Investment in the stock market is currently very popular due to its economic gain. Numerous researchers' and academicians' work is focused on financial time series prediction due to its data availability and profitability. Therefore, this study presents the design and implementation of a novel binary classification framework to predict stock market trends. The framework is composed of data preprocessing, feature engineering, feature selection and classification algorithms. The model is built on multiple sector stock market companies' data collected from NASDAQ over a period of ten years. Various feature selection algorithms are applied in combination with several machine learning algorithms. Furthermore, as the new contribution, we have constructed two new features which have been found to be promising in terms of improving overall performance. Ultimately, a collaboration of feature selection and classification techniques is employed. The application of Principal Component Analysis (PCA) with Multilayer Perceptron and Support Vector Machine (SVM) to added featured datasets shows 100% accuracy on the majority of datasets. In summary, an intensive comparison is presented among the various feature selection and classification algorithms.
The prediction of stock prices has become an exciting area for researchers as well as academicians due to its economic impact and potential business profits. This study proposes a novel multiclass classification ensemble learning approach for predicting stock prices based on historical data using feature engineering. The proposed approach comprises four main steps, which are pre-processing, feature selection, feature engineering, and ensemble methods. We use 11 datasets from Nasdaq and S&P 500 to ensure the accuracy of the proposed approach. Furthermore, eight feature selection algorithms are studied and implemented. More importantly, a feature engineering concept is applied to construct two new features, which are appears to be very auspicious in terms of improving classification accuracy, and this is considered the first study to use feature engineering for multiclass classification using ensemble methods. Finally, seven ensemble machine learning (ML) algorithms are used and compared to discover the ultimate collaboration prediction model. Besides, the best feature selection algorithm is proposed. This study proposes a novel multiclass classification approach called Gradient Boosting Machine with Feature Engineering (GBM-wFE) and Principal Component Analysis (PCA) as the feature selection. We find that GBM-wFE outperforms the previous studies and the overall prediction results are auspicious, as MAPE of 0.0406% is achieved, which is considered the best result compared to the available studies in the literature.
The stock market has been a crucial factor of investments in the financial domain. Risk modeling and profit generation heavily rely on the sophisticated and intricate stock movement prediction task. Stock Price forecasting is complex that could have a significant influence on the financial market. The Machine Learning (ML) type of artificial intelligence (AI) provides a more accurate forecast for binary and multiclass classification. Different effective methods have been recommended to resolve the problem in the binary classification case but the multiclass classification case is a more delicate one. This paper discusses the application of multiclass classifier mappings such as One v/s All (OvA) and One v/s One (OvO) for stock movement prediction. The proposed approach comprises four main steps: data collection, assign a multi-label (up, down, or same), discover the best classifier methods, and comparison of classifiers on evaluation metrics of 10k cross-validation for stock price movement. In this study, a stock NASDAQ dataset for about ten years of ten companies from yahoo finance on daily basis is used. The resultant Stock Price prediction uncovers Neural Network classifier has good performance in some case whereas Multiclass (One V/s One) and (One V/s All) have overall better performance among all other classifiers as AdaBoost, Support Vector Machine, OneR, Bagging, Simple Logistic, Hoeffding trees, PART, Decision Tree and Random Forest. The Precision, Recall, F-Measure, and ROC area comparison results show that Multiclass (One V/s All) is better than Multiclass (One V/s one). The proposed method Multiclass classification (One v/s All) yields an accuracy of 97.63% for average prediction performance on all ten stock companies, also the highest accuracy achieved as 98.7% for QCOM. The individual stock-wise evaluation of the Multiclass (One V/s All) classifier is found to achieve the highest accuracy among all other classifiers which is outperforming all the recent proposals.
There are many studies about using lemmatization and spell-checker with spell-correction regarding English, Arabic, and Persian languages but only few studies found regarding low-resource languages such as Kurdish language and more specifically for Kurmanji dialect, which increased the need of creating such systems. Lemmatization is the process of determining a base or dictionary form (lemma) for a specific surface pattern, whereas spell-checkers and spell-correctors determine whether a word is correctly spelled also correct a range of spelling errors, respectively. This research aims to present a lemmatization and a word-level error correction system for Kurdish Kurmanji Dialect, which are the first tools for this dialect based on our knowledge. The proposed approach for lemmatization is built on morphological rules, and a hybrid approach that relies on the n-gram language model and the Jaccard Coefficient Similarity algorithm was applied to the spell-checker and spell-correction. The process results for lemmatization, as detailed in this article, rates of 97.7% and 99.3% accuracy for noun and verb lemmatization, correspondingly. Furthermore, for spell-checker and spell-correction, accordingly, accuracy rates of 100% and 90.77% are attained.
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