Accepted by EMNLP 201

Ensemble Committees for Stock Return Classification and Prediction

This paper considers a portfolio trading strategy formulated by algorithms in the field of machine learning. The profitability of the strategy is measured by the algorithm's capability to consistently and accurately identify stock indices with positive or negative returns, and to generate a preferred portfolio allocation on the basis of a learned model. Stocks are characterized by time series data sets consisting of technical variables that reflect market conditions in a previous time interval, which are utilized produce binary classification decisions in subsequent intervals. The learned model is constructed as a committee of random forest classifiers, a non-linear support vector machine classifier, a relevance vector machine classifier, and a constituent ensemble of k-nearest neighbors classifiers. The Global Industry Classification Standard (GICS) is used to explore the ensemble model's efficacy within the context of various fields of investment including Energy, Materials, Financials, and Information Technology. Data from 2006 to 2012, inclusive, are considered, which are chosen for providing a range of market circumstances for evaluating the model. The model is observed to achieve an accuracy of approximately 70% when predicting stock price returns three months in advance.Comment: 15 pages, 4 figures, Neukom Institute Computational Undergraduate Research prize - second plac