Predicting Stock Market Price Research Articles

CAGTRADE: Predicting Stock Market Price Movement with a CNN-Attention-GRU Model

CAGTRADE: Predicting Stock Market Price Movement with a CNN-Attention-GRU Model

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.

Stock Price Prediction with Denoising Autoencoder and Transformers

Predicting stock market price movement has been a challenging problem for time series forecasting due to its inherent volatility. The introduction of machine learning techniques, such as the use of a recurrent neural network (RNN), have since dominated the field for stock trend forecasting and stock price prediction. RNNs have inherent model limitations that are solved with the introduction of the transformer model, which has since been used in many sequential classification and generative tasks. This study demonstrates the viability of a transformer-based model on the field of stock price prediction by comparing the predictions of a transformer-based model with some RNN-based baseline models on the hourly closing price of Apple (AAPL) stock prices. A dimension reducing simple autoencoder was also incorporated into the transformer model to increase performance. Although RNN-based models are shown to outperform the transformer model in accuracy, the transformer model leads in directional stock accuracy. This demonstrates that the transformer is applicable to compete with RNN based models within the field, along with the incorporation of an autoencoder to further integrate the transformer’s power to this field.

STOCK MARKET PRICE PREDICTION USING MACHINE LEARNING TECHNIQUES

Predicting stock market prices is a challenging task in the financial sector, where the Efficient Market Hypothesis (EMH) posits the impossibility of accurate prediction due to the inherent uncertainty and complexity of stock price behaviour. However, introducing Machine Learning algorithms has shown the feasibility of stock market price forecasting. This study employs advanced Machine Learning models that can predict stock price movements with the right level of accuracy if the correct parameter tuning and appropriate predictor models are developed. In this research work, the LSTM model, which is a type of Recurrent Neural Network (RNN), time series forecasting Facebook Prophet algorithm and Random Forest Regressor model have been implemented on 10 Dhaka Stock Market (DSEbd) listed companies and six international giants for predicting the stock and forecasting the future price. The dataset of domestic companies is extracted from the graphical representation of the DSEbd website, and the international companies' dataset is imported from Yahoo Finance. In this experiment, Facebook Prophet demonstrates a long period of forecasting with reasonable accuracy, capturing daily, weekly, and yearly seasonality, including holiday effects for market trend analysis. Remarkably, the LSTM model exhibits significant accuracy, yielding the best results with evaluation metrics, including RMSE (0.35), MAPE (0.50%), and MAE (0.30). The experimental results underscore the efficiency of LSTM for future stock forecasting, observed over 15 days of upcoming market prices. A comparison of the results shows that the LSTM model efficiently forecasts the next day's closing price.

SMP-DL: a novel stock market prediction approach based on deep learning for effective trend forecasting

As the economy has grown rapidly in recent years, more and more people have begun putting their money into the stock market. Thus, predicting trends in the stock market is regarded as a crucial endeavor, and one that has proven to be more fruitful than others. Profitable investments will result in rising stock prices. Investors face significant difficulties making stock market-related predictions due to the lack of movement and noise in the data. In this paper, a new system for predicting stock market prices is introduced, namely stock market prediction based on deep leaning (SMP-DL). SMP-DL splits into two stages, which are (i) data preprocessing (DP) and (ii) stock price’s prediction (SP2). In the first stage, data are preprocessed to obtain cleaned ones through several stages which are detect and reject missing value, feature selection, and data normalization. Then, in the second stage (e.g., SP2), the cleaned data will pass through the used predicted model. In SP2, long short-term memory (LSTM) combined with bidirectional gated recurrent unit (BiGRU) to predict the closing price of stock market. The obtained results showed that the proposed system perform well when compared to other existing methods. As RMSE, MSE, MAE, and R2 values are 0.2883, 0.0831, 0.2099, and 0.9948. Moreover, the proposed method was applied using different datasets and it performs well.

Predicting Saudi Stock Market Index by Using Multivariate Time Series Based on Deep Learning

Time-series (TS) predictions use historical data to forecast future values. Various industries, including stock market trading, power load forecasting, medical monitoring, and intrusion detection, frequently rely on this method. The prediction of stock-market prices is significantly influenced by multiple variables, such as the performance of other markets and the economic situation of a country. This study focuses on predicting the indices of the stock market of the Kingdom of Saudi Arabia (KSA) using various variables, including opening, lowest, highest, and closing prices. Successfully achieving investment goals depends on selecting the right stocks to buy, sell, or hold. The output of this project is the projected closing prices over the next seven days, which aids investors in making informed decisions. Exponential smoothing (ES) was employed in this study to eliminate noise from the input data. This study utilized exponential smoothing (ES) to eliminate noise from data obtained from the Saudi Stock Exchange, also known as Tadawul. Subsequently, a sliding-window method with five steps was applied to transform the task of time series forecasting into a supervised learning problem. Finally, a multivariate long short-term memory (LSTM) deep-learning (DL) algorithm was employed to predict stock market prices. The proposed multivariate LSTMDL model achieved prediction rates of 97.49% and 92.19% for the univariate model, demonstrating its effectiveness in stock market price forecasting. These results also highlight the accuracy of DL and the utilization of multiple information sources in stock-market prediction.

Exchange Stock Price Prediction using Time Series data: A Survey

Stocks, which are ownership shares in the company, can be issued and traded on the platform known as a stock exchange by publicly traded companies. A centralized marketplace is provided by a stock exchange where stocks can be traded by buyers and sellers coming together, thereby ensuring liquidity and price transparency. Large studies have been conducted for a long time in regions such as foreign exchange, stock prices, and weather reviews where time series forecasting information has been comprised. In our research work, we will use time series data because time series allows us to compare what elements have an effect on certain variables from time to time. The primary goal of this review paper is to provide a comprehensive overview of the research conducted by various scholars on predicting stock market prices using time series data. Evaluating time series data can provide valuable insights into the changes in a particular asset, security, or financial variable over time. Forecasting techniques using time series analysis are employed in both fundamental and technical analysis.

RETRACTED: Machine learning-based probabilistic profitable model in algorithmic trading

Machine learning models are nowadays becoming ubiquitous in algorithmic trading and investment management. These models are mostly used in the pre-trade analysis phase to determine the buy or sell decisions using various machine learning techniques. We aim to implement a machine learning-driven approach using various technical indicators to predict stock market prices and then accordingly make a decision about buying or selling. First, an effective trading strategy is discussed that selects the potentially profitable stocks, and then the technical indicators such as simple moving average (SMA), exponential moving average (EMA), relative strength index (RSI), and moving average convergence divergence (MACD) are calculated for those potentially profitable stocks. Then supervised machine learning algorithms such as multiple linear regression, support vector machine regression, and decision tree regression are applied, where the close price of the stock is predicted using technical indicators for the next day, and based on that buy or sell signals are generated. The model is then tested on 12 different SNP500 stocks, one for every month in 2018, with the mean squared error (MSE) varying between 30.33 and 48.16 and the root MSE varying between 5.51 and 6.93, where the error is calculated on the difference in the number of days when the stock price actually increases and the predicted number of days for various models.

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.

Stock Selection through Hidden Markov Model: A Case of Pakistan Stock Exchange

To foresee hidden regimes inside the data, Hidden Markov Model is widely used. Many researchers had used various data mining methods to predict stock market prices. This research will describe usage of Hidden Markov Model (HMM) via MATLAB to forecast stock prices for stock’ selection and portfolios development. In this study listed companies of PSX (Pakistan Stock Exchange) are explored specifically KSE-100 Index companies. From January 2012 to June 2022 monthly closing stock prices are used in this study. Many studies based on the historical data have been conducted in developed markets, but we managed the contextual study of the Pakistan Stock Exchange to recognize the stream and variations happening for individual stocks in stock market of Pakistan. Outcomes of the research suggested a strong relationship between stock prices as projected by the Model.

Extending machine learning prediction capabilities by explainable AI in financial time series prediction

Prediction with higher accuracy is vital for stock market prediction. Recently, considerable amount of effort has been poured into employing machine learning (ML) techniques for successfully predicting stock market price direction. No matter how successful the proposed prediction model is, it can be argued that there occur two major drawbacks for further increasing the prediction accuracy. The first one can be referred as the black box nature of ML techniques, in other words inference from the predictions cannot be explained. Furthermore, due to the complex characteristics of the predicted time series, no matter how sophisticated techniques are employed, it would be very difficult to achieve a marginal increase in accuracy that would meaningfully offset the additional computational burden it brings in. For these two reasons, instead of chasing incremental improvements in accuracy, we propose utilizing an “eXplainable Artificial Intelligence” (XAI) approach which can be employed for assessing the reliability of the predictions hence allowing decision maker to abstain from poor decisions which are responsible for declining overall prediction performance. If there would be a measure of how sure the prediction model is on any prediction, the predictions with a relatively higher reliability could be used to make a decision while lower quality decisions could be avoided. In this study, a novel two-stage stacking ensemble model for stock market direction prediction based on ML, empirical mode decomposition (EMD) and XAI is proposed. Our experiments have shown that, proposed prediction model supported with local interpretable model-agnostic explanations (LIME) achieved the highest accuracy of 0.9913 when only the most trusted predictions have been considered on KOSPI dataset and analogous successful results have been obtained from five other major stock market indices.

A Hybrid Framework Using PCA, EMD and LSTM Methods for Stock Market Price Prediction with Sentiment Analysis

The aim of investors is to obtain the maximum return when buying or selling stocks in the market. However, stock price shows non-linearity and non-stationarity and is difficult to accurately predict. To address this issue, a hybrid prediction model was formulated combining principal component analysis (PCA), empirical mode decomposition (EMD) and long short-term memory (LSTM) called PCA-EMD-LSTM to predict one step ahead of the closing price of the stock market in Thailand. In this research, news sentiment analysis was also applied to improve the performance of the proposed framework, based on financial and economic news using FinBERT. Experiments with stock market price in Thailand collected from 2018–2022 were examined and various statistical indicators were used as evaluation criteria. The obtained results showed that the proposed framework yielded the best performance compared to baseline methods for predicting stock market price. In addition, an adoption of news sentiment analysis can help to enhance performance of the original LSTM model.

Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms

The creation of trustworthy models of the equities market enables investors to make better-informed choices. A trading model may lessen the risks that are connected with investing and make it possible for traders to choose companies that offer the highest dividends. However, due to the high degree of correlation between stock prices, analysis of the stock market is made more difficult by batch processing approaches. The prediction of the stock market has entered a technologically advanced era with the advent of technological marvels such as global digitization. For this reason, artificial intelligence models have become very important due to the continuous increase in market capitalization. The novelty of the proposed study is the development of the robustness time series model based on deep leaning for forecasting future values of stock marketing. The primary purpose of this study was to develop an intelligent framework with the capability of predicting the direction in which stock market prices will move based on financial time series as inputs. Among the cutting-edge technologies, artificial intelligence has become the backbone of many different models that predict the direction of markets. In particular, deep learning strategies have been effective at forecasting market behavior. In this article, we propose a framework based on long short-term memory (LSTM) and a hybrid of a convolutional neural network (CNN-LSTM) with LSTM to predict the closing prices of Tesla, Inc. and Apple, Inc. These predictions were made using data collected over the past two years. The mean squared error (MSE), root mean squared error (RMSE), normalization root mean squared error (NRMSE), and Pearson’s correlation (R) measures were used in the computation of the findings of the deep learning stock prediction models. Between the two deep learning models, the CNN-LSTM model scored slightly better (Tesla: R-squared = 98.37%; Apple: R-squared = 99.48%). The CNN-LSTM model showed a superior performance compared with the single deep learning LSTM and existing systems in predicting stock market prices.

AEI-DNET: A Novel DenseNet Model with an Autoencoder for the Stock Market Predictions Using Stock Technical Indicators

Predicting stock market prices is an important and interesting task in academic and financial research. The volatile nature of the stock market means that predicting stock market prices is a challenging task. However, recent advancements in machine learning, especially in deep learning techniques, have made it possible for researchers to use such techniques to predict future stock trends based on historical financial data, social media news, financial news, and stock technical indicators (STIs). This work focused on the prediction of closing stock prices based on using ten years of Yahoo Finance data of ten renowned stocks and STIs by using 1D DenseNet and an autoencoder. The calculated STIs were first used as the input for the autoencoder for dimensionality reduction, resulting in less correlation between the STIs. These STIs, along with the Yahoo finance data, were then fed into the 1D DenseNet. The resultant features obtained from the 1D DenseNet were then used as input for the softmax layer residing inside the 1D DenseNet framework for the prediction of closing stock prices for short-, medium-, and long-term perspectives. Based on the predicted trends of the stock prices, our model presented the user with one of three suggested signals, i.e., buy, sell, or hold. The experimental results showed that the proposed approach outperformed the state-of-the-art techniques by obtaining a minimum MAPE value of 0.41.

Cascade correlation neural network with deterministic weight modification for predicting stock market price

A new weight updating approach is proposed for cascade correlation neural network (CCNN) in this paper. The deterministic weight modification (DWM) algorithm is used to adjust the connection weights of CCNN. The introduced new method can improve the global convergence capability of the conventional CCNN and optimally reduced the system error. The proposed DWM+CCNN prediction algorithm is applied well-known stock market dataset in order to evaluate the robustness and efficiency. The experimental results are confirmed that the proposed DWM+CCNN algorithm is achieves higher performance in terms of convergence rate and the capability of global converges.

Predicting Stock Market Price of Bangladesh: A Comparative Study of Linear Classification Models

Stock price prediction is a popular research domain for its complex data structure and confounding factors. The use of Data science tools enormously increased along with the advancement of data mining and artificial intelligence tools. Classification is a famous machine learning tool with vast potential use in the stock market. However, predicting stock price through a perfect classification model is vital as different stock market data have individual patterns and dependencies as precise information about increasing or decreasing the market can significantly influence selling or buying the shares. The linear model of classification including logistic regression classification (LR), linear discriminant analysis (LDA), partial last-square discriminant analysis (PLS-DA), penalized discriminant analysis (PDA), and nearest Shrunken discriminant analysis are considered in this study to compare according to predict the stock market price of top six banks stock prices of Bangladesh. The existing literature recommends that PLS-DA fit well if data contain a high correlation among the predictors. On the contrary, PDA performs better if there is any multicollinearity problem or chance of overfitting; LDA gave better approximation when data got multivariate normality, and the nearest shrunken method fit well if there is any existence of high dimensionality. Interestingly, this study's data contain all the mentioned characteristics; still, LR gives less misclassification rate or apparent error rate. Thus, this study recommends that one may choose LR among the linear classification model if there is a high correlation, multicollinearity, multivariate normality, and high-dimensionality among predictors.

STOCK PRICE TREND PREDICTION USING SUPPORT VECTOR MACHINE AND CORAL REEF OPTIMIZATION ALGORITHM

Due to non-linearity and non-stationary characteristics of stock market time series data, prior approaches have not been adequate enough for predicting stock market prices. Support vector machines are classifier that have been reported in the literature as having good recognition accuracy and have been applied in the area of predicting financial stock market prices and was found efficient. It is however noted that the performance of the SVM is affected by the values of the hyper-parameters used by the SVM. There is the need to find a way for searching for the best hyper-parameters that optimizes the performance of an SVM model. Coral Reef Optimization (CRO) is one of many nature-inspired algorithms used extensively to solve optimization problems. It is very effective in solving optimization problems because it is able to achieve global optimization. This paper’s contribution is the development of Coral Reef search algorithms for the improvement of the hyper-parameters of the SVM used for stock price trend prediction. The Algorithm is validated using stock data of two banks. The results obtained out-performed un-optimized SVM, and have the same performance as that of SVM optimized with the FireFly optimization algorithm.
 

Short-term stock market price trend prediction using a comprehensive deep learning system

In the era of big data, deep learning for predicting stock market prices and trends has become even more popular than before. We collected 2 years of data from Chinese stock market and proposed a comprehensive customization of feature engineering and deep learning-based model for predicting price trend of stock markets. The proposed solution is comprehensive as it includes pre-processing of the stock market dataset, utilization of multiple feature engineering techniques, combined with a customized deep learning based system for stock market price trend prediction. We conducted comprehensive evaluations on frequently used machine learning models and conclude that our proposed solution outperforms due to the comprehensive feature engineering that we built. The system achieves overall high accuracy for stock market trend prediction. With the detailed design and evaluation of prediction term lengths, feature engineering, and data pre-processing methods, this work contributes to the stock analysis research community both in the financial and technical domains.

An improved deep learning model for predicting stock market price time series

As an important component of the economic market, the stock market has been concerned by many researchers. How to get the trend of the stock market and predict the stock price is a problem that many researchers are studying. In previous works, the prediction methods are mainly focused on statistical models and traditional neural network models which are relatively popular in recent years. Deep learning is not often used in the field of financial time series, but it has a strong learning ability and is suitable for complex time series such as financial time series. In particular, the LSTM network has the function of long-term memory because of its cyclic structure, so it is very suitable for financial time series prediction in theory. In the study, a novel stock closing price forecasting framework is proposed, which has a higher prediction than traditional models. The data processing part, the deep learning predictor part, and the predictor optimization method are the components of this deep hybrid framework. Data processing includes empirical wavelet transform (EWT) based preprocessing and outlier robust extreme learning machine (ORELM) model based post-processing. Long short-term memory (LSTM) network based deep learning network predictor, as the main part of the mixed frame, is jointly optimized by dropout strategy and particle swarm optimization (PSO) algorithm. Each algorithm in the hybrid framework can give full play to its own functions to achieve better prediction accuracy. In order to verify the performance of the model, three challenging datasets are selected for forecasting experiments. Some comparative models are also selected to prove the effectiveness of the proposed framework. Experimental results show that the hybrid framework proposed in the study has the best prediction accuracy and can be applied to stock market monitoring or financial data analysis and research.

Stock Market Prediction Using Machine Learning

The basic tool aimed at increasing the rate of investor’s interest in stock markets is by developing a vibrant application for analyzing and predicting stock market prices. In this report we explain, the development and implementation of a stock market price prediction application using machine learning algorithm. In this report, we try to analyze existing and new methods of stock market prediction. We take three different approaches for solving the problem: Fundamental analysis, Technical Analysis and The application of Machine Learning. We found evidence in support of the weak form of the Efficient Market Hypothesis. We can use Fundamental Analysis and Machine Learning to guide an investor’s decisions. We demonstrate a common flaw in Technical Analysis methodology to show that it produces limited useful information. Based on our findings, algorithmic trading programs are developed and simulated using Quant. During the past few decades, various machine learning techniques have been applied to study the highly theoretical and speculative nature of stock market by capturing and using repetitive patterns. Different companies use different types of analysis tools for forecasting and the main aim is the accuracy, with which they predict which set of stocks would yield the maximum amount of profit.