Stock Market Price Prediction Research Articles

Stock Market and Cryptocurrency Price Prediction

Stock Market and Cryptocurrency Price Prediction

Stock Market Price Prediction and Forecasting Using Stacked LSTM

Stock price movement is non-linear and complex. Several research works have been carried out to predict stock prices. Traditional approaches such as Linear Regression and Support Vector Regression were used but accuracy was not adequate. Researchers have tried to improve stock price prediction using ARIMA. Due to very high variations in stock prices, deep learning techniques are applied due to its proven accuracy in various analytics fields. Artificial Neural Network was deployed to predict stock prices but as stock prices are time-series based, recurrent neural network was applied to further improve prediction accuracy. Therefore, data scientists and analysts found that Deep Learning outperformed Machine Learning which is also proofed by all the collected research papers, and it is the most suitable methodologies to apply to the stock market forecasting domain. This paper explores different stacked LSTM Models for non-stationary financial time series in stock price prediction. This study is to predict stock market prices to make more acquaint and precise investment decisions. The experimental result will show that, this method can get quite accurate result, particularly effective in stock prediction. The proposed LSTM model will be designed to overcome gradient explosion, gradient vanishing, and save long-term memory. Firstly, The Model will have a comparative analysis between different LSTM models and integrating the model based on the result obtained from the analysis. The results will suggest that the developed stacked LSTM produces better predictive power and generalization. Key Words: stock market prediction, stacked LSTM, Deep Learning, Data pre-processing techniques, Data normalization, Neural Networks, Data Visualization, Training and Testing Set, Time series analysis, Predictive models, Future Forecasting.

Predicting the Direction of Stock Markets Employing Back Propagation in Neural Networks

Stock Market prediction is a category of time series prediction which extremely challenging due to the dependence of stock prices on several financial, socio-economic and political parameters etc. Moreover, small inaccuracies in stock market price predictions may result in huge losses to firms which use stock market price prediction results for financial analysis and investments. Off late, artificial intelligence and machine learning based techniques are being used widely for stock market prediction due to relatively higher accuracy compared to conventional statistical techniques. The proposed work employs the steepest descent based scaled conjugate gradient (SCG) algorithm along with the data pre-processing using the discrete wavelet transform (DWT) for stock market prediction. It has been shown that the proposed system attains lesser mean square percentage error compared to previously existing technique. Keywords—Stock Market Forecasting, Artificial Neural Network (ANN), Back Propagation, Scaled Conjugate Gradient (SCG), Discrete Wavelet Transform (DWT), Mean Absolute Percentage Error (MAPE).

An Analytic Review on Stock Market Price Prediction using Machine Learning and Deep Learning Techniques

: Anticipating stock market trends is a challenging endeavor that requires a lot of attention because correctly predicting stock prices can lead to significant rewards if the right judgments are made. Due to non-stationary, loud, and chaotic data, stock market prediction is challenging. Investors need help to forecast where they should spend their money to make a profit. Investment methods in the stock market are intricate and based on the analysis of large datasets. Expert analysts and investors have placed a high value on developments in stock price prediction. Due to intrinsically noisy settings and increased volatility concerning market trends, the stock market forecast for assessing trends is tricky. The intricacies of stock prices are influenced by several elements, including quarterly earnings releases, market news, and other altering habits. Traders use a number of technical indicators based on stocks that are collected on a daily basis to make decisions. Even though these indicators are used to analyze stock returns, predicting daily, and weekly market patterns are difficult. Machine learning techniques have been extensively studied in recent years to see if they might boost market predictions compared to legacy or conventional methods. The existing methodologies have devised several strategies for predicting stock market trends. Various machine learning and deep learning algorithms, such as SVM, DT, LR, NN, kNN, ANN, and CNN, can boost performance in predicting the stock market. Based on a survey of current literature, this work aims to identify future directions for machine learning stock market prediction research. This research aims to provide a systematic literature review process to discover relevant peer-reviewed journal papers from the last two decades and classify studies with similar methods and situations into the machine learning approach and deep learning. In the current article, the methods and the performance of those adopted methods will be identified for measuring the effectiveness of those techniques.

Stock Market Price Prediction

Abstract: Investing in the stock market can be a convoluted and refined method of conducting business. Stock prediction is an extremely difficult and complex endeavor since stock values can fluctuate abruptly owing to a variety of reasons, making the stock market incredibly unpredictable.This paper explores predictive models for the stock market, aiming to forecast stock prices using machine learning algorithms. By analyzing historical market data and employing various predictive techniques, thestudy aims to enhance accuracy in predicting future stock movements. this paper contributes understanding into the potential of LSTM models for enhancing stock market prediction accuracy and reliability.

Comparative Analysis of LSTM, GRU, and ARIMA Models for Stock Market Price Prediction

This study delves into the efficacy of various machine learning and statistical models that have captured the attention of financial analysts. Two of them, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are variations of Recurrent Neural Networks while the Autoregressive Integrated Moving Average (ARIMA) is a statistical model. These models will be used to forecast stock market data across different economic sectors. In the dynamic landscape of financial markets, accurate forecasting is crucial. This research paper contributes to quantitative finance by conducting a comprehensive comparative analysis of these models on historical stock market data from three sectors: extraction, manufacturing, and service (which are considered the primary, secondary, and tertiary sectors of the economy respectively). The models' performances are evaluated using mean squared error (MSE) on six selected stocks representing these sectors. Results reveal the power of recurrent neural networks in capturing intricate patterns. Moreover, the results will explore whether or not the efficacy of each model is impacted by the sector of the economy that it is forecasting data for.

Blockchain-Based Secure Stock Market Price Prediction Using Next Generation Optimized LSTM Model

Stock forecasting has long drawn people’s attention because the stock market is a crucial source of financing for publicly traded corporations and a sizable investment market. To fully use the evidence from dissimilar typical prices and recover the stock forecasting effect, a Blockchain-based secure stock value forecasting model TL-EMD-LSTM-MA (TELM) is projected. Other methods are selected for prediction according to the oscillation frequencies of the details, and the high-frequency components use the depth of the transfer learning method to train the stacked LSTM. Deep transferable learning-trained stacked LSTMs incorporate data from several equities and have a deeper understanding of the marketplace or commerce, which can significantly lower forecasting mistakes. Furthermore, it is possible to more accurately estimate the low-frequency components and the overall trend of the stock by employing the average movement approach. 500 stocks in the stock market are shown, as well as indices such as the Stock Exchange Index and Stock Exchange Component Index, the outcomes demonstrate that compared with other models, TELM has the least prediction error and the highest goodness of fit. Finally, simulate the stock trading process based on the stock closing price predicted by TELM, and the results show that TELM investment has low risk and high returns.

Stock Market Price Prediction Using Machine Learning and Deep Learning

This project aims to address the long-standing challenge of stock market prediction by leveraging the power of machine learning. The stock market is influenced by a multitude of factors, making it difficult for investors to accurately predict trends and make informed decisions. To mitigate this risk, we have chosen to analyse NSE real-time series data using the ARIMA model, a popular forecasting method. Our ultimate goal is to develop a user-friendly web application using Flask, which will allow investors to select a specific firm and receive a forecast for its future stock price. This application will utilize web scraping techniques to retrieve real-time data from the NSE, and the ARIMA model will be applied to predict future outcomes. By combining these powerful tools, we hope to significantly reduce the potential danger associated with stock market prediction and enable investors to make more informed decisions.

Stock Market Price Prediction

Abstract: In stock request valuation, the end is to prognosticate the unborn value of the fiscal stocks of a company. Machine learning itself employs different models to make vaccinating easier and more authentic. The paper focuses on the use of retrogression and LSTM-grounded machine literacy to prognosticate stock values. stock request valuation, the end is to prognosticate the unborn value of the fiscal stocks of a company. Machine learning itself employs different models to make vaticination easier and further authentic. The paper focuses on the use of retrogression and LSTM-grounded machine literacv accinatingsticate stock mores

Stock Market Price Prediction by LSTM & Linear Regression Algorithm using Machine Learning

In this project analyse existing and new methods of stock market prediction. Take three different approaches at the problem: Fundamental analysis,

Stock Price Prediction using Bat Algorithm

There is no such thing as a safe path of investment in the stock market because it is highly unpredictable, which has been a major concern of investors globally. As a result, stock market or stock price prediction has been a hot topic for scholars and researchers and a popular topic for investors worldwide.

Forecasting oil, coal, and natural gas prices in the pre-and post-COVID scenarios: Contextual evidence from India using time series forecasting tools

Stock market price prediction is considered a critically important issue for designing future investments and consumption plans. Besides, given the fact that the COVID-19 pandemic has adversely impacted stock markets worldwide, especially over the past two years, investment decisions have become more challenging for risky. Hence, we propose a two-phase framework for forecasting prices of oil, coal, and natural gas in India, both for pre-and post-COVID-19 scenarios. Notably, the Autoregressive Integrated Moving Average, Simple Exponential Smoothing, and K- Nearest Neighbor approaches are utilized for analyses using data from January 2020 to May 2022. Besides, the various outcomes from the analytical exercises are matched with root mean squared error and mean absolute and percentage errors. Overall, the empirical outcomes show that the Autoregressive Integrated Moving Average method is appropriate for predicting India's oil, coal, and natural gas prices. Moreover, the predictive precision of oil, coal, and natural gas in the pre-COVID-19 period seems to be better than in that the post-COVID-19 stage. Additionally, prices of these energy resources are forecasted to increase through the year 2025. Finally, in line with the findings, significant policy recommendations are made.

ANN based modeling for stock market prediction

Analyzing stock market data and using recent developed algorithms for predicting the changes and forecasting the results is a difficult task nowaday. An efficient and faster method for selecting appropriate stock price improves market value as well as helps investors for benefits. In this paper new method implementing state of the art Artificial Neural Networks (ANNs) known as Legendre Neural Network (LENN) and Chebyshev Functional Link based Artificial Neural Network (CHFLANN) has been described and used for the analysis of stock market data. The process has been efficiently implemented in MATLAB language for representing results by evaluating accuracy and F-measure for the processed data. Prior to it the database collected is passed through a series of processes starting from normalization, expansion and then dividing it into train and test datasets to get the processed data used for implementing the learning algorithm for the stock market price evaluation and prediction purpose. In the results second order expansion systems have been used showing accuracy results as well as Mean Square Error (MSE) rates to find best method among proposed schemes. Also, our proposed models have been compared with the statistical model AutoRegressive Integrated Moving Average (ARIMA) and the basic Functional Link based Artificial Neural Network (FLANN) to analyze their performance based on the metrics like Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and R-Squared (R2).

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.

Stock market price prediction model based on grey prediction and ARIMA

Nowadays more and more people like to invest in volatile assets, and it is the goal of every market trader to maximize the total return by developing a reasonable investment strategy. We first predicted the daily value of gold and bitcoin for five years based on known data, we built two models, one is Improved Metabolic Gray Model (Abbreviated as IGM), the other is Time Series Model ARIMA. The application of the model helps investors make investment decisions and improve economic returns.

Algorithmic Trading Stock Price Model

Abstract: Stock market price prediction is a difficult undertaking that generally requires a lot of human-computer interaction. Traditional batch processing methods cannot be used effectively for stock market analysis due to the linked nature of stock prices. This project presents an online learning technique that employs a recurrent neural network of some sort (RNN) called Long Short-Term Memory (LSTM), which uses stochastic gradient descent to update the weights for individual data points. When compared to existing stock price prediction systems, this will yield more accurate results. With varying sizes of data, the network is trained and evaluated for accuracy, and the results are tallied. A comparison with respect to accuracy is then performed against an Artificial Neural Network. Traditional approaches to securities market analysis and stock value prediction embrace basic analysis, that appearance at a stock's past performance and therefore the general believability of the corporate itself, and applied mathematics analysis, that is exclusively involved with computation and distinguishing patterns available value variation.

Efficient Prediction of Stock Price Using Artificial Neural Network Optimized Using Biogeography-Based Optimization Algorithm

Stock market price prediction has always draws more attention from researchers and analysts. Prediction of stock price is extremely tough task due to the nature of stock data. Therefore, it is needed to develop an efficient model for predicting stock price. This paper explored the use of Feed Forward Neural Network (FFNN) and bio inspired algorithms to develop two efficient models for prediction. The proposed model is based on the ten indicators derived from historical data. Particle Swarm Optimization (PSO) algorithm which inspired from the behavior of bird flocking and Biogeography Based Optimization (BBO) algorithm driven by the geographical distribution of biological organisms is adopted to optimize the parameters of FFNN. Prediction ability of the proposed models is evaluated by using statistical measures. The experimental results demonstrate that the proposed BBO-FFNN is superior to PSO-FFNN and existing methods taken for comparison in terms of prediction accuracy. It is proved that the proposed BBO-FFNN can effectively enhance stock prediction and reduce the prediction error.

Machine learning techniques and methodology analysis for stock market price prediction

Prediction of market movements is a very emerging topic in research nowadays. The one handsome efficient hypothesis says that accurate stock prediction is impossible but, in another hand, if appropriate techniques and algorithms can be used then it can be predicted with high accuracy. Moreover, predicting profitable and accurate value is a challenging task for investors because price prediction is volatile in nature. It could be affected by various factors like the global economy, politics, disasters, etc. A lot of literature survey on technical analysis is available which identify the movement patterns of the market. Various hybrid machine learning approaches in combination have been used to forecast the beginning values of the market trends and also for the future long- term values.This paper gives a comparative assessment of 40 research papers that recommend strategies such as calculating methodologies, stock prediction algorithms, Datasets, results, and more which leads to improved accuracy and lowers the error percentage of stock market prediction.

Stock Price Prediction Using Long Short Term Memory

Stock market price prediction is difficult and complex task. Prediction in stock market is very complex and unstable Process. Stock Price are most of the time tend to follow patterns those are more or less regular in stock price curve. Machine Learning techniques use different predictive models and algorithms to predict and automate things to reduce human effort. This research paper focuses on the use of Long Short Term Memory (LSTM) to predict the future stock market company price of stock using each day closing price analysis. LSTM is very helpful in sequential data models. In this paper LSTM algorithm has been used to train and forecast the future stock prices.

A Survey on Stock Market Price Prediction System using Machine Learning Techniques

Abstract: Prediction of stock prices is one of the most researched topics and gathers interest from academia and the industry alike. In the finance world stock trading is one of the most important activities. Stock market prediction is an act of trying to determine the future value of a stock other financial instrument traded on a financial exchange. This paper explains the prediction of a stock using Machine Learning. The technical and fundamental or the time series analysis is used by the most of the stockbrokers while making the stock predictions. The programming language is used to predict the stock market using machine learning is Python. In this paper we propose a Machine Learning (ML) approach that will be trained from the available stocks data and gain intelligence and then uses the acquired knowledge for an accurate prediction. The paper focuses on the use of Linear Regression, Moving Average, K-Nearest Neighbours, Auto ARIMA, Prophet, and LSTM based Machine learning techniques to predict stock values. Factors considered are open, close, low, high and volume. The models are evaluated using standard strategic indicators: RMSE and MAPE. The low values of these two indicators show that the models are efficient in predicting stock closing price. 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 price prediction. This work contributes to the stock analysis research community both in the financial and technical domains. Keywords: Stock Market, Machine Learning, Prediction, LSTM, Python, Analysis, Linear Regression, Feature Engineering.