A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series

The purpose of this study is to examine whether crude oil spot and futures prices of the same and different grades are cointegrated using a residual-based cointegration test that allows for one structural break in the cointegrating vector and high-frequency data. We choose the US WTI and the UK Brent as the representative crudes for this analysis since these two crudes have well-established spot and futures markets. We find that spot and future prices of the same grade as well as spot and futures prices of different grades are cointegrated. We examine potential causes of structural change as revealed by the cointegration test in terms of events that have impacted on world oil markets as well as discuss the implications of the results for hedge managers, investors and regulators.

Cointegration Between Oil Spot and Future Prices of the Same and Different Grades in the Presence of Structural Change

Neural network based forecasting methods have become ubiquitous in large-scale industrial forecasting applications over the last years. As the prevalence of neural network based solutions among the best entries in the recent M4 competition shows, the recent popularity of neural forecasting methods is not limited to industry and has also reached academia. This article aims at providing an introduction and an overview of some of the advances that have permitted the resurgence of neural networks in machine learning. Building on these foundations, the article then gives an overview of the recent literature on neural networks for forecasting and applications.

Neural forecasting: Introduction and literature overview.

In December 2019, first case of the COVID-19 was reported in Wuhan, Hubei province in China. Soon world health organization has declared contagious coronavirus disease (a.k.a. COVID-19) as a global pandemic in the month of March 2020. Over the span of eleven months, it has rapidly spread out all over the world with total confirmed cases of ~ 41.39 M and causing a total fatality of ~1.13 M. At present, the entire mankind is facing serious threat and it is believed that COVID-19 may have been around for quite some time. Therefore, it has become imperative to forecast the global impact of COVID-19 in the near future. The present work proposes state-of-art deep learning Recurrent Neural Networks (RNN) models to predict the country-wise cumulative confirmed cases, cumulative recovered cases and the cumulative fatalities. The Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) cells along with Recurrent Neural Networks (RNN) were developed to predict the future trends of the COVID-19. We have used publicly available data from John Hopkins University's COVID-19 database. In this work, we emphasize the importance of various factors such as age, preventive measures, and healthcare facilities, population density, etc. that play vital role in rapid spread of COVID-19 pandemic. Therefore, our forecasted results are very helpful for countries to better prepare themselves to control the pandemic.

Forecasting of COVID-19 using deep layer Recurrent Neural Networks (RNNs) with Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) cells.

Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic

Text-based crude oil price forecasting: A deep learning approach

Abstract In recent years, Bitcoin has received substantial attention as potentially high-earning investment. However, its volatile price movement exhibits great financial risks. Therefore, how to accurately predict and capture changing trends in the Bitcoin market is of substantial importance to investors and policy makers. However, empirical works in the Bitcoin forecasting and trading support systems are at an early stage. To fill this void, this study proposes a novel data decomposition-based hybrid bidirectional deep-learning model in forecasting the daily price change in the Bitcoin market and conducting algorithmic trading on the market. Two primary steps are involved in our methodology framework, namely, data decomposition for inner factors extraction and bidirectional deep learning for forecasting the Bitcoin price. Results demonstrate that the proposed model outperforms other benchmark models, including econometric models, machine-learning models, and deep-learning models. Furthermore, the proposed model achieved higher investment returns than all benchmark models and the buy-and-hold strategy in a trading simulation. The robustness of the model is verified through multiple forecasting periods and testing intervals. 

/pdf/hybrid-data-decomposition-based-deep-learning-for-bitcoin-1hfmzk2p.pdf

Hybrid data decomposition-based deep learning for Bitcoin prediction and algorithm trading

Balance between Profit and Fairness: Regulation of Online Food Delivery (OFD) Platforms

Crude Oil Price Movement and Volatility Forecasting based on Online News

Using a database of more than 1.1 million comments from the largest Chinese stock discussion forum Eastmoney, we explore the value of “brand” in the Chinese stock market by empirically investigating the impact of brand attention on stock performance and the moderation role of investor sentiment, as well as the heterogeneity of these effects across companies with different brand values. It finds that brand attention has a positive impact on stock returns and stock trading volume. Investor sentiment has a positive impact on stock returns while having a negative impact on stock trading volume. Investor sentiment positively moderates the impact of brand attention on stock returns, but negatively moderates the impact of brand attention on stock trading volume. The impact of brand attention and investor sentiment on stock performance varies across companies with different brand values. It is more pronounced in companies with high brand value than in companies with a relatively low brand value, while the negative effect of investor sentiment on stock trading volume is lower in companies with high brand value, which highlights the importance of brand-building to improve stock performance. 

Xuerong Li

Papers

Text-based crude oil price forecasting: A deep learning approach

Hybrid data decomposition-based deep learning for Bitcoin prediction and algorithm trading

Balance between Profit and Fairness: Regulation of Online Food Delivery (OFD) Platforms

Crude Oil Price Movement and Volatility Forecasting based on Online News

The Value of “Brand” in the Chinese Stock Market: The Impact of Brand Attention on Stock Performance and the Moderation Role of Investor Sentiment