scholarly journals How Stable are Financial Prediction Models? Evidence from US and International Stock Market Data

2003 ◽  
Author(s):  
Bradley Paye ◽  
Allan G. Timmermann
Keyword(s):  
Stock Market ◽  
Prediction Models ◽  
Market Data ◽  
Financial Prediction

scholarly journals THE EFFICIENCY OF ENSEMBLE CLASSIFIERS IN PREDICTING THE JOHANNESBURG STOCK EXCHANGE ALL-SHARE INDEX DIRECTION

2019 ◽  
Vol 07 (02) ◽  
pp. 1950001
Author(s):  
THABANG MOKOALELI-MOKOTELI ◽  
SHAUN RAMSUMAR ◽  
HIMA VADAPALLI
Keyword(s):  
Logistic Regression ◽  
Stock Market ◽  
Prediction Models ◽  
Stock Exchange ◽  
Machine Learning Techniques ◽  
Ensemble Prediction ◽  
Support Vector ◽  
Ensemble Classifiers ◽  
Ensemble Models ◽  
Johannesburg Stock Exchange

The success of investors in obtaining huge financial rewards from the stock market depends on their ability to predict the direction of the stock market index. The purpose of this study is to evaluate the efficacy of several ensemble prediction models (Boosted, RUS-Boosted, Subspace Disc, Bagged, and Subspace KNN) in predicting the daily direction of the Johannesburg Stock Exchange (JSE) All-Share index compared to other commonly used machine learning techniques including support vector machines (SVM), logistic regression and [Formula: see text]-nearest neighbor (KNN). The findings in this study show that, among all ensemble models, Boosted algorithm is the best performer followed by RUS-Boosted. When compared to the other techniques, ensemble technique (represented by Boosted) outperformed these techniques, followed by KNN, logistic regression and SVM, respectively. These findings suggest that investors should include ensemble models among the index prediction models if they want to make huge profits in the stock markets. However, not all investors can benefit from this as models may suffer from alpha decay as more and more investors use them, implying that the successful algorithms have limited shelf life.

Forecasting the Market with Machine Learning Algorithms: An Application of NMC-BERT-LSTM-DQN-X Algorithm in Quantitative Trading

2022 ◽  
Vol 16 (4) ◽  
pp. 1-22
Author(s):  
Chang Liu ◽  
Jie Yan ◽  
Feiyue Guo ◽  
Min Guo
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Mean Square Error ◽  
Short Term Memory ◽  
The State ◽  
Machine Learning Algorithms ◽  
Future Market ◽  
Mean Square ◽  
Market Data ◽  
Market Trends

Although machine learning (ML) algorithms have been widely used in forecasting the trend of stock market indices, they failed to consider the following crucial aspects for market forecasting: (1) that investors’ emotions and attitudes toward future market trends have material impacts on market trend forecasting (2) the length of past market data should be dynamically adjusted according to the market status and (3) the transition of market statutes should be considered when forecasting market trends. In this study, we proposed an innovative ML method to forecast China's stock market trends by addressing the three issues above. Specifically, sentimental factors (see Appendix [1] for full trans) were first collected to measure investors’ emotions and attitudes. Then, a non-stationary Markov chain (NMC) model was used to capture dynamic transitions of market statutes. We choose the state-of-the-art (SOTA) method, namely, Bidirectional Encoder Representations from Transformers ( BERT ), to predict the state of the market at time t , and a long short-term memory ( LSTM ) model was used to estimate the varying length of past market data in market trend prediction, where the input of LSTM (the state of the market at time t ) was the output of BERT and probabilities for opening and closing of the gates in the LSTM model were based on outputs of the NMC model. Finally, the optimum parameters of the proposed algorithm were calculated using a reinforced learning-based deep Q-Network. Compared to existing forecasting methods, the proposed algorithm achieves better results with a forecasting accuracy of 61.77%, annualized return of 29.25%, and maximum losses of −8.29%. Furthermore, the proposed model achieved the lowest forecasting error: mean square error (0.095), root mean square error (0.0739), mean absolute error (0.104), and mean absolute percent error (15.1%). As a result, the proposed market forecasting model can help investors obtain more accurate market forecast information.

Finding Trading Patterns in Stock Market Data

2004 ◽  
Vol 24 (5) ◽  
pp. 45-55 ◽  
Author(s):  
K.V. Nesbitt ◽  
S. Barrass
Keyword(s):  
Stock Market ◽  
Market Data ◽  
Trading Patterns

Mining for Core Patterns in Stock Market Data

2009 ◽  
Author(s):  
Jianfei Wu ◽  
Anne Denton ◽  
Omar Elariss ◽  
Dianxiang Xu
Keyword(s):  
Stock Market ◽  
Market Data

Contingent Rule: Information Aesthetic, Performance and Hypermediacy

Leonardo ◽  
2011 ◽  
Vol 44 (3) ◽  
pp. 286-287 ◽  
Author(s):  
Eung Suk Kim ◽  
Joonsung Yoon
Keyword(s):  
Stock Market ◽  
Real Time ◽  
Information Visualization ◽  
Market Data ◽  
The Real ◽  
Proposed Model ◽  
Aesthetic Performance

This paper proposes a model of information aesthetic performance in the context of hypermediacy. It addresses the need to consider the features of performance in recently emerging information visualization artwork. By analyzing an artwork, the real-time stock market data-based Contingent Rule, the authors discuss aesthetic effects of performance as well as information visualization. The proposed model could contribute to a better understanding of information visualization in terms of Jay David Bolter and Richard Grusin's ‘hypermediacy.’ This research provides a new guideline for reviewing information visualization.

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