Financial Market Prediction Using Feed Forward Neural Network

N., Col.) Kumar P.; Seshadri, Gayathri; Hariharan, Aravind; Mohandas, V. P.; Balasubramanian, P.

doi:10.1007/978-3-642-20209-4_11

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Luxhoj, et al [10] proposed an econometric model of ANN to forecast the sales price. Pelikan [11], Ginzburg [12], and Kumar et al [13] were developed a feed forward neural networks to forecast the power consumption and enhanced the prediction precision of time series data. Tsaih [14] developed a novel ANN model by integrating the concept of artificial intelligence (AI) to predict the index of American stock market.…”

Section: Literature Reviewmentioning

confidence: 99%

Prophecy of Stock Price of Bharat Immunological & Biological Corporation Ltd using Hybrid ANN and PSO Model

Siddique

Mishra

2021

JEMT

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The prediction of the time series has always attracted much interest from investors and researchers to evaluate financial risk. Stock market movements are extremely complex and are influenced by different factors. Hence it is very important to find the most important factors for the stock market. But the high level of noise and complexity of the financial data makes this job very difficult. Many authors have already used artificial neural network for this kind of forecasting tasks, but hybridization model of artificial neural network is considered to be widely used and better performing forecasting model among others. The dormant high noises data mess up the performance, so to enhance the prediction accuracy. We considered a set of seven technical attribute of stock market to perform the hybrid model of Artificial Neural Network (ANN) and Particle Swarm Optimization algorithms. The efficiency of the proposed method is measured by the stock price of Bharat Immunological & Biological Corporation Ltd with 3945 number of daily transactional data. Empirical prediction analysis shows that the proposed model enhances the performance in comparison to simple ANN model.

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Section: Literature Reviewmentioning

confidence: 99%

Prophecy of Stock Price of Bharat Immunological & Biological Corporation Ltd using Hybrid ANN and PSO Model

Siddique

Mishra

2021

JEMT

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“…Machine learning approaches like support vector machine, feed-forward neural networks, etc. were used by many researchers for predicting stock prices (Henrique et al, 2018;Hu et al, 2013;Jabin, 2014;Kumar et al, 2011). However, due to their inability to remembering context, recurrent neural networks became popular for predicting stock price (Chen et al, 2018;Li et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Analysis of L2 Regularization Hyper Parameter for Stock Price Prediction

Saud

Shakya

2021

J. Inst. Sci. Tech.

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Nowadays stock price prediction is an active area of research among machine learning researchers. One of the main problems with machine learning models is overfitting. Regularization techniques are widely used approaches to avoid over-fitted models. L2 regularization is one of the most popular and widely used regularization techniques. Regularization hyperparameter (ʎ) is one key parameter to be optimized for a well-generalized machine learning model. Hyperparameters can’t be learned by machine learning models during the learning process. We need to find their optimal value through experiments. This research work analyzed the L2 regularization hyperparameter used with a gated recurrent unit (GRU) network for stock price prediction. We experimented with five stocks from the Nepal Stock Exchange (NEPSE) and observed that stock price can be predicted with lower mean squared errors (MSEs) when the value of ʎ was around 0.0005. Therefore, this research paper recommended using ʎ=0.0005 with L2 regularization for stock price prediction.

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“…Ever since the seminal paper of Markowitz (1952), selecting portfolios which are optimal in terms of risk-adjusted returns has been in the centre of interest of both academics and financial practitioners. There are many approaches to algorithmic trading on profitable portfolios ranging from prediction based trading (Gestel et al 2001;Kim 2003;Kumar et al 2011) to mean reverting trading (Chan 2013;d'Aspremont 2007;Fogarasi and Levendovszky 2013).…”

Section: Introductionmentioning

confidence: 99%

Applying Independent Component Analysis and Predictive Systems for Algorithmic Trading

2017

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In this paper, a Nonlinear AutoRegressive network with eXogenous inputs and a support vector machine are proposed for algorithmic trading by predicting the future value of financial time series. These architectures are capable of modeling and predicting vector autoregressive VAR(p) time series. In order to avoid overfitting, the input is pre-processed by independent component analysis to filter out the most noise like component. In this way, the accuracy of the prediction and the trading performance is increased. The proposed algorithms have a small number of free parameters which makes fast learning and trading possible. The method is not only tested on single asset price series, but also on predicting the value of mean reverting portfolios obtained by maximizing the predictability parameter of VAR(1) processes. The tests were first performed on artificially generated data and then on real data selected from exchange traded fund time series including bid-ask spread. In both cases the proposed method could achieve positive returns. Keywords Algorithmic trading • Financial time series • Neural network • Support vector machine • Independent component analysis • Mean reverting portfolio B Attila Ceffer

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Financial Market Prediction Using Feed Forward Neural Network

Cited by 5 publications

References 9 publications

Prophecy of Stock Price of Bharat Immunological & Biological Corporation Ltd using Hybrid ANN and PSO Model

Prophecy of Stock Price of Bharat Immunological & Biological Corporation Ltd using Hybrid ANN and PSO Model

Analysis of L2 Regularization Hyper Parameter for Stock Price Prediction

Applying Independent Component Analysis and Predictive Systems for Algorithmic Trading

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