Stock market prediction by using artificial neural network

Yetis, Yunus; Kaplan, Halid; Jamshidi, Mo

doi:10.1109/wac.2014.6936118

Cited by 55 publications

(25 citation statements)

References 4 publications

(3 reference statements)

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“…ANNs are adept at processing massive amounts of information in parallel and have the ability to derive meaning from complicated or imprecise data by recognizing complex patterns and trends. Trained ANNs are able to perform visual recognition [1], character recognition [2], voice-activated assistance [3], stock market forecasting [4], and are used in self-driving cars [5]. Industry pioneers are designing ANNs that can also be used in solar radiation forecasting, object classification and matching, event filtering, facial recognition, combat automation, target identification and weapon optimization [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

Effects of memristive synapse radiation interactions on learning in spiking neural networks

2021

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This study uses advanced modeling and simulation to explore the effects of external events such as radiation interactions on the synaptic devices in an electronic spiking neural network. Specifically, the networks are trained using the spike-timing-dependent plasticity (STDP) learning rule to recognize spatio-temporal patterns (STPs) representing 25 and 100-pixel characters. Memristive synapses based on a TiO2 non-linear drift model designed in Verilog-A are utilized, with STDP learning behavior achieved through bi-phasic pre- and post-synaptic action potentials. The models are modified to include experimentally observed state-altering and ionizing radiation effects on the device. It is found that radiation interactions tend to make the connection between afferents stronger by increasing the conductance of synapses overall, subsequently distorting the STDP learning curve. In the absence of consistent STPs, these effects accumulate over time and make the synaptic weight evolutions unstable. With STPs at lower flux intensities, the network can recover and relearn with constant training. However, higher flux can overwhelm the leaky integrate-and-fire post-synaptic neuron circuits and reduce stability of the network.

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Section: Introductionmentioning

confidence: 99%

Effects of memristive synapse radiation interactions on learning in spiking neural networks

2021

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“…Paper [9], clearly depicts that these neural networks are inspired by different biological neural networks which provided a platform for integrating intelligent systems. In recent years, profound implementation of neural network has been there to achieve different tasks, e.g., weather forecasting [3], stock market prediction [10], diagnosis of breast cancer [6]. As discussed by the authors of the paper [7], different models have been developed to analyze the data from several diabetic patients.…”

Section: Introductionmentioning

confidence: 99%

QTc as Predictor of Cardiac Autonomy Function in Type-2 Diabetic Patient Using Neural Network

Ranjan¹,

Seth²,

Kumar³

2021

Aksh

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The Cardiac autonomic neuropathy is a major issue in patients diagnosed with diabetes. Prolonged QTc is inclined to ventricular arrhythmia and sudden cardiac arrest. QTc prolongation shows a positive relationship with a degree of cardiac autonomic neuropathy in diabetic patients. This study intends to make a prediction based on the consecutive QTc prolongation of diabetic patients based on their ECG report taken in 3-5 years. A good prediction of the model designed with the help of the input and output data has been obtained with the help of an artificial neural network. The least mean square error can give us the best prediction result of our targeted output data.

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“…Many artificial intelligence methods have been employed to predict stock market prices. Artificial neural networks (ANN) remain a popular choice for this task and are widely studied [1] and have been shown to exhibit good performance [2].…”

Section: Introductionmentioning

confidence: 99%

A Stock Market Trading System Using Deep Neural Network

Yong

Rahim

Abdullah

2017

Communications in Computer and Information Science

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The stock market prediction is a lucrative field of interest with promising profit and covered with landmines for the unprecedented. The markets are complex, non-linear and chaotic in nature which poses huge difficulties to predict the prices accurately. In this paper, a stock trading system utilizing feed-forward deep neural network (DNN) to forecast index price of Singapore stock market using the FTSE Straits Time Index (STI) in t days ahead is proposed and tested through market simulations on historical daily prices. There are 40 input nodes of DNN which are the past 10 days' opening, closing, minimum and maximum prices and consist of 3 hidden layers with 10 neurons per layer. The training algorithm used is stochastic gradient descent with back-propagation and is accelerated with multi-core processing. A trading system is proposed which utilizes the DNN forecasting results with defined entry and exit rules to enter a trade. DNN performance is evaluated using RMSE and MAPE. The overall trading system shows promising results with a profit factor of 18.67, 70.83% profitable trades and Sharpe ratio of 5.34 based on market simulation on test data.

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Stock market prediction by using artificial neural network

Cited by 55 publications

References 4 publications

Effects of memristive synapse radiation interactions on learning in spiking neural networks

Effects of memristive synapse radiation interactions on learning in spiking neural networks

QTc as Predictor of Cardiac Autonomy Function in Type-2 Diabetic Patient Using Neural Network

A Stock Market Trading System Using Deep Neural Network

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