They used the data containing samples of 2928 trading days, ranging from January 1989 to December 1998, and give their selected features and formulas. They also applied optimization of feature discretization, as a technique that is similar to dimensionality reduction. The strengths of their work are that they introduced GA to optimize the ANN. First, the amount of input features and processing elements in the hidden layer are 12 and not adjustable. Another limitation is in the learning process of ANN, and the authors only focused on two factors in optimization. While they still believed that GA has great potential for feature discretization optimization.
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 DotBig 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.
The final algorithm of data preprocessing using RFE and PCA can be illustrated as Algorithm 1. The RFE algorithm is known to have suffered from the over-fitting problem. To eliminate the over-fitting issue, we will run the RFE algorithm multiple times on randomly selected stocks as the training set and ensure all the features we select are high-weighted. https://dotbig.com/markets/stocks/SPOT/ Resampling can be built as an optimization step as an outer layer of the RFE algorithm. The function FE is corresponding to the feature extension block. For the feature extension procedure, we apply three different processing methods to translate the findings from the financial domain to a technical module in our system design.
- The primary limitation of their research was that they only analyzed 20 U.S.-based stocks, the model might not be generalized to other stock market or need further revalidation to see if it suffered from overfitting problems.
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- The algorithmic detail is elaborated, respectively, the first algorithm is the hybrid feature engineering part for preparing high-quality training and testing data.
- Not all the technical indices are applicable for all three of the feature extension methods; this procedure only applies the meaningful extension methods on technical indices.
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We further test the effectiveness of feature extension, i.e., if polarize, max–min scale, and calculate fluctuation percentage works better than original technical indices. The best case to leverage this test is the weekly prediction since it has the least effective feature selected. From the result we got from the last section, we know the best cross-validation score appears when selecting 8 features. The test consists of two steps, and the first step is to test the feature set formed by original features only, in this case, only SLOWK, SLOWD, and RSI_5 are included. The next step is to test the feature set of all 8 features we selected in the previous subsection. We leveraged the test by defining the simplest DNN model with three layers.
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Nekoeiqachkanloo et al. in proposed a system with two different approaches for stock investment. First, it is a comprehensive system that consists of data pre-processing and two https://dotbig.com/ different algorithms to suggest the best investment portions. Second, the system also embedded with a forecasting component, which also retains the features of the time series.
This step is to retain as many effective features as possible and meanwhile eliminate the computational complexity of training the model. This research work also evaluates the best combination of i and j, which has relatively better prediction accuracy, meanwhile, cuts the computational consumption. After the PCA step, the system will get a reshaped matrix with j columns. The very first step before leveraging PCA is feature pre-processing. Because some of the features after RFE are percentage data, while others are very large numbers, i.e., the output from RFE are in different units. Thus, before feeding the data into the PCA algorithm , a feature pre-processing is necessary.
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Pimenta et al. in leveraged an automated investing method by using multi-objective genetic programming and applied it in the stock market. The dataset was obtained from Brazilian stock exchange market , and the primary https://dotbig.com/ techniques they exploited were a combination of multi-objective optimization, genetic programming, and technical trading rules. For optimization, they leveraged genetic programming to optimize decision rules.
Stock markets play an essential role in growing industries that ultimately affect the economy through transferring available funds from units that have excess funds to those who are suffering from funds deficit . In other words, capital markets facilitate funds movement between the above-mentioned units. This process leads to the enhancement of available financial resources which in turn affects the economic DotBig growth positively. Crowd gathering on Wall Street after the 1929 crash, one of the worst stock market crashes in history. The Paris Bourse, now part of Euronext, is an order-driven, electronic stock exchange. In 1986, the CATS trading system was introduced, and the order matching system was fully automated. The NASDAQ is an electronic exchange, where all of the trading is done over a computer network.
Proposed model evaluation—PCA effectiveness
When finding the best parameter combination, they also used a grid search method, which is k cross-validation. Besides, the evaluation of different feature selection methods is also comprehensive. As the authors mentioned in their conclusion part, they only considered the technical indicators but not macro and micro factors in the financial domain. The source of datasets Stock Price Online that the authors used was similar to our dataset, which makes their evaluation results useful to our research. They also mentioned a method called k cross-validation when testing hyper-parameter combinations. From the confusion matrices in Fig.9, we can see all the machine learning models perform well when training with the full feature set we selected by RFE.
While not all the indices are applicable for expanding, we only choose the proper method for certain features to perform the feature extension , according to Table2. The algorithmic detail is elaborated, stock price of Spotify respectively, the first algorithm is the hybrid feature engineering part for preparing high-quality training and testing data. It corresponds to the Feature extension, RFE, and PCA blocks in Fig.3.
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Not all the technical indices are applicable for all three of the feature extension methods; this procedure only applies the meaningful extension methods on technical indices. We choose meaningful extension methods while looking at how the indices are calculated.