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Stock price forecasting - Mathematical models --- Finance - Econometric models --- Autoregression (statistics) --- Stock price forecasting --- Finance --- Modèles économétriques. --- Autoregression (Statistics) --- Autorégression (statistique) --- Mathematical models. --- Econometric models. --- Modèles économétriques. --- Autorégression (statistique)

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Stock price forecasting --- Investments --- Actions (Titres de société) --- Investissements --- Prix --- Prévision --- Banques Bankwezen --- Finances Financiën --- Monnaie Munt --- US / United States of America - USA - Verenigde Staten - Etats Unis --- 333.613 --- 339.4 --- Forecasting, Stock price --- Security price forecasting --- Stocks --- Business forecasting --- Investing --- Investment management --- Portfolio --- Finance --- Disinvestment --- Loans --- Saving and investment --- Speculation --- Activiteiten van de nationale en internationale markten. Beursnoteringen van aandelen en obligaties. --- Vermogensbeheer. Financiële analyse. Verspreiding van de beleggingsrisico's. --- Prices --- Forecasting --- Investments. --- Stock price forecasting. --- Actions (Titres de société) --- Prévision --- Activiteiten van de nationale en internationale markten. Beursnoteringen van aandelen en obligaties --- Vermogensbeheer. Financiële analyse. Verspreiding van de beleggingsrisico's

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Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these approaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain the discovered rules - an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data. Data Mining in Finance introduces a new approach, combining relational data mining with the analysis of statistical significance of discovered rules. This reduces the search space and speeds up the algorithms. The book also presents interactive and fuzzy-logic tools for `mining' the knowledge from the experts, further reducing the search space. Data Mining in Finance contains a number of practical examples of forecasting S&P 500, exchange rates, stock directions, and rating stocks for portfolio, allowing interested readers to start building their own models. This book is an excellent reference for researchers and professionals in the fields of artificial intelligence, machine learning, data mining, knowledge discovery, and applied mathematics.

Investments --- Stock price forecasting --- Data mining --- Data processing --- AA / International- internationaal --- 305.970 --- 303.0 --- 301 --- Algemeenheden: Autoregression and moving average representation. ARIMA. ARMAX. Lagrange multiplier. Wald. Function (mis) specification. Autocorrelation. Homoscedasticity. Heteroscedasticity. ARCH. GARCH. Integration and co-integration. Unit roots. --- Statistische technieken in econometrie. Wiskundige statistiek (algemene werken en handboeken). --- Techniek van statistische inlichtingen. Organisatie van de statistische enquêtes. Statistische kritiek. --- Investments -- Data processing. --- Stock price forecasting -- Data processing. --- Data mining. --- Data processing. --- Data structures (Computer scienc. --- Artificial intelligence. --- Finance. --- Data Structures and Information Theory. --- Artificial Intelligence. --- Finance, general. --- Data structures (Computer science) --- Forecasting, Stock price --- Security price forecasting --- Stocks --- Business forecasting --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Techniek van statistische inlichtingen. Organisatie van de statistische enquêtes. Statistische kritiek --- Statistische technieken in econometrie. Wiskundige statistiek (algemene werken en handboeken) --- Algemeenheden: Autoregression and moving average representation. ARIMA. ARMAX. Lagrange multiplier. Wald. Function (mis) specification. Autocorrelation. Homoscedasticity. Heteroscedasticity. ARCH. GARCH. Integration and co-integration. Unit roots --- Prices --- Forecasting --- Data structures (Computer science). --- Funding --- Funds --- Economics --- Currency question --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Information structures (Computer science) --- Structures, Data (Computer science) --- Structures, Information (Computer science) --- File organization (Computer science) --- Abstract data types (Computer science) --- Investments - Data processing --- Stock price forecasting - Data processing