Techniques for Stock Market Prediction: A Review

Stock market forecasting has long been viewed as a vital real-life topic in economics world. There are many challenges in stock market prediction systems such as the Efficient Market Hypothesis (EMH), Nonlinearity, complex, diverse datasets, and parameter optimization. A stock's value on the stock market fluctuates due to many factors like previous trends of the stock, the current news, twitter feeds, any online customer feedbacks etc. In this paper, the literature is critically analysed on approaches used for stock market prediction in terms of stock datasets, features used, evaluation metrics used, statistical, machine learning and deep learning techniques along with the directions for the future. The focus of this review is on trend and value prediction for stocks. Overall, 68 research papers have been considered for review from years 1998-2023. From the review, Indian stock market datasets are found to be most frequently used datasets. Evaluation metrics used commonly are accuracy and Mean Absolute Percentage Error. ARIMA is reported as the most used frequently statistical technique for stick market prediction. Long-Short Term Memory and Support Vector Machine are the commonly used algorithms in stock market prediction. The advantages and disadvantages of frequently used evaluation metrics, machine learning, deep learning and statistical approaches are also included in this survey

Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm

Algorithms and the Foundations of Software technolog

A Survey of Forex and Stock Price Prediction Using Deep Learning

The prediction of stock and foreign exchange (Forex) had always been a hot and profitable area of study. Deep learning application had proven to yields better accuracy and return in the field of financial prediction and forecasting. In this survey we selected papers from the DBLP database for comparison and analysis. We classified papers according to different deep learning methods, which included: Convolutional neural network (CNN), Long Short-Term Memory (LSTM), Deep neural network (DNN), Recurrent Neural Network (RNN), Reinforcement Learning, and other deep learning methods such as HAN, NLP, and Wavenet. Furthermore, this paper reviewed the dataset, variable, model, and results of each article. The survey presented the results through the most used performance metrics: RMSE, MAPE, MAE, MSE, accuracy, Sharpe ratio, and return rate. We identified that recent models that combined LSTM with other methods, for example, DNN, are widely researched. Reinforcement learning and other deep learning method yielded great returns and performances. We conclude that in recent years the trend of using deep-learning based method for financial modeling is exponentially rising

Đánh giá hiệu suất mô hình phức hợp LSTM-GRU: nghiên cứu điển hình về dự báo chỉ số đo lường xu hướng biến động giá cổ phiếu trên sàn giao dịch chứng khoán Hồ Chí Minh

Thị trường chứng khoán là một hệ thống chuyển động phi tuyến rất phức tạp và quy luật biến động của nó bị ảnh hưởng bởi rất nhiều yếu tố, vì vậy việc dự đoán chỉ số giá cổ phiếu là một nhiệm vụ rất khó khăn. Mô hình mạng nơ-ron với bộ nhớ ngắn hạn định hướng dài hạn (LSTM), mạng nơ-ron hồi tiếp với nút cổng (GRU) và các phức hợp được thiết kế bằng ngôn ngữ lập trình Python với các gói phụ trợ có sẵn, cho thấy kết quả dự báo với độ chính xác cao, hiệu suất của mô hình LSTM-GRU Hybrid cho kết quả tốt nhất. Thông qua mô hình LSTM-GRU Hybrid, nghiên cứu dự báo xu hướng biến động chỉ số VNIndex 100 ngày tiếp theo cho kết quả chỉ số VNIndex có xu hướng tăng. Điều đó gián tiếp chỉ ra rằng thị trường chứng khoán Việt Nam có dấu hiệu khởi sắc trở lại cùng với các chính sách mới của Chính phủ

Developing and Applying Hybrid Deep Learning Models for Computer-Aided Diagnosis of Medical Image Data

The dissertation discusses three methods to address the challenges of applying deep learning models to medical imaging. The first method involves the development of a new joint deep learning model, J-Net, to achieve lesion segmentation and classification simultaneously. The J-Net model outperforms the individual models in accuracy with small datasets. The second method performs automatic image detection using a two-stage deep learning model to produce clean data. The third method involves developing multi-stage deep learning algorithms to generate synthetic medical image data, which can be used to overcome the lack of large, diverse datasets. These methods demonstrate that building enhanced training datasets can play a vital role in improving the performance of deep-learning models in medical imaging applications

Predicting Forex Currency Fluctuations Using a Novel Bio-inspired Modular Neural Network

This thesis explores the intricate interplay of rational choice theory (RCT), brain modularity, and artificial neural networks (ANNs) for modelling and forecasting hourly rate fluctuations in the foreign exchange (Forex) market. While RCT traditionally models human decision-making by emphasising self-interest and rational choices, this study extends its scope to encompass emotions, recognising their significant impact on investor decisions. Recent advances in neuro- science, particularly in understanding the cognitive and emotional processes associated with decision-making, have inspired computational methods to emulate these processes. ANNs, in particular, have shown promise in simulating neuroscience findings and translating them into effective models for financial market dynamics. However, their monolithic architectures of ANNs, characterised by fixed struc- tures, pose challenges in adaptability and flexibility when faced with data perturbations, limiting overall performance. To address these limitations, this thesis proposes a Modular Convolutional orthogonal Recurrent Neural Net- work with Monte Carlo dropout-ANN (MCoRNNMCD-ANN) inspired by recent neuroscience findings. A comprehensive literature review contextualises the challenges associated with monolithic architectures, leading to the identification of neural network structures that could enhance predictions of Forex price fluctuations, such as in the most prominently traded currencies, the EUR/GBP pairing. The proposed MCoRNNMCD-ANN is thoroughly evaluated through a detailed comparative analysis against state-of-the-art techniques, such as BiCuDNNL- STM, CNN–LSTM, LSTM–GRU, CLSTM, and ensemble modelling and single- monolithic CNN and RNN models. Results indicate that the MCoRNNMCD- ANN outperforms competitors. For instance, reducing prediction errors in test sets from 19.70% to an impressive 195.51%, measured by objective evaluation metrics like a mean square error. This innovative neurobiologically-inspired model not only capitalises on modularity but also integrates partial transfer learning to improve forecasting ac- curacy in anticipating Forex price fluctuations when less data occurs in the EUR/USD currency pair. The proposed bio-inspired modular approach, incorporating transfer learning in a similar task, brings advantages such as robust forecasts and enhanced generalisation performance, especially valuable in domains where prior knowledge guides modular learning processes. The proposed model presents a promising avenue for advancing predictive modelling in Forex predictions by incorporating transfer learning principles