[1] Quanto Option Pricing on a Multivariate Levy Process Model with a Generative Artificial Intelligence基于生成人工智能的多元Levy过程模型的Quanto期权定价来源:ARXIV_20240229[2] Neural Networks for Portfolio Level Risk Management用于投资组合级风险管理的神经网络来源:ARXIV_20240229[3] FinAgentFinAgent来源:ARXIV_20240229[4] MambaStock曼巴股票来源:ARXIV_20240301[5] Transforming Stock Price Forecasting: Deep Learning Architectures and Strategic Feature Engineering转变股价预测：深度学习架构与战略特征工程来源:SSRN_20240303[6] CNN-BiLSTM and Time Delay Embedding: A Single-Step Hybrid Deep Learning Model for Stock Price ForecastingCNN-BiLSTM和时延嵌入：一个用于股价预测的单步混合深度学习模型来源:SSRN_20240303[7] A Lightweight Multi-Head Attention Transformer for Stock Price Forecasting一种用于股价预测的轻量级多头注意力转换器来源:SSRN_20240303[8] CNN-BiLSTM-GRU and Phase Space Reconstruction In Retail Stock Price Forecasting零售股价预测中的CNN BiLSTM GRU和相空间重构来源:SSRN_20240303
[1] Quanto Option Pricing on a Multivariate Levy Process Model with a Generative Artificial Intelligence
标题:基于生成人工智能的多元Levy过程模型的Quanto期权定价作者:Young Shin Kim, Hyun-Gyoon Kim来源:ARXIV_20240229Abstract : In this study, we discuss a machine learning technique to price exotic options with two underlying assets based on a non Gaussian Levy process model. We introduce a new multivariate Levy process model named the generalized normal tempered stable (gNTS) process, which is defined by time changed multivariate Brownian motion. Since the probability density function (PDF) of the gNTS process is......(摘要翻译及全文见知识星球)Keywords :
[2] Neural Networks for Portfolio Level Risk Management
标题:用于投资组合级风险管理的神经网络作者:Vikranth Lokeshwar Dhandapani, Shashi Jain来源:ARXIV_20240229Abstract : In this paper, we present an artificial neural network framework for portfolio compression of a large portfolio of European options with varying maturities (target portfolio) by a significantly smaller portfolio of European options with shorter or same maturity (compressed portfolio), which also represents a self replicating static hedge portfolio of the target portfolio. For the proposed machine learning architecture, which is......(摘要翻译及全文见知识星球)Keywords :
[3] FinAgent
标题:FinAgent作者:Wentao Zhang, Lingxuan Zhao, Haochong Xia, Shuo Sun, Jiaze Sun, Molei Qin, Xinyi Li, Yuqing Zhao, Yilei Zhao, Xinyu Cai, Longtao Zheng, Xinrun Wang, Bo An来源:ARXIV_20240229
Abstract : Financial trading is a crucial component of the markets, informed by a multimodal information landscape encompassing news, prices, and Kline charts, and encompasses diverse tasks such as quantitative trading and high frequency trading with various assets. While advanced AI techniques like deep learning and reinforcement learning are extensively utilized in finance, their application in financial trading tasks often faces challenges due......(摘要翻译及全文见知识星球)Keywords :
[4] MambaStock
标题:曼巴股票作者:Zhuangwei Shi来源:ARXIV_20240301Abstract : The stock market plays a pivotal role in economic development, yet its intricate volatility poses challenges for investors. Consequently, research and accurate predictions of stock price movements are crucial for mitigating risks. Traditional time series models fall short in capturing nonlinearity, leading to unsatisfactory stock predictions. This limitation has spurred the widespread adoption of neural networks for stock prediction, owing to......(摘要翻译及全文见知识星球)Keywords :
[5] Transforming Stock Price Forecasting: Deep Learning Architectures and Strategic Feature Engineering
标题:转变股价预测：深度学习架构与战略特征工程作者:Anh Q. Nguyen,Son Ha来源:SSRN_20240303Abstract : This paper delves into the multifaceted landscape of stock prices, as their nature makes accurate forecasting a considerable challenge for investors and researchers. The analysis encompasses three key stock datasets: AAPL, AMZN, and MSFT. Each stock incorporates raw datasets with OHLCV information and enhanced datasets with subsets of raw data, technical, and macroeconomic indicators. Whilst, adjusted closing price is the target......(摘要翻译及全文见知识星球)Keywords : Stock Prices, Technical Indicators, Enhanced Dataset, Deep Learning, BiLSTM Model, Forecasting Principles
[6] CNN-BiLSTM and Time Delay Embedding: A Single-Step Hybrid Deep Learning Model for Stock Price Forecasting
标题:CNN-BiLSTM和时延嵌入：一个用于股价预测的单步混合深度学习模型作者:Anh Q. Nguyen,Son Ha来源:SSRN_20240303Abstract : The intricacies of the stock markets are driven by numerous determining fac- tors, making it a captivating research area for econometric and scientific study. Moreover, successfully predicting stock values is similar to foreseeing the future of enterprises. Therefore, the recent boom of Artificial Intelligence (AI) within the digital economy has rejuvenated interest in forecasting stock prices utilising state-of-the-art Machine Learning (ML)......(摘要翻译及全文见知识星球)Keywords : Deep Learning, CNN-BiLSTM, Time Delay Embedding, Stock Markets
[7] A Lightweight Multi-Head Attention Transformer for Stock Price Forecasting
标题:一种用于股价预测的轻量级多头注意力转换器作者:Anh Q. Nguyen,Son Ha来源:SSRN_20240303Abstract : Despite the potential growth and implementation of AI in live trading machines of the financial industry, researchers face challenges in understanding and foreseeing the chaotic nature of stock prices. Hence, this research proposes a distinctive lightweight Transformer model with sustainable architecture consisting mainly of positional encoding and advanced training techniques to mitigate model overfitting, hence offering prompt forecasting results through a......(摘要翻译及全文见知识星球)Keywords : Lightweight Transformer, Positional Encoding, Univariate Approach, Time Series, Flash Crashes
[8] CNN-BiLSTM-GRU and Phase Space Reconstruction In Retail Stock Price Forecasting
标题:零售股价预测中的CNN BiLSTM GRU和相空间重构作者:Anh Q. Nguyen,Son Ha来源:SSRN_20240303Abstract : Stock markets’ nature encompasses a great complexity of influential factors, making it an appealing field for econometrical researchers and investors, as forecasting stock prices means foreseeing the growth and profitability of companies. With the advent of AI in finance, stock price forecasting using dynamic Machine Learning (ML) architectures has seen a resurgence. Accordingly, this paper offers CNN-BiLSTM-GRU, a hybrid Deep Learning......(摘要翻译及全文见知识星球)Keywords : Machine Learning, CNN-BiLSTM-GRU, Phase Space Reconstruction, Chaos Theory, Retail Stocks
