A comparison of global, recurrent and smoothed-piecewise neural models for Istanbul stock exchange (ISE) prediction

Yümlü, Serdar; Gürgen, Fikret; Okay, Nesrin

doi:10.1016/j.patrec.2005.03.026

Cited by 52 publications

(16 citation statements)

References 7 publications

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“…In finance, there are typically two kinds of information (Petersen, 2004): soft information usually refers to text, including opinions, ideas, and market commentary, whereas hard information is recorded as numbers, such as financial measures and historical prices. Most financial studies related to risk analysis are based on hard information, especially on time series modeling (Christoffersen and Diebold, 2000;Lee and Tong, 2011;Wu et al, 2014;Yümlü et al, 2005). Despite of using only hard information, some literature incorporates soft textual information to predict financial risk (Kogan et al, 2009;Leidner and Schilder, 2010;.…”

Section: Introductionmentioning

confidence: 99%

Financial Keyword Expansion via Continuous Word Vector Representations

Tsai

Wang

2014

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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This paper proposes to apply the continuous vector representations of words for discovering keywords from a financial sentiment lexicon. In order to capture more keywords, we also incorporate syntactic information into the Continuous Bag-ofWords (CBOW) model. Experimental results on a task of financial risk prediction using the discovered keywords demonstrate that the proposed approach is good at predicting financial risk.

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Section: Introductionmentioning

confidence: 99%

Financial Keyword Expansion via Continuous Word Vector Representations

Tsai

Wang

2014

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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“…In addition, it helps find the optimal model to fit the time series data and apply this model to predict the future observations of data based on past data series. For example, financial market prediction by computations of the next value of trade sales each month [22][23][24]. The main aspect of time series is actually that observation values are not created independently or ordered randomly; the data in time series are representing sequences of measurements arranged according to time intervals.…”

Section: Time Series Forecastingmentioning

confidence: 99%

The application of dynamic self-organised multilayer network inspired by the Immune Algorithm for weather signals forecast

Hussain

Al‐Jumeily

Alaskar

2015

2015 Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE)

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“…Neural networks (NN) are considered as a superior method for time-series prediction, as compared to pure statistical methods, owing to their non-linear nature and capacity for data-based training (Yümlü et al, 2005;Ho et al, 2002;Dunis and Williams, 2002). In summary, the ability of a neural network to approximate and produce complex mappings between non-linear data is particularly fitting to this identified problem domain.…”

Section: Introductionmentioning

confidence: 99%

Predicting financial time series data using artificial immune system-inspired neural networks

Alaskar

Lamb

Hussain

et al. 2015

IJAISC

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This paper investigates a set of approaches for the prediction of noisy time series data; specifically, the prediction of financial signals. A novel dynamic self-organised multilayer neural network based on the immune algorithm for financial time series prediction is presented, combining the properties of both recurrent and self-organised neural networks. In an attempt to overcome inherent stability and convergence problems, the network is derived to ensure that it reaches a unique equilibrium state. The accuracy of the comparative evaluation is enhanced in terms of profit earning; empirical testing used in this work includes normalised mean square error (NMSE) to evaluate forecast fitness and also evaluates predictions against financial metrics to assess profit generation. Extensive simulations for multi-step prediction in stationary and non-stationary time series were performed. The resulting forecast made by the proposed network shows substantial profits on financial historical signals when compared to various solely neural network approaches. These simulations suggest that dynamic immunology-based self-organised neural networks have a better ability to capture the chaotic movement in financial signals.Keywords: financial signals; artificial immune systems; immunology-inspired computing; higher order neural network; time series data.Reference to this paper should be made as follows: Al-askar, H., Lamb, D., Hussain, A.J., Al-Jumeily, D., Randles, M. and Fergus, P. (2015) 'Predicting financial time series data using artificial immune system-inspired neural networks', Int.

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A comparison of global, recurrent and smoothed-piecewise neural models for Istanbul stock exchange (ISE) prediction

Cited by 52 publications

References 7 publications

Financial Keyword Expansion via Continuous Word Vector Representations

Financial Keyword Expansion via Continuous Word Vector Representations

The application of dynamic self-organised multilayer network inspired by the Immune Algorithm for weather signals forecast

Predicting financial time series data using artificial immune system-inspired neural networks

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