Stock market prediction based on statistical data using machine learning algorithms

Akhtar, Mobin; Zamani, Abu Sarwar; Khan, Shakir; Shatat, Abdallah Saleh Ali; Dilshad, Sara; Samdani, Faizan

doi:10.1016/j.jksus.2022.101940

Cited by 35 publications

(13 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Experimental data show that the FHAN way in this paper has better performance in stock price prediction than other neural network models and LSTM-attention [18]. References [19,20] presented a novel Convolutional, BiGRU, and Capsule network-based deep learning model, HCovBi-Caps, to classify the hate speech, and multichannel CNN modeling is discussed in [21][22][23] and a new multichannel convolution neural network (MCCNN) model is proposed for extracting the relationship.…”

Section: Introductionmentioning

confidence: 94%

See 1 more Smart Citation

Multilayer Graph-Based Deep Learning Approach for Stock Price Prediction

Alshara

2022

Security and Communication Networks

View full text Add to dashboard Cite

Although a strategy for predicting stock prices using relational data has been described recently, no practical way for selecting aggregating various forms of relational data to forecast stock prices has been discovered. The authors present an upgraded multilayer node graph attention network (FHAN) model that incorporates the Fraudar algorithm and provides insight into the interaction between several items. The model, which regards businesses as nodes and interactions as edges, aggregates data from various connection types and adds it to each company’s node representation, which is then automatically fed into the task-specific layer select information. The testing findings indicate that this approach is more accurate in stock price prediction than the currently popular neural network methodology. The experiment compares several distinct neural network algorithms. This approach is more accurate than the previous method when ideal parameters are used. The average rise is around 4%, while the largest increase is 24%.

show abstract

Section: Introductionmentioning

confidence: 94%

“…e feature extraction module is used to portray the enterprise's current status using the historical pattern of stock price [22] volatility. LSTM is employed as a feature extraction module in this investigation.…”

Section: Feature Extraction Modulementioning

confidence: 99%

Multilayer Graph-Based Deep Learning Approach for Stock Price Prediction

Alshara

2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…Some academics have also recommended an introduction to equity derivatives and an introduction to derivatives. This study focuses on all equity derivatives traded on stock exchanges [19]. The author describes the evolution of the stock derivatives market over time.…”

Section: Literature Reviewmentioning

confidence: 99%

A Model for Calculating the Effectiveness of Writing and Hedging SBI Derivatives

Patra¹,

Patra²

2023

Int. Res. J. multidiscip. Technovation

View full text Add to dashboard Cite

Options in the stock market are a form of risk management that can help protect investors from various potential liabilities. Increased demand for derivatives is reflected in higher trading volumes every day. Over time, it has been easier for regular investors to get their hands on derivatives. The major Indian exchanges trade a wide range of financial goods, including stock derivatives. This article explains how to trade F&O on the National Stock Exchange (NSE) in India. To write options, the NSE typically employs call-and-put options. It may be able to design ways to achieve this goal by studying the State Bank of India (SBI) options chain for the first quarter of fiscal years 22 and 23. Based on the current stock price, the suggested computational approach writes call (CE) and put (PE) options for the upcoming month's settlement date. CE and PE were written at prices twenty rupees higher and lower than the stock options strike price, respectively. Furthermore, the pricing for both products has been reduced to zero rupees. According to our research, selling options to firms with minimal volatility is a good idea.

show abstract

“…Sharma et al surveyed several efficient regression approaches, e.g., linear, polynomial, RBF, and sigmoid regression to predict the stock market [3]. Mobin Akhtar et al mainly used a Support Vector Machine to improve the overall accuracy of stock price prediction [4]. Jayanth Balaji et al mainly focused on deep learning methods and found that LSTM, GRU, CNN, and ELM performed well in terms of RMSE, DA, and MdAPE [5].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Different Machine Learning Scenarios for Stock Price Prediction of Netflix

Chen¹

2023

BCPBM

View full text Add to dashboard Cite

Buying stocks based on relatively accurate predictions to make a profit is what investors have been yearning for. However, due to the volatility and stochastic intrinsic of the stock, the price is also full of uncertainty, which is difficult to predict. With the improvement of computer performance and the popularization of machine learning methods nowadays, effective stock prediction methods emerge one after another. In this paper, random forest, XGBoost, and LSTM techniques are utilized for predicting the closing price of Netflix, which is one of the major stocks in Nasdaq and has been fluctuating this year. The closing price of the previous five days and the last day’s volume are used as inputs. The models are evaluated by MSE and R-square. According to the analysis, similar model evaluation results in random forest and LSTM indicate that the models are efficient. Furthermore, the prediction could be improved by considering the fluctuations of stocks simultaneously and introducing more variables in various dimensions. These results shed light on guiding further exploration of predicting stock price movements based on advanced machine learning methods.

show abstract

Stock market prediction based on statistical data using machine learning algorithms

Cited by 35 publications

References 13 publications

Multilayer Graph-Based Deep Learning Approach for Stock Price Prediction

Multilayer Graph-Based Deep Learning Approach for Stock Price Prediction

A Model for Calculating the Effectiveness of Writing and Hedging SBI Derivatives

Comparison of Different Machine Learning Scenarios for Stock Price Prediction of Netflix

Contact Info

Product

Resources

About