An empirical study on the various stock market prediction methods

Investment in the stock market is one of the much-admired investment actions. However, prediction of the stock market has remained a hard task because of the non-linearity exhibited. The non-linearity is due to multiple affecting factors such as global economy, political situations, sector performance, economic numbers, foreign institution investment, domestic institution investment, and so on. A proper set of such representative factors must be analyzed to make an efficient prediction model. Marginal improvement of prediction accuracy can be gainful for investors. This review provides a detailed analysis of research papers presenting stock market prediction techniques. These techniques are assessed in the time series analysis and sentiment analysis section. A detailed discussion on research gaps and issues is presented. The reviewed articles are analyzed based on the use of prediction techniques, optimization algorithms, feature selection methods, datasets, toolset, evaluation matrices, and input parameters. The techniques are further investigated to analyze relations of prediction methods with feature selection algorithm, datasets, feature selection methods, and input parameters. In addition, major problems raised in the present techniques are also discussed. This survey will provide researchers with deeper insight into various aspects of current stock market prediction methods

Can crude oil serve as a hedging asset for underlying securities? - Research on the heterogenous correlation between crude oil and stock index

In the increasingly frequent global financial turmoil, investors prefer to invest in stable assets to hedge risks. Crude oil naturally has dual use value as a general commodity and as a financial asset, which has attracted wide attention. In this paper, we adopt a wavelet coherence analysis to study the standard of crude oil as a hedging asset and analyze the dynamic correlation of crude oil and stock market price fluctuations in the four economies of the United States, Japan, China and Hong Kong at different frequencies. The empirical evidence shows that crude oil can be conditionally used as a hedging asset for underlying securities. From the perspective of space, crude oil is suitable for investors in China's stock market as a hedging asset, while for stock markets in the US, Japan and Hong Kong, the ability of crude oil to hedge risk has been greatly weakened. From the perspective of investment term, although crude oil cannot be regarded as a hedging asset for long-term investment, it can still play a hedging role in the short term. When the market is in a state of panic, the ability of oil to hedge risk is stronger

Critical review of text mining and sentiment analysis for stock market prediction

The paper is aimed at a critical review of the literature dealing with text mining and sentiment analysis for stock market prediction. The aim of this work is to create a critical review of the literature, especially with regard to the latest findings of research articles in the selected topic strictly focused on stock markets represented by stock indices or stock titles. This requires examining and critically analyzing the methods used in the analysis of sentiment from textual data, with special regard to the possibility of generalization and transferability of research results. For this reason, an analytical approach is also used in working with the literature and a critical approach in its organization, especially for completeness, coherence, and consistency. Based on the selected criteria, 260 articles corresponding to the subject area are selected from the world databases of Web of Science and Scopus. These studies are graphically captured through bibliometric analysis. Subsequently, the selection of articles was narrowed to 49. The outputs are synthesized and the main findings and limits of the current state of research are highlighted with possible future directions of subsequent research

Recent Advances in Stock Market Prediction Using Text Mining: A Survey

Market prediction offers great profit avenues and is a fundamental stimulus for most researchers in this area. To predict the market, most researchers use either technical or fundamental analysis. Technical analysis focuses on analyzing the direction of prices to predict future prices, while fundamental analysis depends on analyzing unstructured textual information like financial news and earning reports. More and more valuable market information has now become publicly available online. This draws a picture of the significance of text mining strategies to extract significant information to analyze market behavior. While many papers reviewed the prediction techniques based on technical analysis methods, the papers that concentrate on the use of text mining methods were scarce. In contrast to the other current review articles that concentrate on discussing many methods used for forecasting the stock market, this study aims to compare many machine learning (ML) and deep learning (DL) methods used for sentiment analysis to find which method could be more effective in prediction and for which types and amount of data. The study also clarifies the recent research findings and its potential future directions by giving a detailed analysis of the textual data processing and future research opportunity for each reviewed study

Marketing plan of Oriental Yuhong Company

The COVID-19 outbreak, which started in 2020 and has continued globally since then, has largely changed the way human beings live and work, and has also caused different impacts and effects in various industries. The purpose of this project is to develop a new marketing plan to address the decline in operating revenue and profit of Oriental Yuhong Company in 2022. This thesis uses literature review method, case study method and questionnaire method, and conducts external, internal and competitive analysis, and uses 4P marketing model to formulate a new marketing plan. The final conclusion of this thesis: Firstly, the company needs to reposition the new product market and focus on R&D innovation. Then, vigorously develop the application of the Internet, improve the enterprise network information system, and establish the service system of O2O marketing mode. And it should pay attention to the change of consumer demand in key markets and keep up with the change of policy and general environment.O surto de COVID-19, que começou em 2020 e tem continuado a nível mundial desde então, alterou em grande medida a forma como os seres humanos vivem e trabalham, tendo também causado diferentes impactos e efeitos em vários sectores. O objetivo deste projeto é desenvolver um novo plano de marketing para fazer face ao declínio das receitas operacionais e dos lucros da Oriental Yuhong Company em 2022. Esta tese utiliza o método de revisão da literatura, o método de estudo de casos e o método de questionário, e realiza análises externas, internas e competitivas, e utiliza o modelo de marketing 4P para formular um novo plano de marketing. A conclusão final desta tese: Em primeiro lugar, a empresa precisa de reposicionar o mercado de novos produtos e concentrar-se na inovação de I&D. Depois, desenvolver vigorosamente a aplicação da Internet, melhorar o sistema de informação da rede da empresa e estabelecer o sistema de serviço do modo de marketing O2O. Além disso, deve prestar atenção à evolução da procura dos consumidores nos principais mercados e acompanhar a evolução da política e do ambiente geral

Combination of Facebook Prophet and Attention-Based LSTM with Multi- Source data for Indian Stock Market Prediction

The stock market prediction has been the subject of interest to various researchers and analysts due to its highly unpredictable nature and serves as a perfect example for time series forecasting. Over the years deep learning models such as Long-Term Short-Term Memory and statistical models such as Autoregressive Integrated Moving Average have shown promising results in predicting future stock prices. But the results from these models cannot be generalized as they fail to incorporate the dynamics of the market and influence of several external factors such as political, social, investor\u27s emotion, etc on stock markets. Recently Facebook’s creation of the Prophet model solely for time series forecasting has been successful in fitting the trends and seasonality of the data accurately compared to vanilla models. This research proposes a unique combination of the newly developed Facebook Prophet model and Attention-Based Long-Term Short-Term Memory model to predict the adjacent closing price of NIFTY 50 stocks to fit both the seasonality and non-linearity component of stock price data. Further to encompass both market and investor sentiments influencing stock prediction, data from five sources are collected from 01/01/2015 to 31/12/2019 namely historic stock price, technical indicators, news articles scraped from multiple news sources, and tweets collected from a verified Twitter account. To extract sentiments from unlabelled news and tweet data this research takes upon an unsupervised approach by implementing a pre-trained Bidirectional Encoder Representations from Transformers base uncased model. The proposed model is trained and validated on eight combinations of the dataset created by merging data from multiple sources and compared with the performance of the baseline Facebook Prophet model trained and tested with data from a single source i.e., historic stock prices. The proposed model resulted in the least Mean Absolute Percentage Error ranging from 3.3 to 7.7 for all the combinations of the data in comparison to the baseline model which achieved the highest Mean Absolute Percentage Error of 11.67

China's New Sources of Economic Growth: Vol. 1. Reform, Resources and Climate Change

China’s change to a new model of growth, now called the ‘new normal’, was always going to be hard. Events over the past year show how hard it is. The attempts to moderate the extremes of high investment and low consumption, the correction of overcapacity in the heavy industries that were the mainstays of the old model of growth, the hauling in of the immense debt hangover from the fiscal and monetary expansion that pulled China out of the Great Crash of 2008 would all have been hard at any time. They are harder when changes in economic policy and structure coincide with stagnation in global trade and rising protectionist sentiment in developed countries, extraordinarily rapid demographic change and recognition of the urgency of easing the environmental damage from the old model. China’s economy has slowed and there are worries that the authorities will not be able to contain the slowdown within preferred limits. This year’s Update explores the challenge of the slowdown in growth and the change in economic structure. Leading experts on China’s economy and environment review change within China’s new model of growth, and its interaction with ageing, environmental pressure, new patterns of urbanisation, and debt problems at different levels of government. It illuminates some new developments in China’s economy, including the transformational potential of internet banking, and the dynamics of financial market instability. China’s economic development since 1978 is full of exciting change, and this year’s China Update is again the way to know it as it is happenin

Real-Time Stock Market Recommendation & Prediction using Multi Source Data

Stock investors must be cognizant of both the current price of their stock and the price at which they want to sell it in the future. This does not stop investors to monitor past price patterns and apply their knowledge to the present. ’Past performance is not an indicator of future success’, as the saying goes. To put it another way, historical stock data alone isn’t enough to forecast future stock prices. Another key factor to consider in a trading strategy is the impact of market psychology. Financial data, which is a type of multimedia data, provides a wealth of information that has been widely used for data analysis tasks. However, predicting stock prices remains a popular study topic for investors and financial scholars. Forecasting stock prices has become an extremely difficult undertaking because of the significant noise, nonlinearity, and volatility of stock price statistic data