Stock market prediction system with modular neural networks

Kimoto, Tsunenobu; Asakawa, Kazuo; Yoda, Minami; Takeoka, M.

doi:10.1109/ijcnn.1990.137535

Cited by 505 publications

(199 citation statements)

References 3 publications

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“…Last, we note that our work is very different from another great body of existing work in literature (Kimoto et al 1993;Tay and Cao 2001;Cao and Tay 2003;Tsang et al 2004;Lu et al 2009), which attempted to make financial time series forecasting and stock price predictions by applying machine learning techniques, such as neural networks (Kimoto et al 1993), decision trees (Tsang et al 2004), and support vector machines (SVM) (Tay and Cao 2001;Cao and Tay 2003;Lu et al 2009), etc. The key difference between these work and ours is that their learning goal is to make explicit predictions of future prices/trends while our learning goal is to directly optimize portfolio without predicting prices explicitly.…”

Section: Learning To Select Portfoliomentioning

confidence: 86%

PAMR: Passive aggressive mean reversion strategy for portfolio selection

et al. 2012

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This article proposes a novel online portfolio selection strategy named "Passive Aggressive Mean Reversion" (PAMR). Unlike traditional trend following approaches, the proposed approach relies upon the mean reversion relation of financial markets. Equipped with online passive aggressive learning technique from machine learning, the proposed portfolio selection strategy can effectively exploit the mean reversion property of markets. By analyzing PAMR's update scheme, we find that it nicely trades off between portfolio return and volatility risk and reflects the mean reversion trading principle. We also present several variants of PAMR algorithm, including a mixture algorithm which mixes PAMR and other strategies. We conduct extensive numerical experiments to evaluate the empirical performance of the proposed algorithms on various real datasets. The encouraging results show that in most cases the proposed PAMR strategy outperforms all benchmarks and almost all state-of-the-art portfolio selection strategies under various performance metrics. In addition to its superior performance, the proposed PAMR runs extremely fast and thus is very suitable for real-life online trading applications. The experimental testbed including source codes and data sets is available at http://www.cais.ntu.edu.sg/~chhoi/PAMR/.

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Section: Learning To Select Portfoliomentioning

confidence: 86%

PAMR: Passive aggressive mean reversion strategy for portfolio selection

et al. 2012

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“…Skabar and Cloete (2002), describe a methodology in which neural networks can be used indirectly, through a genetic algorithm based on weight optimisation procedure, in order to determine buy and sell points for fi nancial commodities traded on a stock exchange. A number of studies applied the simulation of trading agents based on ANNs (White 1988;Kimoto et al 1990;Weigend and Gershenfeld 1994). The traditional approach to supervise neural network weight optimisation is the well-known back propagation algorithm (Rumelhart, McClelland 1986), while Beltratti and Terna (1996), suggest the use of genetic search for neural network weight optimisation in this fi eld.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Bank Stock Market Prices With a Hybrid Method: The Case of Alpha Bank / Vertybinių Popierių Kainų Prognozavimas Hibridiniu Metodu: Alpha Bank Pavyzdys

Koutroumanidis

Ιωάννου

Zafeiriou

2011

Journal of Business Economics and Management

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Abstract. The present study aims at constructing Confi dence Intervals (C.I) for the predicted values of a Time Series with the application of a Hybrid method. The presented methodology is complicated and thus is completed in different stages. Initially the Artifi cial Neural Networks (ANNs) is applied on the raw time series in order to estimate C.I of the forecasts. Then, the Bootstrap method is employed on the residuals generated by the preceded process. On the upper and lower limit of the estimated C.I., two new ANNs are employed in order to make point estimations (of the upper and lower limits) using of Object Oriented Programming. For the empirical analysis daily observations of the closing prices of Alpha Bank stocks have been used. The sample period is extended from 28/01/2004 until 30/11/2005. The nonstationarity of the time series employed in our study is not a forbidding condition for the estimation of the confi dence intervals, in our case, since the level of bootstrap still provides a satisfactory approximation for the roots arbitrarily close to unity (Berkowitz, Kilian 1996). The accuracy of the forecasts was surveyed with the use of different criteria and the results were satisfactory.

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“…A collection of small networks, however, has been demonstrated to be an effective alternative to large backpropagation networks (Kimoto et al, 1990;Lendaris & Harb, 1990). The network architecture used involves two levels of modularization, the database modularization and the encoding modularization (Wu et al, 1991b).…”

Section: Modular Network Architecturementioning

confidence: 99%

Protein classification artificial neural system

Wu¹,

Whitson²,

McLarty³

et al. 1992

Protein Science

116

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A neural network classification method is developed as an alternative approach to the large database search/ organization problem. The system, termed Protein Classification Artificial Neural System (ProCANS), has been implemented on a Cray supercomputer for rapid superfamily classification of unknown proteins based on the information content of the neural interconnections. The system employs an n-gram hashing function that is similar to the k-tuple method for sequence encoding. A collection of modular back-propagation networks is used to store the large amount of sequence patterns. The system has been trained and tested with the first 2,148 of the 8,309 entries of the annotated Protein Identification Resource protein sequence database (release 29). The entries included the electron transfer proteins and the six enzyme groups (oxidoreductases, transferases, hydrolases, lyases, isomerases, and ligases), with a total of 620 superfamilies. After a total training time of seven Cray central processing unit (CPU) hours, the system has reached a predictive accuracy of 90%. The classification is fast (i.e., 0.1 Cray CPU second per sequence), as it only involves a forward-feeding through the networks. The classification time on a full-scale system embedded with all known superfamilies is estimated to be within 1 CPU second. Although the training time will grow linearly with the number of entries, the classification time is expected to remain low even if there is a 10-100-fold increase of sequence entries. The neural database, which consists of a set of weight matrices of the networks, together with the ProCANS software, can be ported to other computers and made available to the genome community. The rapid and accurate superfamily classification would be valuable to the organization of protein sequence databases and to the gene recognition in large sequencing projects.Keywords: database search; neural networks; protein classification; sequence analysis; superfamilyThe continuing rapid growth of the molecular sequencing data has generated a pressing need for advanced computational tools to analyze and manage the data. An ideal computer tool should allow the interpretation of genomic information from the sequences and permit easy organization of the information into a database to facilitate information retrieval. Currently, a database search for sequence similarities represents the most direct computational approach to the analysis of genomic information (Doolittle, Quicksearch method (Devereux, 1988) provides an even faster but less sensitive search against the database that is represented with a sparse hash table. A BLAST approach (Altschul et al., 1990), which directly approximates alignments that optimize a measure of local similarity, also permits fast sequence comparisons. In contrast to the above methods that are designed for pairwise comparisons, a profile analysis method (Gribskov et al., 1987) provides search against information from protein families instead of individual proteins using dynamic programming ...

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Stock market prediction system with modular neural networks

Cited by 505 publications

References 3 publications

PAMR: Passive aggressive mean reversion strategy for portfolio selection

PAMR: Passive aggressive mean reversion strategy for portfolio selection

Forecasting Bank Stock Market Prices With a Hybrid Method: The Case of Alpha Bank / Vertybinių Popierių Kainų Prognozavimas Hibridiniu Metodu: Alpha Bank Pavyzdys

Protein classification artificial neural system

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