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The optimization techniques, such as principal component analysis were also applied in short-term stock price prediction . This type of previous dotbig works belongs to the feature engineering domain and can be considered as the inspiration of feature extension ideas in our research.
In the previous works, researchers have been using a variety of models for predicting stock price trends. In the related works, often a thorough statistical analysis is performed based on a special dataset and conclude new features rather than performing feature selections. Some data, such as the percentage of a certain index fluctuation has been proven to be effective on stock performance.
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Accurate information on 6000+ stocks, including all the companies in the S&P500 index. See stock prices, news, financials, forecasts, charts and more. While these curves have different patterns, for the length of 2 weeks, the cross-validation score increases with the number of features selected. Godaddy stock If the prediction term length is 1 week, the cross-validation score will decrease if selected over 8 features. For every other day price trend prediction, the best cross-validation score is achieved by selecting 48 features. Biweekly prediction requires 29 features to achieve the best score.
The dataset in this research is a sub dataset of NASDAQ Index in Taiwan Economic Journal Database in 2008. The feature selection part was using a hybrid method, supported sequential forward search played the role of the wrapper. 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 https://dotbig.com/markets/stocks/GDDY/ structuring. One of the limitations was that the performance of SVM was compared to back-propagation neural network only and did not compare to the other machine learning algorithms. In this empirical paper, an exploration of the relationship between stock market prices and online search trends is attempted, to contribute to this ongoing discussion. This report complements the previous work of Ikeda , who found that most of the US industrial sector stocks are fractal.
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The model training using the full 29 features takes 28.5 s per epoch on average. While it only takes 18 s on average per epoch training on the feature set of five principal components. PCA has significantly improved the training efficiency of the LSTM model by 36.8%. We will leverage a discussion in the next section about complexity analysis. In the first step, we select all 29 effective features and train the NN model without performing PCA. It creates a baseline of the accuracy and training time for comparison.
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Last but not least, their input features are a mix of fundamental features and technical indices that aim to fill in the gap between the https://www.cnbc.com/money-in-motion/ financial domain and technical domain. Instead of evaluating the proposed system on a large dataset, they chose 25 well-known stocks.
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But, at the end of the day, these factors resulted in more investors selling stock than buying, which is why we saw stock prices generally dotbig decline. Financial ratios of a listed company are used to present the growth ability, earning ability, solvency ability, etc.
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