Stock Market Trends Research Articles

Validating the impact of accounting disclosures on stock market: A deep neural network approach

Firms disclose information either voluntarily or due to the regulator's mandatory requirements, and such disclosures form good sources to know the prospects of a firm. Information in the disclosures and analysts' opinions influence investor-trading behavior, and consequently, affects the asset prices. As sentiments factored in disclosures are a source of market action, this study aims to capture the sentiments from disclosure information to assess asset prices' impact. The paper adopts a deep neural network-based prediction model for conducting sentiment analysis on heterogeneous datasets. We construct a sentiment simulation model of voluntary disclosures to know whether the managers can use the market sentiment as a strategic input to boost market performance by suitably drafting the tone and content of disclosures without compromising their quality and veracity. The Deep Neural Networks with LSTM algorithm is found to outperform the Deep Neural Networks with RNN and other baseline machine learning classifiers in terms of predictive accuracy of the NSE NIFTY50. The variable importance computed also validates that market news, combined with historical indicators, predicts the stock market trend closer to the actual trend.

Harnessing artificial intelligence for the next generation of 3D printed medicines.

Artificial intelligence (AI) is redefining how we exist in the world. In almost every sector of society, AI is performing tasks with super-human speed and intellect; from the prediction of stock market trends to driverless vehicles, diagnosis of disease, and robotic surgery. Despite this growing success, the pharmaceutical field is yet to truly harness AI. Development and manufacture of medicines remains largely in a 'one size fits all' paradigm, in which mass-produced, identical formulations are expected to meet individual patient needs. Recently, 3D printing (3DP) has illuminated a path for on-demand production of fully customisable medicines. Due to its flexibility, pharmaceutical 3DP presents innumerable options during formulation development that generally require expert navigation. Leveraging AI within pharmaceutical 3DP removes the need for human expertise, as optimal process parameters can be accurately predicted by machine learning. AI can also be incorporated into a pharmaceutical 3DP 'Internet of Things', moving the personalised production of medicines into an intelligent, streamlined, and autonomous pipeline. Supportive infrastructure, such as The Cloud and blockchain, will also play a vital role. Crucially, these technologies will expedite the use of pharmaceutical 3DP in clinical settings and drive the global movement towards personalised medicine and Industry 4.0.

Social Media Behavioral Intelligence using Feature Extraction

Analysis of the behavioral pattern of a people using data of the social media became a trend in last couple of years. Among this popular network, Twitter, Facebook and the Instagram become more and more popular and that’s why these platforms attract the lots of researchers to predict the sentiment regarding major events like election, product brand, movie, stock market and recent trends are some of them. By identifying the attitude associated with the text in terms of positive, negative or the neutral we are able to analyze the opinion behind the content generated by the user and this opinions about the sentiment are very helpful to for the organization or the political parties or among other entities. The task of sentiment analysis is conducted using identifying the polarity associated with the word or document or we can say sentence. This paper consists research work which is designed to improve the accuracy of the model by improving the Naïve Bayes algorithm and I also worked to improve the 3-gram method during my research

Hybrid models for intraday stock price forecasting based on artificial neural networks and metaheuristic algorithms

• Artificial Neural Networks (ANNs) live a vital role in many areas including medical, security systems and stock market. • This paper proposed nine new integrated models for forecasting intraday stock price based on the potential of three ANNs. • Results proved that the PSO-BPNN model yielded the highest prediction accuracy in estimating intraday stock price. • Other models produced low performances in prediction accuracy are 97.2, 98.4, 84.0, 85.2, 84.0, 83.9, 89.9 and 78.6%. Stock market prediction is one of the critical issues in fiscal market. It is important issue for the traders and investors. Artificial Neural Networks (ANNs) associated with nature inspired algorithms are playing an increasingly vital role in many areas including medical field, security systems and stock market. Several prediction models have been developed by researchers to forecast stock market trend. However, few studies have focused on improving stock market prediction accuracy especially when utilizing artificial neural networks to perform the analysis. This paper proposed nine new integrated models for forecasting intraday stock price based on the potential of three ANNs, Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Time Delay Neural Network (TDNN) and nature inspired algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC).The developed models were named as GA-BPNN, PSO-BPNN, ABC-BPNN, GA-RBFNN, PSO-RBFNN, ABC-RBFNN, GA-TDNN, PSO-TDNN and ABC-TDNN. Nature inspired algorithms are employed for optimizing the parameters of ANNs. Technical indicators calculated from historical data are fed as input to developed models. Proposed hybrid models validated on four datasets representing different sectors in NSE. Four statistical metrics, Root Mean Square Error (RMSE), Hit Rate (HR), Error Rate (ER) and prediction accuracy were utilized to gauge the performance of the developed models. Results proved that the PSO-BPNN model yielded the highest prediction accuracy in estimating intraday stock price. The other models, GA-BPNN, ABC-BPNN, GA-RBFNN, PSO-RBFNN, ABC-RBFNN, GA-TDNN, PSO-TDNN and ABC-TDNN produced lower performance with mean prediction accuracy of 97.24%, 98.37%, 84.01%, 85.15%, 84.01%, 83.87%, 89.95% and 78.61% respectively.

Multi-Factorized Semi-Covariance of Stock Markets and Gold Price

Complex models have received significant interest in recent years and are being increasingly used to explain the stochastic phenomenon with upward and downward fluctuation such as the stock market. Different from existing semi-variance methods in traditional integer dimension construction for two variables, this paper proposes a simplified multi-factorized fractional dimension derivation with the exact Excel tool algorithm involving the fractional center moment extension to covariance, which is a complex parameter average that is a multi-factorized extension to Pearson covariance. By examining the peaks and troughs of gold price averages, the proposed algorithm provides more insight into revealing underlying stock market trends to see who is the financial market leader during good economic times. The calculation results demonstrate that the complex covariance is able to distinguish subtle differences among stock market performances and gold prices for the same field that the two variable covariance may overlook. We take London, Tokyo, Shanghai, Toronto, and Nasdaq as the representative examples.

신경망 학습에 기반한 주식 시장 추세의 상승 전환 예측

기존의 여러 연구에 의하면 신경망 기반의 주가 예측기들은 폭락형 주가 패턴에 대해 우수한 예측 성능을 보이는 것으로 나타났다. 이러한 점에 착안하여, 본 논문에서는 신경망 기반의 주가 예측기를 사용하여 시장 폭락 상황에서의 상향 추세 전환을 예측할 수 있는 새로운 기법을 제시한다. 폭락형 패턴에 대해 더욱 일관적인 예측 결과를 얻기 위해, 새롭게 고안한 추가적인 입력 자질들을 사용하여 기존에 사용하던 신경망 기반 예측기의 성능을 개선한다. 과거의 폭락 장세들에 대해 예측 점수들의 통계를 분석하고, 그 결과를 활용하여 테스트 기간 동안 발생한 폭락 장세에서의 상향 추세 전환을 예측한다. 한국 거래소 시장 및 코스닥 시장에서 수집한 데이터를 사용한 실험을 통해, 최근 3년간의 거래 시뮬레이션 결과를 제시한다.

Unsupervised feature extraction with convolutional autoencoder with application to daily stock market prediction

AbstractDue to the volatility and noise of the stock market, accurately obtaining the trend of the stock market is a challenging problem, and gets the attention of many researchers and speculators. Recently, convolutional neural network (CNN) has been used to automatically learn effective features and predict stock market trends. In CNN‐based methods reported so far, less focus has been paid to time series information of the stock, but is very crucial for stock forecasting. In this study, an unsupervised feature extraction method with convolutional autoencoder (CAE) with application to daily stock market prediction is proposed, which has a higher prediction than traditional models. The proposed method mainly consists of the data processing part, unsupervised feature learning part, and the support vector machine model part. Data processing part includes time series data transform into two‐dimensional data and data normalization. CAE network‐based unsupervised feature learning is designed by fusing convolution and autoencoder. In order to verify the performance of the model, various initial financial and economic variables of stock indices are chosen for prediction experiments. The experimental results on different stock indices demonstrate a significant improvement in prediction's performance compared with the baseline methods.

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.