A Machine Learning Approach: Enhancing the Predictive Performance of Pharmaceutical Stock Price Movement during COVID

He, Beilei; Han, Weiyi; Hon, Suet Ying Isabelle

doi:10.4236/jdaip.2022.101001

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…According to the findings, the FM-based model produces performance that is satisfactory and has very little risk error. He et al [3] conducted research on pharmaceutical companies' stock price movement prediction during the COVID-19 pandemic. The researchers gathered extensive financial data unique to the company, including historical and present stock prices, key financial indicators, worldwide industry indices, and information about COVID-19.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning for Predicting the Stock Price Direction with Trading Indicators

Ansary

2023

ENPESJ

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There are a number of possible advantages in utilizing trading indicators and machine learning to predict the direction of stock prices. It is crucial to remember that stock price prediction is difficult by nature and that there is no way to ensure accuracy. The financial markets are extremely information-rich, dynamic, and complex. Large datasets may be processed and analysed by machine learning algorithms far more quickly than by people, which makes it possible to spot patterns or trends that might not be immediately obvious. By using historical price and volume data, the algorithms can be trained to identify patterns that could predict future moves. Because they offer insights into possible market moves, predictive models can help with risk management. Because ML models are always learning from fresh data, they can adjust to changing market conditions. This flexibility is essential in markets where a variety of factors impact the market. Several machine learning models have been used in this experimental effort to monitor the direction of stock prices, and the outcomes are extremely encouraging.

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Section: Literature Reviewmentioning

confidence: 99%

Machine Learning for Predicting the Stock Price Direction with Trading Indicators

Ansary

2023

ENPESJ

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“…Dari data yang ada pada sistem informasi [2], bisa dilakukan pengolahan sehingga menghasilkan informasi dan pengetahuan menggunakan Machine Learning atau pembelajaran mesin. Himpunan data atau data set bisa dilatih agar mengenali pola tertentu sehingga menjadi informasi yang berguna untuk memperoleh insight atau pengetahuan baru dalam mengambil kebijakan [3].…”

Section: Pendahuluanunclassified

Prediksi Kuantitas Penggunaan Obat pada Layanan Kesehatan Menggunakan Algoritma Backpropagation Neural Network

Khairati

Putra²

2022

jsisfotek

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Prediction of the amount of drug use at public health centers is needed to ensure the availability of drugs for patients in service quality management. A good prediction of the amount of medicine needed helps the quality of development planning in the health sector. Scientific developments in the field of Artificial Intelligence (AI) deliver a variety of the best techniques for making predictions. By adopting the workings of neural networks (neurons) in the human brain or Artificial Neural Network (ANN), the Backpropagation Neural Network (BPNN) algorithm is one of the best algorithms in making predictions, including predicting drug use in health services. The problem of this research is how to design the best architectural model such as the number of neurons in the input layer, hidden layer and other parameters so as to produce predictions with optimal accuracy. This study aims to develop an ANN architectural design with the Backpropagation algorithm to predict the need for drug use. The data used is data on drug use reports from 2015 to 2021 at the Andalas Community Health Center (Puskesmas) Padang City. The steps taken to predict are; collect data, pre-process data and perform analysis, design ANN architecture, make predictions. Learning using the backpropagation algorithm through the initial weight initialization process, activation stage, weight training (weight change) and iteration stage. The proportion of the amount of data used for training is 70% data and 30% for testing data. The results of this study indicate that the best ANN architecture is 12-12-1 with an accuracy of predicting the quantity of drug use reaching 97.87% for paracetamol with a Mean Absolute Percentage Error (MAPE) of 2.13%. The prediction results become a reference for the Puskesmas and the Health Office for service planning and development.

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A Methodological Evaluation of Machine Learning Models for Stock Price Predictions

Rathi,

Bhuskute,

Joshi

et al. 2024

2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI)

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A Machine Learning Approach: Enhancing the Predictive Performance of Pharmaceutical Stock Price Movement during COVID

Cited by 4 publications

References 14 publications

Machine Learning for Predicting the Stock Price Direction with Trading Indicators

Machine Learning for Predicting the Stock Price Direction with Trading Indicators

Prediksi Kuantitas Penggunaan Obat pada Layanan Kesehatan Menggunakan Algoritma Backpropagation Neural Network

A Methodological Evaluation of Machine Learning Models for Stock Price Predictions

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