Effectiveness of Artificial Intelligence in Stock Market Prediction based on Machine Learning

@inproceedings{Mokhtari2021EffectivenessOA,
  title={Effectiveness of Artificial Intelligence in Stock Market Prediction based on Machine Learning},
  author={Sohrab Mokhtari and Kang K. Yen and Jin Liu},
  year={2021}
}
This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are employed to predict the stock price trend at the end of a business day based on the historical price data. In contrast, in the fundamental analysis, the classification ML algorithms… Expand
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References

SHOWING 1-10 OF 25 REFERENCES
Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques
TLDR
Experimental results show that the performance of all the prediction models improve when these technical parameters are represented as trend deterministic data, and random forest outperforms other three prediction models on overall performance. Expand
Predicting the daily return direction of the stock market using hybrid machine learning algorithms
TLDR
A comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF based on 60 financial and economic features and 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. Expand
A systematic review of fundamental and technical analysis of stock market predictions
TLDR
Support vector machine and artificial neural network were found to be the most used machine learning algorithms for stock market prediction. Expand
Support Vector Machine Regression for Volatile Stock Market Prediction
TLDR
The experimental results show that the use of standard deviation to calculate a variable margin gives a good predictive result in the prediction of Hang Seng Index. Expand
A new procedure in stock market forecasting based on fuzzy random auto-regression time series model
TLDR
The study found that variability and spread adjustment are important factors in data preparation to improve ac- curacy of the fuzzy random auto-regression model. Expand
Support vector regression with chaos-based firefly algorithm for stock market price forecasting
TLDR
A forecasting model based on chaotic mapping, firefly algorithm, and support vector regression (SVR) is proposed to predict stock market price and performs best based on two error measures, namely mean squared error (MSE) and mean absolute percent error (MAPE). Expand
Stock market prediction system with modular neural networks
TLDR
The authors developed a number of learning algorithms and prediction methods for the TOPIX (Tokyo Stock Exchange Prices Indexes) prediction system, which achieved accurate predictions, and the simulation on stocks trading showed an excellent profit. Expand
Separating Winners from Losers among LowBook-to-Market Stocks using Financial Statement Analysis
This paper combines traditional fundamentals, such as earnings and cash flows, with measures tailored for growth firms, such as earnings stability, growth stability and intensity of R&D, capitalExpand
Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers
This paper examines whether a simple accounting-based fundamental analysis strategy, when applied to a broad portfolio of high book-to-market firms, can shift the distribution of returns earned by anExpand
Impact of large-scale wind power penetration on incentive of individual investors, a supply function equilibrium approach
TLDR
A novel approach, including error correction and the sensitivity analysis method, is introduced and can address the ensured gained profit of an investment in an uncertain power market. Expand
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