55,004 research outputs found
Soft computing techniques applied to finance
Soft computing is progressively gaining presence in the financial world. The number of real and potential applications is very large and, accordingly, so is the presence of applied research papers in the literature. The aim of this paper is both to present relevant application areas, and to serve as an introduction to the subject. This paper provides arguments that justify the growing interest in these techniques among the financial community and introduces domains of application such as stock and currency market prediction, trading, portfolio management, credit scoring or financial distress prediction areas.Publicad
An Improved Stock Price Prediction using Hybrid Market Indicators
In this paper the effect of hybrid market indicators is examined for an improved stock price prediction. The hybrid market indicators consist of technical, fundamental and expert opinion variables as input to artificial neural networks model. The empirical results obtained
with published stock data of Dell and Nokia obtained from New York Stock Exchange shows that the proposed model can be effective to improve accuracy of stock price prediction
Process Framework for Subscriber Management and Retention in Nigerian Telecommunication Industry
in the global telecommunication industry. Hence, a dominant approach for subscriber
management and retention is churn control, since it is cheaper to retain an existing
subscriber than acquiring a new one. Predictive modeling employs the use of data mining
techniques to identify patterns and provide a result that a group of subscribers are likely to
churn in the near future. However, the effectiveness of subscriber retention strategy in an
organization can be further boosted if the reason for churn and the timing of churn can also
be predicted.
In this paper, we propose a data mining process framework that can be used to predict
churn, determine when a subscriber is likely to churn, provides the reason why a subscriber
may churn, and recommend appropriate intervention strategy for customer retention using
a combination of statistical and machine learning techniques. This experiment is carried
out using data from a major telecom operator in Nigeria
An empirical methodology for developing stockmarket trading systems using artificial neural networks
A neural network-based framework for financial model calibration
A data-driven approach called CaNN (Calibration Neural Network) is proposed
to calibrate financial asset price models using an Artificial Neural Network
(ANN). Determining optimal values of the model parameters is formulated as
training hidden neurons within a machine learning framework, based on available
financial option prices. The framework consists of two parts: a forward pass in
which we train the weights of the ANN off-line, valuing options under many
different asset model parameter settings; and a backward pass, in which we
evaluate the trained ANN-solver on-line, aiming to find the weights of the
neurons in the input layer. The rapid on-line learning of implied volatility by
ANNs, in combination with the use of an adapted parallel global optimization
method, tackles the computation bottleneck and provides a fast and reliable
technique for calibrating model parameters while avoiding, as much as possible,
getting stuck in local minima. Numerical experiments confirm that this
machine-learning framework can be employed to calibrate parameters of
high-dimensional stochastic volatility models efficiently and accurately.Comment: 34 pages, 9 figures, 11 table
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