Predicting Stock Market Movements Research Articles

Topological Approach for Detection of Structural Breakpoints in Philippine Stock Price Data Surrounding the COVID-19 Pandemic

Topological data analysis (TDA) is a novel but rigorous framework used to identify the underlying shape and structure of a huge data set. In this study, we demonstrated how TDA can be useful in analyzing and predicting stock market movement. The study collected time-series data from the Philippine Stock Exchange of companies that were heavily affected by lockdown measures implemented during the COVID-19 pandemic and those that thrived. The study found that during market crashes, persistence diagrams revealed a presence of persistence weakening phenomenon. Moreover, by analyzing the stock price movement given an interval of time, the persistence landscape graphs produced high points or bumps during a market drop, which corresponds to structural breakpoints in the time series. Indeed, persistent homology can be used to predict early warning signs of probable market crashes.

Predicting the Direction of NEPSE Index Movement with News Headlines Using Machine Learning

Predicting stock market movement direction is a challenging task due to its fuzzy, chaotic, volatile, nonlinear, and complex nature. However, with advancements in artificial intelligence, abundant data availability, and improved computational capabilities, creating robust models capable of accurately predicting stock market movement is now feasible. This study aims to construct a predictive model using news headlines to predict stock market movement direction. It conducts a comparative analysis of five supervised classification machine learning algorithms—logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and artificial neural network (ANN)—to predict the next day’s movement direction of the close price of the Nepal Stock Exchange (NEPSE) index. Sentiment scores from news headlines are computed using the Valence Aware Dictionary for Sentiment Reasoning (VADER) and TextBlob sentiment analyzer. The models’ performance is evaluated based on sensitivity, specificity, accuracy, and the area under the receiver operating characteristic (ROC) curve (AUC). Experimental results reveal that all five models perform equally well when using sentiment scores from the TextBlob analyzer. Similarly, all models exhibit almost identical performance when using sentiment scores from the VADER analyzer, except for minor variations in AUC in SVM vs. LR and SVM vs. ANN. Moreover, models perform relatively better when using sentiment scores from the TextBlob analyzer compared to the VADER analyzer. These findings are further validated through statistical tests.

Stock Price Prediction and Pattern Detection Using Deep Learning

Accurately predicting stock market movements remains a significant challenge, yet one that continues to attract intense interest. This project leverages data mining and warehousing techniques to explore historical stock price data, aiming to uncover recurring patterns, forecast potential future trends, and evaluate the consistency of a specific stock's behaviour relative to its identified patterns. By employing various data mining algorithms and implementing effective warehousing strategies, the project seeks to extract valuable insights that can inform investment decisions and potentially contribute to superior market performance. But among these goals are some serious obstacles. Accurate prediction is hampered by the noise and uncertainties that are present in stock market data. Furthermore, fine-tuning and experimentation are necessary to achieve optimal results when optimizing the hyperparameters of long short-term memory networks (LSTM networks). Moreover, strong preprocessing methods are required to handle datasets that contain inconsistent or missing data. Lastly, proficient data visualization and user interface design abilities are required when creating an educational and engaging dashboard with Plotly Dash.

Implications of Deep Learning for Stock Market Forecasting

This research explores the effectiveness of using deep learning in predicting stock market movements. This research uses rigorous methods to bring out the performance of deep learning models, compare them with traditional methods, and identify critical factors that influence stock market predictions. The research results show that deep learning models, especially LSTM and CNN-LSTM architectures, can achieve satisfactory levels of accuracy and outperform traditional methods by capturing patterns in complex stock market data. In addition, this research identifies external and internal factors that influence predictions of stock market movements. This research's practical and theoretical implications highlight the potential of deep learning in improving investment decision-making and understanding financial market dynamics. Recommendations for future research include exploration of advanced deep learning techniques, integration with traditional methods, emphasis on risk management strategies, continuous evaluation of model performance, and provision of training and education to encourage analysts and investors to adopt this technology. By implementing these recommendations, the potential of deep learning models in financial analysis can be optimized, ultimately improving market efficiency and investment returns.

An Intelligent Approach for Predicting Stock Market Movements in Emerging Markets Using Optimized Technical Indicators and Neural Networks

Abstract Integrating big data analytics and machine learning algorithms has become increasingly important in the fast-changing landscape of stock market investment. The numerical findings showcase the tangible impact of our methodology on the accuracy and efficiency of stock market trend predictions. Identifying and selecting the most salient features (technical indicators) is critical in predicting the trend direction of exchange-traded funds (ETFs) in emerging markets, leveraging financial and economic indicators. Our methodology encompasses an array of statistical techniques strategically employed to identify critical technical indicators with significant implications for time series problems. We improve the efficacy of our model by performing systematic evaluations of statistical and machine learning methods across multiple sets of features or technical indicators, resulting in a more accurate trend prediction mechanism. Notably, our approach not only achieves a substantial reduction in the computational cost of the proposed neural network model by selecting only 5% of the total technical indicators for predicting ETF trends but also enhances the accuracy rate by approximately 2%.

Predicting stock market movements using neural networks: A review and application study

In the rapidly evolving landscape of financial markets, the quest for accurate stock market predictions has never been more critical. This paper delves into the transformative potential of neural network models in forecasting stock market movements, offering a comprehensive examination of their effectiveness compared to traditional predictive models. With a focus on the evolution of stock market prediction methodologies, this study aims to uncover the nuanced dynamics of neural networks, their comparative analysis with other models, and the pivotal role of data preprocessing in enhancing prediction accuracy. Employing a qualitative analysis framework, the research meticulously synthesizes findings from selected studies, highlighting the superior performance of neural network models in capturing complex market patterns and adapting to volatility. The results underscore the significant impact of data quality and quantity, architectural nuances of neural networks, and the strategic implications for investors navigating the stock market's unpredictability. Despite the promising outcomes, the study acknowledges inherent challenges in the real-world application of these models, including data imperfections and the complexity of financial ecosystems. Conclusively, the paper advocates for ongoing innovation, interdisciplinary collaboration, and the strategic integration of advanced neural network architectures to overcome existing limitations. Recommendations emphasize the critical need for high-quality, diverse datasets and continuous model refinement to harness the full predictive power of neural networks in stock market forecasting. This study not only illuminates the path forward for investors and financial analysts but also sets the stage for future research in this dynamic field.

MACHINE LEARNING FOR STOCK MARKET FORECASTING: A REVIEW OF MODELS AND ACCURACY

As financial markets become increasingly complex and dynamic, the application of machine learning (ML) techniques for stock market forecasting has garnered significant attention. This paper presents a comprehensive review of various ML models employed in the realm of stock market forecasting, focusing on their methodologies and the accuracy achieved in predicting market trends. The review begins by examining traditional time-series models such as autoregressive integrated moving average (ARIMA) and moving average convergence divergence (MACD) and their limitations in capturing the intricate patterns present in financial data. Subsequently, the discussion transitions to more advanced ML models, including support vector machines (SVM), artificial neural networks (ANN), and ensemble methods like random forests and gradient boosting. Each model's strengths and weaknesses are scrutinized in the context of stock market forecasting. The paper explores the pivotal role of feature selection and engineering in enhancing the predictive power of ML models. Feature sets encompassing financial indicators, macroeconomic variables, sentiment analysis from news articles, and social media data are analyzed for their impact on forecasting accuracy. Additionally, the incorporation of technical indicators and alternative data sources is explored as potential avenues to improve model robustness. A critical aspect of this review is the assessment of accuracy in predicting stock market movements. The evaluation is conducted through a comparative analysis of model performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and accuracy rates. The study also addresses the challenge of model overfitting and proposes strategies to mitigate this issue for more reliable predictions. This review provides a nuanced understanding of the landscape of ML models for stock market forecasting, highlighting the diverse approaches, challenges, and opportunities in the quest for improved accuracy. It contributes valuable insights for researchers, practitioners, and investors seeking to leverage the potential of ML in navigating the complexities of financial markets.
 Keywords: Machine Learning, Stock Market, Forecasting, Models, Review.

Sentiment Analysis for Financial Markets

Abstract: Predicting movements in the stock market is a novel use of sentiment analysis's growing body of knowledge. The purpose of this study is to investigate the potential of NLP for predicting stock market movements by analyzing textual data sources such as news articles, social media posts, as well as earnings reports. The research examines current approaches, applications, and difficulties in sentiment analysis by drawing on extensive surveys and reviews [1], [2]. It also investigates the use of pre-trained models in NLP and the potential biases of such models [6]. Important research findings [3], [17] suggest that NLP-based sentiment analysis models, especially those employing deep learning architectures, show promising results in financial forecasting. There are, however, several difficulties associated with these models. These include the requirement of huge datasets and the elimination of biases. This study has far-reaching ramifications. One benefit is a more nuanced comprehension of the potential and pitfalls of natural language processing for sentiment analysis in the financial markets, which is useful for both analysts and investors. Second, it provides opportunities for more study to enhance the precision and dependability of such models, which ultimately aids in the development of more steady and well-informed monetary judgements.

Stock market prediction-COVID-19 scenario with lexicon-based approach

Stock market forecasting remains a difficult problem in the economics industry due to its incredible stochastic nature. The creation of such an expert system aids investors in making investment decisions about a certain company. Due to the complexity of the stock market, using a single data source is insufficient to accurately reflect all of the variables that influence stock fluctuations. However, predicting stock market movement is a challenging undertaking that requires extensive data analysis, particularly from a big data perspective. In order to address these problems and produce a feasible solution, appropriate statistical models and artificially intelligent algorithms are needed. This paper aims to propose a novel stock market prediction by the following four stages; they are, preprocessing, feature extraction, improved feature level fusion and prediction. The input data is first put through a preparation step in which stock, news, and Twitter data (related to the COVID-19 epidemic) are processed. Under the big data perspective, the input data is taken into account. These pre-processed data are then put through the feature extraction, The improved aspect-based lexicon generation, PMI, and n-gram-based features in this case are derived from the news and Twitter data, while technical indicator-based features are derived from the stock data. The improved feature-level fusion phase is then applied to the extracted features. The ensemble classifiers, which include DBN, CNN, and DRN, were proposed during the prediction phase. Additionally, a SI-MRFO model is suggested to enhance the efficiency of the prediction model by adjusting the best classifier weights. Finally, SI-MRFO model’s effectiveness compared to the existing models with regard to MAE, MAPE, MSE and MSLE. The SI-MRFO accomplished the minimal MAE rate for the 90th learning percentage is approximately 0.015 while other models acquire maximum ratings.

Machine Learning-Based Momentum Strategy for High Frequency Trading and Buy/Sell Calls Using Algorithmic Trading

Stock Market Prediction is beneficial to investors. It provides shareholders with useful assistance in making suitable decisions about whether to purchase or sell shares. Accurate stock price prediction is extremely challenging because of multiple factors. Stock prices are influenced by a variety of factors, but the price at any given time is determined by supply and demand in the market. Due to the growing volume of data, it is now impractical, if not impossible for humans to manually analyze data for certain tasks like predicting stock market movements, necessitating automation. The stock price data represents a financial time series data which becomes more difficult to predict due to its characteristics and dynamic nature. Thus, this also means that there is a lot of data to find patterns in. This gives rise to the concept of algorithmic trading, which uses automated, pre-programmed trading strategies to execute orders. Machine learning algorithms explore large amounts of data and search for a model that will achieve the programmer’s goal. Our project’s goal is to use machine learning to implement a momentum strategy. We attempted to predict trading signals using machine learning techniques based on a set of technical indicators and rules.

A Novel Ensemble Learning Approach for Stock Market Prediction Based on Sentiment Analysis and the Sliding Window Method

Financial news disclosures provide valuable information for traders and investors while making stock market investment decisions. Essential but challenging, the stock market prediction problem has attracted significant attention from both researchers and practitioners. Conventional machine learning models often fail to interpret the content of financial news due to the complexity and ambiguity of natural language used in the news. Inspired by the success of recurrent neural networks (RNNs) in sequential data processing, we propose an ensemble RNN approach (long short-term memory, gated recurrent unit, and SimpleRNN) to predict stock market movements. To avoid extracting tens of thousands of features using traditional natural language processing methods, we apply sentiment analysis and the sliding window method to extract only the most representative features. Our experimental results confirm the effectiveness of these two methods for feature extraction and show that the proposed ensemble approach is able to outperform other models under comparison.

Understanding Investors Behaviour Based On Changing Market Scenario Due to COVID-19

COVID-19 has created a misbalance in the global economy by affecting multiple sectors. It has also affected the mindset and, consequently, the behavior of economic agents, one of whom is the investors. This study aims to learn about investors’ investment behavior in COVID-19 by looking at their preferred sources of information, which influence investment decisions, and their preferred investment avenues. A deep learning model is also proposed for accurately predicting stock market movement. For this study, data was collected from 50 investors. Factors such as the respondents’ age, salary, educational background, investment experience, and income were considered when selecting the sample. According to the findings, most investors trust modern investment vehicles such as stocks and mutual funds. For most investors, liquidity and fund safety have emerged as the most important factors influencing their investment goals. A deep learning model proposed is a combination of CNN network plus bidirectional LSTM network.

A Bibliometric Analysis of Artificial Intelligence-Based Stock Market Prediction

The primary purpose of this study is to conduct a scientometrics analysis of stock market forecasts based on artificial intelligence. This research examined 1,301 publications that were published between January 2002 and June 2022. We investigated 183 journal articles among 1,329 papers. In addition to entering the keywords into Scopus, a comprehensive dataset of relevant research papers was compiled. These papers discussed the optimization of investment portfolios, artificial intelligence-based stock market forecasts, investor emotions, and market monitoring. We found the most prolific documents by affiliation, the most prolific author, the most cited papers, nations, institutions, co-authorship maps, inter-country co-authorship maps, and keywords occurrences in this study. Co-authorship analysis network maps and keyword occurrence linkages are generated using the VOS-viewer software. According to our findings, it is evident from the review that the body of literature is becoming more specific and extensive. Primarily, neural networks, support vector machines, and neuro-fuzzy systems are employed to predict the future price of a stock market index based on the composite index's historical prices. Artificial intelligence techniques are able to consider challenges facing financial systems when forecasting time series. Our findings provide actionable guidance on how artificial intelligence can be used to predict stock market movements for market participants, including traders, investors, and financial institutions.

Are Twitter sentiments during COVID-19 pandemic a critical determinant to predict stock market movements? A machine learning approach

The problem of stock market prediction is a challenging task owing to its complex nature and the numerous indirect factors at play. The sentiments regarding socio-political issues such as wars and pandemics can affect stock prices. The spread of the COVID-19 pandemic continues to take a toll on the economy and fluctuations in sentiment of the concerns about the health impacts of the disease can be captured from the microblogging platform, Twitter. We examined how these sentiments during the Covid-19 pandemic and the health impacts arising from the disease along with other macroeconomic indicators provide useful information to predict the stock indices in a more accurate manner. We developed a machine learning model namely, long-short term memory (LSTM) networks to predict the impact of the Covid-19 induced sentiments on the stock values of different sectors in the United States and India. We did the same predictions using the timeseries statistical models such as autoregressive moving average model and the linear regression model. We then compared the performance of the LSTM and the timeseries statistical models to find that the machine learning model has produced more accurate predictions of the stock indices. The performance of the models across the sectors and between the United States and India are compared to draw economic inferences.

Design Analysis and Implementation of Stock Market Forecasting System using Improved Soft Computing Technique

In this paper, a stock market prediction model was created utilizing artificial neural networks. Many people nowadays are attempting to predict future trends in bonds, currencies, equities, and stock markets. It is quite challenging for a capitalist and an industry to forecast changes in stock market prices. Due to the numerous economic, political, and psychological aspects at play, forecasting future value changes on the stock markets is quite challenging. In addition, stock market forecasting is a difficult endeavor because it relies on a wide range of known and unknown variables. Many approaches, including technical analysis, fundamental analysis, time series analysis, and statistical analysis are used to attempt to predict the share price; however, none of these methods has been demonstrated to be a consistently effective prediction tool. Artificial neural networks (ANNs), a subfield of artificial intelligence, are one of the most modern and promising methods for resolving financial issues, such as categorizing corporate bonds and anticipating stock market indexes and bankruptcy (AI). Artificial neural networks (ANN) are a prominent technology used to forecast the future of the stock market. In order to understand financial time series, it is often essential to extract relevant information from enormous data sets using artificial neural networks. An outcome prediction neural network with three layers is trained using the back propagation method. Analysis shows that ANN outperforms every other prediction technique now available to academics in terms of stock market price predictions. It is concluded that ANN is a useful technique for predicting stock market movements globally.

Modeling of Hyperparameter Tuned Hybrid CNN and LSTM for Prediction Model

The stock market is an important domain in which the investors are focused to, therefore accurate prediction of stock market trends remains a hot research area among business-people and researchers. Because of the non-stationary features of the stock market, the stock price prediction is considered a challenging task and is affected by several factors. Anticipating stock market trends is a difficult 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, noisy, and chaotic data, stock market prediction is a huge difficulty, and as a result, investors find it difficult to invest their money in order to make a profit. In order to predict stock market movements, a number of strategies have been established. Earlier studies based on statistical models and machine learning techniques have been focused on short term stock price prediction. In this aspect, this study designs a novel hyperparameter tuned hybrid convolutional neural network with long short term memory (HPT-HCLSTM) for stock price prediction. The proposed HPT-HCLSTM technique encompasses three different processes namely pre-processing, prediction, and parameter optimization. The HPT-HCLSTM technique employs the HCLSTM technique for the prediction of stock prices. In addition, teaching and learning based optimization (TLBO) algorithm is applied for the hyperparameter optimization of the HCLSTM technique and thereby results in minimal error values. In order to demonstrate the enhanced prediction performance of the HPT-HCLSTM technique, a wide range of simulations were carried out and the results highlighted the better performance of the HPT-HCLSTM technique under several aspects. The HPT-HCLSTM technique is found to be a proper tool for forecasting stock prices. the HPT-HCLSTM technique has showcased better performance with the increased R2 value of 0.9154.

A dynamic predictor selection algorithm for predicting stock market movement

Although training a deep network with financial time series is not hard, the important issue is, how much the prediction for the truly new data can be trusted with a trained network. In this study, we propose a dynamic predictor selection algorithm (DPSA) that dynamically evaluates and selects the prediction model (predictor) for stock daily movement trend prediction. We first build an initial set of potential candidate predictors based on the convolutional long short-term memory networks (ConvLSTMs) by using different values of parameters. To evaluate the candidate predictors, we propose a kernel time-weighted fuzzy c-means clustering algorithm (KTFCM), which improves the kernel FCM algorithm (KFCM), to organize the historical samples according to their relevance to the target sample, which makes the historical samples that are closely related to the target sample have more influence on the predictors. Then, we use the well-organized historical samples to evaluate the candidate predictors. The predictor that yields the best accuracy is selected to predict the target sample. The proposed DPSA algorithm takes less than one minute in total for training the networks, evaluating and selecting the predictors, and performing prediction, which greatly shortens the time of the deep learning prediction. We perform the comparative experiments for the proposed DPSA algorithm and seven popular methods. These experiments test a large real-life financial time series data of various stock markets. The experiment results show that DPSA achieves the best accuracy and the highest return compared to the seven other popular methods.

Predicting Stock Market Movements Using Artificial Neural Networks

Knowing about the future returns attract every investor. Investors can take appropriate decisions once they know what would be the future returns based on their investments. To know the returns, the earlier studies have proposed basic models like Efficient Marker Hypothesis and Random Walk Model, whereas these theories have their own limitations in predicting the direction of the stock and the next day value of the stock. Later with the evolution of Machine learning and Deep Learning Techniques, there were many experiments which were made to study the stock markets. The present research paper aims at applying the Deep Learning technique of Artificial Neural Network to predict the direction of the stock index. The data consist of daily open price, close price, high price, low price and volume of NIFTY 50, S&P 500, New York Stock Index, Korean Stock Index, Dow Jones Index and Shanghai Stock Index from Jan 2015 to May 2020. The open price of the index is fed as input to the Artificial Neural Network. The model is evaluated on different performance metrics of Accuracy, Precision, Recall and F1-Score.

Predicting Stock Market Movements Using Machine Learning Techniques

The purpose of this paper is to compare the performance of various state-of-the-art machine learning techniques in predicting the behavior of stock-market returns. To do so, we gathered ten years of daily historical data (2488 observations per stock) for the top ten most liquid stocks in Casablanca Stock Exchange (Morocco) and trained six machines learning classifiers (ridge regression, LASSO regression, support-vector machine, k-nearest neighbors, random forest, and adaptive boosting) and an ensemble of them (i.e. ensemble learning) in order to predict one-day-ahead, one-week-ahead, and one-month-ahead prices direction (i.e. positive or negative returns). The performance of each algorithm is then evaluated using accuracy, precision, recall, and F1 scores. Applying the Diebold-Mariano test at a significance level of 5%, we have found that support-vector machine, random forest, and adaptive boosting perform equally well and outperform all other single classifiers for short-term predictions (one-day-ahead and one-week-ahead). However, for monthly predictions, all methods display similar predictive accuracy. In addition, our study suggests that ensemble learning significantly improves all performance metrics for the three prediction horizons. We have also found that for all models the performance significantly decreases as the prediction horizon increases.

Stock Prediction Using Technical And Sentimental Analysis

Predicting stock market movements is a well-known problem of interest. Now-a- days social media is perfectly representing the public sentiment and opinion about current events. Especially, Twitter has attracted a lot of attention from researchers for studying the public sentiments. Stock market prediction on the basis of public sentiments expressed on Twitter has been an intriguing field of research. The approach through sentimental analysis is to observe how well the changes in stock prices i.e. the rise and fall are correlated to the opinion of people that are expressed by them on Twitter. Sentimental analysis helps in analyzing the public sentiments on Twitter, this approach is our approach through using make of sentimental analysis. Another approach in the same topic of our project is using technical analysis. We model the stock price movement as a function of these input features and solve it as a regression problem in a multiple kernel learning regression framework. The machine learning coupled with fundamental and/ or technical analysis also yields satisfactory results for stock market prediction. We also evaluated the model for taking buy-sell decision at the end of day which is also known as intraday trading.