Stock Market Forecasting Research Articles

Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications

The financial sector has greatly impacted the monetary well-being of consumers, traders, and financial institutions. In the current era, artificial intelligence is redefining the limits of the financial markets based on state-of-the-art machine learning and deep learning algorithms. There is extensive use of these techniques in financial instrument price prediction, market trend analysis, establishing investment opportunities, portfolio optimization, etc. Investors and traders are using machine learning and deep learning models for forecasting financial instrument movements. With the widespread adoption of AI in finance, it is imperative to summarize the recent machine learning and deep learning models, which motivated us to present this comprehensive review of the practical applications of machine learning in the financial industry. This article examines algorithms such as supervised and unsupervised machine learning algorithms, ensemble algorithms, time series analysis algorithms, and deep learning algorithms for stock price prediction and solving classification problems. The contributions of this review article are as follows: (a) it provides a description of machine learning and deep learning models used in the financial sector; (b) it provides a generic framework for stock price prediction and classification; and (c) it implements an ensemble model—“Random Forest + XG-Boost + LSTM”—for forecasting TAINIWALCHM and AGROPHOS stock prices and performs a comparative analysis with popular machine learning and deep learning models.

Forecasting stock market volatility with various geopolitical risks categories: New evidence from machine learning models

This paper investigates how geopolitical risks influence the prediction performance on the US stock market volatility with machine learning models. Further, it compares the predictive performance of individual and combination forecast methods. With SHAP algorithm, it could identify which factor has a great impact and fully extract the information of geopolitical risks in predicting. Empirical results show that military build-ups and escalation of war have great importance on predicting realized volatility among various geopolitical risks. The research further emphasizes the superior performance of machine learning and forecast combination methods, especially SVR method and trimmed mean combination. In addition, by allocating portfolio according to the machine learning-based volatility forecasts, particularly elastic net and random forest, a mean-variance investor can achieve sizeable financial benefits. Our paper provides substantial implications for political risk management and volatility forecasting.

Forecasting Stock Market Indices Using Gated Recurrent Unit (GRU) Based Ensemble Models: LSTM-GRU

A "time sequence analysis" is a particular method for looking at a group of data points gathered over a long period of time. Instead of merely randomly or infrequently, time series analyzers gather information from data points over a predetermined length of time at scheduled times. But this kind of research requires more than just accumulating data over time. Data in time series may be analyzed to illustrate how variables change over time, which makes them different from other types of data. To put it another way, time is a crucial element since it demonstrates how the data changes over the period of the information and the outcomes. It offers a predetermined architecture of data dependencies as well as an extra data source. Time Series forecasting is a crucial field in deep learning because many forecasting issues have a temporal component. A time series is a collection of observations that are made sequentially across time. In this study, we examine distinct machine learning, deep learning and ensemble model algorithms to predict Nike stock price. We are going to use the Nike stock price data from January 2006 to January 2018 and make predictions accordingly. The outcome demonstrates that the hybrid LSTM-GRU model outperformed the other models in terms of performance.

Forecasting Stock Market Volatility via Causal Reasoning

Abstract Studies have shown that Internet financial news has become an important reference for investors in investment behavior. In order to simulate trading experiments that mimic the real stock market, this paper develops a stock volatility prediction model based on causal reasoning. It also gathers and cleans news and stock market data from the Internet, such as opening price, closing price, and change. The findings of the study indicate that the level of stock market volatility can be significantly influenced by online financial news. The proposed model can analyze the effects of news and stock market data in an explainable manner.

DEEP REINFORCEMENT LEARNING ON STOCK DATA

This study proposes using Deep Reinforcement Learning (DRL) for stock trading decisions and prediction. DRL is a machine learning technique that enables agents to learn optimal strategies by interacting with their environment. The proposed model surpasses traditional models and can make informed trading decisions in real-time. The study highlights the feasibility of applying DRL in financial markets and its advantages in strategic decision- making. The model's ability to learn from market dynamics makes it a promising approach for stock market forecasting. Overall, this paper provides valuable insights into the use of DRL for stock trading decisions and prediction, establishing a strong case for its adoption in financial markets. Keywords: reinforcement learning, stock market, deep reinforcement learning.

Multi-Step Vector Output Prediction of Time Series Using EMA LSTM

This research paper proposes a novel method, Exponential Moving Average Long Short-Term Memory (EMA LSTM), for multi-step vector output prediction of time series data using deep learning. The method combines the LSTM with the exponential moving average (EMA) technique to reduce noise in the data and improve the accuracy of prediction. The research compares the performance of EMA LSTM to other commonly used deep learning models, including LSTM, GRU, RNN, and CNN, and evaluates the results using statistical tests. The dataset used in this study contains daily stock market prices for several years, with inputs of 60, 90, and 120 previous days, and predictions for the next 20 and 30 days. The results show that the EMA LSTM method outperforms other models in terms of accuracy, with lower RMSE and MAPE values. This study has important implications for real-world applications, such as stock market forecasting and climate prediction, and highlights the importance of careful preprocessing of the data to improve the performance of deep learning models.

Which is more important in stock market forecasting: Attention or sentiment?

We examine the predictability of investors' attention and sentiment on four major stock indexes' returns in China between 2008 and 2018. We create investors' attention and sentiment variables by using the investors' posts from Guba, which is the largest stock forum in China. We find that both investor attention and sentiment can improve the stock return predictability with in-sample and out-of-sample periods. However, investor sentiment is more informative than investor attention in stock return prediction. These results remain significant by testing posts announced in trading and non-trading sessions. Our paper provides new empirical evidence for stock return forecasting in a data-rich environment.

Forecasting Using Point-valued Time Series and Fuzzy-valued Time Series Models

The point-valued time series (PTS) is simply about one value in each time or period of the data, but when the data have two values at each time, the suitable time series is called the interval-valued time series (ITS). An example of ITS is the daily close and open stock prices. If the time series data are of typed linguistic data, for example, “low increase”, “medium increase” and “high increase”, the time series data are called a fuzzy-valued time series or being well-known as fuzzy time series (FTS). The aim of this study is to compare the PTS and FTS models for forecasting stock prices in the stock market. The stock market is one of the essential investments in the economy. The movement of stock price can be leading or declining which respectively expands or contracts a country’s economy. The movement may also represent the scenario or event happening in a company. Therefore, forecasting stock price movement is highly important since it will assist investors and sellers to make planning in their investment’s decision. Therefore, the objectives of this study are to identify the best forecasting models in point-valued time series (PTS) and fuzzy time series (FTS) based on forecasting measurement error. Eight stock price forecasting models consisting of four models from PTS and four models from FTS. Meanwhile, four forecasting measurement errors were discussed in the analysis as a criterion for choosing the best forecasting model. A set of daily historical data from the Bursa Malaysia website was used as a basis for analysis. The finding shows the simple exponential smoothing model is the best PTS model. In the meantime, the Cheng model based on Sturges’ rule is the best FTS model. However, among these two types of models, the Cheng model is found to be the best model with forecasting measurement errors of 0.0001 (MSFE), 0.0108 (RMSFE) and 1.1918 (MAPFE). The results reveal that besides the PTS model, the FTS model is an alternative model to forecast stock market movement. Moreover, these FTS and PTS models can also be applied to solving other forecasting problems.

GRU Neural Network Based on CEEMDAN–Wavelet for Stock Price Prediction

Stock indices are considered to be an important indicator of financial market volatility in various countries. Therefore, the stock market forecast is one of the challenging issues to decrease the uncertainty of the future direction of financial markets. In recent years, many scholars attempted to use different conventional statistical and deep learning methods to predict stock indices. However, the non-linear financial noise data will usually cause stochastic deterioration and time lag in forecast results, resulting in existing neural networks that do not demonstrate good prediction results. For this reason, we propose a novel framework to combine the gated recurrent unit (GRU) neural network with the complete ensemble empirical mode decomposition of adaptive noise (CEEMDAN) to predict the stock indices with better accuracy, in which the wavelet threshold method is especially used to denoise high-frequency noises in the sub-signals to exclude noise interference for future data predictions. Firstly, we choose representative datasets collected from the closing prices of S&P500 and CSI 300 stock indices to evaluate the proposed GRU-CEEMDAN–wavelet model. Additionally, we compare the improved model to the traditional ARIMA and several modified neural network models using different gate structures. The result shows that the mean values of MSE and MAE for GRU based on CEEMDAN–wavelet are the smallest by significance analysis. Overall, we found that our model could improve prediction accuracy and alleviates the time lag problem.

Stock Market Forecasting Using LSTM Neural Network

This study investigates the widespread use of machine learning techniques, including recurrent neural networks (RNNs) and long short-term memory (LSTM) models to the analysis of stock market data. By utilizing RNN and LSTM model capabilities to identify temporal relationships and patterns in stock market data, it seeks to overcome conventional techniques' constraints. The research provides empirical proof of the efficiency of RNNs and LSTM models in enhancing investment decision-making by analyzing the project outcomes using real-world stock market data. The inclusion of RNNs and LSTM models in this research paper strengthens the exploration of machine learning techniques in stock market analysis.

Co-evolution of neural architectures and features for stock market forecasting: A multi-objective decision perspective

In a multi-objective setting, a portfolio manager’s highly consequential decisions can benefit from assessing alternative forecasting models of stock index movement. The present investigation proposes a new approach to identify a set of non-dominated neural network models for further selection by the decision-maker. A new co-evolution approach is proposed to simultaneously select the features and topology of neural networks (collectively referred to as neural architecture), where the features are viewed from a topological perspective as input neurons. Further, the co-evolution is posed as a multi-criteria problem to evolve sparse and efficacious neural architectures. The well-known dominance and decomposition based multi-objective evolutionary algorithms are augmented with a non-geometric crossover operator to diversify and balance the search for neural architectures across conflicting criteria. Moreover, the co-evolution is augmented to accommodate the data-based implications of distinct market behaviors prior to and during the ongoing COVID-19 pandemic. A detailed comparative evaluation is carried out with the conventional sequential approach of feature selection followed by neural topology design, as well as a scalarized co-evolution approach. The results on three market indices (NASDAQ, NYSE, and S&P500) in pre- and peri-COVID time windows convincingly demonstrate that the proposed co-evolution approach can evolve a set of non-dominated neural forecasting models with better generalization capabilities.

Stock Market Forecasting

Abstract: Stock market forecasting seeks to determine the worth of a firm’s financial stocks in the future. Machine learning is being used in recent developments in stock market forecasting technology to produce forecasts based on the values of current stock market indices by training on their previous values. Future stock price projections can be difficult to make when trying to anticipate the stock market. It is incredibly challenging to forecast the stock market since shares fluctuate so frequently. Every day and frequently, stock. Foreseeing trends in the stock market is often correct using this method. This study forecasts the closing prices of numerous corporations using Long-Short Term Memory (LSTM) methodologies. These models are assessed using RMSE, which is one of the commonly used error measures. LSTM works better than SVR, as shown by the experiment's findings

Two-Stage Hybrid Feature Selection Approach Using Levy’s Flight Based Chicken Swarm Optimization for Stock Market Forecasting

Two-Stage Hybrid Feature Selection Approach Using Levy’s Flight Based Chicken Swarm Optimization for Stock Market Forecasting

English

In the field of vision, applying accurate- time tracking technology in sequence images is of excellent use value to many fields, for example. It has broad prospects for development in alcohol and tobacco, passenger flow statistics, traffic surveillance, etc. In this paper, a Visual tracking simulation is carried out on corresponding detection, extraction, recognition and tracking of the moving target in an image sequence to obtain the moving parameters and trajectory. The tracked moving object performs further processing and analysis accordingly and provides a reliable database for the next more advanced task. Therefore, visual tracking plays an important role in the localization analysis of moving objects in the military, work, scientific research, intelligent transportation and other fields. The computer has undergone four development stages and made a qualitative leap from hardware to software. Stock price fluctuation represents current market trends and corporate development that can be used to determine whether to sell or acquire stocks. A stock market estimate is one of the most complex and vital duties because of the nonlinear or dynamic nature of the market. Therefore, according to the requirements of target tracking in video, a particle filter algorithm is selected to optimize the technology and is addressed for efficient outcomes through simulation. The news, blogs, sentiment analysis, and other media are connected to this relationship between supply and demand. Sentiment analysis stock market forecast focused on the stock market’s autonomous performance.

An International Study of Application of Long Short-Term Memory (LSTM) Neural Networks for the prediction of stock and forex markets

This study focuses on the use of Long Short-Term Memory (LSTM) neural networks for stock and currency market forecasting. Accurate projections are difficult to make because of the complex dynamics and non-linear interactions that characterise financial markets. A Recurrent Neural Networks (RNN) variation called LSTM is particularly good at identifying temporal dependencies and long-term patterns in sequential data. The goal of LSTM models is to discover significant insights and produce precise predictions using past price data. In addition to discussing data pretreatment methods, model creation, and assessment metrics related to stock and FX market prediction, this work examines the benefits of LSTM in capturing market dynamics. Case studies and empirical analysis are used to investigate the capabilities and constraints of LSTM models in forecasting market movements.

Analysis of Factors Affecting the CSI300 Index Based on KPCA and Various Machine Learning Algorithms

This paper utilises Kernel Principal Component Analysis (KPCA) and various machine learning algorithms to analyze the importance of factors affecting the Chinese Securities Index 300 (CSI300). Based on previous research, this paper constructs an indicator system consisting of 4 secondary and 21 tertiary indicators affecting the CSI300. The data is then reduced through KPCA and processed by various machine learning algorithms, including LightGBM, XGBoost, SVM, and Random Forest, to compare their predictive ability and feature importance. The results indicate that: (1) Under appropriate model parameter settings, the LightGBM model performs the best, while the other algorithms, such as the XGBoost, SVM, and Random Forest models, perform worse and with greater variability than the former. (2) This paper identifies the most significant indicator factors that affect the CSI300 index, such as closing price, price-to-book ratio, and turnover rate. Conversely, some factors, such as the buy-to-sell ratio, exhibit lower importance. These research findings have certain reference and guiding significance for improving the accuracy and reliability of stock market forecasting and practical and theoretical research in financial markets.

Forecasting Stock Market Out-of-Sample with Regularised Regression Training Techniques

Forecasting Stock Market Out-of-Sample with Regularised Regression Training Techniques

Stock Trend Prediction Using KNN Algorithm

Stock forecasting has always been a difficult task for statisticians and financial analysts. The key strategy used to make this prediction is buying stocks with a high probability of price growth and selling stocks with a high probability of price decline. There are typically two approaches to stock market forecasting. One of them is fundamental analysis, which is dependent on a company's methodology and fundamental data. The performance of the supervised machine learning algorithm KNN (K-Nearest Neighbor) is evaluated by the author in this study. Stock trading is one of the most significant activities in the world of finance. Trying to anticipate the future value of a stock or other financial instrument traded on a financial exchange is known as stock market prediction. Python is the computer language used to make stock market predictions using machine learning. In this article, we present a Machine Learning (ML) approach that will be trained using the stock market data that is currently accessible, gain intelligence, and then use the learned information to make an accurate prediction. This study employs prices with both daily and up-to-the-minute frequencies and a machine learning method known as K-Nearest Neighbor to forecast stock prices for both large and small capitalizations and in the three separate marketplaces.

Multivariate Index Relationship Exploration Using Advanced Machine Learning Techniques

Abstract: Stock market forecasts have always attracted the attention of many analysts and researchers. Popular theory suggests that stock markets are inherently straightforward, and trying to predict them is a trivial matter. It is a difficult problem in itself. The market behaves like a voting machine in the short term, but in the long term it behaves like a pair of scales. Therefore, there is room to predict market movements over a longer period of time. Applying machine learning techniques and other algorithms to stock price analysis and forecasting is a promising area. This white paper begins with a brief overview of the stock market and a taxonomy of stock market forecasting methods. We then highlight some research findings on resource analysis and forecasting. We discuss technical, fundamental, short- term and long-term approaches to equity analysis. Finally, we present some challenges and research opportunities in this area.

A Comparison of Linear Regression, LSTM model and ARIMA model in Predicting Stock Price A Case Study: HSBC’s Stock Price

It is widely assumed that the stock market is a key aspect of the financial market and stock price forecasting has become a popular topic of an in-depth study by financial technologists. With the advent of financial markets, stock prices face several analytical and forecasting challenges. In this paper, the stock price trend forecast analysis is carried out and the survey object was the transaction data of HSBC from 2010 to 2019. Linear regression, LSTM model and ARIMA model are used to forecast stock price trends. The models’ prediction accuracy is demonstrated after the cross-validation process by combining error indicators and trading performance. As a consequence of the analysis, we discover that LSTM has the lowest error value, which implies that it is ideally suited to stock market forecasting. On the contrary, linear regression holds the largest mean square error, implying that this method has the weakest fitting for the stock market.