Short-Term Stock Price-Trend Prediction Using Meta-Learning

The paper emphasizes on stock price trend prediction based on the online textual news. Cognitive process uses existing knowledge and generates new knowledge. Contextual features (CF) from news sites are extracted & recommendations based on the interpretations are generated. A Naïve bays classification algorithm is used to classify the news sentiments. A News Sentiment Index (NSI) is calculated and effect of the news on particular stock is calculated to predict the trend. Along with news sentiment index, technical quality of the same stock is calculated by various statistical technical indicators which are called as Stock Technical Index (STI). The weighted index of NSI and STI is used to predict the trend of stock price. In the previous recommendation systems, the context of the recommendation is not considered. However, it is shown in this research that if the authors consider the news context while recommendation, the performance of the recommendation system will drastically improve. The results are compared with traditional systems and it shows significant improvement.

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Stock price trend prediction using Bayesian regularised radial basis function network model

Spanish Journal of Finance and Accounting / Revista Española de Financiación y Contabilidad ◽

10.1080/02102412.2016.1260859 ◽

2016 ◽

Vol 46 (2) ◽

pp. 189-211

Author(s):

G Sheelapriya ◽

R Murugesan

Keyword(s):

Radial Basis Function ◽

Network Model ◽

Basis Function ◽

Stock Price ◽

Radial Basis Function Network ◽

Trend Prediction ◽

Price Trend ◽

Radial Basis ◽

Function Network

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Neural Network Algorithm Based Method for Stock Price Trend Prediction

Journal of Applied Sciences ◽

10.3923/jas.2013.5384.5390 ◽

2013 ◽

Vol 13 (22) ◽

pp. 5384-5390 ◽

Cited By ~ 1

Author(s):

Nan Ma ◽

Yun Zhai ◽

Wen-Fa Li ◽

Cui-Hua Li ◽

Shan-shan Wang ◽

...

Keyword(s):

Neural Network ◽

Stock Price ◽

Trend Prediction ◽

Price Trend ◽

Network Algorithm ◽

Neural Network Algorithm

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Study on Auto Sector Stock Price Trend Prediction by using Decision Tree

New Approaches in Engineering Research Vol. 5 ◽

10.9734/bpi/naer/v5/2384f ◽

2021 ◽

pp. 57-67

Author(s):

Manish M. Goswami ◽

Sachin Jambhulkar

Keyword(s):

Decision Tree ◽

Stock Price ◽

Trend Prediction ◽

Price Trend

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STOCK PRICE TREND PREDICTION USING SUPPORT VECTOR MACHINE AND CORAL REEF OPTIMIZATION ALGORITHM

Journal of Natural Sciences Engineering and Technology ◽

10.51406/jnset.v18i1.2033 ◽

2020 ◽

Vol 18 (1) ◽

pp. 68-87

Author(s):

A. DEJI-OLARERIN ◽

O. FOLORUNSO ◽

O. R. VINCENT ◽

O. M. OLAYIWOLA

Keyword(s):

Coral Reef ◽

Stock Market ◽

Optimization Algorithm ◽

Stock Price ◽

Optimization Problems ◽

Series Data ◽

Support Vector ◽

Trend Prediction ◽

Market Prices ◽

Price Trend

Due to non-linearity and non-stationary characteristics of stock market time series data, prior approaches have not been adequate enough for predicting stock market prices. Support vector machines are classifier that have been reported in the literature as having good recognition accuracy and have been applied in the area of predicting financial stock market prices and was found efficient. It is however noted that the performance of the SVM is affected by the values of the hyper-parameters used by the SVM. There is the need to find a way for searching for the best hyper-parameters that optimizes the performance of an SVM model. Coral Reef Optimization (CRO) is one of many nature-inspired algorithms used extensively to solve optimization problems. It is very effective in solving optimization problems because it is able to achieve global optimization. This paper’s contribution is the development of Coral Reef search algorithms for the improvement of the hyper-parameters of the SVM used for stock price trend prediction. The Algorithm is validated using stock data of two banks. The results obtained out-performed un-optimized SVM, and have the same performance as that of SVM optimized with the FireFly optimization algorithm.

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