Stock Market Prediction Model Research Articles

Stock Market Prediction Model Based on Deep Learning and Enhancement of Interpretabilit

Stock Market Prediction Model Based on Deep Learning and Enhancement of Interpretabilit

Galformer: a transformer with generative decoding and a hybrid loss function for multi-step stock market index prediction

The prediction of stock market fluctuations is crucial for decision-making in various financial fields. Deep learning algorithms have demonstrated outstanding performance in stock market index prediction. Recent research has also highlighted the potential of the Transformer model in enhancing prediction accuracy. However, the Transformer faces challenges in multi-step stock market forecasting, including limitations in inference speed for long sequence prediction and the inadequacy of traditional loss functions to capture the characteristics of noisy, nonlinear stock history data. To address these issues, we introduce an innovative transformer-based model with generative decoding and a hybrid loss function, named “Galformer,” tailored for the multi-step prediction of stock market indices. Galformer possesses two distinctive characteristics: (1) a novel generative style decoder that predicts long time-series sequences in a single forward operation, significantly boosting the speed of predicting long sequences; (2) a novel loss function that combines quantitative error and trend accuracy of the predicted results, providing feedback and optimizing the transformer-based model. Experimental results on four typical stock market indices, namely the CSI 300 Index, S&P 500 Index, Dow Jones Industrial Average Index (DJI), and Nasdaq Composite Index (IXIC), affirm that Galformer outperforms other classical methods, effectively optimizing the Transformer model for stock market prediction.

A deep fusion model for stock market prediction with news headlines and time series data

A deep fusion model for stock market prediction with news headlines and time series data

Advancing Stock Market Predictions with Time Series Analysis including LSTM and ARIMA

Predicting stock market prices accurately is a major task for investors and traders seeking to optimize their decision-making processes. This research focuses on the comparative analysis of advanced machine learning (ML) techniques, particularly, the Long Short-Term Memory (LSTM) model and Autoregressive Integrated Moving Average (ARIMA) model for predicting stock market prices. The study enforces thorough data collection and preprocessing to ensure the quality and reliability of the historical stock price data, forming a robust foundation for the predictive models. The core contribution of this paper lies in its systematic and comparative analysis of these two models. A range of performance metrics, including Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), are employed to assess and contrast the predictive accuracy and efficiency of the LSTM and ARIMA models. The research findings indicate that the ARIMA model, contrary to expectations, outperforms the LSTM model in this study, achieving lower RMSE and MAE values. Specifically, the ARIMA model demonstrates a Test RMSE of 4.336 and a Test MAE of 3.45926, indicating its superior predictive accuracy compared to the LSTM model. Furthermore, the study sets its findings against the backdrop of existing literature by comparing the performance of its models with those reported in previous research. This comparison shows better results achieved by our stock market prediction models. By addressing limitations observed in prior studies and demonstrating practical applicability, this research contributes to advancing stock market prediction methodologies, offering valuable insights for investors and traders.

Research on the application of deep learning techniques in stock market prediction and investment decision-making in financial management

Introduction: This paper introduces a deep learning approach based on Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory Networks (BiLSTM), and attention mechanism for stock market prediction and investment decision making in financial management. These methods leverage the advantages of deep learning to capture complex patterns and dependencies in financial time series data. Stock market prediction and investment decision-making have always been important issues in financial management.Methods: Traditional statistical models often struggle to handle nonlinear relationships and complex temporal dependencies, thus necessitating the use of deep learning methods to improve prediction accuracy and decision effectiveness. This paper adopts a hybrid deep learning model incorporating CNN, BiLSTM, and attention mechanism. CNN can extract meaningful features from historical price or trading volume data, while BiLSTM can capture dependencies between past and future sequences. The attention mechanism allows the model to focus on the most relevant parts of the data. These methods are integrated to create a comprehensive stock market prediction model. We validate the effectiveness of the proposed methods through experiments on real stock market data. Compared to traditional models, the deep learning model utilizing CNN, BiLSTM, and attention mechanism demonstrates superior performance in stock market prediction and investment decision-making.Results and Discussion: Through ablation experiments on the dataset, our deep learning model achieves the best performance across all metrics. For example, the Mean Absolute Error (MAE) is 15.20, the Mean Absolute Percentage Error (MAPE) is 4.12%, the Root Mean Square Error (RMSE) is 2.13, and the Mean Squared Error (MSE) is 4.56. This indicates that these methods can predict stock market trends and price fluctuations more accurately, providing financial managers with more reliable decision guidance. This research holds significant implications for the field of financial management. It offers investors and financial institutions an innovative approach to better understand and predict stock market behavior, enabling them to make wiser investment decisions.

Forecasting Model of Sustainable Financial Market Trend Based on Neural Network Algorithm

Recent advances in computer technology have resulted in the ongoing gathering of massive amounts of data and information. Because the financial market creates so much real-time data, including transaction records, we have a great potential to get important insights from analysing that data, especially in the banking industry. As a result, the goal of this work is to use the financial data that is now accessible to create a unique stock market prediction model. We employ the deep learning approach because of its exceptional capacity to learn from large datasets. This study proposes a hybrid approach that integrates the Archimedes optimisation algorithm (AOA) with a long short-term memory (LSTM) network. So far, heuristic-based trial and error has been extensively used to estimate the temporal window size and architectural components of long short-term memory networks. This paper investigates the temporal properties of financial market data by providing a systematic way to selecting the topology and time window size for the LSTM network. The experimental results demonstrate that the hybrid LSTM network and AOA model outperform the benchmark model.

Analysis of ARIMA, LightGBM, XGBoost, and LSTM models for stock prediction

As a matter of fact, stock market prediction has always been a popular problem. Many investors and scholars think that its impossible because they believe its random and doesnt have any patterns. However, many studies have found that long term prediction is possible, and that the existence of patterns in stocks makes it able to be predicted. Because of the possibility of predicting the stock market, many studies and investors have thought of new methodologies, ranging from statistical, economical, and others, employing techniques from a variety of practices. One methodology that has recently gained momentum is machine learning, which shows great promise and improvement. This study looks at four prevalent stock market prediction models, which include ARIMA, LightGBM, XGBoost, and LSTMs, explains some research done with them, the problems they have, and future improvements. It finally briefly discusses other methods researchers have used to predict the stock market that werent explained in the paper.

STOCK PRICE PREDICTION OF NIFTY 50

In this project a forecasting system is presented which can predict the change in stock market value of Nifty Fifty index using the historical data of various factors influencing the stock value. Traditional stock market prediction models only factor in the trends of the historical data of the targeted stock value which is then fed to a time series algorithm to get a forecast. Since the real-world stock market is constantly being influenced by various factors, it is not possible to take all those factors into account and make the forecast, let alone using the target stock by itself. So, the objective of this project is to analyse and use the trends in the factors that majorly influence Nifty Fifty index like international stock indices and commodities that affect the economy addon Recurrent Neural Networks (RNN) which has the architecture that can retain the past information and use it in the prediction along with the observations at the current time step. This will allow the predictions to be more accurate and make it possible to make a better decision when investing in the Nifty Fifty index.

Deep Learning-Based Stock Market Prediction and Investment Model for Financial Management

This study explores the potential application of deep learning techniques in stock market prediction and investment decision-making. The authors used multi-temporary stock data (MTS) for effective multi-scale feature extraction in reverse cross attention (RCA), combined with improved whale optimization algorithm (IWOA) to select the optimal parameters for the bidirectional long short-term memory network (BiLSTM) and constructed an innovative RCA-BiLSTM stock intelligent trend prediction model. At the same time, a complete RCA-BiLSTM-DQN stock intelligent prediction and investment model was established by combining the deep Q network (DQN) investment strategy. The research results indicate that the model has excellent sequence modeling and decision learning capabilities, which can capture the nonlinear characteristics and complex correlations of the market and provide more accurate prediction results. It can continuously improve the robustness and stability of the model through adaptive learning and automatic optimization.

Stock Market Prediction Model using LSTM

Abstract: The stock market is a complex and dynamic financial system that constantly undergoes fluctuations driven by various economic, political, and social factors. Accurate prediction of stock prices is a challenging yet essential task for investors, traders, and financial analysts. The objective of this project is to design and implement an LSTM-based stock market prediction system that leverages historical market data to forecast future stock prices. The methodology involves the creation of a deep learning model leveraging LSTM layers for temporal dependency modeling, Dense layers for feature extraction, Dropout layers for regularization, and a Sequential architecture for seamless integration. The model is trained on historical stock data, incorporating essential features such as past prices, trading volumes, and market indicators. The model's performance is rigorously evaluated through back testing and out-of-sample testing using historical stock data. The results indicate the model's ability to make predictions that outperform traditional buy-and-hold strategies. This research contributes to the ongoing efforts to harness the power of machine learning and artificial intelligence in predicting stock market trends and demonstrates the potential for improved decision-making in the financial industry. It is hoped that this model will serve as a valuable resource for market participants seeking to navigate the complexities of the stock market.

Financial stock market forecast using evaluated linear regression based machine learning technique

The objective of Stock Market Forecasting (SMF) is to forecast the future value of a company's financial stocks. The availability of Machine Learning (ML), particularly obtains forecasts regarding specific prices of current stock market indexes through training on actual high economic value, constitutes a significant improvement in stock market prediction automation. ML itself utilizes a wide range of models to improve as well as evaluate prediction. Regarding the nonlinearities as well as discontinuities of the factors that are anticipated to effect stock markets, the selection of a modest price of global financial data is frequently acknowledged as a significant basic phase in any stock market prediction model. The fundamental emphasis of the study is the application of Evaluated Linear Regression based Machine Learning (ELR-ML) technique to forecast stock financial values of the Standard and Poor's 500 (s & p 500) index with Open, close, low, high, and volume factors.

A performance comparison of machine learning models for stock market prediction with novel investment strategy.

Stock market forecasting is one of the most challenging problems in today's financial markets. According to the efficient market hypothesis, it is almost impossible to predict the stock market with 100% accuracy. However, Machine Learning (ML) methods can improve stock market predictions to some extent. In this paper, a novel strategy is proposed to improve the prediction efficiency of ML models for financial markets. Nine ML models are used to predict the direction of the stock market. First, these models are trained and validated using the traditional methodology on a historic data captured over a 1-day time frame. Then, the models are trained using the proposed methodology. Following the traditional methodology, Logistic Regression achieved the highest accuracy of 85.51% followed by XG Boost and Random Forest. With the proposed strategy, the Random Forest model achieved the highest accuracy of 91.27% followed by XG Boost, ADA Boost and ANN. In the later part of the paper, it is shown that only classification report is not sufficient to validate the performance of ML model for stock market prediction. A simulation model of the financial market is used in order to evaluate the risk, maximum draw down and returns associate with each ML model. The overall results demonstrated that the proposed strategy not only improves the stock market returns but also reduces the risks associated with each ML model.

A hybrid stock market prediction model based on GNG and reinforcement learning

The stock market is a dynamic, complex, and chaotic environment, which makes predictions for the stock market difficult. Many prediction methods are applied to the stock market, but most are supervised learning and cannot effectively parse the trading information present in the stock market. This paper proposes a prediction model that combines unsupervised learning with reinforcement learning to address this problem. Firstly, we capture the stock trend from historical stock data and construct the trading environment state of the market by the growing neural gas (GNG) algorithm in unsupervised learning. Secondly, the reward function is restructured to provide timely feedback on the trading information present in the stock trading market. Finally, a novel trading agent algorithm, Triple Q-learning, is designed to execute the corresponding trading behavior and make comprehensive predictions of the stock market based on the environment state constructed by GNG. Experimental results on several stock datasets demonstrate that the proposed model outperforms other comparative models in this paper.

A multivariate LSTM-based deep learning model for stock market prediction

Stocks represent ownership in a company and a proportionate claim on its assets and earn-ings. Investors trade stocks via an exchange by buying at a price and selling at a higher price. Due to market volatility forecast it is a necessity for trading to determine the direc-tion of the stock price in order to maximize profit and minimize loss. Traditional methods of stock price predictions include technical and fundamental analysis. The technical deals with historical price movement while fundamental analysis uses the relationship between financial information about the company. However, these predictions methods sometimes fail to yield desired result sometimes due to the influence of factors such as national poli-cies, global and regional economics, psychological, human among many. This work propos-es a prediction model for stock market using LSTM algorithm. Multivariate time series stock price data is obtained from Nigerian Stock Exchange Index to implement the model. The experimental result of the technique is measured using MAPE, MAE, MSE and rRMSE performance metrics. The accuracy of the result shows that the proposed system outper-forms existing traditional and deep learning methods.

Intelligent Techniques for Predicting Stock Market Prices: A Critical Survey

The stock market is an exciting field of interest to many people regardless of their occupational background. It is a market where individuals with adequate knowledge can join and earn an additional income. Nowadays, life expenses have increased. Hence, the number of people investing in stocks is increasing dramatically. Anyone may indeed start participating in the stock market at any time, yet it is not ensured that they will profit from this investment. The stock market is a risky field of investment, given that it is unknown whether the stock will rise or fall. Stock market prediction using Artificial Intelligence techniques is a possible way to help people anticipate stock market directions. Current research showed that many factors aid in changing the stock market value in general and specifically in the Saudi stock market. To our knowledge, most research studies only consider historical data in predicting stock market trends. However, this research aims to enhance the accuracy of the daily closing price for three Saudi stock market sectors by considering historical and sentimental data. Several intelligent algorithms are considered, and their performance indicators are discussed and contrasted against each other. This research concluded that more accurate stock market prediction models could be produced by employing historical and sentimental data.

HM-SMF: An Efficient Strategy Optimization using a Hybrid Machine Learning Model for Stock Market Prediction

Stock market forecasting is a significant task, and investing in the stock marketplace is a significant part of monetary research due to its high risk. Therefore, accurate forecasting of stock market analysis is still a challenge. Due to stable and volatile data, stock market forecasting remains a major challenge for investors. Recent machine learning (ML) models have been able to reduce the risk of stock market forecasting. However, diversity remains a key challenge in developing better erudition models and extracts more intellectually priceless qualities to auxiliary advanced predictability. In this paper, we propose an efficient strategy optimization using a hybrid ML model for stock market prediction (HM-SMP). The first contribution of the proposed HM-SMP model is to introduce chaos-enhanced firefly bowerbird optimization (CEFBO) algorithm for optimal feature selection among multiple features which reduce the data dimensionality. Second, we develop a hybrid multi-objective capuchin with a recurrent neural network (HC-RNN) for the prediction of the stock market which enhances the prediction accuracy. We use supervised RNN to predict the closing price. Finally, to estimate the presence of the proposed HM-SMP model through the benchmark, stock market datasets and the performance can be compared with the existing state-of-the-art models in terms of accuracy, precision, recall, and F-measure.

Machine learning approaches in stock market prediction: A systematic literature review

Predicting the stock market has been done for a long time using traditional methods by analyzing fundamental and technical aspects. With machine learning, stock market predictions are made more accessible and more accurate. Various machine learn- ing approaches have been applied in stock market prediction. This study aims to review relevant works about machine learning approaches in stock market prediction. To achieve this aim, we did a systematic literature review. This study review 30 studies regarding machine learning approaches/models in stock market prediction. Approaches that were used included neural networks and support vector machines. The result of this study is that neural networks are the most used model for stock market prediction. However, this does not mean that other models cannot be used for predicting the stock market.

Metaheuristic enabled intelligent model for stock market prediction via integrating volatility spillover: India and its Asian and European counterparts

Recently, the price of a stock market changes often owing to a variety of factors. As a result, making an accurate stock price prediction is a difficult process. Hence, this research work proposes a novel intellectual stock market prediction model that incorporates the volatility spillover over Indian and its Asian countries. This intellectual model mainly involves two phases like data library construction and stock market prediction. For stock market prediction, a Neural Network (NN) model is employed and this model intake the data of calculated indicators in the data library and makes the prediction of Indian market. To attain more precise prediction, the NN weight is optimally chosen via novel hybrid algorithm namely Fly Updated Whale Optimization Algorithm (FU-WOA) that is the hybridization of WOA and Firefly Algorithm (FF). At last, the suggested model performance is exploited by comparing other conventional models in the view of various metrics. Especially, the computational cost of the proposed hybrid FU-WOA–NN model is 38.12%, 15.96%, 15.52%, 41.22%, 16.07% and 16.33% better than existing LM-NN, FF-NN, GWO-NN, WOA-NN, PCA as well as ARIMA methods respectively.

A cooperative deep learning model for stock market prediction using deep autoencoder and sentiment analysis.

Stock market prediction is a challenging and complex problem that has received the attention of researchers due to the high returns resulting from an improved prediction. Even though machine learning models are popular in this domain dynamic and the volatile nature of the stock markets limits the accuracy of stock prediction. Studies show that incorporating news sentiment in stock market predictions enhances performance compared to models using stock features alone. There is a need to develop an architecture that facilitates noise removal from stock data, captures market sentiments, and ensures prediction to a reasonable degree of accuracy. The proposed cooperative deep-learning architecture comprises a deep autoencoder, lexicon-based software for sentiment analysis of news headlines, and LSTM/GRU layers for prediction. The autoencoder is used to denoise the historical stock data, and the denoised data is transferred into the deep learning model along with news sentiments. The stock data is concatenated with the sentiment score and is fed to the LSTM/GRU model for output prediction. The model's performance is evaluated using the standard measures used in the literature. The results show that the combined model using deep autoencoder with news sentiments performs better than the standalone LSTM/GRU models. The performance of our model also compares favorably with state-of-the-art models in the literature.

Design and Modeling of Stock Market Forecasting Using Hybrid Optimization Techniques

In this paper, an artificial neural network-based stock market prediction model was developed. Today, a lot of individuals are making predictions about the direction of the bond, currency, equity, and stock markets. Forecasting fluctuations in stock market values is quite difficult for businesspeople and industries. Forecasting future value changes on the stock markets is exceedingly difficult since there are so many different economic, political, and psychological factors at play. Stock market forecasting is also a difficult endeavour since it depends on so many various known and unknown variables. There are several ways used to try to anticipate the share price, including technical analysis, fundamental analysis, time series analysis, and statistical analysis; however, none of these approaches has been shown to be a consistently reliable prediction tool. We built three alternative Adaptive Neuro-Fuzzy Inference System (ANFIS) models to compare the outcomes. The average of the tuned models is used to create an ensemble model. Although comparable applications have been attempted in the literature, the data set is extremely difficult to work with because it only contains sharp peaks and falls with no seasonality. In this study, fuzzy c-means clustering, subtractive clustering, and grid partitioning are all used. The experiments we ran were designed to assess the effectiveness of various construction techniques used to our ANFIS models. When evaluating the outcomes, the metrics of R-squared and mean standard error are mostly taken into consideration. In the experiments, R-squared values of over.90 are attained.