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- LR algorithm cost less training time than other algorithms while it can achieve a similar prediction result with other costly models such as SVM and MLP.
- Table5 indicates that the overall prediction accuracy is not drastically affected by reducing the dimension.
- Many strategies can be classified as either fundamental analysis or technical analysis.
- Besides comparing the performance across popular machine learning models, we also evaluated how the PCA algorithm optimizes the training procedure of the proposed LSTM model.
- All investments involve risks, including the loss of principal invested.
- Another advantage of this work is that they designed a detailed procedure of parameter adjustment with performance under different parameter values.
In this research, our objective is to build a state-of-art prediction model for price trend prediction, which focuses on short-term price DotBig trend prediction. A stock market crash is often defined as a sharp dip in share prices of stocks listed on the stock exchanges.
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Besides the different result structure, the datasets that previous works researched on are also different from our work. Some of the previous works involve news data to perform sentiment analysis and exploit the SE part as another system component to support their prediction model. Hafezi et al. in built a bat-neural network multi-agent system (BN-NMAS) to predict stock price. They also applied the Bat algorithm for optimizing neural network weights. The authors illustrated their overall structure and logic of system design in clear flowcharts. While there were very few previous works that had performed on DAX data, it would be difficult to recognize if the model they proposed still has the generality if migrated on other datasets.
Ayo leveraged analysis on the stock data from the New York Stock Exchange , while the weakness is they only performed analysis on closing price, which is a feature embedded with high noise. As concluded by Fama in , financial time series prediction is known to be a notoriously difficult task due to the generally accepted, semi-strong form of market efficiency and the high level of noise. Back in 2003, Wang et al. in already applied artificial neural networks on stock market price prediction and focused on volume, as a specific feature of stock market. One of the key findings by them was that the volume was not found to be effective in improving the forecasting performance on the datasets they used, which was S&P 500 and DJI. Ince and Trafalis in targeted short-term forecasting and applied support vector machine model on the stock price prediction. Their main contribution is performing a comparison between multi-layer perceptron and SVM then found that most of the scenarios SVM outperformed MLP, while the result was also affected by different trading strategies.
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We leveraged the test by defining the simplest DNN model with three layers. In this research, we focus on the short-term price trend prediction. We mark the price trend by comparing the current closing price with the closing price of n trading days ago, the range of n is from 1 to 10 since our research is focusing on the short-term. If the price trend goes up, we mark it as 1 or mark as 0 in the opposite case. To be more specified, we use the indices from the indices of n−1th day to predict the price trend of the nth day.
After the feature extension procedure, the expanded features will be combined with the most commonly used technical indices, i.e., input data with output data, and feed into RFE DotBig block as input data in the next step. Growth investors seek out companies with exceptionally high growth potential, hoping to realize maximum appreciation in share price.
Another advantage of this work is that they designed a detailed procedure of parameter adjustment with performance under different parameter values. The clear structure of the feature selection model is also heuristic to the primary stage of model structuring. One of the limitations was that DotBig the performance of SVM was compared to back-propagation neural network only and did not compare to the other machine learning algorithms. Lei in exploited Wavelet Neural Network to predict stock price trends. The author also applied Rough Set for attribute reduction as an optimization.
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They obtained these datasets from three open-sourced APIs and an R package named TTR. A thorough study of ensemble methods specified for short-term stock price https://dotbig.com/ prediction. With background knowledge, the authors selected eight technical indicators in this study then performed a thoughtful evaluation of five datasets.
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