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on Forecasting |
| By: | Cheng Zhang; Nilam Nur Amir Sjarif; Roslina Binti Ibrahim |
| Abstract: | Forecasting financial time series has long been a challenging problem that has attracted attention from both researchers and practitioners. Statistical and machine learning techniques have both been explored to develop effective forecasting models in the past few decades. With recent developments in deep learning models, financial time series forecasting models have advanced significantly, and these developments are often difficult to keep up with. Hence, we have conducted this literature review to provide a comprehensive assessment of recent research from 2020 to 2022 on deep learning models used to predict prices based on financial time series. Our review presents different data sources and neural network structures, as well as their implementation details. Our goals are to ensure that interested researchers remain up-to-date on recent developments in the field and facilitate the selection of baselines based on models used in prior studies. Additionally, we provide suggestions for future research based on the content in this review. |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2305.04811&r=for |
| By: | Ali Lashgari |
| Abstract: | Forecasting financial time series (FTS) is an essential field in finance and economics that anticipates market movements in financial markets. This paper investigates the accuracy of text mining and technical analyses in forecasting financial time series. It focuses on the S&P500 stock market index during the pandemic, which tracks the performance of the largest publicly traded companies in the US. The study compares two methods of forecasting the future price of the S&P500: text mining, which uses NLP techniques to extract meaningful insights from financial news, and technical analysis, which uses historical price and volume data to make predictions. The study examines the advantages and limitations of both methods and analyze their performance in predicting the S&P500. The FinBERT model outperforms other models in terms of S&P500 price prediction, as evidenced by its lower RMSE value, and has the potential to revolutionize financial analysis and prediction using financial news data. Keywords: ARIMA, BERT, FinBERT, Forecasting Financial Time Series, GARCH, LSTM, Technical Analysis, Text Mining JEL classifications: G4, C8 |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2304.14544&r=for |
| By: | Mira Watermeyer; Thomas M\"obius; Oliver Grothe; Felix M\"usgens |
| Abstract: | The accurate prediction of short-term electricity prices is vital for effective trading strategies, power plant scheduling, profit maximisation and efficient system operation. However, uncertainties in supply and demand make such predictions challenging. We propose a hybrid model that combines a techno-economic energy system model with stochastic models to address this challenge. The techno-economic model in our hybrid approach provides a deep understanding of the market. It captures the underlying factors and their impacts on electricity prices, which is impossible with statistical models alone. The statistical models incorporate non-techno-economic aspects, such as the expectations and speculative behaviour of market participants, through the interpretation of prices. The hybrid model generates both conventional point predictions and probabilistic forecasts, providing a comprehensive understanding of the market landscape. Probabilistic forecasts are particularly valuable because they account for market uncertainty, facilitating informed decision-making and risk management. Our model delivers state-of-the-art results, helping market participants to make informed decisions and operate their systems more efficiently. |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2304.09336&r=for |
| By: | Tianqi Pang; Kehui Tan; Chenyou Fan |
| Abstract: | Carbon futures has recently emerged as a novel financial asset in the trading markets such as the European Union and China. Monitoring the trend of the carbon price has become critical for both national policy-making as well as industrial manufacturing planning. However, various geopolitical, social, and economic factors can impose substantial influence on the carbon price. Due to its volatility and non-linearity, predicting accurate carbon prices is generally a difficult task. In this study, we propose to improve carbon price forecasting with several novel practices. First, we collect various influencing factors, including commodity prices, export volumes such as oil and natural gas, and prosperity indices. Then we select the most significant factors and disclose their optimal grouping for explainability. Finally, we use the Sparse Quantile Group Lasso and Adaptive Sparse Quantile Group Lasso for robust price predictions. We demonstrate through extensive experimental studies that our proposed methods outperform existing ones. Also, our quantile predictions provide a complete profile of future prices at different levels, which better describes the distributions of the carbon market. |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2305.03224&r=for |