Stock Market Trends Research Articles

A Survey of Sentiment Analysis Through Machine Learning for Forecasting Indian Stock Market Trend

A Survey of Sentiment Analysis Through Machine Learning for Forecasting Indian Stock Market Trend

Analysis of Trends in Stock Market

Abstract: In the period of AI applications for each and every fragment of examination, breaking down financial exchange costs and patterns has become more famous than previously. We have gathered information of securities exchange utilizing python back end and have proposed a far reaching customization of element designing and profound learning based model for anticipating the pattern of market, The proposed arrangement incorporates pre-handling of the securities exchange dataset, usage of various element designing strategies, joined with a redid profound learning based framework for pattern forecast, Evaluations have been directed on the models and reason that our proposed arrangement beats because of element designing that we constructed. Framework accomplishes decent exactness without the distant element of information overfitting and because of definite assessment of term lengths and strategies, this adds to the stock examination research local area both in monetary and specialized areas. Keywords: Machine-learning, Deep-learning, Python, API, Metrics, Feature Engineering, Stock Market.

Robotics and Algorithmic Trading: A New Era in Stock Market Trend Analysis

This paper uses machine learning to examine how robots and algorithmic trading have transformed stock market trend analysis. The main goals are to assess how these sophisticated systems improve prediction accuracy, trading efficiency, market liquidity, and their problems and policy consequences. The research synthesizes academic, industrial, and technical literature using secondary sources. Significant results show that robots and algorithmic trading have enhanced trading speed, accuracy, and market efficiency while increasing market volatility data quality and model overfitting issues. Machine learning improves trend analysis by spotting complicated patterns and improving trading techniques. These advances need solid regulatory frameworks to control risks, including market instability and ethical issues. Policy implications include circuit breakers and transparency standards to promote fair and stable markets. This study emphasizes balancing technology innovation with regulation to provide a safe and fair trade environment.

Stochastic Modeling in Stock Market Trend Prediction

n Modern centuries a lot of predicting techniques take been proposed and applied for the stock market movement prediction. In this paper, the pattern examinations of the financial exchange forecast are introduced by utilizing Hidden Markov Model with the one day distinction in close incentive for a particular period. The likelihood esteems π gives the pattern level of the stock costs which is determined for all the notice arrangement and stowed away successions. It supports for decision makers to make decisions in case of indecision on the basis of the proportion of probability values found from the steady state probability distribution.

Study of machine learning algorithms for potential stock trading strategy frameworks

Abstract: Purpose: This paper discusses major stock market trends and provides information on stock market forecasting. Stock market forecasting is essentially an attempt to forecast the future value of the stock market. Doing this manually can be a strenuous task, and thus we need some software and algorithms to make our task easier. This paper also lists a few of those algorithms, formulas, and calculations associated with them. These algorithms and models primarily revolve around the concept of Machine Learning (ML) and Deep Learning. Research Methodology: This study is based on descriptive, quantitative, and cross-sectional research design. We used a multivariate algorithm model and indicators to examine stocks for investing or trading and their efficiency. It concludes with the recommendations for enhancing trading strategies using machine learning algorithms. Results: This study suggests that after comparing and combining the various algorithms using experimental analysis, the random forest algorithm is the most suitable algorithm for forecasting a stock's market prices based on various data points from historical data. Limitations: The applicability of the study was only hampered by unforeseeable tragic events such as economic crisis, market collapse, etc Contribution: Successful stock prediction will be a substantial benefit for stock market institutions and provide real-world answers to the challenges that stock investors face. As a result, gaining significant knowledge on the subject is quite beneficial for us. Keywords: 1. Algorithms 2. Algo-trading 3. Deep learning 4. Machine learning 5. Price prediction 6. Stock market 7. Trading 8. Trends

Research on the Application of Generative Adversarial Networks in the Generation of Stock Market Forecast Trend Images

Investors make capital investment by buying stocks and expect to get a certain income from the stock market. When buying stocks, they need to draw up investment plans based on various information such as stock market historical transaction data and related news data of listed companies and collect and analyze these data. The data are relatively cumbersome and require a lot of time and effort. If you only rely on subjective analysis, the reference factors are often not comprehensive enough. At the same time, Internet social media, such as the speech in stock forums, also affect the judgment and behavior of investors, and investor sentiment will have a positive or negative effect on the stock market. This has an impact on the trend of stock prices. Therefore, this article proposes a stock market prediction model that uses data preprocessing technology based on past stock market transaction data to establish a stock market prediction model, and secondly, an image description generation model based on a generative confrontation network is designed. The model includes a generator and a discriminator. A time-varying preattention mechanism is proposed in the generator. This mechanism allows each image feature to pay attention to the image features of other stock markets to predict stock market trends so that the decoder can better understand the relational information in the image. The discriminator is based on the recurrent neural network and considers the degree of matching between the input sentence and the 4 reference sentences and the image features. Experiments show that the accuracy of the model is higher than that of the stock pretrend forecast model based on historical data, which proves the effectiveness of the data used in this paper in the stock price trend forecast.

Predicting Stock Indices Trends using Neuro-fuzzy systems in COVID-19

Predicting the ebb and flow of stock markets is a complex and challenging exercise owing to the disruptive and uncertain behavior of stock prices. The COVID-19 pandemic is an example of an event that, had a drastic impact on global stock markets, due to business activities and trading being severely affected. It is important, therefore, to be able to predict how stock markets behave in a crisis period. We find that stock markets obtain the worst returns in countries where there are higher reported positive cases of coronavirus. This study employs adaptive neuro-fuzzy inference systems (ANFIS), comprising of a controller and the stock market process, to predict the behavior of selected stock indices. After training ANFIS and evaluating the resultant data, we estimate statistical errors and found that 100 training epochs provide marginally better results. To test the accuracy of our results, we used hit rate success and report that the neuro-fuzzy system predicts stock market trends with an average accuracy of 65.84%, an improvement over earlier techniques reported in the literature. Finally, we compute the rate of return using a buy-and-hold strategy and a neuro-fuzzy system, and identify that market indices outperform by employing the proposed method

Forecasting Stock Index Using a Volume-Aware Positional Attention-Based Recurrent Neural Network

With the rapid development of deep learning, more researchers have attempted to apply nonlinear learning methods such as recurrent neural networks (RNNs) and attention mechanisms to capture the complex patterns hidden in stock market trends. Most existing approaches to this task employ an attention mechanism that primarily relies on the information extracted from input features but fails to consider the other important factors (e.g. trading volume and position), which can potentially enhance these attention-based approaches. Motivated by the observation, we extend the attention mechanism with features needed for stock performance prediction in this paper. Specifically, we propose a volume-aware positional attention-based recurrent neural network (VPA-RNN) for this task. First, we propose a generic method of adding position awareness to the attention mechanism. Next, the trading volume is incorporated into the original attention distribution to form a revised distribution. To evaluate the effectiveness of VPA-RNN, we collected real stock market data for stock indexes S&P 500 and DJIA, and the experimental results show that the proposed VPA-RNN can significantly outperform several existing highly competitive methods.

Performance analysis of LSTM model on equity domain data

Predicting the stock market trend is a difficult problem because of its dynamic nature and other external factorsaffecting it. This research work is done to assess the trend in stock prices and to check whether using differenttrading prices like open, high and low instead of using just the closing price gives more accurate results. For thispurpose, we have used the long short term memory (LSTM) model and trained this model by using historic stockprices data. We created three different data frames from the historic stock price data (from the year 2000-2019)such that the first data frame contains median stock price calculated by finding the median of all the trading pricesfor each row of data. Similarly, the second data frame contains max stock prices among all the trading prices andthe third data frame contains min stock price among the trading price. Then three different LSTM models arecreated and trained using these three data frames. We then test these three models by using them to predict thestock prices for the year 2020 and plot a graph with traces for the actual stock prices of the year 2020 and predictstock prices by these three models. It was observed from the plotted graph that the traces for the actual andpredicted stock prices were very close to each other which means that the models are giving satisfactory results.

A Systematic Study on Sentiment Analysis based on Text Mining and Deep learning for Predictions in Stock Market Trends Through Social and News Media Data

Abstract: Stock Market has always been one of the most active fields of research, many companies and organizations have focused their research in trying to find better ways to predict market trends. The stock market has been the instrument to measure the performance of a company and many have tried to develop methods that reduce risk for the investors. Since, the implementation of concepts like Deep Learning and Natural Language Processing has been made possible due to modern computing there has been a revolution in forecasting market trends. Also, the democratization of knowledge related to companies made possible due to the internet has provided the stake holders a means to learn about assets they choose to invest in through news media and social media also stock trading has become easier due to apps like robin hood etc. Every company now a days has some kind of social media presence or is usually reported by news media. This presence can lead to the growth of the companies by creating positive sentiment and also many losses by creating negative sentiments due to some public events. Our goal in this paper is to study the influence of news media and social media on market trends using sentiment analysis. Keywords: Deep Learning, Natural Language Processing, Stock Market, Sentiment analysis

Maritime Stock Prices and Information Flows: A Cointegration Study

In this study, the issue of how global maritime stock prices influence the stock prices of large transportation companies in the U.S. and other large markets is examined. Maritime stocks are chosen because they are central in global trade and thus may be good indicators of future global stock market and economic trends. Maritime companies are often owned by families or governments and are traded in stock markets with lower standards of accountability, hence information flows from maritime stocks may be slower than flows from other stocks. Cointegration and vector error-correction analysis is used to analyze the short-term and long-term relationships between maritime stocks, rail stocks, and trucking stocks. Evidence is found of a gradual diffusion of information from maritime stock prices to large rail or trucking stocks. This suggests that price changes in maritime stocks may help predict changes in prices in non-maritime transportation stocks.

Exploring what stock markets tell us about GDP in theory and practice

This paper explores the stock market-GDP relationship from basic theory to simple empirics to better understand what stock market movements tell us about underlying GDP in real time. We present a simple theoretical model to make key relationships clear, then explore US GDP and US stock market (S&P 500) performance through a range of analytical tools from visual inspection to correlations, regressions, counting and extreme value calculations to a few illustrative narrative investigations. We find that the S&P 500 is weakly correlated with real GDP as well as with vintage GDP releases contemporaneous, but more strongly and statistically significantly with one lag as theory predicts. We also find that the S&P 500 is more closely related both contemporaneously and with a lag to final, revised GDP numbers - only known months later - than to vintage GDP estimates, suggesting that stock market trends are informative about true GDP.

The shrinking stock market

The total number of publicly traded firms in the United States has fallen by half since the late 1990s. As a result, the size of the market is roughly the same as it was in 1991 despite the overall economy tripling in size since that time. This study examines this trend showing the long-term shrinking stock market trend is associated with higher levels of cash dividend payouts by firms, slower rates of profit and revenue growth, and less firm-level risk, all of which point to an average firm that is later in its lifecycle. The loss of firms has a more substantial impact on revenue and profit growth rates for the marginal firm in the market than the addition of new firms does. These results have important implications for the broader economy and support studies which find that smaller firms are being purchased by larger firms rather than going public.

Evaluating the Performance of Factor Pricing Models for Different Stock Market Trends: Evidence from China

ABSTRACT This paper examines the performance of three famous factor pricing models in markets of bull, bear, and consolidation in China. Empirical results show that these models explain the time-series variations in portfolio returns in bearish market reasonably well, but fail to explain the cross-sectional variations. Another two findings are revealed by instability tests. First, the three models are more unstable in trending (i.e., bearish and bullish) markets under time-series regression due to the higher stock price synchronicity. Second, greater instability causes the unitary parameter estimates less reliable and brings about difficulties in explaining the cross-sectional portfolio returns in trending markets.

Emotions, moods and hyperreality: social media and the stock market during the first phase of COVID-19 pandemic

PurposeThis paper aims to expand the emerging literature on COVID-19 and the financial markets by searching for a relationship between the uncertainty of the first phase of the COVID-19 pandemic experienced through social media and the extreme volatility of the Italian stock market.Design/methodology/approachThe authors analyze the relationship between social media and stock market trends during the first phase of the COVID-19 pandemic through the lens of social theory and Baudrillard's simulacra and hyperreality theory. The authors conducted the data analysis in two phases: the emotional and Granger correlation analysis by using the KPI6 software to analyze 3,275,588 tweets for the predominant emotion on each day and observe its relationship with the stock market.FindingsThe research results show a significant Granger causality relation between tweets on a particular day and the closing price of the FTSE MIB during the first phase of the COVID-19 epidemic. The results highlight a strong relationship between social media hyperreality and the real world. The study confirms the role of social media in predicting stock market volatility.Research limitations/implicationsThe findings have theoretical and practical implications as they reveal the relevance of social media in our society and its relationship with businesses and economies. In an emergency, social media, as an expression of users' feelings and emotions, can generate a state of hyperreality that is strong correlated with reality. Since social media allows users to publish and share messages without any filter and mediation, the hyperreality generated is affected by highly subjective elements.Originality/valueThis research is different from the previous ones on the same topic because unlike previous studies, conducted under normal or simulated scenarios, this study is focused on the first phase of an unpredictable and unforeseen emergency event: the COVID-19 pandemic. This research adopts a multidisciplinary approach and integrates previous studies on the economic and financial effects generated by social media by applying well-known theories to a new and unexplored context. The study reveals the significant impact generated by social media on stock markets during a global pandemic.

Market Efficiency Dynamics and Chaotic Behavior of Dhaka Stock Exchange: Evidence from Mutual Information and Lyapunov Exponents Models

This study investigates the evidence of market efficiency dynamics and chaotic behavior of the Dhaka Stock Exchange benchmark index (DSEX) over the 2000-2020 period. We employed the newly developed model of mutual informational and global correlation coefficient in addition to the traditional linear and nonlinear techniques. Results suggest there is evidence of serial dependence in the DSEX returns. We attempted the Lyapunov exponent model to evaluate the possibility of chaos and nonlinear dynamics in the market. The results conspicuously represent the existence of chaotic behavior- a nonlinearity-based profitability pattern revealed in the DSEX return series in its short run behavior. By applying two technical trading indicators, we justify the predicting trend of the Bangladesh stock market and conclude that investors active in the Dhaka Stock Exchange can earn abnormal returns. Findings have practical implications for general investors and professional fund managers to exploit the profitable opportunities and reshuffle the investment decisions. Results also convey the message to the regulatory body to initiate the strategies for intervening in the operating mechanisms to reduce the market inefficiency.

Constructing a stock-price forecast CNN model with gold and crude oil indicators

In this study, we propose algorithms to predict future stock market trends based on 8 different input features, including financial technology indicators, gold prices, a gold price volatility index, crude oil price, a crude oil price volatility index, and other characteristic data using two different labeling methods with separate classification algorithms of two and three output categories, respectively including predicted stock price changes (up and down) and recommended trading actions (buy, sell, and hold), and analyze the validity of these characteristic data in terms of their ability to predict future trends. The S&P 500 (GSPC) is the target of these forecasts. Sample data from 2010 to 2018 are divided 8:2, between training and verification data, while data from 2019 are used to test the proposed approach. CNN and LSTM models are used for comparison of classification accuracy and investment returns, respectively. Bayesian optimization (BO) hyperparameters are used to improve the accuracy of the model and increase the return on investment (ROI) of the output predictions.The purpose of this study is to verify whether using gold prices, a gold volatility index, crude oil price, and a crude oil price volatility indices as input features can enable a deep learning model accurately to predict future stock price trends, and to discuss separately the applicability of CNN and LSTM models to the abovementioned characteristics and financial indicators. We also present the results of experiments conducted to evaluate the proposed method in terms of classification accuracy and confusion matrix. In the case of three-category classification, the model takes feature data as input to outputs a predicted trading order on whether to buy, sell, or hold a given set of stocks tomorrow as well as the timing of entry and exit from each position, and also backtests the data outside the sample to find the combination of characteristics and indicators best maximizing ROI. Using this three-category method, we obtain a comprehensive ROI for a given set of individual stocks and assess whether each type of stock is suitable for the prediction model based on input features such as gold and crude oil or the fields that are suitable for the given feature.Experimental results show that the proposed approach as able to predict whether stock price will rise or fall in the next 10 days, and the model accuracy rate can reach 67%. The results of experiments on the proposed combined CNN model with eight features, referred to as CNN8, achieved an ROI on 2019 data outside the sample period of up to 13.23%, which was superior to the 12.08% and 11.06% obtained by the models designed CNN4 (CNN with four input features) and LSTM8(LSTM with eight input features), respectively. The F1 score increased from 0.75 0.79 as a result of applying BO. The results indicate that considering the price of gold, the gold volatility index, crude oil price, and crude oil price volatility index can help obtain better ROI for companies in certain fields, such as the semiconductor, petroleum, and automotive industries, rather than merely considering financial indicators. However, for companies related to apparel, fast food, and copy processing, the input characteristics of purely financial technical indicators were found to be suitable.

DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

The explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

CRYPTOCURRENCY MARKET TRENDS AND FUNDAMENTAL ECONOMIC INDICATORS: CORRELATION AND REGRESSION ANALYSIS

Abstract. The first cryptocurrency was born in 2008. Already today, virtual financial assets and tokens are a significant part of trading in global financial markets. The cryptocurrency market capitalization currently exceeds 600 billion U.S. dollars. However, there is a lot of discussion about cryptocurrency functions and the correlation between Bitcoin prices and the basic economic indices. Therefore, the purpose of the paper is to define the statistical substantiation of the influence of fundamental economic indicators on the market of virtual financial assets and the possibility of using cryptocurrency as the investment assets. This article is based on the theoretical principles and methods of econometric analysis; the system approach methods to define the main vehicles and trends of the international financial market. The study presents correlation analysis, regression models with paired and multiple variables. For these models, R-Studio instruments are the main tools of quality estimation and results interpretation. The article shows the results of the correlation analysis of Bitcoin’s U.S. dollar price dynamics and changes in the main stock, monetary market indicators, cryptocurrencies market tendency, levels of the United States fundamental economic indicators for the period from 2014 to 2021. Traditional multifactorial regression models are used to determine the level and the impact of individual indicators of the world stock market at the U.S. dollar price of Bitcoin. A comparison of the level of volatility of key investment financial assets in the market of cryptocurrencies and stock markets is carried out. The authors determine the level of correlation dependence and make a regression model of the impact of fundamental economic indicators and stock market trends on the dynamics of U.S. dollar prices for key cryptocurrencies. The article presents conclusions on trends and problems of using cryptocurrencies as an investment asset, considering volatility and profitability. Implementation of the results allows to clarify the economic essence of cryptocurrencies as a specific financial vehicle, as well as improving the existing models of investment management, considering the statistical characteristics of the virtual financial assets. The main direction of further research is to build models of medium-term prediction of prices for the main cryptocurrencies as an investment asset in conditions of changes in global financial markets, which must consider the fundamental economic indicators of the world economy and trends on key stock and commodity markets. Keywords: virtual financial asset, cryptocurrency, bitcoin, econometric model, financial market, economic indicator, investment asset. JEL Classification D53, E44, G15, C58 Formulas: 3; fig.: 3; tabl.: 3; bibl.: 31.

Modeling the forecast of the stock market trend

Modeling the forecast of the stock market trend