The reason why derivative markets have grown so stupendously is democracy. By its natural evolution toward a fiat currency, democracy has increased market volatility, forcing individuals and firms to seek protection in a whole new array of derivative instruments. Among those are gold futures, which I put forward here as a measure of the depreciation of the US dollar since the move to fiat money. Oil’s price gyrations are explained as a product of that move as well. I isolate the options embedded in Collateralized Debt Obligations (CDOs) to show that the US sub-prime mortgage crisis resulted from a knowledge problem faced by investors. And I discuss the role of credit default swaps in that crisis, while pointing to the dangers of new derivatives regulations.

The Derivatives Markets

Abstract Accurately predicting the prices of financial time series is essential and challenging for the financial sector. Owing to recent advancements in deep learning techniques, deep learning models are gradually replacing traditional statistical and machine learning models as the first choice for price forecasting tasks. This shift in model selection has led to a notable rise in research related to applying deep learning models to price forecasting, resulting in a rapid accumulation of new knowledge. Therefore, we conducted a literature review of relevant studies over the past 3 years with a view to aiding researchers and practitioners in the field. This review delves deeply into deep learning‐based forecasting models, presenting information on model architectures, practical applications, and their respective advantages and disadvantages. In particular, detailed information is provided on advanced models for price forecasting, such as Transformers, generative adversarial networks (GANs), graph neural networks (GNNs), and deep quantum neural networks (DQNNs). The present contribution also includes potential directions for future research, such as examining the effectiveness of deep learning models with complex structures for price forecasting, extending from point prediction to interval prediction using deep learning models, scrutinizing the reliability and validity of decomposition ensembles, and exploring the influence of data volume on model performance. This article is categorized under: Technologies &gt; Prediction Technologies &gt; Artificial Intelligence 

Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022

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.

pdf/deep-learning-techniques-for-financial-time-series-348xfol6.pdf

Deep learning techniques for financial time series forecasting: A review of recent advancements: 2020-2022

Significance of effective development in China Connection between the capital market and development Current performance of the Chinese capital market Significant parameters for the Chinese capital market Opinions on capital market performance and its potential Prospects on the capital market and China's development

The Chinese Capital Market: Performance, Parameters for Further Evolution, and Implications for Development

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. 

/pdf/deep-learning-techniques-for-financial-time-series-17cahxe9.pdf

Deep learning techniques for financial time series forecasting: A review
  of recent advancements: 2020-2022

As rapid growth, Chinese financial derivative market is holding increasingly large proportions in entire domestic capital market as well as in global shares. To the nature of derivative instruments, plenty of market data features (such as prices and trading volumes) and off-market factors (such as financial news and policies) can directly impact on the price and risk in Chinese financial derivative markets, which is becoming more and more infeasible to model by using only traditional financial models and hand-crafted features. To alleviate the issue, in this paper we introduce some state-of-art deep neural network architectures and model two significant futures market price and risk indicators that are widely used by Chinese regulators, which are turn-over ratio (ratio of daily trading volumes and daily open interest volumes) and price basis (gap between futures price and corresponding spot product price). The extensive experimental results show that deep learning methods perform better prediction accuracy than traditional methods, among which convolutional LSTM achieves better results in most cases as it can capture local time-variant patterns. In addition, we also propose methods to exploit alternative off-market features (such as social media emotions and Baidu Search Index) with DNN models, which are proven beneficial to the price and risk prediction by rendering extra information than only market data.

Modeling Price and Risk in Chinese Financial Derivative Market with Deep Neural Network Architectures

Financial markets are very sensitive to emerging news related to stock because investors need to continuously monitor financial events when deciding buying and selling stocks. Tracking important events has done mostly using rule-based methods in the past, which is time-consuming in the face of huge online news data. To track this issue, in this paper, a novel document embedding technology based on TF-IDF and BERT incorporating online text cluster algorithm to form an automated event detection system is proposed. Embedding technology is first used to encode text to vectors and then an online text cluster algorithm - SinglePass is implemented to accomplish topic tracking. Experiment results show that the proposed algorithms can effectively detect and track online topics. In addition, both domestic and international events such as the outbreak of novel coronavirus (COVID-19) and Sino-U.S. trade war and their impact on capital market in China are analyzed, which demonstrate the practical and economic value of proposed system.

Online topic detection and tracking system and its application on stock market in china

Financial News Annotation by Weakly-Supervised Hierarchical Multi-label Learning

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Papers

Modeling Price and Risk in Chinese Financial Derivative Market with Deep Neural Network Architectures

Online topic detection and tracking system and its application on stock market in china

Financial News Annotation by Weakly-Supervised Hierarchical Multi-label Learning