Stock Market Trends Research Articles

LSTM based stock prediction using weighted and categorized financial news.

A significant correlation between financial news with stock market trends has been explored extensively. However, very little research has been conducted for stock prediction models that utilize news categories, weighted according to their relevance with the target stock. In this paper, we show that prediction accuracy can be enhanced by incorporating weighted news categories simultaneously into the prediction model. We suggest utilizing news categories associated with the structural hierarchy of the stock market: that is, news categories for the market, sector, and stock-related news. In this context, Long Short-Term Memory (LSTM) based Weighted and Categorized News Stock prediction model (WCN-LSTM) is proposed. The model incorporates news categories with their learned weights simultaneously. To enhance the effectiveness, sophisticated features are integrated into WCN-LSTM. These include, hybrid input, lexicon-based sentiment analysis, and deep learning to impose sequential learning. Experiments have been performed for the case of the Pakistan Stock Exchange (PSX) using different sentiment dictionaries and time steps. Accuracy and F1-score are used to evaluate the prediction model. We have analyzed the WCN-LSTM results thoroughly and identified that WCN-LSTM performs better than the baseline model. Moreover, the sentiment lexicon HIV4 along with time steps 3 and 7, optimized the prediction accuracy. We have conducted statistical analysis to quantitatively assess our findings. A qualitative comparison of WCN-LSTM with existing prediction models is also presented to highlight its superiority and novelty over its counterparts.

The myth of wartime prosperity: Evidence from the Canadian experience

This paper investigates therelationship between prosperity and national account data during wartime, focusing on Canada. In particular, we build off of existing literature arguing that military outlays must be excluded for real output measures to reasonably approximate economic prosperity. We analyse all non‐war components of Canadian gross national product during both world wars and estimate a novel price deflator for World War II in order to take into account wartime price controls. This allows us to obtain a new estimate of real output in Canada excluding military outlays. We then compare the trends in our new real output series with domestic private investment and stock market trends, all three of which either fell or grew at an anemic pace in Canada during both world wars. Combined, we argue that this provides evidence against the idea of wartime prosperity and more specifically, against the notion of World War II ending the Great Depression in Canada.

MODELING ASSETS PRICING USING PURE JUMP INVERSE-GAUSSIAN PROCESS

In general, stock price changes are rare occurrences, and a Poisson process can be used to describe those price changes. This study focuses on asset price modeling utilizing just jump Inverse-Gaussian processes. The goals are to analyze the price-movement patterns of the stock and gauge its relative volatility using the Inverse-Gaussian jump model. The Inverse-Gaussian jump model was employed to analyze the data. The outcome from Table 2 shows that there are more jumps than zero in the estimation of the inverse Gaussian process. This implies that the stock has been continuously rising. For Nestle Nigeria Limited (628.15), BCC (131.27), NB (110.6), 7UP (79.944), and Guinness (79.944), the average percentage increase in stock price is exceptionally high. The estimated model demonstrates that jump risk can be diversified. It is significantly different for all variables, according to the computed goodness of fit (Chi Square) test. This demonstrates unequivocally that there are daily increases in stock prices. As a result, we draw the conclusion that stock prices have increased recently. The analysis suggests that investors recognize that Nigerian stocks are producing strong returns regardless of stock market trends. To attract more stockholders, more efforts should be made to boost their production.

Stock market indices in the market capitalization management of corporate agricultural enterprises

In globalization and production intensification, corporate agricultural enterprises increasingly seek investment resources for their development. Given the lack of sufficient funds from domestic investors and the transnational nature of economic activity, agricultural corporations are entering international capital markets. On the other hand, implementing corporate strategies for market capitalization management also involves optimizing or constantly increasing the market value of the business, which makes issuers of securities dependent on the stock market, financial risks, and psychological moods of investors. Since general trends determine the market conditions on the stock exchange, the financial management of corporate agricultural enterprises should consider the dynamics of share prices of both the leading issuers on the stock exchange and their industry representatives. Therefore, stock exchange indices are generalized indicators of this. The article aims to study stock market indices in the market capitalization management of corporate agricultural enterprises. It has been shown that stock indices are an essential financial market instrument, allowing investors to analyze market dynamics, assess risks, and formulate investment strategies. With the development of technology and the informatization of securities trading, the role of indices is only growing, and they reflect the dynamics of the economy and the latest investment demands. In corporate market capitalization management, stock indices play a crucial role by providing information to financial analysts, underwriters, and top management about general stock market trends and industry investment expectations. We have provided a brief genesis of stock market indices and defined their primary functions and principles of construction. The methodology for calculating stock exchange indices of corporate agricultural enterprises has been proposed. The trends in the development of Ukrainian agro-corporations for 2012-2022 have been identified, and the prominent trends for 2023 have been forecast. It has been defined that in order to prevent the deepening of the crisis in the corporate agrarian sector in 2023, along with state strategies and targeted programs, strategies for managing the market capitalization of corporate agricultural enterprises should be developed and/or refined based on strategic risk management, crisis management, international investment management, and behavioral finance, taking into account stock exchange indices as one of the strategic indicators of development in a globalized turbulent world. Keywords: stock indices, stock market, market capitalization, corporate agricultural enterprises, financial management

Current trends in the Russian stock market

Current trends in the Russian stock market

A federated learning-enabled predictive analysis to forecast stock market trends

This article proposes a federated learning framework to build Random Forest, Support Vector Machine, and Linear Regression models for stock market prediction. The performance of the federated learning is compared against centralised and decentralised learning frameworks to figure out the best fitting approach for stock market prediction. According to the results, federated learning outperforms both centralised and decentralised frameworks in terms of Mean Square Error if Random Forest (MSE = 0.021) and Support Vector Machine techniques (MSE = 37.596) are used, while centralised learning (MSE = 0.011) outperforms federated and decentralised frameworks if a linear regression model is used. Moreover, federated learning gives a better model training delay as compared to the benchmarks if Linear Regression (time = 9.7 s) and Random Forest models (time = 515 s) are used, whereas decentralised learning gives a minimised model training delay (time = 3847 s) for Support Vector Machine.

A Study of Stock Market Prediction through Sentiment Analysis

In the modern world, the current state and course of economic development and growth are determined by the fortunes and vagaries of the stock markets. In this research study, the authors provide a model that can aid in making reliable and error-free predictions of stock market trends. The described approach uses sentiment analytics based on financial news and past stock market patterns. The proposed structure has been used to forecast stock market patterns that incorporates sentiment analysis taken from news and previous stock market patterns to provide more precise results. The model shown here has provided a two-step process- the Naive Bayes algorithm and forecasting future values of stocks using evaluation findings on text polarity and historical stock value movement information. A novel idea known as the KNN-LR Hybrid algorithm has been introduced to achieve better outcomes when evaluating the accuracy and efficacy of other machine learning algorithms.

A new BRB model for technical analysis of the stock market

To predict the trend of stock prices, a belief rule base (BRB) assessment model based on different technical indicators is proposed in this paper. The proposed BRB-based model includes three BRBs, denoted as BRB_1, BRB_2 and BRB_3. BRB_1 is used to capture the relationship between the price trend of the moving average (MA) and buy/sell decisions. BRB_2 is used to investigate the conditions of moving average convergence and divergence (MACD). BRB_3 is employed to represent the stochastic indicator (KD) states. The above three indicators are commonly used in stock analysis, and they usually need to be used together to achieve a more accurate analysis of the stock price trend. In the BRB model, the initial values of some parameters are provided by experts to construct the elementary algorithm logic, but these are unlikely to result in an accurate assessment. Therefore, on the basis of the maximum likelihood (ML) algorithm, an optimal algorithm for training the parameters of the assessment model is further proposed. Taking the trend of the Chinese stock market as the research object, an average MSE of 0.3242 is obtained using this model. The results indicate the potential application of the proposed model in the financial industry.

Intelligent Techniques for Predicting Stock Market Prices: A Critical Survey

The stock market is an exciting field of interest to many people regardless of their occupational background. It is a market where individuals with adequate knowledge can join and earn an additional income. Nowadays, life expenses have increased. Hence, the number of people investing in stocks is increasing dramatically. Anyone may indeed start participating in the stock market at any time, yet it is not ensured that they will profit from this investment. The stock market is a risky field of investment, given that it is unknown whether the stock will rise or fall. Stock market prediction using Artificial Intelligence techniques is a possible way to help people anticipate stock market directions. Current research showed that many factors aid in changing the stock market value in general and specifically in the Saudi stock market. To our knowledge, most research studies only consider historical data in predicting stock market trends. However, this research aims to enhance the accuracy of the daily closing price for three Saudi stock market sectors by considering historical and sentimental data. Several intelligent algorithms are considered, and their performance indicators are discussed and contrasted against each other. This research concluded that more accurate stock market prediction models could be produced by employing historical and sentimental data.

Stock market analysis with the usage of machine learning and deep learning algorithms

In this work we are focusing on listing out various works in the understanding of various parameters and context to get the overview of stock market analysis in the context of machine learning (ML) and deep learning (DL) models. The work focusses on the stock market analysis along with methodologies and algorithms used to understand the trends and the corresponding results as part of those studies. The importance of this work is to summarize and analyse the parameters which are highly influenced the understandingof the stock market trends. The outcome of the work is understanding the important factors which directly and indirectly influences the stock value raise and drop. The work highlights the methodologies and the algorithms used to stock market data analysis and efficient and effective recommendation of stable stocks to the customers. Further we are listing out the research gaps and future enhancements of the studies which are left over in the earlier works. The work pops up the limitations of some of the works in the existing works along with significance of hyper parameter techniques to clearly identify the features through which we can get more possibilities of better analysis of the data.

Stock Market Prediction using Reinforcement Learning with Sentiment Analysis

This work creates a new Deep Q-learning model with augmented sentiment analysis and stock trend labelling (DQS model). The novelty of this study is as following. We form the stock price prediction problem as trend prediction instead of predicting its accurate price. By benchmarking multiple machine learning methods, stock market trend label is proven to be effective and can be predicted accurately. We use news titles and apply Valence Aware Dictionary for Sentiment Reasoning (VADER) to project the sentiment of the news about stock under study. The input feature to a customized Deep Q-learning model incorporates stock market trend label and sentiment analysis score label. Our study shows that a trading agent using DQS model achieved 83% more portfolio value than a DQ model using only stock technical indicators. The trading agent based on DQS model achieved a Sharpe ratio of 3.65 comparing with 1.6 achieved by a traditional DQ model-based trading agent. This indicates the DQS model combining with input features proposed by our study can achieve excellent risk-free investment portfolio.

Effective short-term forecasts of Saudi stock price trends using technical indicators and large-scale multivariate time series.

Forecasting the stock market trend and movement is a challenging task due to multiple factors, including the stock's natural volatility and nonlinearity. It concerns discovering the market's hidden patterns with respect to time to enable proactive decision-making and better futuristic insights. Recurrent neural network-based methods have been a prime candidate for solving complex and nonlinear sequences, including the task of modeling multivariate time series forecasts. Due to the lack of comprehensive and reference work in short-term forecasts for the Saudi stock price and trends, this article introduces a comprehensive and accurate forecasting methodology tailored to the Saudi stock market. Two steps were configured to render effective short-term forecasts. First, a custom-built feature engineering streamline was constructed to preprocess the raw stock data and enable financial-related technical indicators, followed by a stride-based sliding window to produce multivariate time series data ready for the modeling phase. Second, a well-architected Gated Recurrent Unit (GRU) model was constructed and carefully calibrated to yield accurate multi-step forecasts, which was trained using the recently published historical multivariate time-series data from the primary Saudi stock market index (TASI index), in addition to being benchmarked against a suitable baseline model, namely Vector Autoregression Moving-Average with Exogenous Regressors (VARMAX). The output predictions from the proposed GRU model and the VARMAX model were evaluated using a set of regression-based metrics to assess and interpret the model precision. The empirical results demonstrate that the proposed methodology yields outstanding short-term forecasts of the Saudi stock price trends price compared to existing efforts related to this work.

The Impact of Stock market on Economic Growth: Evidence of Bangladesh

The study investigates the impact of well-functioning stock market in Bangladesh economy (2003-2022). The research has taken account of secondary data and employed regression analysis to measure the intensity of possible influence that Dhaka stock market capitalization has on GDP. It has been demonstrated that the trends in stock market follow parallel momentum along with GDP. The genesis of a functional financial system resides in relevant capital markets that promotes and revitalizes platforms for smooth channeling of funds. A knowledge-based investment environment for investors would pursue higher efficiency and liquidity in the markets. The data collected for the study are analyzed quantitatively and significant statistical evidence have been found that the idea conforms to the analysis. Upper trend in stock market cap reflects inherent profitability and accessibility to resources of listed business companies hence, economic growth is enhanced.

РОЛЬ ФИНАНСОВОГО РЫНКА В ОБЕСПЕЧЕНИИ ЭКОНОМИЧЕСКОГО РОСТА И РАЗВИТИЯ РОССИЙСКОЙ ФЕДЕРАЦИИ (ЧАСТЬ 2)

The article presents the results of a study of the main changes taking place in ensuring economic growth and development of the Russian Federation insofar the financial market. The relevance of the issues is due to the need of studying methodological approaches to the impact of finance as a factor in the acceleration, or, conversely, inhibition of socio-economic development of the world and Russian society. The role and functions of the stock market depend on the entities that are involved in a specific segment of the financial system. For individuals, the securities market acts as a tool for investing personal capital in order to increase it. For companies, the stock market is one of the main methods of attracting external financing. And it is precisely the functions of accumulation and attraction of funds that serve as the basis for creating prospects for the development of the securities market in the state economy. The current trend of the Russian stock market is characterized by a downward trend. It is due to the influence of various challenges that form obstacles to its further development. Hence the decline in the level of investment attractiveness of securities of Russian issuers. Therefore, it is necessary to develop measures aimed at improving the efficiency of the system of state regulation of the securities market.

IMPACT OF THE RUSSIA-UKRAINE CRISIS ON FOOD INFLATION AND STOCK MARKETS IN ASIAN ECONOMIES: WITH REFERENCE TO INDIA, CHINA, JAPAN AND THAILAND

The current geopolitical risk posed by the Russia-Ukraine war has significant implications on the stock markets and the commodities markets of several countries. Using monthly data from October 2020 to the present time period, we analyse the impact of Russia-Ukraine crisis on food inflation and stock market return volatility in the context of Asian countries, namely, India, China, Japan and Thailand with the help of regression analysis based on the OLS technique. Our main findings show us the impact of the Russia-Ukraine war on the food inflation to be visible in China and Japan whereas the war's effect on the stock markets of all the countries selected is shown to be insignificant. We observe the highest and lowest monthly food inflation in China, the highest monthly stock return volatility in Thailand and the lowest monthly stock return volatility in Japan among the selected countries. For the case of India, the war doesn't have much effect on its food inflation and stock market return volatility. We suggest reducing food import dependency by strengthening domestic production and going for alternative food suppliers so as to not be affected by the international food prices that are subjected to the turmoil of war. It is imperative to re-diversify portfolios based on the volatility trend of stock market returns in order to shield oneself from the perils of stock market volatility caused by wars.

Predicting Stock Market Trends: A Bivariate Regression Analysis of Trading Volume Impact

Stock market prediction presents a perennial challenge for investors attempting to forecast the future value of traded stocks at the exchange. The allure of successful predictions, which can yield substantial profits, has driven this pursuit since the inception of stock markets. The 2008 financial crisis highlighted the complexity of market behaviour, emphasizing the need for a deeper understanding of stock market prediction. Regression analysis, a quantitative research method, quantifies relationships between dependent and independent variables. This study conducted a bivariate regression analysis to assess the predictive potential of traded volume on equity price movements within the Nigerian Exchange Limited (NGX or The Exchange), using Guaranty Trust Holding Company Plc (GTCO) historical data. The results revealed a statistically significant correlation (r=0.59) between volume (X) and price (Y). The regression equation (Y = 44.164 + 0.179X) demonstrated that 34.5% of price variance can be predicted from traded volume, indicating a reasonably strong relationship. These findings suggest a moderately robust association between trading volume and stock price movements. This implies that, to some extent, trading volume can guide predictions of stock prices. As investors seek to outperform the market, using linear Regression for stock price prediction remains an engaging area for further research, aiming to enhance return on investment. In conclusion, our linear regression model can generate more precise stock price predictions with a more extensive dataset. Keywords: Efficient market hypothesis, linear Regression, Price prediction, trading volume, Stock market.

Analyzing Stock Market Trends with Time Series Analysis

The stock market is a vital component of modern economies, serving as a mechanism for companies to raise capital and for investors to participate in the growth of those companies. It is a place where investors can buy and sell ownership shares in publicly traded companies. Companies issue shares of their stock to raise capital for growth and expansion, and investors can buy and sell these shares to earn a return on their investment. The stock market serves several important functions in the economy. It provides a mechanism for companies to raise capital by issuing shares of their stock to fund growth and expansion. Investors can buy shares of stock and earn a return on their investment through dividends and/or capital gains. The stock market provides investors with a liquid market where they can buy and sell shares of stock quickly and easily. The stock market helps to determine the price of a company's stock based on supply and demand.Predicting the trend of the stock market is important for investors, traders, and policymakers. It helps investors and traders make informed decisions about buying, selling, or holding stocks, which helps them maximize their returns and minimize their risks. (Source: Investopedia). Predicting the trend of the stock market can help investors and traders identify potential risks and take measures to mitigate them. This can help them avoid losses and protect their investments. The stock market is often seen as a barometer of the economy. Predicting the trend of the stock market can help policymakers monitor the health of the economy and take measures to address any issues. The stock market is influenced by a wide range of factors, including investor sentiment, economic data, and geopolitical events. Predicting the trend of the stock market can provide insights into market sentiment and help investors and traders understand the underlying drivers of stock prices. The stock market is also often used as a proxy for corporate earnings. Predicting the trend of the stock market can help investors and analysts forecast future corporate earnings and make more accurate valuations of individual stocks.

A Comparative Study of Deep Learning Algorithms for Forecasting Indian Stock Market Trends

A Comparative Study of Deep Learning Algorithms for Forecasting Indian Stock Market Trends

Predicting Stock Market Trends with Machine Learning: A Comprehensive Study

Predicting Stock Market Trends with Machine Learning: A Comprehensive Study

Comprehensive Study of Machine learning Algorithms for Stock Market Prediction during COVID-19

The stock market has always drawn investors' attention. Stock trend forecasting tools are in high demand because they facilitate the direct acquisition of profits. The more precise the results, the greater the likelihood of generating a greater profit. Statistically, only 3% of the Indian population invests in stocks, and at the time of COVID-19, that number was even lower, as the stock market was not based on general patterns and equations but rather on the emotional quotient of the people. Such a circumstance increased the stock market's vulnerability. The pandemic factor, such as the case of COVID-19, influences the stock market's trends. Technical analysis of market trends is a technique for interpreting past and present prices to forecast likely future prices. Several deep learning and machine learning algorithms are utilized to generate stock market forecasts, wherein LSTM and ARIMA models have been proven to produce reasonably accurate results. The prior works focused on individual models and their components to provide forecasts. Thus, this paper aims to compare the two well-established models and provide investors with models that work well with data and have appropriate parameter values. The LSTM and ARIMA models are presented because they provide appropriate results using technical analysis of the data set, and the results are to be compared. The closing values from the historic stock prices of the top three Indian hospitality industries were used as the data set. The results show that the individual models work well when the data matches the model and the appropriate parameter is used.