Adaptive Stock Forecasting Model using Modified Backpropagation Neural Network (MBNN)

Gurav, Uma; Sidnal, Nandini

doi:10.1109/ctems.2018.8769290

Cited by 3 publications

(5 citation statements)

References 11 publications

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“…This section presents the performance evaluation of the proposed SASPF model over other prediction models. The GAN-FD model [15] was chosen for comparison as it achieved much better results than existing LSTM based stock forecasting methods [21,[24][25][26][27][28], as investors' sentiments are considered as a major contributing parameter. Results obtained for sentiment index (positive, negative, neutral and compound).…”

Section: Resultsmentioning

confidence: 99%

Sentiment Aware Stock Price Forecasting using an SA-RNN-LBL Learning Model

Gurav¹,

Kotrappa²

2020

Eng. Technol. Appl. Sci. Res.

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Stock market historical information is often utilized in technical analyses for identifying and evaluating patterns that could be utilized to achieve profits in trading. Although technical analysis utilizing various measures has been proven to be helpful for forecasting and predicting price trends, its utilization in formulating trading orders and rules in an automated system is complex due to the indeterminate nature of the rules. Moreover, it is hard to define a specific combination of technical measures that identify better trading rules and points, since stocks might be affected by different external factors. Thus, it is important to incorporate investors’ sentiments in forecasting operations, considering dynamically the varying stock behavior. This paper presents a sentiment aware stock forecasting model using a Log BiLinear (LBL) model for learning short term stock market sentiment patterns, and a Recurrent Neural Network (RNN) for learning long-term stock market sentiment patterns. The Sentiment Aware Stock Price Forecasting (SASPF) model achieves a much superior performance compared to standard deep learning based stock price forecasting models.

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Section: Resultsmentioning

confidence: 99%

Sentiment Aware Stock Price Forecasting using an SA-RNN-LBL Learning Model

Gurav¹,

Kotrappa²

2020

Eng. Technol. Appl. Sci. Res.

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“…Besides offering profit potential, such investments also entail significant risks. Therefore, the analysis of stock price movements became a main point for investors because the fluctuations in stock prices did not guarantee the profits or losses incurred (Ardana et al, 2019;Gurav, 2018).…”

Section: A Introductionmentioning

confidence: 99%

“…Fluctuating stock prices challenged investors to apply appropriate methods to model historical stock price data and predict future periods. In the last decade, the application of Machine Learning (ML) techniques such as Support Vector Machine (SVM), Artificial Neural Network (ANN), and Reinforcement Learning (RL) has been widely conducted and considered effective in predicting financial data (Fauzi et al, 2023;Gurav, 2018).…”

Section: A Introductionmentioning

confidence: 99%

“…Backpropagation Neural Network (BPNN), an extension of Artificial Neural Network (ANN), was a machine learning method that simulated the composition and functionality of the human brain. ANN could accurately forecast and differentiate nonlinear data patterns without previous experience, making it widely accepted and applicable (Gurav, 2018). Moreover, BPNN features efficient learning, generalization, and parallel processing capabilities in solving complex problems.…”

Section: A Introductionmentioning

confidence: 99%

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Projection of PT Aneka Tambang Tbk Share Risk Value Based on Backpropagation Artificial Neural Network Forecasting Result

Haris,

Setyaningsih,

Fauzi

et al. 2024

JTAM

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PT Aneka Tambang Tbk (ANTAM) received an award as the most sought-after stock issuer in Indonesia in 2016. That stock continued to attract investors in 2022 due to a 105% increase in net profit and a 19% increase in sales from the previous year. Despite the upward trend, investors still had doubts due to the fluctuating movement of ANTAM's stock prices. Therefore, forecasting was needed to determine the future movement of stock prices. The Backpropagation Neural Network method had good capabilities for fluctuating data types. However, this method has the disadvantage of a lengthy iteration process. To handle this limitation, The Nguyen-Widrow weighted setting was applied to address this constraint. The expected Shortfall (ES) method used the forecasting results to measure investment risk. This research uses ANTAM stock closing price data from May 2, 2018, to May 31, 2023. Based on the analysis results, the best architecture was obtained with a configuration of 5-11-1, using Nguyen-Widrow weight initialization and a combination of a learning rate of 0.5 and momentum of 0.9. This architecture yielded a prediction error based on the Mean Absolute Percentage Error (MAPE) of 1.9947%. Risk measurement with the ES method based on the prediction for the next 60 periods showed that at a 95% confidence level, the risk value was 0.002181; at a 90% confidence level, it was 0.002165; at an 85% confidence level, it was 0.002148, and at an 80% confidence level, it was 0.002132.

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“…Moreover, stock price is predicted mainly to determine the company's future value or the other financial assets that can be marketed on the exchange [3] [7]. Meanwhile it is also observed that the stock market is heavily characterized through the various properties such as high frequency MC (Multi-polynomial Component), nonlinearities and discontinuities since fluctuations occurs due to the various scenario such as political changes, Country economic conditions and the expectation of traders [4] [17], hence it is highly improbable to predict the stock values. Moreover the investors buy the stocks which deals with the infrastructure projects, construction firms, contractors hiring, paperwork handling.…”

Section: Introductionmentioning

confidence: 99%

RNNLBL : A Recurrent Neural Network and Log Bilinear based Efficient Stock Forecasting Model

Gurav*,

2020

IJITEE

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Recent years have seen the wide use of Time series forecasting (TSF) for predicting the future price stock, modeling and analyzing of finance time series helps in guiding the trades and investors decision. Moreover considering the stock as the dynamic environment, it is pronounced as the non-linearity of time series which affects the stock price forecast immediately. Hence, in this research work we propose intelligent TSF model, which helps in forecasting the early prediction of stock prices. The proposed stock price forecasting model employed both short-term (i.e. recent behavior fluctuation) using log bilinear (LBL) model and long-term (i.e., historical) behavior using recurrent neural network (RNN) based LSTM (long short term memory )model. Subsequently, this model is mainly helpful for the home brokers since they do not possess enough knowledge about the stock market. Proposed RNNLBL hybrid model shows the satisfying forecasting performance, these results in overall profit for the investors and trades. Furthermore, proposed model possesses a promising forecasting in case of the non-linear time series since the pattern of non-linear pattern are highly improbable to capture through these state-of-art stock price forecasting models.

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Adaptive Stock Forecasting Model using Modified Backpropagation Neural Network (MBNN)

Cited by 3 publications

References 11 publications

Sentiment Aware Stock Price Forecasting using an SA-RNN-LBL Learning Model

Sentiment Aware Stock Price Forecasting using an SA-RNN-LBL Learning Model

Projection of PT Aneka Tambang Tbk Share Risk Value Based on Backpropagation Artificial Neural Network Forecasting Result

RNNLBL : A Recurrent Neural Network and Log Bilinear based Efficient Stock Forecasting Model

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