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Thai stock news classification based on price changes and sentiments
(2022-01-01)
Netisopakul, Ponrudee
;
Saewong, Woranun
This research investigates the daily stock news influences toward a company's stock price direction in the Stock Exchange of Thailand. First, machine learning's text classification methods, namely, naïve Bayes, decision tree, random forest, support vector machine, and the three-layer and the five-layer backpropagation neural networks, are applied to predict the stock price directions using stock news collected during the year 2018. Then, the stock news sentiment is incorporated to help improve the prediction accuracy. Last, a meaningful grouping of stock news is carried out to further improve the direction prediction. The testing dataset collected from January to March 2019 stock news are used for model evaluations. The best accuracy obtained from the baseline dataset using stock news only is 78.6%. When dataset is augmented with sentiments and grouped, the best accuracy increases to 90.6%.
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Improved Identification of Imbalanced Multiple Annotation Intent Labels with a Hybrid BLSTM and CNN Model and Hybrid Loss Function
(2021-01-01)
Vatathanavaro, Supawit
;
Pasupa, Kitsuchart
;
Sirirattanajakarin, Sorratat
;
Suntisrivaraporn, Boontawee
Payment or fund transfer transactions can be annotated by users when they are made through a mobile banking app, for example, SCB Easy app—a mobile banking app by Siam Commercial Bank—allows users to annotate transactions with 40 character texts. The AI<sup>2</sup> framework was used to identify user intentions with the transactions, so that the bank can offer the right product to the right customer at the right time. The framework employed Long Short-Term Memory (LSTM). Commonly, one annotated sample can be interpreted as representing multiple intents, thus we had a multiple label classification problem. However, the original model did not consider the class imbalance, that caused the model to bias toward the majority class. We introduced a new hybrid Bidirectional LSTM and Convolutional Neural Network model in conjunction with a new hybrid loss function to tackle the imbalance. Our model with hybrid loss function performed better than the AI<sup>2</sup> framework with a 4.5% improvement in F<inf>1</inf> -score. Moreover, our hybrid loss function enabled the model to classify minority classes better, when the imbalance ratio became higher, compared with a conventional cross-entropy loss function. In other words, our hybrid loss function made the model to be more efficient in real-world multiple label imbalance problem.
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Effectiveness of Six Text Classifiers for Predicting SET Stock Price Direction
(2020-01-01)
Netisopakul, Ponrudee
;
Saewong, Woranun
Six text classification methods were compared to find the best model for predicting Stock Exchange of Thailand stock prices. News headlines, on individual stocks, were classified as causing “change” and “no-change” based on a preset change threshold, 2.5%. The training dataset was collected by matching stock news in 2018 with stock names and filling in stock price changes. 258 news were associated with a “change” and 636 news with “no-change”. The Thai text news items were preprocessed and converted to TF-IDF vector representation. Six machine learning text classification methods are applied to create six text classifier models and create a confusion matrix, then compared with actual changes to obtain accuracy scores. We found that a deep learning classifier (with 85.6% accuracy) scored better than other classifiers for one day price movement to assist short-term investments.
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Thai stock news sentiment classification using wordpair features
(2015-01-01)
Chattupan, Apinan
;
Netisopakul, Ponrudee
Thai stock brokers issue daily stock news for their customers. One broker labels these news with plus, minus and zero sign to indicate the type of recommendation. This paper proposed to classify Thai stock news by extracting important texts from the news. The extracted text is in a form of a 'wordpair'. Three wordpair sets, manual wordpairs extraction (ME), manual wordpairs addition (MA), and automate wordpairs combination (AC), are constructed and compared for their precision, recall and f-measure. Using this broker's news as a training set and unseen stock news from other brokers as a testing set, the experiment shows that all three sets have similar results for the training set but the second and the third set have better classification results in classifying stock news from unseen brokers.
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Text processing simplified ARTMAP neural network
(2005-02-01)
Kreesuradej, Worapoj
;
Kunasit, Puangpaka
This paper proposes text processing simplified ARTMAP neural network. The algorithm works directly on textual information without transforming to numerical value. The input layer of the neural network can directly receive a qualitative value without mapping the qualitative value into numerical value. Then, based on simplified fuzzy ARTMAP neural network and the concept of similarity measure for symbolic objects, the proposed neural network can assigns class labels to the objects correctly.

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