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

A new stock market analysis method based on evidential reasoning and hierarchical belief rule base to support investment decision making

Stock market analysis is helpful for investors to make reasonable decisions and maintain market stability, and it usually involves not only quantitative data but also qualitative information, so the analysis method needs to have the ability to deal with both types of information comprehensively. In addition, due to the inherent risk of stock investment, it is necessary to ensure that the analysis results can be traced and interpreted. To solve the above problems, a stock market analysis method based on evidential reasoning (ER) and hierarchical belief rule base (HBRB) is proposed in this paper. First, an evaluation model is constructed based on expert knowledge and ER to evaluate stock market sentiment. Then, a stock market decision model based on HBRB is constructed to support investment decision making, such as buying and selling stocks and holding positions. Finally, the Shanghai Stock Index from 2010 to 2019 is used as an example to verify the applicability and effectiveness of the proposed stock market analysis method for investment decision support. Experimental research demonstrates that the proposed method can help analyze the stock market comprehensively and support investors to make investment decisions effectively.

Stock index trend prediction based on TabNet feature selection and long short-term memory

In this study, we propose a predictive model TabLSTM that combines machine learning methods such as TabNet and Long Short-Term Memory Neural Network (LSTM) with a complete factor library for stock index trend prediction. Our motivation is based on the notion that there are numerous interrelated factors in the stock market, and the factors that affect each stock are different. Therefore, a complete factor library and an efficient feature selection technique are necessary to predict stock index. In this paper, we first build a factor database that includes macro, micro and technical indicators. Successively, we calculate the factor importance through TabNet and rank them. Based on a prespecified threshold, the optimal factors set will include only the highest-ranked factors. Finally, using the optimal factors set as input information, LSTM is employed to predict the future trend of 4 stock indices. Empirical validation of the model shows that the combination of TabNet for factors selection and LSTM outperforms existing methods. Moreover, constructing a factor database is necessary for stock index prediction. The application of our method does not only show the feasibility to predict stock indices across different financial markets, yet it also provides an complete factor database and a comprehensive architecture for stock index trend prediction, which may provide some references for stock forecasting and quantitative investments.

Micro Learning Support Vector Machine for Pattern Classification: A High-Speed Algorithm

The support vector machine theory has been developed into a very mature system at present. The original support vector machine to solve the optimization problem is transformed into a direct calculation formula of line in this paper and the model is o(n²) time complexity. In the model of this article, weited theory, multiclassification problem and online learning have all become the direct inference, and we have applied the new model to the UCI data set. We hope that in the future, this model will be useful in real-world problems such as stock forecasting, which require nonlinear hi-speed algorithms.

The Prediction of Enterprise Stock Change Trend by Deep Neural Network Model

This study aims to accurately predict the changing trend of stocks in stock trading so that company investors can obtain higher returns. In building a financial forecasting model, historical data and learned parameters are used to predict future stock prices. Firstly, the relevant theories of stock forecasting are discussed, and problems in stock forecasting are raised. Secondly, the inadequacies of deep neural network (DNN) models are discussed. A prediction trend model of enterprise stock is established based on long short-term memory (LSTM). The uniqueness and innovation lie in using the stock returns of Bank of China securities in 2022 as the training data set. LSTM prediction models are used to perform error analysis on company data training. The 20-day change trend of the company’s stock returns under different models is predicted and analyzed. The results show that as the number of iterations increases, the loss rate of the LSTM training curve keeps decreasing until 0. The average return price of the LSTM prediction model is 14.01. This figure is closest to the average real return price of 13.89. Through the forecast trend analysis under different models, LSTM predicts that the stock change trend of the enterprise model is closest to the changing trend of the actual earnings price. The prediction accuracy is better than other prediction models. In addition, this study explores the characteristics of high noise and complexity of corporate stock time series, designs a DNN prediction model, and verifies the feasibility of the LSTM model to predict corporate stock changes with high accuracy.

An Intelligent Fusion Model with Portfolio Selection and Machine Learning for Stock Market Prediction

Developing reliable equity market models allows investors to make more informed decisions. A trading model can reduce the risks associated with investment and allow traders to choose the best-paying stocks. However, stock market analysis is complicated with batch processing techniques since stock prices are highly correlated. In recent years, advances in machine learning have given us a lot of chances to use forecasting theory and risk optimization together. The study postulates a unique two-stage framework. First, the mean-variance approach is utilized to select probable stocks (portfolio construction), thereby minimizing investment risk. Second, we present an online machine learning technique, a combination of “perceptron” and “passive-aggressive algorithm,” to predict future stock price movements for the upcoming period. We have calculated the classification reports, AUC score, accuracy, and Hamming loss for the proposed framework in the real-world datasets of 20 health sector indices for four different geographical reasons for the performance evaluation. Lastly, we conduct a numerical comparison of our method's outcomes to those generated via conventional solutions by previous studies. Our aftermath reveals that learning-based ensemble strategies with portfolio selection are effective in comparison.

Generative Adversarial Network to evaluate quantity of information in financial markets

Nowadays, the information obtainable from the markets are potentially limitless. Economic theory has always supported the possible advantage obtainable from having more information than competitors, however quantifying the advantage that these can give has always been a problem. In particular, in this paper we study the amount of information obtainable from the markets taking into account only the time series of the prices, through the use of a specific Generative Adversarial Network. We consider two types of financial instruments traded on the market, stocks and cryptocurrencies: the first are traded in a market subject to opening and closing hours, whereas cryptocurrencies are traded in a 24/7 market. Our goal is to use this GAN to be able to “convert” the amount of information that the different instruments can have in discriminative and predictive power, useful to improve forecast. Finally, we demonstrate that by using the initial dataset with the 5 most important feature useds by traders, the prices of cryptocurrencies present higher discriminatory and predictive power than stocks, while by adding a feature the situation can be completely reversed.

A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network

Stock prices are volatile due to different factors that are involved in the stock market, such as geopolitical tension, company earnings, and commodity prices, affecting stock price. Sometimes stock prices react to domestic uncertainty such as reserve bank policy, government policy, inflation, and global market uncertainty. The volatility estimation of stock is one of the challenging tasks for traders. Accurate prediction of stock price helps investors to reduce the risk in portfolio or investment. Stock prices are nonlinear. To deal with nonlinearity in data, we propose a hybrid stock prediction model using the prediction rule ensembles (PRE) technique and deep neural network (DNN). First, stock technical indicators are considered to identify the uptrend in stock prices. We considered moving average technical indicators: moving average 20 days, moving average 50 days, and moving average 200 days. Second, using the PRE technique-computed different rules for stock prediction, we selected the rules with the lowest root mean square error (RMSE) score. Third, the three-layer DNN is considered for stock prediction. We have fine-tuned the hyperparameters of DNN, such as the number of layers, learning rate, neurons, and number of epochs in the model. Fourth, the average results of the PRE and DNN prediction model are combined. The hybrid stock prediction model results are computed using the mean absolute error (MAE) and RMSE metric. The performance of the hybrid stock prediction model is better than the single prediction model, namely DNN and ANN, with a 5% to 7% improvement in RMSE score. The Indian stock price data are considered for the work.

A Fusion Framework for Forecasting Financial Market Direction Using Enhanced Ensemble Models and Technical Indicators

People continuously hunt for a precise and productive strategy to control the stock exchange because the monetary trade is recognised for its unbelievably different character and unpredictability. Even a minor gain in predicting performance will be extremely profitable and significant. Our novel study implemented six boosting techniques, i.e., XGBoost, AdaBoost, Gradient Boosting, LightGBM, CatBoost, and Histogram-based Gradient Boosting, and these boosting techniques were hybridised using a stacking framework to find out the direction of the stock market. Five different stock datasets were selected from four different countries and were used for our experiment. We used two-way overfitting protection during our model building process, i.e., dynamic reduction technique and cross-validation technique. For model evaluation purposes, we used the performance metrics, i.e., accuracy, ROC curve (AUC), F-score, precision, and recall. The aim of our study was to propose and select a predictive model whose training and testing accuracy difference was minimal in all stocks. The findings revealed that the meta-classifier Meta-LightGBM had training and testing accuracy differences that were very low among all stocks. As a result, a proper model selection might allow investors the freedom to invest in a certain stock in order to successfully control risk and create short-term, sustainable profits.

Stock price forecasting based on Hausdorff fractional grey model with convolution and neural network

Forecast of stock prices can guide investors' investment decisions. Due to the high-dimensional and long-memory characteristics of stock data, it is difficult to predict. The fractional grey model with convolution (FGMC (1, m)) can be used to predict time series, because of its memory and ability to process high-dimensional data. However, the FGMC (1, m) model has some disadvantages, including complex calculation, loss of information, and approximate background values. In this paper, Hausdorff fractional derivative and Newton-Cotes formula are used to optimize these shortcomings and can get a Hausdorff fractional grey model with convolution (HFGMC (1, m)) model. The HFGMC (1, m)-LLE-BP model is proposed in this paper. HFGMC (1, m) provides a solution that can reduce the complexity of the cumulative generator matrix calculation and preserve the global information of the sequence. Newton-Cotes formula is used to calculate the background value, which can solve the shortcomings of approximate background values. The HFGMC (1, m) model is used to predict the linear component of the sequence, and the BP neural network is used to predict the nonlinear component of the sequence. In addition, because of the high-dimensional and nonlinear characteristics of stock data, a local linear embedding (LLE) algorithm is used to remove redundant information in high-dimensional non-linear data. The experimental results show that the HFGMC (1, m)-LLE-BP model is effective for predicting the stock price in different trends.

Sentiment Analysis with Machine Learning Methods on Social Media

Social media has become an important part of our everyday life due to the widespread use of the Internet. Of the social media services, Twitter is among the most used ones around the world. People share their opinions by writing tweets about numerous subjects, such as politics, sports, economy, etc. Millions of tweets per day create a huge dataset, which drew attention of the data scientists to focus on these data for sentiment analysis. The sentiment analysis focuses to identify the social media posts of users about a specific topic and categorize them as positive, negative or neutral. Thus, the study aims to investigate the effect of types of text representation on the performance of sentiment analysis. In this study, two datasets were used in the experiments. The first one is the user reviews about movies from the IMDB, which has been labeled by Kotzias, and the second one is the Twitter tweets, including the tweets of users about health topic in English in 2019, collected using the Twitter API. The Python programming language was used in the study both for implementing the classification models using the Naïve Bayes (NB), Support Vector Machines (SVM) and Artificial Neural Networks (ANN) algorithms, and for categorizing the sentiments as positive, negative and neutral. The feature extraction from the dataset was performed using Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec (W2V) modeling techniques. The success percentages of the classification algorithms were compared at the end. According to the experimental results, Artificial Neural Network had the best accuracy performance in both datasets compared to the others.