Stock Market Trends Research Articles

Stock market trend detection and automatic decision-making through a network-based classification model

Many complex systems observed in nature and society can be described in terms of network. A salient feature of networks is the presence of community patterns. Network-based models have already been applied in the analysis of data from very diverse areas, from epidemics modeling to periodicity detection in meteorological data. In this paper, inspired by the formation of community structures, such as the metabolic networks and the anatomical and functional connectome observed in biological neural networks, we present a model which makes use of connector hubs to detect price trend reversals and to automatize decision-making processes in stock market trading operations for selecting a good investment strategy and improve the returns. It starts by mapping the historical stock price time series as a network, where each node represents a price variation range and the edges are generated according to the time sequential order in which these ranges occur. Afterwards, communities of the constructed network so far are detected, which represent the up and down trends of the stock prices. The model has two phases: (1) Trend detection phase, where the price trend communities are detected and trend labels are generated; and (2) Operating phase. In this phase, the proposed technique predicts trend labels to future stock prices, in such a way that these trends can be used as triggers to perform buying and selling operations of the stock. We evaluate the model by applying it on historical data from 10 of the most traded stocks from both NYSE and the Brazilian Stock Exchange (Bovespa). The obtained results are promising, with the model’s best returns being able to outperform the stock price returns for the same period in 15 out of the 20 cases under consideration.

Stock Price Trend Analysis Method Based on Change Cycle Clustering

With the continuous development of financial informatization and the continuous improvement of China's economic system, analyzing and mining financial data has become an important means to study financial problems. Compared with other industries in the financial field, stock data is easier to collect and store, and its application is more convenient. Analyzing and predicting the changing trend of the future stock market through the historical data of stocks is helpful to reduce the risk of investors and increase income. It has become a research hotspot in the financial field. Using the historical price data of domestic stocks, a method based on a clustering algorithm is proposed to analyze the periodic characteristics of stock price changes. This method can provide a basis for the prediction of stock price and the detection of trading behaviour in the stock market.

Relationship between Stock Market & Housing Market Trends and Liquidity

Relationship between Stock Market & Housing Market Trends and Liquidity

Associative Classification Model for Forecasting Stock Market Trends

This paper proposes an associative classification model based on three technical indicators to forecast future trends of stock market. Our methodology assessed the performance of nine technical indicators, using a portfolio of ten stocks and a 12-year time series. The experimental results showed that the use of a set of technical indicators leads to higher classification rates compared to the use of sole technical indicators, reaching an accuracy of 88.77%. The proposed approach also uses a multidimensional data cube that allows automatic updating of stock market asset values, which are essential to keep the forecast updated. The results indicate that our approach can support investors and analysts to operate in the stock market.

ANALYSIS OF THE BRAZILIAN STOCK MARKET THROUGH GRAPH CENTRALITY MEASURES

ABSTRACT This article aims to analyze the shares that make up the Brazilian IBRX100 index, verifying which sectors had the greatest influence on the Stock Exchange in 2018, 2019, and 2020. For this purpose, the theory of graph centrality measures was used to discover the most central shares. A balance analysis of the graphs was also performed, since balanced graphs are more stable, generating a more predictable stock portfolio. This study may help investors to compose a safer stock portfolio and identify which stocks are most correlated with each other. The most central shares can aid in perceiving stock market trends.

Stock Prediction Using Ml

Stock market price data is generated in huge volume and it changes every second. Stock market is a complex and challenging system where people will either gain money or lose their entire life savings. In this work, an attempt is made for prediction of stock market trend. Two models are built one for daily prediction and the other one is for monthly prediction. Supervised machine learning algorithms are used to build the models. As part of the daily prediction model, historical prices are combined with sentiments. Up to 70% of accuracy is observed using supervised machine learning algorithms on daily prediction model. Monthly prediction model tries to evaluate whether there is any similarity between any two months trend. Evaluation proves that trend of one month is least correlated with the trend of another month.

Crude oil stock market trend reversal forecast based on Wall Street Journal articles with keyword indication

The research aims to find correlations between articles in the Wall Street Journal (WSJ) and oil price change. Exactly how much the WSJ induces, how much impact it has, or how much it predicts future trend reversals. Current speculative trading and the willingness to buy or sell following certain events have a decisive influence on stock market prices. In the case of oil, we cannot talk about purely speculative pricing, but its sensitivity to market processes and the decisions of market participants, and possibly only possible decisions, is indisputable. In this way, we search for and determine correlations based on the above.

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 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 indices classification using fundamental data, while classifying the indices using technical indicators, with data derived from Yahoo Finance on the top 100 indices in the NASDAQ stock market from January 2000 to December 2020.

Associative classification model for forecasting stock market trends

Associative classification model for forecasting stock market trends

The connection between stock market prices and political support: evidence from Japan

ABSTRACT This study analyzes the causality between stock market prices and political support, with a focus on Japan. Japan offers a critical setting to control for the causality of political connectedness, because electoral institutions proscribe corporate donations and politicians holding concurrent corporate executive positions and corporate donations. By narrowing the causality from stock market trends to political components, a vector autoregressive linear non-Gaussian acyclic model reveals that stock prices are the cause (parent) and ruling party support is the outcome (child).

PRICE PREDICTION OF DIFFERENT CRYPTOCURRENCIES USING TECHNICAL TRADE INDICATORS AND MACHINE LEARNING

From the past two years with increasing geopolitical and economic issues, global currency values have been falling and stock markets have been having a poor run & investors losing wealth. This has led to a renewal of interest in digital currencies. Cryptocurrency one of the most prominent digital currency has found itself in spotlight with investors wanting a piece of it and business establishments accepting it as a source of payment due to its stable performance in the last few years. This research has been done on predicting cryptocurrency prices using machine learning based neural network which has a lowest the model loss over 100 epochs during training and Technical Trade Indicators (TTI) graphs depicts a real BTC value 5 to 10 times in 300-days of current fiscal year has further supported this increasing trader confidence and a shift in global cryptocurrency graph by predicted BTC values. On the same lines, we are analyzing bitcoin prices using Machine Learning and Sentiment Analysis. We also study stock market trends in order to better predict bitcoin prices quantitively. In this work we analyze the impact of global currencies like US Dollar, foreign exchanges on Bitcoin prices and whether Bitcoin has the stability to dethrone global currencies and become the single medium of transaction. This work is adequate enough to aid in predicting price and with results obtained from predicting Bitcoin prices using machine learning based neural network achieving an accuracy of 94.89% under all circumstances of technical trade indication thereby bringing down its price prediction by over 13.7% in April 2020 itself during evaluation.

Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction.

Predicting stock market (SM) trends is an issue of great interest among researchers, investors and traders since the successful prediction of SMs’ direction may promise various benefits. Because of the fairly nonlinear nature of the historical data, accurate estimation of the SM direction is a rather challenging issue. The aim of this study is to present a novel machine learning (ML) model to forecast the movement of the Borsa Istanbul (BIST) 100 index. Modeling was performed by multilayer perceptron–genetic algorithms (MLP–GA) and multilayer perceptron–particle swarm optimization (MLP–PSO) in two scenarios considering Tanh (x) and the default Gaussian function as the output function. The historical financial time series data utilized in this research is from 1996 to 2020, consisting of nine technical indicators. Results are assessed using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and correlation coefficient values to compare the accuracy and performance of the developed models. Based on the results, the involvement of the Tanh (x) as the output function, improved the accuracy of models compared with the default Gaussian function, significantly. MLP–PSO with population size 125, followed by MLP–GA with population size 50, provided higher accuracy for testing, reporting RMSE of 0.732583 and 0.733063, MAPE of 28.16%, 29.09% and correlation coefficient of 0.694 and 0.695, respectively. According to the results, using the hybrid ML method could successfully improve the prediction accuracy.

Fuzzy Crow Search Algorithm-Based Deep LSTM for Bitcoin Prediction

Prediction of stock market trends is considered as an important task and is of great attention as predicting stock prices successfully may lead to attractive profits by making proper decisions. Stock market prediction is a major challenge owing to non-stationary, blaring, and chaotic data and thus, the prediction becomes challenging among the investors to invest the money for making profits. Initially, the blockchain network is fed to the blockchain network bridge from which the bitcoin data is acquired that is followed with the bitcoin prediction. Bitcoin prediction is performed using the proposed FuzzyCSA-based Deep Long short-term memory (LSTM). At first, the flow strength indicators are extracted based on Double exponential moving average (DEMA), Rate of Change (ROCR), Average True Range (ATR), Simple Moving Average (SMA), and Moving Average Convergence Divergence (MACD) from the blockchain data. Based on the extracted features, the prediction is done using FuzzyCSA-based Deep LSTM, which is the combination of FuzzyCSA with Deep LSTM. Then, the CSA is modified using the fuzzy operator for determining the optimal weights in Deep LSTM. The experimentation of the proposed method is performed from the openly available dataset. The analysis of the method in terms of Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) reveals that the proposed FuzzyCSA-based Deep LSTM acquired a minimal MAE of 0.4811, and the minimal RMSE of 0.3905, respectively.

Application of Cloud Model in Qualitative Forecasting for Stock Market Trends.

Forecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. The use of technical analysis for financial forecasting has been successfully employed by many researchers. The existing qualitative based methods developed based on fuzzy reasoning techniques cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. Extended fuzzy sets (e.g., fuzzy probabilistic set) study the fuzziness of the membership grade to a concept. The cloud model, based on probability measure space, automatically produces random membership grades of a concept through a cloud generator. In this paper, a cloud model-based approach was proposed to confirm accurate stock based on Japanese candlestick. By incorporating probability statistics and fuzzy set theories, the cloud model can aid the required transformation between the qualitative concepts and quantitative data. The degree of certainty associated with candlestick patterns can be calculated through repeated assessments by employing the normal cloud model. The hybrid weighting method comprising the fuzzy time series, and Heikin–Ashi candlestick was employed for determining the weights of the indicators in the multi-criteria decision-making process. Fuzzy membership functions are constructed by the cloud model to deal effectively with uncertainty and vagueness of the stock historical data with the aim to predict the next open, high, low, and close prices for the stock. The experimental results prove the feasibility and high forecasting accuracy of the proposed model.

Predicting stock trend using an integrated term frequency–inverse document frequency-based feature weight matrix with neural networks

The financial market consists of various money-making strategies wherein trading through a stock market is an important example. The complex non-linear behaviors of volatile stock markets attract researchers to study inherent patterns. As the primary motivation for investment in such markets is to gain higher profits, potential stocks are given considerable attention using various weighting strategies that can enhance future returns. Term frequency–inverse document frequency (TF–IDF) is a statistical approach with remarkable applications in the financial domain for information retrieval from textual data; it identifies the importance of a term in the given document of a corpus. However, the application of TF–IDF for the numerical data representation is explored to a limited extent. In this article, we propose to extend the applicability of TF–IDF for the numerical time-series stock market data; we process the data and prepare them to be suitable for TF–IDF. We utilize this statistical approach to derive feature weight matrix from the historical stock market data and further, integrate it with the widely explored neural network architectures namely, backpropagation neural network (BPNN), long short-term memory (LSTM), and gated recurrent unit (GRU) for predicting stock market trend. Simulation results show that the proposed integrated approach using TF–IDF-based feature weight matrix and neural networks outperforms the considered recent approaches. The results are statistically supported with p-value less than .01 using a Wilcoxon signed-rank test; our proposed approach is supported with illustrative examples to develop better understanding of the work. Also, remarks on the conclusions and potential future scope are discussed.

Short-term stock market price trend prediction using a comprehensive deep learning system

In the era of big data, deep learning for predicting stock market prices and trends has become even more popular than before. We collected 2 years of data from Chinese stock market and proposed a comprehensive customization of feature engineering and deep learning-based model for predicting price trend of stock markets. The proposed solution is comprehensive as it includes pre-processing of the stock market dataset, utilization of multiple feature engineering techniques, combined with a customized deep learning based system for stock market price trend prediction. We conducted comprehensive evaluations on frequently used machine learning models and conclude that our proposed solution outperforms due to the comprehensive feature engineering that we built. The system achieves overall high accuracy for stock market trend prediction. With the detailed design and evaluation of prediction term lengths, feature engineering, and data pre-processing methods, this work contributes to the stock analysis research community both in the financial and technical domains.

Dancing with Trump in the Stock Market

It is always deemed crucial to identify the key factors that could have significant impact on the stock market trend. Recently, an interesting phenomenon has emerged that some of President Trump’s posts in Twitter can surge into a dominant role on the stock market for a certain time period, although studies along this line are still in their infancy. Therefore, in this article, we study whether and how this new-rising information can help boost the performance of stock market prediction. Specifically, we have found that the echoing reinforced effect of financial news with Trump’s market-related tweets can influence the market movement—that is, some of Trump’s tweets directly impact the stock market in a short time, and the impact can be further intensified when it echoes with other financial news reports. Along this line, we propose a deep information echoing model to predict the hourly stock market trend, such as the rise and fall of the Dow Jones Industrial Average. In particular, to model the discovered echoing reinforced impact, we design a novel information echoing module with a gating mechanism in a sequential deep learning framework to capture the fused knowledge from both Trump’s tweets and financial news. Extensive experiments have been conducted on the real-world U.S. stock market data to validate the effectiveness of our model and its interpretability in understanding the usability of Trump’s posts. Our proposed deep echoing model outperforms other baselines by achieving the best accuracy of 60.42% and obtains remarkable accumulated profits in a trading simulation, which confirms our assumption that Trump’s tweets contain indicative information for short-term market trends. Furthermore, we find that Trump’s tweets about trade and political events are more likely to be associated with short-term market movement, and it seems interesting that the impact would not degrade as time passes.

Examining the Effects of Pandemics on Stock Market Trends through Sentiment Analysis

Examining the Effects of Pandemics on Stock Market Trends through Sentiment Analysis

Forecasting Stock Market Trend using Machine Learning Algorithms with Technical Indicators

Forecasting Stock Market Trend using Machine Learning Algorithms with Technical Indicators

CRYPTOCURRENCY INSTITUALIZATION: REGULATION, LEGAL STATUS, ACCOUNTING AND TAXATION

Introduction. The article gives a critical assessment of the situation and discusses the problematic aspects and perspectives of the institutionalization of cryptocurrency. Given the growing public interest in cryptocurrencies circulation, stock market trends, benefits and opportunities for the economy, as well as the associated threats and challenges, regulatory policy and the legislative framework are imperative. It is impossible to solve the problem of legal, accounting and tax consequences of cryptocurrency transactions without determining the legal status of a cryptocurrency. The lack of standards and clear recommendations of the relevant methodological authorities necessitated the search and systematization of international accounting, financial reporting and taxation practice on cryptocurrency transactions. Methods. The authors applied the dialectical method of cognition and general scientific principles of complex research, which include empirical, general logical, theoretical and cognitive methods. These methodological tools are used to identify approaches, assess trends in cryptocurrency circulation and cryptocurrency market, consider the nature and disclosure of cryptocurrency, identify methodological problems of integration of cryptocurrency and related transactions in the system of accounting and financial reporting, differentiation of approaches to determining legal status. Results. Undoubtedly, cryptocurrency has potential. However, to realize this potential, stimulate the growth, increase the accessibility and the level of trust, effectively scale of the business and its interaction with regulatory authorities, it is necessary to create a more institutionally friendly environment. Without appropriate legislative regulation of circulation and determination of the legal status of cryptocurrencies, as well as in the absence of clear recommendations of methodological bodies in the field of accounting and taxation, many legal, theoretical, and applied issues of accounting and taxation cannot be resolved. Discussion. Domestic lawmakers, regulators and scientists have to analyze actively the experience of other countries, to study scientific developments and to conduct further research in order to normalize these relations in the European and global context.