On Mixed Model for Improvement in Stock Price Forecasting

Stock market trading is an activity in which investors need fast and accurate information to make effective decisions.But the fact is that forecasting stock prices by using various models has been suffering from low accuracy,slow convergence,and complex parameters.This study aims to employ a mixed model to improve the accuracy of stock price prediction.We present how to use a random walk based on jump-diffusion,to obtain stock predictions with a good-fitting degree by adjusting different parameters.Aimed at getting better parameters and then using the time series model to predict the data,we employed the time series model to smooth the sequence utilizing logarithm and difference,which successfully resulted in drawing the auto-correlation figure and partial the auto-correlation figure.According to the comparative analysis,which focuses on checking the mean absolute error,including root mean square error and R square evaluation index,we have drawn a clear conclusion that our mixed model prediction effect is relatively good.In the context of Chinese stocks,the hybrid random walk model is very suitable for predicting stocks.It can“interpret”the randomness of stocks very well,and it also has an unparalleled prediction effect compared with other models.Based on the time series model’s application in this paper,the abovementioned series is more suitable for predicting trends.

Improving Stock Price Forecasting Using a Large Volume of News Headline Text

Previous research in the area of using deep learning algorithms to forecast stock prices was focused on news headlines,company reports,and a mix of daily stock fundamentals,but few studies achieved excellent results.This study uses a convolutional neural network(CNN)to predict stock prices by considering a great amount of data,consisting of financial news headlines.We call our model N-CNN to distinguish it from a CNN.The main concept is to narrow the diversity of specific stock prices as they are impacted by news headlines,then horizontally expand the news headline data to a higher level for increased reliability.This model solves the problem that the number of news stories produced by a single stock does not meet the standard of previous research.In addition,we then use the number of news headlines for every stock on the China stock exchange as input to predict the probability of the highest next day stock price fluctuations.In the second half of this paper,we compare a traditional Long Short-Term Memory(LSTM)model for daily technical indicators with an LSTM model compensated by the N-CNN model.Experiments show that the final result obtained by the compensation formula can further reduce the root-mean-square error of LSTM.

Forecasting Tesla’s Stock Price Using the ARIMA Model

The stock market is an important economic information center.The economic benefits generated by stock price prediction have attracted much attention.Although the stock market cannot be predicted accurately,the stock market’s prediction of the trend of stock prices helps in grasping the operation law of the stock market and the influence mechanism on the economy.The autoregressive integrated moving average(ARIMA)model is one of the most widely accepted and used time series forecasting models.Therefore,this paper first compares the return on investment(ROI)of Apple and Tesla,revealing that the ROI of Tesla is much greater than that of Apple,and subsequently focuses on ARIMA model’s prediction on the available time series data,thus concluding that the ARIMA model is better than the Naïve method in predicting the change in Tesla’s stock price trend.

Stock Price Forecasting: An Echo State Network Approach

Forecasting stock prices using deep learning models suffers from pro-blems such as low accuracy,slow convergence,and complex network structures.This study developed an echo state network(ESN)model to mitigate such pro-blems.We compared our ESN with a long short-term memory(LSTM)network by forecasting the stock data of Kweichow Moutai,a leading enterprise in China’s liquor industry.By analyzing data for 120,240,and 300 days,we generated fore-cast data for the next 40,80,and 100 days,respectively,using both ESN and LSTM.In terms of accuracy,ESN had the unique advantage of capturing non-linear data.Mean absolute error(MAE)was used to present the accuracy results.The MAEs of the data forecast by ESN were 0.024,0.024,and 0.025,which were,respectively,0.065,0.007,and 0.009 less than those of LSTM.In terms of con-vergence,ESN has a reservoir state-space structure,which makes it perform faster than other models.Root-mean-square error(RMSE)was used to present the con-vergence time.In our experiment,the RMSEs of ESN were 0.22,0.27,and 0.26,which were,respectively,0.08,0.01,and 0.12 less than those of LSTM.In terms of network structure,ESN consists only of input,reservoir,and output spaces,making it a much simpler model than the others.The proposed ESN was found to be an effective model that,compared to others,converges faster,forecasts more accurately,and builds time-series analyses more easily.

Stock Price Forecasting with Artificial Neural Networks Long Short-Term Memory: A Bibliometric Analysis and Systematic Literature Review

This study maps the academic literature on Stock Price Forecasting with Long-Term Memory Artificial Neural Networks—RNA LSTM. The objective is to know if it is suitable for time series studies, especially for stock price projection. Through bibliometric analysis and systematic literature review, it is observed that 333 authors wrote on the topic between 2018 and March 2022, and the journals Expert Systems with Applications, IEEE Access, Big Data Journal and Neural Computing and Applications, published the most relevant articles. Of the 99 articles published in this period, 43 are associated with Chinese institutions, the most cited being that of Kim and Won, who studies the volatility of returns and the market capitalization of South Korean stocks. The basis of 65% of the studies is the comparison between the RNN LSTM and other artificial neural networks. The daily closing price of shares is the most analyzed type of data, and the American (21%) and Chinese (20%) stock exchanges are the most studied. 57% of the studies include improvements to existing neural network models and 42% new projection models.

Survey of feature selection and extraction techniques for stock market prediction

In stock market forecasting,the identification of critical features that affect the performance of machine learning(ML)models is crucial to achieve accurate stock price predictions.Several review papers in the literature have focused on various ML,statistical,and deep learning-based methods used in stock market forecasting.However,no survey study has explored feature selection and extraction techniques for stock market forecasting.This survey presents a detailed analysis of 32 research works that use a combination of feature study and ML approaches in various stock market applications.We conduct a systematic search for articles in the Scopus and Web of Science databases for the years 2011–2022.We review a variety of feature selection and feature extraction approaches that have been successfully applied in the stock market analyses presented in the articles.We also describe the combination of feature analysis techniques and ML methods and evaluate their performance.Moreover,we present other survey articles,stock market input and output data,and analyses based on various factors.We find that correlation criteria,random forest,principal component analysis,and autoencoder are the most widely used feature selection and extraction techniques with the best prediction accuracy for various stock market applications.

ST-Trader:A Spatial-Temporal Deep Neural Network for Modeling Stock Market Movement

Stocks that are fundamentally connected with each other tend to move together.Considering such common trends is believed to benefit stock movement forecasting tasks.However,such signals are not trivial to model because the connections among stocks are not physically presented and need to be estimated from volatile data.Motivated by this observation,we propose a framework that incorporates the inter-connection of firms to forecast stock prices.To effectively utilize a large set of fundamental features,we further design a novel pipeline.First,we use variational autoencoder(VAE)to reduce the dimension of stock fundamental information and then cluster stocks into a graph structure(fundamentally clustering).Second,a hybrid model of graph convolutional network and long-short term memory network(GCN-LSTM)with an adjacency graph matrix(learnt from VAE)is proposed for graph-structured stock market forecasting.Experiments on minute-level U.S.stock market data demonstrate that our model effectively captures both spatial and temporal signals and achieves superior improvement over baseline methods.The proposed model is promising for other applications in which there is a possible but hidden spatial dependency to improve time-series prediction.

Predicting the daily return direction of the stock market using hybrid machine learning algorithms

Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on forecasting daily stock market returns,especially when using powerful machine learning techniques,such as deep neural networks(DNNs),to perform the analyses.DNNs employ various deep learning algorithms based on the combination of network structure,activation function,and model parameters,with their performance depending on the format of the data representation.This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF(ticker symbol:SPY)based on 60 financial and economic features.DNNs and traditional artificial neural networks(ANNs)are then deployed over the entire preprocessed but untransformed dataset,along with two datasets transformed via principal component analysis(PCA),to predict the daily direction of future stock market index returns.While controlling for overfitting,a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000.Moreover,a set of hypothesis testing procedures are implemented on the classification,and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset,as well as several other hybrid machine learning algorithms.In addition,the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested,including in a comparison against two standard benchmarks.

Estimating stock closing indices using a GA-weighted condensed polynomial neural network

Accurate forecasting of changes in stock market indices can provide financial managers and individual investors with strategically valuable information.However,predicting the closing prices of stock indices remains a challenging task because stock price movements are characterized by high volatility and nonlinearity.This paper proposes a novel condensed polynomial neural network(CPNN)for the task of forecasting stock closing price indices.We developed a model that uses partial descriptions(PDs)and is limited to only two layers for the PNN architecture.The outputs of these PDs along with the original features are fed to a single output neuron,and the synaptic weight values and biases of the CPNN are optimized by a genetic algorithm.The proposed model was evaluated by predicting the next day’s closing price of five fast-growing stock indices:the BSE,DJIA,NASDAQ,FTSE,and TAIEX.In comparative testing,the proposed model proved its ability to provide closing price predictions with superior accuracy.Further,the Deibold-Mariano test justified the statistical significance of the model,establishing that this approach can be adopted as a competent financial forecasting tool.

A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction

Accurate prediction of stock market behavior is a challenging issue for financial forecasting.Artificial neural networks,such as multilayer perceptron have been established as better approximation and classification models for this domain.This study proposes a chemical reaction optimization(CRO)based neuro-fuzzy network model for prediction of stock indices.The input vectors to the model are fuzzified by applying a Gaussian membership function,and each input is associated with a degree of membership to different classes.A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this model.CRO was chosen because it requires fewer control parameters and has a faster convergence rate.Five statistical parameters are used to evaluate the performance of the model,and the model is validated by forecasting the daily closing indices for five major stock markets.The performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be superior.We conducted the Deibold-Mariano test to check the statistical significance of the proposed model,and it was found to be significant.This model can be used as a promising tool for financial forecasting.

Prediction of the Technology Company’s Stock Price through the Deep Learning Method

This work aims to utilize deep learning methods CNN and LSTM to predict the adjusted close price of eight technical companies. The proposed model is a CNN-LSTM hybrid model, which combined CNN and LSTM layers in the model. It was compared with the single LSTM model and double LSTM model to evaluate its performance. The results showed the CNN-LSTM made a great prediction and can predict a more accurate value than the other two models, but it still can be improved to reduce overfitting problems.

Stock Price Forecasting and Rule Extraction Based on L1-Orthogonal Regularized GRU Decision Tree Interpretation Model

Neural network is widely used in stock price forecasting,but it lacks interpretability because of its“black box”characteristics.In this paper,L1-orthogonal regularization method is used in the GRU model.A decision tree,GRU-DT,was conducted to represent the prediction process of a neural network,and some rule screening algorithms were proposed to find out significant rules in the prediction.In the empirical study,the data of 10 different industries in China’s CSI 300 were selected for stock price trend prediction,and extracted rules were compared and analyzed.And the method of technical index discretization was used to make rules easy for decision-making.Empirical results show that the AUC of the model is stable between 0.72 and 0.74,and the value of F1 and Accuracy are stable between 0.68 and 0.70,indicating that discretized technical indicators can predict the short-term trend of stock price effectively.And the fidelity of GRU-DT to the GRU model reaches 0.99.The prediction rules of different industries have some commonness and individuality.

Profit Guided or Statistical Error Guided? A Study of Stock Index Forecasting Using Support Vector Regression

Stock index forecasting has been one of the most widely investigated topics in the field of financial forecasting. Related studies typically advocate for tuning the parameters of forecasting models by minimizing learning errors measured using statistical metrics such as the mean squared error or mean absolute percentage error. The authors argue that statistical metrics used to guide parameter tuning of forecasting models may not be meaningful, given the fact that the ultimate goal of forecasting is to facilitate investment decisions with expected profits in the future. The authors therefore introduce the Sharpe ratio into the process of model building and take it as the profit metric to guide parameter tuning rather than using the commonly adopted statistical metrics. The authors consider three widely used trading strategies, which include a na¨?ve strategy, a filter strategy and a dual moving average strategy, as investment scenarios. To verify the effectiveness of the proposed profit guided approach, the authors carry out simulation experiments using three global mainstream stock market indices. The results show that profit guided forecasting models are competitive, and in many cases produce significantly better performances than statistical error guided models. This implies thatprofit guided stock index forecasting is a worthwhile alternative over traditional stock index forecasting practices.

Prediction of Shanghai Stock Index Based on Investor Sentiment and CNN-LSTM Model

In view of the breakthrough progress of the depth learning method in image and other fields,this paper attempts to introduce the depth learning method into stock price forecasting to provide investors with reasonable investment suggestions.This paper proposes a stock prediction hybrid model named ISI-CNN-LSTM considering investor sentiment based on the combination of long short-term memory(LSTM) and convolutional neural network(CNN).The model adopts an end-to-end network structure,using LSTM to extract the temporal features in the data and CNN to mine the deep features in the data can effectively improve the prediction ability of the model by increasing investor sentiment in the network structure.The empirical part makes a comparative experimental analysis based on Shanghai stock index in China.By comparing the experimental prediction results and evaluation indicators,it verifies the prediction effectiveness and feasibility of ISI-CNN-LSTM network model.