Machine Learning Model for Stock Market Prediction

Kanade, Ashwini

doi:10.22214/ijraset.2020.6030

Cited by 5 publications

(2 citation statements)

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“…The third type of work (Classification), classifies indicator data from a stock as "Buy", "Hold", or "Sell" through deep learning and neural network based classifications [31], [32], [33], [34], [16]. Prediction results can also represent an "Up" or "Down" trend so that investors can make decisions on investment entry positions by applying two single non-linear classifiers ANN, SVM and one RF ensemble approach to predict the direction of the next day's movement [35], [5], [36].…”

Section: Using a Clustering Algorithm Shares Will Be Grouped Against An Investment Decision Making Criterion (Clustering)mentioning

confidence: 99%

The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

Wiranata

Djunaidy

2021

Jurnal Ilmu Komputer dan Informasi

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This literature review identifies and analyzes research topic trends, types of data sets, learning algorithm, methods improvements, and frameworks used in stock exchange prediction. A total of 81 studies were investigated, which were published regarding stock predictions in the period January 2015 to June 2020 which took into account the inclusion and exclusion criteria. The literature review methodology is carried out in three major phases: review planning, implementation, and report preparation, in nine steps from defining systematic review requirements to presentation of results. Estimation or regression, clustering, association, classification, and preprocessing analysis of data sets are the five main focuses revealed in the main study of stock prediction research. The classification method gets a share of 35.80% from related studies, the estimation method is 56.79%, data analytics is 4.94%, the rest is clustering and association is 1.23%. Furthermore, the use of the technical indicator data set is 74.07%, the rest are combinations of datasets. To develop a stock prediction model 48 different methods have been applied, 9 of the most widely applied methods were identified. The best method in terms of accuracy and also small error rate such as SVM, DNN, CNN, RNN, LSTM, bagging ensembles such as RF, boosting ensembles such as XGBoost, ensemble majority vote and the meta-learner approach is ensemble Stacking. Several techniques are proposed to improve prediction accuracy by combining several methods, using boosting algorithms, adding feature selection and using parameter and hyper-parameter optimization.

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Section: Using a Clustering Algorithm Shares Will Be Grouped Against An Investment Decision Making Criterion (Clustering)mentioning

confidence: 99%

The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

Wiranata

Djunaidy

2021

Jurnal Ilmu Komputer dan Informasi

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“…We desire to provide an efficient and effective solution that would overcome the manual trading drawbacks by building a Trading Bot [5]. Our strategy employs three actor critic models: Advanced Actor Critic(A2C) [6], [7], Twin Delayed DDPG (TD3) [8], [9] and Soft Actor Critic (SAC) [10].…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning Models for Automated Stock Trading

Singh

Goyal

Goel

et al. 2022

Advances in Transdisciplinary Engineering

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This paper proposes an automated trading strategy using reinforcement learning. The stock market has become one of the largest financial institutions. These institutions embrace machine learning solutions based on artificial intelligence for market monitoring, credit quality, fraud detection, and many other areas. We desire to provide an efficient and effective solution that would overcome the manual trading drawbacks by building a Trading Bot. In this paper, we will propose a stock trading strategy that uses reinforcement learning algorithms to maximize the profit. The strategy employs three actor critic models: Advanced Actor Critic(A2C), Twin Delayed DDPG (TD3) and Soft Actor Critic (SAC). Our strategy picks the most optimal model based on the current market situation. The performance of our trading bot is evaluated and compared with Markowitz portfolio theory.

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Real Estate Market Prediction Using Deep Learning Models

Rimal,

Bhandari

et al. 2024

Ann. Data. Sci.

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Machine Learning Model for Stock Market Prediction

Cited by 5 publications

References 0 publications

The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

Deep Reinforcement Learning Models for Automated Stock Trading

Real Estate Market Prediction Using Deep Learning Models

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