Forecasting Direction of Stock Index Using Two Stage Hybridization of Machine Learning Models

Misra, Puneet; Chaurasia, Siddharth

doi:10.1109/icrito.2018.8748530

Cited by 5 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The GA-SVM hybrid model outperformed the SVM by a large margin. The accuracy of GA-SVM was 61.7328 percent for a particular company like Tata Consultancy Services (TCS), while the accuracy of the SVM was 58.09%.The study in [5] seeks to anticipate the direction of movement for the SP BSE Sensex index for the next day in two steps. The first step determined whether the expected movement would be up or down based on six technical indicators.…”

Section: Literature Reviewmentioning

confidence: 99%

Stock Price Prediction Using Machine Learning algorithm with Web Interface (GUI)

Mittal¹,

Joshi²,

Savani³

2022

Artificial Intelligence and Communication Technologies

View full text Add to dashboard Cite

Predicting the stock market trend is difficult due to the stock market’s high level of uncertainty. Many approaches for stock price and market prediction have been developed in the past. Different algorithms such as Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Linear regression, and Decision trees are utilized in this study to forecast the future closing price of stocks for a specific firm. In this paper, the accuracy of these mentioned algorithms is also being compared. A seven-day study is also performed, illustrating the actual and anticipated closing prices for two companies using different algorithms. The outcome of the previous forecast is compared to the other algorithms, and a final algorithm is chosen based on it. A final model is created by taking many technical parameters in consideration in order to have more precise anticipated the price. Based on the final model, a 10-days examination of the actual and anticipated closing prices for ten different firms is also performed. Finally, A web application-based Graphical User Interface (GUI) was built using the Stream-lit library. The GUI includes features such as a forecast graph, a graph for historical data, and a company selection as well as the start and the end date.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Stock Price Prediction Using Machine Learning algorithm with Web Interface (GUI)

Mittal¹,

Joshi²,

Savani³

2022

Artificial Intelligence and Communication Technologies

View full text Add to dashboard Cite

show abstract

“…Findings indicate that Random Forest is the most favoured algorithm, followed by Support Vector Machines, Kernel Factory, Adaboost, Neural Networks, K-Nearest Neighbors, and Logistic Regression. Misra & Chaurasia (2019) sought to predict movement direction for the next day's high price for the S&P BSE Sensex index. Random Forest, Support Vector Machine, and Artificial Neural Network methods were adopted, with the S&P BSE Sensex index being the dataset.…”

Section: Papers Reviewedmentioning

confidence: 99%

“…SVMs can learn from the high dimensional feature space. Mapping from lower to higher feature dimensional space is achieved using kernel function (Misra & Chaurasia, 2019;Pun & Shahi, 2018;Cakra, 2015).…”

Section: Introductionmentioning

confidence: 99%

Intelligent Models for Stock Price Prediction

Ansah

Denwar

Appati

2022

Journal of Information Technology Research

View full text Add to dashboard Cite

Prediction of the stock price is a crucial task as predicting it may lead to profits. Stock price prediction is a challenge owing to non-stationary and chaotic data. Thus, the projection becomes challenging among the investors and shareholders to invest the money to make profits. This paper is a review of stock price prediction, focusing on metrics, models, and datasets. It presents a detailed review of 30 research papers suggesting the methodologies, such as Support Vector Machine Random Forest, Linear Regression, Recursive Neural Network, and Long Short-Term Movement based on the stock price prediction. Aside from predictions, the limitations, and future works are discussed in the papers reviewed. The commonly used technique for achieving effective stock price prediction is the RF, LSTM, and SVM techniques. Despite the research efforts, the current stock price prediction technique has many limits. From this survey, it is observed that the stock market prediction is a complicated task, and other factors should be considered to accurately and efficiently predict the future.

show abstract

“…The essence of these two directions is to predict the rise and fall of stocks. At the same time, quantitative investment and machine learning are more and more integrated [2][3][4][5][6], and the research of stock forecasting using technical indicators and machine learning algorithm is developing rapidly.…”

mentioning

confidence: 99%

Research on stock trend prediction method based on optimized random forest

Yin

Zhang

2021

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

As a complex hot problem in the financial field, stock trend forecasting uses a large amount of data and many related indicators; hence it is difficult to obtain sustainable and effective results only by relying on empirical analysis. Researchers in the field of machine learning have proved that random forest can form better judgements on this kind of problem, and it has an auxiliary role in the prediction of stock trend. This study uses historical trading data of four listed companies in the USA stock market, and the purpose of this study is to improve the performance of random forest model in medium-and long-term stock trend prediction. This study applies the exponential smoothing method to process the initial data, calculates the relevant technical indicators as the characteristics to be selected, and proposes the D-RF-RS method to optimize random forest. As the random forest is an ensemble learning model and is closely related to decision tree, D-RF-RS method uses a decision tree to screen the importance of features, and obtains the effective strong feature set of the model as input. Then, the parameter combination of the model is optimized through random parameter search. The experimental results show that the average accuracy of random forest is increased by 0.17 after the above process optimization, which is 0.18 higher than the average accuracy of light gradient boosting machine model. Combined with the performance of the ROC curve and Precision-Recall curve, the stability of the model is also guaranteed, which further demonstrates the advantages of random forest in medium-and long-term trend prediction of the stock market.

show abstract

Forecasting Direction of Stock Index Using Two Stage Hybridization of Machine Learning Models

Cited by 5 publications

References 12 publications

Stock Price Prediction Using Machine Learning algorithm with Web Interface (GUI)

Stock Price Prediction Using Machine Learning algorithm with Web Interface (GUI)

Intelligent Models for Stock Price Prediction

Research on stock trend prediction method based on optimized random forest

Contact Info

Product

Resources

About