27 research outputs found
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High frequency trading from an evolutionary perspective: financial markets as adaptive systems
The recent rapid growth of algorithmic high‐frequency trading strategies makes it a very interesting time to revisit the long‐standing debates about the efficiency of stock prices and the best way to model the actions of market participants. To evaluate the evolution of stock price predictability at the millisecond timeframe and to examine whether it is consistent with the newly formed adaptive market hypothesis, we develop three artificial stock markets using a strongly typed genetic programming (STGP) trading algorithm. We simulate real‐life trading by applying STGP to millisecond data of the three highest capitalized stocks: Apple, Exxon Mobil, and Google and observe that profit opportunities at the millisecond time frame are better modelled through an evolutionary process involving natural selection, adaptation, learning, and dynamic evolution than by using conventional analytical techniques. We use combinations of forecasting techniques as benchmarks to demonstrate that different heuristics enable artificial traders to be ecologically rational, making adaptive decisions that combine forecasting accuracy with speed
Kernel principal component analysis and support vector machines for stock price prediction
Linear classification tikhonov regularization knowledge-based support vector machine for tornado forecasting
Knowledge-based model, Linear classification, Knowledge set, Prior knowledge, Tikhonov regularization, Support vector machines,
Unsupervised Neural Network Training Via a Potential Reduction Approach
A new training algorithm based on quadratic optimization under box constraints is presented to nd the optimal weights of an unsupervised neural network architecture based on Linsker's model. Linsker's network is a multilayer network. All of the units in the network are linear. The training of Linsker's model can be reduced into a nite number of concave quadratic optimization problems. Specically at each neuron the problem is to maximize the variance of its output. We present two algorithms for this problem which are inspired by an interior point potential reduction approach to combinatorial optimization problems suggested by Warners et al. [25] Computational experience by using randomly generated instances are reported. Key words: interior point methods, potential reduction methods, quadratic programming, unsupervised learning, selforganization. The major part of the research was done while the rst author visited the department SSOR of the T.U. Delft. y The rst author gratef..