Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities

@inproceedings{Dolphin2021MeasuringFT,
  title={Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities},
  author={Rian Dolphin and Barry Smyth and Yang Xu and Ruihai Dong},
  booktitle={ICCBR},
  year={2021}
}
Forecasting stock returns is a challenging problem due to the highly stochastic nature of the market and the vast array of factors and events that can influence trading volume and prices. Nevertheless it has proven to be an attractive target for machine learning research because of the potential for even modest levels of prediction accuracy to deliver significant benefits. In this paper, we describe a case-based reasoning approach to predicting stock market returns using only historical pricing… Expand

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SHOWING 1-10 OF 38 REFERENCES
Data mining for financial prediction and trading: application to single and multiple markets
TLDR
This study investigated the implications for portfolio management using an implicit learning technique (neural nets) and an explicit approach (CBR) and found that a trading strategy coupled with a forecasting system offers the possibility for returns in excess of a passive buy-and-hold approach. Expand
A Similarity-Based Approach for Financial Time Series Analysis and Forecasting
TLDR
A new feature extractor based on visual features associated with a boosted instance-based learning classifier to predict a share’s behavior is proposed, thus improving the human analyst understanding and validation of the results, and outperformed existing methods in terms of accuracy, running time and scalability. Expand
Trend discovery in financial time series data using a case based fuzzy decision tree
TLDR
A novel case based fuzzy decision tree model, CBFDT, is developed to predict the time series behavior in the future and is experimentally compared with other approaches on Standard & Poor's 500 index and some stocks in S&P500. Expand
A neural network with a case based dynamic window for stock trading prediction
TLDR
The empirical results show that the CBDW can assist the BPN to reduce the false alarm of buying or selling decisions, and is a first attempt in the literature to predict the sell/buy decision points instead of stock price itself. Expand
An integrated framework of deep learning and knowledge graph for prediction of stock price trend: An application in Chinese stock exchange market
TLDR
A deep neural network model using the desensitized transaction records and public market information to predict stock price trend is proposed and achieves the best performance in comparison with other prediction baselines. Expand
Stock price prediction using LSTM, RNN and CNN-sliding window model
TLDR
This work uses three different deep learning architectures for the price prediction of NSE listed companies and compares their performance and applies a sliding window approach for predicting future values on a short term basis. Expand
Stock Market Forecasting Using Computational Intelligence: A Survey
TLDR
This paper presents an up-to-date survey of existing literature on stock market forecasting based on computational intelligent methods and presents the outlines of proposed work with the aim to enhance the performance of existing techniques. Expand
Geometric Case Based Reasoning for Stock Market Prediction
TLDR
This method overcomes the limitation of conventional case-based reasoning in that it uses Euclidean distance and does not consider how nearest neighbors are similar to the target case in terms of changes between previous and current features in a time series. Expand
Explainable Text-Driven Neural Network for Stock Prediction
  • Linyi Yang, Z. Zhang, +4 authors Ruihai Dong
  • Computer Science
  • 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)
  • 2018
TLDR
Thorough empirical studies based upon historical prices of several individual stocks demonstrate the superiority of the proposed dual-layer attention-based neural network method in stock price prediction compared to state-of-the-art methods. Expand
Predicting financial activity with evolutionary fuzzy case-based reasoning
TLDR
A hybrid decision model using case-based reasoning augmented with genetic algorithms (GAs) and the fuzzy k nearest neighbor (fuzzy k-NN) methods for predicting the financial activity rate is proposed. Expand
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