[1] A Spatio Temporal Machine Learning Model for Mortgage Credit Risk抵押贷款信用风险的时空机器学习模型来源:ARXIV_20241007[2] Leveraging Fundamental Analysis for Stock Trend Prediction for Profit利用基本面分析预测股票走势获利来源:ARXIV_20241008[3] Improving Portfolio Optimization Results with Bandit Networks利用Bandit网络提高投资组合优化结果来源:ARXIV_20241008[4] Application of AI in Credit Risk Scoring for Small Business Loans人工智能在小企业贷款信用风险评分中的应用来源:ARXIV_20241010[5] Statistical Arbitrage in Rank Space秩空间中的统计套利来源:ARXIV_20241010[6] SARFSARF来源:ARXIV_20241010[7] Stock Price Prediction and Traditional Models股票价格预测与传统模型来源:ARXIV_20241011[8] A Dynamic Approach to Stock Price Prediction股票价格预测的动态方法来源:ARXIV_20241011[9] Blockchain Based Ad Auctions and Bayesian Persuasion基于区块链的广告拍卖与贝叶斯说服来源:ARXIV_20241011
[1] A Spatio Temporal Machine Learning Model for Mortgage Credit Risk
标题:抵押贷款信用风险的时空机器学习模型作者:Pascal Kündig, Fabio Sigrist来源:ARXIV_20241007Abstract : We introduce a novel machine learning model for credit risk by combining tree boosting with a latent spatio temporal Gaussian process model accounting for frailty correlation. This allows for modeling non linearities and interactions among predictor variables in a flexible data driven manner and for accounting for spatio temporal variation that is not explained by observable predictor variables. We also show......(摘要翻译及全文见知识星球)Keywords :
[2] Leveraging Fundamental Analysis for Stock Trend Prediction for Profit
标题:利用基本面分析预测股票走势获利作者:John Phan, Hung-Fu Chang来源:ARXIV_20241008Abstract : This paper investigates the application of machine learning models, Long Short Term Memory (LSTM), one dimensional Convolutional Neural Networks (1D CNN), and Logistic Regression (LR), for predicting stock trends based on fundamental analysis. Unlike most existing studies that predominantly utilize technical or sentiment analysis, we emphasize the use of a company s financial statements and intrinsic value for trend forecasting. Using......(摘要翻译及全文见知识星球)Keywords :
[3] Improving Portfolio Optimization Results with Bandit Networks
标题:利用Bandit网络提高投资组合优化结果作者:Gustavo de Freitas Fonseca, Lucas Coelho e Silva, Paulo André Lima de Castro来源:ARXIV_20241008Abstract : In Reinforcement Learning (RL), multi armed Bandit (MAB) problems have found applications across diverse domains such as recommender systems, healthcare, and finance. Traditional MAB algorithms typically assume stationary reward distributions, which limits their effectiveness in real world scenarios characterized by non stationary dynamics. This paper addresses this limitation by introducing and evaluating novel Bandit algorithms designed for non stationary environments. First,......(摘要翻译及全文见知识星球)Keywords :
[4] Application of AI in Credit Risk Scoring for Small Business Loans
标题:人工智能在小企业贷款信用风险评分中的应用作者:Nigar Karimova来源:ARXIV_20241010Abstract : The research investigates how the application of a machine learning random forest model improves the accuracy and precision of a Delphi model. The context of the research is Azerbaijani SMEs and the data for the study has been obtained from a financial institution which had gathered it from the enterprises (as there is no public data on local SMEs, it was......(摘要翻译及全文见知识星球)Keywords :
[5] Statistical Arbitrage in Rank Space
标题:秩空间中的统计套利作者:Y.-F. Li, G. Papanicolaou来源:ARXIV_20241010Abstract : Equity market dynamics are conventionally investigated in name space where stocks are indexed by company names. In contrast, by indexing stocks based on their ranks in capitalization, we gain a different perspective of market dynamics in rank space. Here, we demonstrate the superior performance of statistical arbitrage in rank space over name space, driven by a robust market representation and enhanced......(摘要翻译及全文见知识星球)Keywords :
[6] SARF
标题:SARF作者:Saber Talazadeh, Dragan Perakovic来源:ARXIV_20241010Abstract : Stock trend forecasting, a challenging problem in the financial domain, involves ex tensive data and related indicators. Relying solely on empirical analysis often yields unsustainable and ineffective results. Machine learning researchers have demonstrated that the application of random forest algorithm can enhance predictions in this context, playing a crucial auxiliary role in forecasting stock trends. This study introduces a new approach......(摘要翻译及全文见知识星球)Keywords :
[7] Stock Price Prediction and Traditional Models
标题:股票价格预测与传统模型作者:Opeyemi Sheu Alamu, Md Kamrul Siam来源:ARXIV_20241011Abstract : A comparative analysis of deep learning models and traditional statistical methods for stock price prediction uses data from the Nigerian stock exchange. Historical data, including daily prices and trading volumes, are employed to implement models such as Long Short Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Autoregressive Integrated Moving Average (ARIMA), and Autoregressive Moving Average (ARMA). These models are assessed......(摘要翻译及全文见知识星球)Keywords :
[8] A Dynamic Approach to Stock Price Prediction
标题:股票价格预测的动态方法作者:Diego Vallarino来源:ARXIV_20241011Abstract : This study evaluates the effectiveness of a Mixture of Experts (MoE) model for stock price prediction by comparing it to a Recurrent Neural Network (RNN) and a linear regression model. The MoE framework combines an RNN for volatile stocks and a linear model for stable stocks, dynamically adjusting the weight of each model through a gating network. Results indicate that the......(摘要翻译及全文见知识星球)Keywords :
[9] Blockchain Based Ad Auctions and Bayesian Persuasion
标题:基于区块链的广告拍卖与贝叶斯说服作者:Xinyu Li来源:ARXIV_20241011Abstract : This paper explores how ad platforms can utilize Bayesian persuasion within blockchain based auction systems to strategically influence advertiser behavior despite increased transparency. By integrating game theoretic models with machine learning techniques and the principles of blockchain technology, we analyze the role of strategic information disclosure in ad auctions. Our findings demonstrate that even in environments with inherent transparency, ad platforms......(摘要翻译及全文见知识星球)Keywords :
