A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction

Daradkeh, Mohammad

doi:10.3390/electronics11020250

Cited by 17 publications

(9 citation statements)

References 53 publications

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“…By integrating news events, sentiment trends, and quantitative financial data, the author enhance stock trend prediction accuracy. Using this integrated approach, the CNN-BiLSTM model outperforms benchmarked machine learning models by 11.6% in real estate and 25.6% in communications sectors [18]. The proposed BLSTM-based Seq2Seq Model predicts stock closing prices, surpassing K-Nearest neighbor, decision tree, and linear regression models.…”

Section: Introductionmentioning

confidence: 99%

Monte carlo simulation with bilstm for day-ahead stock portfolio management

Shaikh,

Ramadass

2024

IJEECS

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<div>Predicting stock price movement and optimizing day-ahead stock portfolios are challenging tasks due to the inherent complexity and volatility of financial markets. This study proposes a novel approach that combines bidirectional long short-term memory (BiLSTM) neural networks with monte carlo simulation (MCS) to enhance day-ahead stock portfolio management. In the proposed methodology, historical data of the top-performing 10 stocks from different sectors of the National Stock Exchange of India (NSEI) is obtained from 1 January 2004 to 30 June 2023 and utilized to train a BiLSTM model. This model effectively extracts intricate patterns and trends from the time series, leading to more accurate and robust stock price predictions. MCS generates different scenarios, considering various market conditions and uncertainties. These scenarios provide a comprehensive view of the portfolio’s performance under different conditions, thus mitigating the risk of relying solely on a single prediction. The study evaluates the proposed framework and compares its performance against traditional portfolio management strategies. Results demonstrate that the MCS with the BiLSTM approach outperforms traditional methods in terms of risk-adjusted returns and portfolio stability.</div>

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Section: Introductionmentioning

confidence: 99%

Monte carlo simulation with bilstm for day-ahead stock portfolio management

Shaikh,

Ramadass

2024

IJEECS

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“…Recent advances in technology and their contribution to finance have caused a significantly increasing impact on financial markets, especially international stock markets and on the world economy. Accordingly, stock market investigation recreates a critical role to achieve knowledge about the stock market, inventing trading strategies and the stocks ingrained value determination [1]. In addition, the stock index and stock market movement predictions are important skills among successful traders'.…”

Section: Introductionmentioning

confidence: 99%

Stock index forecasting using DACLAMNN: A new intelligent highly accurate hybrid ACLSTM/Markov neural network predictor

Safari,

Badamchizadeh

2023

Cognitive Comp and Systems

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The authors present the investigation of a new hybrid predictive model of Duplex Attention‐based Coupled LSTM Markov Averaged Neural Network, known as DACLMANN. The financial field, particularly the stock market, heavily relies on accurate predictive models. DACLMANN comprises four essential components: two LSTM blocks, an Averagiser and a Markov Neural Network block. The first LSTM block is composed of two hidden layers, each containing 50 neurons and a dense layer with 25 neurons. The second LSTM block consists of two hidden layers, each with 100 neurons, and a dense layer with 50 neurons. The Averagiser plays a crucial role by averaging the closing prices and predicted values from the first LSTM block, resulting in a 90% gain. These averaged values are then fed into the second LSTM block for further prediction. Finally, the predictions undergo evaluation using the Markov model, yielding the final prediction. To assess the performance of DACLMANN, it was tested on 22 years of stock prices for the AMZN index. The evaluation metrics used by the authors include an R2 of 0.76, mean absolute error of 6.81216, root mean square error of 8.6040, Precision of 1, Accuracy of 1, Recall of 1 and F1 of 1. Additionally, DACLMANN achieved a Mean Absolute Percentage Error of less than 0.043% and an RMSPE of less than 2.1%. These results not only demonstrate the effectiveness of the proposed model but also authenticate the prediction outcomes. DACLMANN offers several advantages over traditional predictive models in the stock market. By combining the strengths of Duplex Attention‐based Coupled LSTM, Averagiser, and Markov Neural Network, DACLMANN leverages the power of deep learning, attention mechanisms, and sequential modelling. This hybrid approach enables DACLMANN to capture intricate patterns and dependencies present in stock market data, leading to more accurate and reliable predictions. The robust evaluation metrics further validate the superiority of DACLMANN in predicting stock prices.

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“…To address this issue, we employ predictive analysis to determine the speed at which specific breaking news spreads after the event occurs and subsequently analyze public sentiment towards it, enabling us to forecast price trends. Here's an article which talks about combining financial data with news events and sentiment trends to build strategies [6]. The second approach involves abnormal fluctuations in stock prices caused by market sentiment, investor buying and selling behavior, or technical factors unrelated to publicly disclosed breaking news.…”

Section: Introductionmentioning

confidence: 99%

Stock Price Anomalies Analysis and Detection Based on Machine Learning

Xia,

Chen

2023

HBEM

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Through long years of development in the financial industry, more people and investments have been immersed here. However, the highly volatile stock market often presents investors with significant surprises. Therefore, identifying anomalies within stock prices in a timely manner is a widely-discussed problem, successfully detecting anomalies can help investors avoid loss, even gaining profit in some cases. In this essay, the performances of the Autoencoder and Light Gradient Boosting Machine are compared in predicting anomalies, employing supervised learning and unsupervised learning respectively. For Autoencoder, we construct layers and use a target loss to make decisions. As for the Light Gradient Boosting Machine, we split the original data into a test set and training set and trained the model. To assign labels, a statistical approach is employed to pre-test the data for normality. From an overall picture, a threshold of 4 achieves the best accuracy performance, proving that the number around four would be a good choice while detecting the strange turning trend in stock price.

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A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction

Cited by 17 publications

References 53 publications

Monte carlo simulation with bilstm for day-ahead stock portfolio management

Monte carlo simulation with bilstm for day-ahead stock portfolio management

Stock index forecasting using DACLAMNN: A new intelligent highly accurate hybrid ACLSTM/Markov neural network predictor

Stock Price Anomalies Analysis and Detection Based on Machine Learning

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