Stock Market Trend Prediction Research Articles

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.

CNN based Stock Market Prediction

Indian Stock market is highly dynamic and especially after globalization stock market modeling has become even more complex due to influence of multiple parameters. In presence of multiple parameters, some parameters have increased influence than others in prediction of stock market trends. This influence of individual parameters and their joint influence over time is better modeled with Convolutional Neural Network Classifiers. This work models the dynamics of stock market in terms of Convolutional Neural Networks and multiple parameters impacting the stock trend. The proposed solution is implemented for Indian stock market for stocks in different sectors to prove its prediction accuracy.

Stock price forecast based on combined model of ARI-MA-LS-SVM

Stock forecasting is a very complex non-stationary, nonlinear time series forecasting, and is often affected by many factors, making it difficult to predict it with a simple model. Support vector machine (SVM) is one of the common data mining methods in the field of machine learning. It has outstanding advantages compared with other methods and it is widely used in various fields. However, there are still many problems in the practical application of the method, and the model itself has many fields that need to be improved. The purpose of this paper is to accurately predict the trend of stock prices, providing a reference model for the trend of stock market and the tracking method of stock price prediction, and provide value reference for research on the forecasting model of stock market and investor’s investment decision. Research using a combined model to predict stock market trends whether will have a significant improvement compared to using a single model to forecast that. The method of this paper is to analyze the shortcomings of current stock market forecasting methods and standard support vector machines firstly, at the same time, based on this, a cumulative auto-regressive moving average is proposed, which combines the least squares support vector machine synthesis model (ARI-MA-LS-SVM) to make basic predictions for the stock market. Secondly, process the data first for the predictive indicators by using cumulative auto-regressive moving average. Then, use the least squares support vector machine of simple indicator system to predict stock price fluctuations. Therefore, it can be concluded that the combined model based on ARI-MA-LS-SVM is more suitable for stock price forecasting than the single forecasting model, and the actual performance is better. At the same time, a large number of simulation experiments show that the algorithm of multiple model’s fusion can achieve the expected effect, which indicate that the model has universal applicability, market applicability and stability feasibility. This model can bring some guidance and reference value for many investors and market regulators.

Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis

The nature of stock market movement has always been ambiguous for investors because of various influential factors. This study aims to significantly reduce the risk of trend prediction with machine learning and deep learning algorithms. Four stock market groups, namely diversified financials, petroleum, non-metallic minerals and basic metals from Tehran stock exchange, are chosen for experimental evaluations. This study compares nine machine learning models (Decision Tree, Random Forest, Adaptive Boosting (Adaboost), eXtreme Gradient Boosting (XGBoost), Support Vector Classifier (SVC), Naive Bayes, K-Nearest Neighbors (KNN), Logistic Regression and Artificial Neural Network (ANN)) and two powerful deep learning methods (Recurrent Neural Network (RNN) and Long short-term memory (LSTM). Ten technical indicators from ten years of historical data are our input values, and two ways are supposed for employing them. Firstly, calculating the indicators by stock trading values as continuous data, and secondly converting indicators to binary data before using. Each prediction model is evaluated by three metrics based on the input ways. The evaluation results indicate that for the continuous data, RNN and LSTM outperform other prediction models with a considerable difference. Also, results show that in the binary data evaluation, those deep learning methods are the best; however, the difference becomes less because of the noticeable improvement of models' performance in the second way.

Ultimate Prediction of Stock Market Price Movement

Investment in the stock market is currently very popular due to its economic gain. Numerous researchers’ and academicians’ work is focused on financial time series prediction due to its data availability and profitability. Therefore, this study presents the design and implementation of a novel binary classification framework to predict stock market trends. The framework is composed of data preprocessing, feature engineering, feature selection and classification algorithms. The model is built on multiple sector stock market companies’ data collected from NASDAQ over a period of ten years. Various feature selection algorithms are applied in combination with several machine learning algorithms. Furthermore, as the new contribution, we have constructed two new features which have been found to be promising in terms of improving overall performance. Ultimately, a collaboration of feature selection and classification techniques is employed. The application of Principal Component Analysis (PCA) with Multilayer Perceptron and Support Vector Machine (SVM) to added featured datasets shows 100% accuracy on the majority of datasets. In summary, an intensive comparison is presented among the various feature selection and classification algorithms.

Predicting Stock Market Trends using Hybrid SVM Model and LSTM with Sentiment Determination using Natural Language Processing

In the financial world, stock trading is one of the most crucial activities. Investors make educated guesses to predict stock market trends by analyzing news, studying the company history, industrial history and a lot of other data. successfully predicting the stock market trends and investing in the right shares at the right time can maximize the investor’s profit or at least minimize the losses. Stock market price data is generated in huge volumes and is affected by various diverse factors. This work proposes two models to predict the stock market prices. The first model is an LSTM model that employs a backpropagation optimized LSTM network to forecast future stock prices. The second model is a hybrid model that combines an SVM model, KNN model and a Random Forest classifier using the Majority Voting algorithm to predict stock market trends. Both models have a sentiment analyzer to factor the news influencing the stock market using Natural Language Processing (NLP). The project aims to help investors who are new to the stock market and don’t possess sufficient knowledge to make share investments as well the experienced investors by predicting stock market trends.

Predicting stock market trends using machine learning algorithms via public sentiment and political situation analysis

Stock market trends can be affected by external factors such as public sentiment and political events. The goal of this research is to find whether or not public sentiment and political situation on a given day can affect stock market trends of individual companies or the overall market. For this purpose, the sentiment and situation features are used in a machine learning model to find the effect of public sentiment and political situation on the prediction accuracy of algorithms for 7 days in future. Besides, interdependencies among companies and stock markets are also studied. For the sake of experimentation, stock market historical data are downloaded from Yahoo! Finance and public sentiments are obtained from Twitter. Important political events data of Pakistan are crawled from Wikipedia. The raw text data are then pre-processed, and the sentiment and situation features are generated to create the final data sets. Ten machine learning algorithms are applied to the final data sets to predict the stock market future trend. The experimental results show that the sentiment feature improves the prediction accuracy of machine learning algorithms by 0–3%, and political situation feature improves the prediction accuracy of algorithms by about 20%. Furthermore, the sentiment attribute is most effective on day 7, while the political situation attribute is most effective on day 5. SMO algorithm is found to show the best performance, while ASC and Bagging show poor performance. The interdependency results indicate that stock markets in the same industry show a medium positive correlation with each other.

The Profitability in the FTSE 100 Index: A New Markov Chain Approach

In this paper, we propose a new method to predict stock market trends based on the multivariate Markov chain (MMC) methodology. Our approach consists of forecasting the one-period ahead FTSE 100 Index behavior, using the MTD-Probit model. The MTD-Probit model is a new approach for estimating MMC, based on multiple categorical data sequences that can be used to forecast financial markets. In this context, we propose a simple trading strategy and analyze its profitability using the White “Reality Check” and the Hansen SPA data snooping bias tests. Our empirical results suggest that the MTD-Probit model applied to the FTSE 100 Index cannot significantly out-perform the buy-and-hold benchmark after data-snooping is controlled.

Stock market trend prediction using a functional time series approach

Thanks to advanced technologies, ultra-high-frequency limit order book (LOB) data are now available to data analysts. An LOB contains comprehensive information on all transactions in a market. We use LOB data to investigate the high-frequency dynamics of market supply and demand (S–D) and inspect their impacts on intra-daily market trends. The intra-daily S–D curves are fitted with B-spline basis functions. Technique of multi-resolution is introduced to capture inhomogeneous curvature of the S–D curves and a lasso-type criterion is employed to select a common basis set. Based on empirical evidence, we model the time varying coefficients in the B-spline interpolation by vector autoregressive models of order . The Xgboost algorithm is employed to extract information from the areas under the S–D curves to predict the intra-daily market trends. In the empirical study, we analyze the LOB data from LOBSTER (https://lobsterdata.com/). The results show that the proposed approach is able to recover the S–D curves and has satisfactory performance on both curve and market trend predictions.

EMP-SA: Ensemble Model based Market Prediction using Sentiment Analysis

Predicting stock market trend is an extremely complicated task and calls for extensive study and insights into the context at hand. Primary requirement for any investor is to assess this trend to help invest for maximizing his returns. The advances in Machine learning and data analytics in particular have changed the way investors can approach this matter. Sentiment analysis or Opinion mining can be carried out by taking into consideration public sentiments regarding the stock market conditions and to understand the ups and down of this most volatile sector. In this paper, public sentiments from Twitter along with news feed related to the stock market conditions for predicting the nature of market is considered to analyse the stock market trend. The data is collected from twitter and various news sites to generate a gross sentiment score regarding the market. The gross sentiment score is used to find a correlation between market price and sentiments to train the proposed models for prediction using Linear and robustness regression techniques such as Ordinary Least squares (OLS), RANSAC, Theil-Sen estimator, Huber Regression and Ridge regression. Ensemble method is used to achieve reliable and better prediction accuracy instead of a single method. Ensemble method combines models and carries out majority voting among them to produce one final model to increase prediction accuracy. The obtained results reveal that public opinion does make a significant impact on market behaviour with the prediction accuracy between 65-91% depending on the dataset.

Short Term Stock Movements with Big Data and Market Sentiments Analytics

Machine Learning Techniques and Big Data analytics are two central points of data science. Big Data is important for organizations to gain insights into it and machine learning techniques are one of the substantial assets for analyzing a massive amount of data. In this paper, a framework has been proposed to improve the short term stock trend prediction accuracy using Logistic Regression model by means of qualitative and quantitative data. This paper makes a comprehensive survey of stock market trend prediction with the accumulation of various data sources by applying machine learning techniques and by using big data analytics approach. The model has been implemented in Big data Framework with Hadoop and Apache Spark. For qualitative data Tweets sentiments and news sentiments has been taken in to account and for quantitative data Google trends and historical data are considered. The proposed system has enhanced the prediction accuracy about 3-4 % in comparison to existing models by supplying Google trend as input data in addition to market sentiments and historical data. The implemented model can help the investors to take short term decisions to make money in the security market and the survey would help in finding the most effective resources which overly influence the stock prices.

Evaluation of Pattern Based Customized Approach for Stock Market Trend Prediction With Big Data and Machine Learning Techniques

The stock market is very volatile and non-stationary and generates huge volumes of data in every second. In this article, the existing machine learning algorithms are analyzed for stock market forecasting and also a new pattern-finding algorithm for forecasting stock trend is developed. Three approaches can be used to solve the problem: fundamental analysis, technical analysis, and the machine learning. Experimental analysis done in this article shows that the machine learning could be useful for investors to make profitable decisions. In order to conduct these processes, a real-time dataset has been obtained from the Indian stock market. This article learns the model from Indian National Stock Exchange (NSE) data obtained from Yahoo API to forecast stock prices and targets to make a profit over time. In this article, two separate algorithms and methodologies are analyzed to forecast stock market trends and iteratively improve the model to achieve higher accuracy. Results are showing that the proposed pattern-based customized algorithm is more accurate (10 to 15%) as compared to other two machine learning techniques, which are also increased as the time window increases.

Modeling public mood and emotion: Stock market trend prediction with anticipatory computing approach

The science and technology is more and more developed. Digital media such as articles, commentary, videos, animations and others on the Internet is becoming more and more important. English semantic analysis has many basic technologies, many applications are also gradually budding in this basic technology. On the other hand, there is no uniform or complete reorganization of the basic technologies in Chinese semantic analysis. Chinese semantic analysis is difficult than English semantic analysis because it is difficult to judge the true meaning of Chinese words and sentences. This study collects articles about common news sites in Taiwan and related to individual stocks. After the data is preprocessed and Skip-gram, each word is converted to word features using Word2Vec. The Lexicon stores the most relevant words around the keyword. In the prediction stage, this study calculates the impact of new articles on the stock price according to the full training lexicon. Finally, this study uses the deep learning approach - LSTM (Long Short-Term Memory) to evaluate the final results. The aim of this study is to adopt anticipatory computing to explore the public mood and emotion from news articles. Then this study can predict the future stock market trend and can be the reference model to the related industries.

Development of stock market trend prediction system using multiple regression

The Stock market trend prediction is an efficient medium for investors, public companies and government to invest money by taking into account the profit and risk. The existing studies on the development of stock-based prediction systems rely on data acquired from social media sources (sentiment-based) and secondary data sources (financial-sites). However, the data acquired from such sources is usually sparse in nature. Moreover, the selection of predictor variables is also poor, which ultimately degrades the performance of prediction model. The problems associated with existing approaches can be overcome by proposing an effective prediction model with improved quality of input data and enhanced selection/inclusion of predictor variables. This work presents the results of stock prediction by applying a multiple regression model using R software. The results obtained show that the proposed system achieved a prediction accuracy of 95% on KSE 100-index dataset, 89% on Lucky Cement, 97% on Abbot Company dataset. Furthermore, user-friendly interface is provided to assist individuals and companies to invest or not in a specific stock.

Application of Markov Chain to stock trend: A study of PT HM Sampoerna, tbk.

One of attractive area for investment is the stock market. Indonesia stock exchange being a stock market in a region that is considered investors will take the opportunity to make investment decisions based on the future expectations of the stock market. Knowing the behaviour of the stock price of a company really important for investor. Therefore, we conduct this research with aimed at knowing the stock market trend movement. In the prediction of stock market trends are widely applied some models; one of model is Markov Chain model. The objective of this research is to apply Markov Chain in PT HM Sampoerna stock price. The data that use in this research is the closing price of PT HM Sampoerna which was obtained from yahoo finance website over a period covering from 1st January 2017 to 31st December 2017. A Markov Chain model was determined based on probability transition matrix and initial state vector. In the long run, the model predicted that PT HM Sampoerna share prices with probability 0.09, 0.40, 0.46, and 0.05 respectively.

EMD2FNN: A strategy combining empirical mode decomposition and factorization machine based neural network for stock market trend prediction

Stock market forecasting is a vital component of financial systems. However, the stock prices are highly noisy and non-stationary due to the fact that stock markets are affected by a variety of factors. Predicting stock market trend is usually subject to big challenges. The goal of this paper is to introduce a new hybrid, end-to-end approach containing two stages, the Empirical Mode Decomposition and Factorization Machine based Neural Network (EMD2FNN), to predict the stock market trend. To illustrate the method, we apply EMD2FNN to predict the daily closing prices from the Shanghai Stock Exchange Composite (SSEC) index, the National Association of Securities Dealers Automated Quotations (NASDAQ) index and the Standard & Poor’s 500 Composite Stock Price Index (S&P 500), which respectively exhibit oscillatory, upward and downward patterns. The results are compared with predictions obtained by other methods, including the neural network (NN) model, the factorization machine based neural network (FNN) model, the empirical mode decomposition based neural network (EMD2NN) model and the wavelet de-noising-based back propagation (WDBP) neural network model. Under the same conditions, the experiments indicate that the proposed methods perform better than the other ones according to the metrics of Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Furthermore, we compute the profitability with a simple long-short trading strategy to examine the trading performance of our models in the metrics of Average Annual Return (AAR), Maximum Drawdown (MD), Sharpe Ratio (SR) and AAR/MD. The performances in two different scenarios, when taking or not taking the transaction cost into consideration, are found economically significant.

A Neural Networks Adoption Framework for Predicting Stock Market Trends: Case of the Zimbabwe Stock Exchange

A Neural Networks Adoption Framework for Predicting Stock Market Trends: Case of the Zimbabwe Stock Exchange

Stock market trend prediction using AHP and weighted kernel LS-SVM

Nowadays, stock market trend prediction represents a challenging subject both in terms of the choice of the prediction model and in terms of constructing the set of features that model will use for prediction. To address both of these aspects, we propose a feature ranking and feature selection approach in combination with weighted kernel least squares support vector machines (LS-SVMs). We introduce the analytic hierarchy process (AHP) into the stock market and propose evaluation criteria which provide the prediction model with relevant knowledge of the underlying processes of the studied stock market. The feature weights obtained by the AHP method are used for feature ranking and selection, and used with the LS-SVMs through a weighted kernel. The test results indicate that the proposed model outperforms the benchmark models. In addition, the set of feature weights obtained by the proposed approach can also independently be incorporated into other kernel-based learners.

Combining Sentiment Analysis with Socialization Bias in Social Networks for Stock Market Trend Prediction

According to the indirect relationship between information and stock trend, information such as comments and tweets can be used for stock trend prediction. When conducting classification on text data, feature sparse issues occur during conversion between tweets and word vectors. Another problem is that the unreliability of average sentiment scores to indicate one day’s sentiment. This is especially caused by the unbalanced number between positive and negative within one day, thus a large bias between sentiment and stock trend arises. In addion, information has social attributes when created and diffused in social networks, bias containing people’s belief in social networks also have become socialization bias. In order to solve those problems, this work proposes a sentiment analysis based prediction model and an inverse bias algorithm. Instead of applying sentiment analysis to add sentiment related features, this work uses SentiWordNet to give an additional weight to the selected features, and applies two kinds of sentiment analysis to inverse the socialization bias. Aiming at labeling the tweets to sentiment related groups to help find socialization bias, this work also proposes an extended wordlist based on a semi-supervised Naïve Bayes classification algorithm. After finishing the inverse socialization bias, stock trends were used to label example sets. Different classification algorithms were compared in this work. The proposed model with SVM linear algorithm proves to yield accuracy of 90.33% at its best performance.