Forecasting Crude Oil Prices with Major S&amp;P 500 Stock Prices: Deep Learning, Gaussian Process, and Vine Copula

Kim, Jong Min; Han, Hope Hyeun; Kim, Sangjin

doi:10.3390/axioms11080375

Cited by 7 publications

(6 citation statements)

References 25 publications

(31 reference statements)

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“…Additionally, the application of task-specific learning rate schedules has been recommended to tailor the fine-tuning process to the specific characteristics of medical datasets [10,17,32,33]. The importance of hyperparameter optimization through automated search techniques, such as grid search and Bayesian optimization, has been underscored, highlighting their role in identifying the most effective fine-tuning configurations [34,35,36]. These findings collectively emphasize the nuanced impact of hyperparameters on fine-tuning success, suggesting the need for tailored approaches based on task specificity and dataset characteristics.…”

Section: Impact Of Hyperparameters On Fine-tuningmentioning

confidence: 99%

Optimizing Llama 7B for Medical Question Answering: A Study on Fine-Tuning Strategies and Performance on the MultiMedQA Dataset

Sulaiman,

Hamzah

2024

Preprint

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This study explores the efficacy of various fine-tuning strategies on the performance of the Llama 7B model, a Large Language Model (LLM), when applied to the task of medical question answering using the MultiMedQA dataset. Through meticulous experimentation, we implemented and evaluated several fine-tuning techniques, including learning rate adjustments, graduated unfreezing, domain-specific vocabulary integration, selective layer fine-tuning, and regularization methods. Our findings reveal that these strategies significantly improve the model's accuracy, precision, recall, and F1 score, indicating a substantial enhancement in the model's ability to understand and respond to complex medical queries. The implications of these improvements are profound, suggesting the potential of fine-tuned LLMs to revolutionize medical information retrieval and support systems by providing more accurate, reliable, and contextually relevant answers. This research not only demonstrates the transformative power of fine-tuning LLMs for specialized applications but also lays the groundwork for future exploration into optimizing LLMs for a variety of domain-specific tasks. Our study contributes to the ongoing dialogue in the field of artificial intelligence and healthcare, highlighting the importance of targeted model optimization in achieving significant advancements in medical question answering.

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Section: Impact Of Hyperparameters On Fine-tuningmentioning

confidence: 99%

Optimizing Llama 7B for Medical Question Answering: A Study on Fine-Tuning Strategies and Performance on the MultiMedQA Dataset

Sulaiman,

Hamzah

2024

Preprint

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“…It accomplishes this by predicting masked words via masked language modeling and by learning relationships between two sentences of the next sentence prediction task. And fine-tuning is the process of adapting the pre-trained BERT model to a particular task [54].…”

Section: ) Bertmentioning

confidence: 99%

SPCM: A Machine Learning Approach for Sentiment-Based Stock Recommendation System

Wang,

Chen

2024

IEEE Access

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Recommendation systems play a pivotal role in delivering user preference information. However, they often face the challenge of information cocoons due to repeated content delivery, particularly prevalent in stock recommendations that are susceptible to investor sentiment. In response to the information cocoons, we propose the Sentiment and Price Combined Model (SPCM), which leverages sentiment features and price factors to predict stock price movements. This novel framework combines collective sentiment analysis with state-of-the-art BERT transformer models and advanced machine learning techniques. Over a three-year period, we collected 40 million stock comments from the Guba platform, extracting investor sentiment conveyed in text information and investigating the impact of metrics such as homophily on stock recommendations. Experimental results indicate that both the volume of posts and the agreement index affect the effectiveness of investor sentiment, while homophily reduces the accuracy of participants' stock price judgments. The recognition accuracy of the BERT-based sentiment analysis model reaches an impressive 84.12%, and the portfolio constructed by SPCM yields a cumulative return four times that of the industry benchmark. Furthermore, homogeneous quantitative metrics also enhance diversification in stock selection.INDEX TERMS BERT; decision making; homophily; machine learning; stock recommendation I. INTRODUCTION

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“…With regards to the extent of prediction errors, the vine copula regression with NLPCA was generally superior to the other proposed approaches. To reduce the dimensions of the data, a Bayesian variable selection and nonlinear principal component analysis (NLPCA) were used [17].…”

Section: Literary Surveymentioning

confidence: 99%

Predicting Saudi Stock Market Index by Using Multivariate Time Series Based on Deep Learning

Jarrah

Derbali

2023

Applied Sciences

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Time-series (TS) predictions use historical data to forecast future values. Various industries, including stock market trading, power load forecasting, medical monitoring, and intrusion detection, frequently rely on this method. The prediction of stock-market prices is significantly influenced by multiple variables, such as the performance of other markets and the economic situation of a country. This study focuses on predicting the indices of the stock market of the Kingdom of Saudi Arabia (KSA) using various variables, including opening, lowest, highest, and closing prices. Successfully achieving investment goals depends on selecting the right stocks to buy, sell, or hold. The output of this project is the projected closing prices over the next seven days, which aids investors in making informed decisions. Exponential smoothing (ES) was employed in this study to eliminate noise from the input data. This study utilized exponential smoothing (ES) to eliminate noise from data obtained from the Saudi Stock Exchange, also known as Tadawul. Subsequently, a sliding-window method with five steps was applied to transform the task of time series forecasting into a supervised learning problem. Finally, a multivariate long short-term memory (LSTM) deep-learning (DL) algorithm was employed to predict stock market prices. The proposed multivariate LSTMDL model achieved prediction rates of 97.49% and 92.19% for the univariate model, demonstrating its effectiveness in stock market price forecasting. These results also highlight the accuracy of DL and the utilization of multiple information sources in stock-market prediction.

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Forecasting Crude Oil Prices with Major S&P 500 Stock Prices: Deep Learning, Gaussian Process, and Vine Copula

Cited by 7 publications

References 25 publications

Optimizing Llama 7B for Medical Question Answering: A Study on Fine-Tuning Strategies and Performance on the MultiMedQA Dataset

Optimizing Llama 7B for Medical Question Answering: A Study on Fine-Tuning Strategies and Performance on the MultiMedQA Dataset

SPCM: A Machine Learning Approach for Sentiment-Based Stock Recommendation System

Predicting Saudi Stock Market Index by Using Multivariate Time Series Based on Deep Learning

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