Stock Market Trends Research Articles

A study on the effect of exchange rates on the domestic stock market and countermeasures

In the domestic stock market, the capital market opened in January 1992, and the proportion of foreign capital has steadily increased, accounting for 30% of the domestic market in Overall stock market trend infers that the domestic stock market is more influenced by foreign issues than domestic issues. The trading trend of foreign capital displays a similar flow to exchange rate fluctuations,; thus, preparing an investment strategy by using the Pearson analyzing method the effect of exchange rates of foreign capital trading, fluctuations in exchange rates, and predicting one of the macroeconomic indicators will yield high returns in the stock market. Therefore, this research was conducted to help investment by predicting foreign variables comparing and analyzing exchange rates and foreign capital trading patterns, and predicting appropriate time for buying and selling.

Stochastic Modeling for the Analysis and Forecasting of Stock Market Trend using Hidden Markov Model

The HMM is generally applied to forecast the hidden system of observation data. In this paper, we deal with the development of HMM for a proper understanding of finance variables in the stock market. Formulation of relationships between and within both the changing share values of Housing Development Finance Corporation Bank Limited (HDFC Bank Ltd) as visible/observed states influenced by the indicators of S&P Bombay Stock Exchange Sensitive Index (Sensex) as invisible/influencing states. Stochastic modeling with hidden Markov models is carried out for exploring various parameters of the model. Mathematical derivations for all the required statistical measures are obtained using the method of moments for the proposed probability distribution. Deducing mathematical formulation of initial probability vector, transition and observed probability matrices were carried out with the empirical data sets. Probability distribution for visible states of various lengths is obtained. It is observed from the empirically analysis that there is the maximum likelihood of rising the share prices of HDFC bank in consecutive two days. Furthermore, an attempt is made to estimate the long-run steady-state behavior of both the SENSEX and HDFC Bank share prices. The share value of HDFC bank will be on rising state from the 19th day onwards and it may be recommended for good investment choice for the long run. The findings of these studies will be valid for effective decision-making in portfolio management.

Forecasting of Stock Market Trends using Machine Learning Techniques

Forecasting of Stock Market Trends using Machine Learning Techniques

Leveraging enterprise knowledge graph to infer web events’ influences via self-supervised learning

Knowledge graph (KG) techniques have achieved successful results in many tasks, especially in semantic web and natural language processing domains. In recent years, representation learning on KG has been successfully applied to e-business applications, such as event-driven automatic investment strategies. However, there is still limited research about learning events’ influence on KG for modern quantitative investment. In this paper, we propose a novel event influence learning framework to predict stock market trends, called ST-Trend, leveraging enterprise knowledge graph to represent company correlation relationships, for mining the deep background knowledge of web events, with three self-supervised learning tasks. In particular, we devise two jointly self-supervised tasks to identify the relations between web events and companies. The first task is for generating ground-truth event-company correlation labels based on the enterprise knowledge graph. The second task is used to train how to identify the correlated companies of an event based on the generated correlation labels, with the encoding of web events, company features, and technical sequential data. We then design the prediction network to infer an event’s influence on stock price trends of the identified correlated companies based on the enterprise KG. Finally, we perform extensive experiments on a massive real-life dataset to validate the effectiveness of our proposed framework, and the experimental results demonstrate its superior performance in predicting stock market trends via considering events’ influences with the enterprise knowledge graph.

Credit Scoring Active Telegram Channels Offering Stock Signals

The impact of personal judgment on the assessment of an individual’s financial situation has been drastically reduced through the development of credit scoring. The systems are capable of deciding based on an applicant’s total score which is a combination of several factors and indicators. Over the past few decades, credit scoring has been considered an essential tool for evaluation in various institutions and has also been able to transform the industry as a whole. Most of the research conducted in the field has taken into account traditional credit scoring, but considering the ever-evolving technological world that we live in and the increasing emergence of new social media networks, such research has now become obsolete. Such technological advancements have not only paved the way for far more sophisticated credit scoring systems but also essentially rendered the previous generations useless. It should be noted that credit scoring and its features have widely been discussed across the globe but, considering the various aspects and models that have to be taken into account, no one best method has been designed or suggested for it so far. This study shows that social media channels tend to perform relatively well in predicting stock market trends when the overall index is growing positively. The research also illustrates that a higher number of days of activity and a large number of signals released do not necessarily mean that the channels can or have credited their offered stock return on a one-month time frame. The methodology used is "CRISP-DM," which consists of six steps. The main variables include social and financial variables that are examined for six months. In the research, we seek to identify, analyze and categorize active telegram channels in stock signals using the data mining model and the RFM method. The k-means algorithm is selected for this category. Then, in each cluster, the importance of social variables and the performance of the channels are extracted by the EXTRATREECLASSIFIER algorithm, and channel performance is measured by considering the changes in the total index.

Stock Prediction using Neural Networks and Evolution Algorithm

Abstract: Various researches and studies have shown that machine learning techniques like neural network have the ability to learn and predict the general trend of stock market. Artificial intelligence and different machine learning techniques have been widely implemented in the field of forecasting stock prices for a long time. However, selecting the best model and best hyperparameters for these models is highly necessary for better accuracy in prediction. Given the huge number of architecture types and hyper-parameters for each model, it is not practical to find the best combination for best accuracy. Therefore, in this research we used evolution algorithm to optimize model architecture and hyper-parameters. Promising results are found in stock prediction. Keywords: Neural Network, Long Short-Term Memory, Recurrent Neural Network, Dense Neural Network, Gated Recurrent Unit, Stock Prediction, Evolution Algorithm.

The Dynamic Impact of COVID-19 Pandemic on Stock Returns: A TVP-VAR-SV Estimation for G7 Countries

The COVID-19 pandemic has profoundly and negatively impacted the global stock markets. Hence, we investigated the time-varying impact of the COVID-19 pandemic on stock returns during the period from January 27, 2020 to December 23, 2021 using the TVP-VAR-SV model and used G7 countries as our research sample. Our results imply that (i) the spread of the COVID-19 pandemic has a significant negative impact on stock returns, but the impact decreases as the time window increases; (ii) the timeliness, compulsoriness, and effectiveness of anti-epidemic policies implemented by governments are the important adjustment factors for stock returns; (iii) the impact of the early stage of the COVID-19 pandemic on the stock market trend gradually weakens as the intermediate time interval increases. In addition, over time, the duration of the negative impact of the COVID-19 pandemic on the stock returns became shorter, and the recovery rate of the impact became faster; (iv) under the managed floating exchange rate regime, the stock returns changed synchronously with the pressures of exchange rate appreciation and depreciation, and under the free-floating exchange rate regime, the effect of the exchange rate on stock returns was almost zero, while the impact of exchange rate channels in eurozone countries was related to the characteristics of national economies. Thus, governments should make greater efforts to improve the compulsion and effectiveness of epidemic prevention policies and strengthen their control over exchange rate fluctuations to alleviate the negative impact of the COVID-19 pandemic on the stock markets.

Machine learning approaches for sentiment analysis

Sentiment Analysis or Opinion Mining is popular task of Natural Language Processing (NLP) performed on textual data generated by users to know the orientation or sentiment of the text. To perform Sentiment Analysis, it is critical to create an accurate and precise model, machine learning techniques are heavily utilized to build an accurate model. Deep learning and transfer learning techniques have been found to have increased utilization and better results, making them one of the most popular research areas around the world. Hotel and restaurant industries analyze reviews to obtain a deeper understanding of their client’s needs, likes and dislikes, whereas specialists use Twitter data and stock market news items to forecast stock market trends. Machine Learning algorithms are most essential part of a Sentiment Analysis model, this survey paper analyze all the widely used Machine Learning Approaches for Sentiment Analysis. A brief introduction on Methodology for Sentiment Analysis is given along with conclusion and future scope and in the field of Sentiment Analysis.

IntelliPortfolio: Intelligent Portfolio for Enhanced Index Tracking Using Clustering and LSTM

Enhanced index tracking (EIT) is an active research area in portfolio management that focuses on adding reliable value relative to the index on the basis of mimicking the behavior of the benchmark index. To solve the EIT problem, many approaches have been proposed. However, it still remains a critical challenge to efficiently generate a portfolio with good quality. In this study, we propose a learning-based approach named IntelliPortfolio for the EIT problem. IntelliPortfolio uses PCA and clustering to select stock and estimates the investment weight for each constituent stock using a long short-term memory (LSTM) network. Two advantages of the proposed algorithm are as follows. (1) It considers both the fundamentals and the price information for stocks and can balance the trade-off between the performance and the diversity of the selected stocks. (2) It uses a LSTM model to estimate investment weights, which is more capable to handle long sequences of input and is more robust to predict the future trend of stock market. Experimental results on the five real-world datasets of the international stock market illustrate the significant performance superiority of the proposed approach in comparison with five state-of-the-art algorithms.

The Influence of Covid-19 Cases and Sentiments on Indonesia Stock Market Trends

This study determined the COVID-19 cases and sentiments effect on the stock price index of selected sectors in Indonesia. The study applied time-series data from the period 2 March-27 October 2020 by collecting data on COVID-19 cases, Indonesia Stock Index (IDX), 10 selected sectors (Jasica) and Google Trends (GT). The researchers used the search terms related to COVID-19 sentiments. The researchers used descriptive analysis for IDX and GT data and applied quantitative analysis with time lag correlation to analyze data based on COVID-19 cases and GT data on the IDX & 10 selected sectors. The researchers used the Manufacture and Mining sectors with the high-or-low negative correlation groups; IDX, Finance, and Miscellaneous Industry with the moderate-or-low negative correlation groups; Trade with the high-or-moderate-or-negligible negative correlation; Agriculture with the high-or-low-or-negligible negative correlation; Consumer Industry with high-or-negligible negative correlation group; Basic Industry and Infrastructure sectors with the moderate-or-negligible negative correlation groups; and Property sector with negligible correlation group. The correlation results showed different effects regarding the sentiments of COVID-19 in each sector.

THE REAL AND THE FICTION IN THE ECONOMY OF WESTERN BALKANS: A RESEARCH METHODOLOGY

This study attempts to propose a Research Model for study linkages across Western Balkans’ economies macroeconomic indicators and the stock market. Do they move independently of each other? Are there any asymmetries following shocks or not? And, finally, do financial crises have any effect on these linkages? The answers to these questions will clarify the distance between the real and the fiction, i.e., macroeconomic developments and stock market trends, in the sensitive area of the six Western Balkan countries: Albania, Bosnia-Herzegovina, North Macedonia, Montenegro, Serbia, and Kosovo. Deciphering stock market trends and linking them to the real economy would be a powerful tool of their economic maturity, in their process of EU accession. In this paper, we proposed a research methodology to investigate the dynamic linkages among the six countries of Western Balkans’ economic indices, stock markets, and interest rates/yields for the 2015-2021 period, via Granger causality tests and VARs, for the full and several sub-periods. Can some of these countries play the role of economic leaders in the area? 1. Are there any impulse responses and variance decompositions from various VARs, indicating independent move of the real economic indices, bond yields, and the stock market in these six countries? 2. Are there signs of disconnection among important real and financial sectors in each of the countries examined? Do these countries have followed their paths and that no significant short-run (or even long-run) linkages of real production growth rates across countries exist or not? Utilizing this methodology has two advantages: it feeds empirical research to capture the economic development in the sensitive, political, economic, and social terms, area of Western Balkans, and contribute to the economic evaluation of the EU Stabilization and Association Process. Keywords: macroeconomic indices, stock market, Western Balkans, EU Accession.

Discovering the influences of the patent innovations on the stock market

With the implementation of the innovation-driven development strategy, increasing technical innovations are patented by the individuals or the companies. As a form of intellectual properties, the patent has attracted attention from individuals and companies. Although there are some researches on the economic function of patent, few quantitative researches discuss on whether patents can work on the company stock market. To discover the relations between the company patents and the stock market, we explore a method to analyze the influence of patent activity on the company stock market. We collect the patent data and the stock data of listed companies, from which patent and market activities are extracted. By the recursive discrete wavelet transform, the patent and market activities are decomposed into multi-scale wavelets. These wavelets are fed into a patent and market activity based stock market trend prediction model, in which the influences of patent activity are analyzed. We compare our model with the state-of-the-art model on 4 measurements for 3 manufacturing datasets. The experimental results show that the patent activities have positive effect on market trend prediction in about 30% manufacturing listed companies and that the measurements of Shanghai/Shenzhen Stock Exchange often outperform that of USA in years 2016–2019 for the manufacturing listed companies.

Forecasting the Market with Machine Learning Algorithms: An Application of NMC-BERT-LSTM-DQN-X Algorithm in Quantitative Trading

Although machine learning (ML) algorithms have been widely used in forecasting the trend of stock market indices, they failed to consider the following crucial aspects for market forecasting: (1) that investors’ emotions and attitudes toward future market trends have material impacts on market trend forecasting (2) the length of past market data should be dynamically adjusted according to the market status and (3) the transition of market statutes should be considered when forecasting market trends. In this study, we proposed an innovative ML method to forecast China's stock market trends by addressing the three issues above. Specifically, sentimental factors (see Appendix [1] for full trans) were first collected to measure investors’ emotions and attitudes. Then, a non-stationary Markov chain (NMC) model was used to capture dynamic transitions of market statutes. We choose the state-of-the-art (SOTA) method, namely, Bidirectional Encoder Representations from Transformers ( BERT ), to predict the state of the market at time t , and a long short-term memory ( LSTM ) model was used to estimate the varying length of past market data in market trend prediction, where the input of LSTM (the state of the market at time t ) was the output of BERT and probabilities for opening and closing of the gates in the LSTM model were based on outputs of the NMC model. Finally, the optimum parameters of the proposed algorithm were calculated using a reinforced learning-based deep Q-Network. Compared to existing forecasting methods, the proposed algorithm achieves better results with a forecasting accuracy of 61.77%, annualized return of 29.25%, and maximum losses of −8.29%. Furthermore, the proposed model achieved the lowest forecasting error: mean square error (0.095), root mean square error (0.0739), mean absolute error (0.104), and mean absolute percent error (15.1%). As a result, the proposed market forecasting model can help investors obtain more accurate market forecast information.

Stock Prediction Based on Technical Indicators Using Deep Learning Model

Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature. The stock data is usually non-stationary, and attributes are non-correlative to each other. Several traditional Stock Technical Indicators (STIs) may incorrectly predict the stock market trends. To study the stock market characteristics using STIs and make efficient trading decisions, a robust model is built. This paper aims to build up an Evolutionary Deep Learning Model (EDLM) to identify stock trends’ prices by using STIs. The proposed model has implemented the Deep Learning (DL) model to establish the concept of Correlation-Tensor. The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange (NSE) – India, a Long Short Term Memory (LSTM) is used. The datasets encompassed the trading days from the 17 of Nov 2008 to the 15 of Nov 2018. This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends. The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one. The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%, 56.25%, and 57.95% on the datasets of HDFC, Yes Bank, and SBI, respectively. Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.

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.

Research on the trend of Chinese stock market based on Monte Carlo simulation method

The quantitative trading strategy of stock market in developed countries has been very mature, but the quantitative trading strategy of China's stock market has only begun to develop in recent years. In this paper, the closing levels of CSI 300 Index from 2002 to 2022 were selected as the research objects. Monte Carlo simulation method was used to simulate the closing levels of the next 30 trading days, and the simulation results of the last trading day were counted and analyzed. It is found that most of the results of Monte Carlo simulation are in the reasonable range, and the occasional extreme case has similar cases in history. Therefore, the use of Monte Carlo simulation can roughly simulate the future period of income and risk situation, to provide a certain degree of help for investors to make decisions.

CURRENT TRANSFORMATIONS OF GLOBAL EXCHANGE TRADING

Objective. The objective of the study is to analyze the current state and identify the main trends in the development of world stock trading. Methods. In the research process, general scientific methods and techniques of research were used: analysis, synthesis, generalization, induction, deduction, abstract-logical method, graphic and tabular methods (for a visual representation of the analyzed material). The results. The article analyzes the main trends in the development of global stock trading. The development trends of the international stock market are considered and characterized. The current state and structure of global exchange trading in commodity and financial derivatives in 2013-2022 was analyzed. Futures and options trading was analyzed. An analysis of the structure of world stock trade by main groups of assets was carried out. The structure of the world stock market was studied by geographical regions. The volume of trade activity in various regions in 2013, 2021-2022 was studied. It was revealed that the largest percentage share of world trade activity during the last decade is occupied by the Asia-Pacific region. The rating of world stock exchanges by the number of contracts and the rating of the largest world stock exchanges by market capitalization were analyzed. The rating of world exchanges by the number of contracts shows that the largest number of transactions is on the exchanges of India, Brazil, the USA, China, South Korea, etc. The main factors affecting the current state of development of stock trading in the world are systematized. It has been established that today the world is actively developing electronic exchange trading and the growth of the cryptocurrency economy, which will continue in the near future and will lead to increased competition among electronic exchanges.

Preliminarily Explore the Steps of Financial Big Data Analytics

Big data analytics is difficult for people since there are so many factors that can influence the ways stock market change. Big data analytics is different from weather forecasting, which can be made by applying the laws of atmospheric change and mass experience and other comprehensive research. However, stock markets do not have a fixed law that allows people to do analysis. They are influenced by governments decision, information on the website, citizens’ behaviors, etc. Hence, this paper helps people learn how to do big data analytics and stock market prediction by three steps, such as sentiment analysis, information extraction and cleaning, and three models for stock market prediction. The implementation process of big data analytics generally includes several stages, such as data acquisition and recording, information extraction and cleaning, data integration and presentation, selection of modeling and analysis methods, interpretation of results, effectiveness of evaluation results and monitoring.[1],[2] The result of the prediction is that LSTM model is the best model in prediction of the trend of stock market.

Associating Fundamental Features with Technical Indicators for Analyzing Quarterly Stock Market Trends Using Machine Learning Algorithms

The stock market is the primary entity driving every major economy across the globe, with each investment designed to capitalize on profit while decreasing its associated risks. As a result of the stock market’s importance, there have been enumerable studies conducted with the goal of predicting the stock market through data analysis techniques including machine learning, neural networks, and time series analysis. This paper uses machine learning algorithms to perform stock market index classification using fundamental data while classifying the indices using technical indicators. The data were derived from Yahoo Finance on the top 100 indices in the NASDAQ stock market from January 2000 to December 2020.

СУЧАСНІ ФІНАНСОВІ ТА ТОВАРНІ РИНКИ: ХАРАКТЕРИСТИКИ ТА ВИМІРИ

Modern financial systems are global in nature. The article reveals the current state of world financial markets. The example of stock index trends shows the main trends in global financial markets as a result of global challenges (the Covid-19 pandemic and the war in Ukraine). The trends of stock market indicators in different regions of the world are analyzed. The article reveals the current factors that affect the trends of stock indices. The influence of economic and political consequences caused by the pandemic and war on the further development of the world economy is studied. The role of geopolitical events on the development of commodity markets, in particular gold and oil, is shown. The main stock indices and how they react to changes in the world economic environment are analyzed. Thus, the article shows that negative global signals are causing concerns about rising inflation around the world, and rising rates are affecting investor sentiment. It has been proven that financial markets can calm down from the global decisions of international leaders and organizations. However, very often geopolitical decisions show signs of indecision, which affects the continuation of hostilities in Ukraine and contributes to the subsequent fluctuations of financial indicators. t is shown that the volatility of financial markets will remain high in the last week of June, due to the planned expiration of contracts for derivative instruments in June and political decisions of the G7 countries, which may ban Russian gold imports on Tuesday, June 28. The article analyzes the characteristics of the trends of major world stock indices and reveals the possible prospects for their development, as well as identifies the main factors of stock market trends at the present stage of development. The current state of the financial market of Ukraine is studied.