In this paper, a C5.0 decision tree and neural network models are proposed to classify recessions in the US with 12 common financial indices and new financial stress indices inferred from the neural network models are...In this paper, a C5.0 decision tree and neural network models are proposed to classify recessions in the US with 12 common financial indices and new financial stress indices inferred from the neural network models are created. A detailed experiment is presented and demonstrates that the neural network models with proper regularization and dropout achieve 98% accuracy in the training set, 97% accuracy in validation set and 100% accuracy in test accuracy. The financial stress indices outperform other existing financial stress indices in many scenes and can accurately locate crisis events even the most recent 2018 US Bear Market. With these models and new indices, contraction can be detected before NBER’s announcement and action could be taken as early as the situation get worse.展开更多
Online short-term rental platforms,such as Airbnb,have been becoming popular,and a better pricing strategy is imperative for hosts of new listings.In this paper,we analyzed the relationship between the description of ...Online short-term rental platforms,such as Airbnb,have been becoming popular,and a better pricing strategy is imperative for hosts of new listings.In this paper,we analyzed the relationship between the description of each listing and its price,and proposed a text-based price recommendation system called TAPE to recommend a reasonable price for newly added listings.We used deep learning techniques(e.g.,feedforward network,long short-term memory,and mean shift)to design and implement TAPE.Using two chronologically extracted datasets of the same four cities,we revealed important factors(e.g.,indoor equipment and high-density area)that positively or negatively affect each property’s price,and evaluated our preliminary and enhanced models.Our models achieved a Root-Mean-Square Error(RMSE)of 33.73 in Boston,20.50 in London,34.68 in Los Angeles,and 26.31 in New York City,which are comparable to an existing model that uses more features.展开更多
文摘In this paper, a C5.0 decision tree and neural network models are proposed to classify recessions in the US with 12 common financial indices and new financial stress indices inferred from the neural network models are created. A detailed experiment is presented and demonstrates that the neural network models with proper regularization and dropout achieve 98% accuracy in the training set, 97% accuracy in validation set and 100% accuracy in test accuracy. The financial stress indices outperform other existing financial stress indices in many scenes and can accurately locate crisis events even the most recent 2018 US Bear Market. With these models and new indices, contraction can be detected before NBER’s announcement and action could be taken as early as the situation get worse.
文摘Online short-term rental platforms,such as Airbnb,have been becoming popular,and a better pricing strategy is imperative for hosts of new listings.In this paper,we analyzed the relationship between the description of each listing and its price,and proposed a text-based price recommendation system called TAPE to recommend a reasonable price for newly added listings.We used deep learning techniques(e.g.,feedforward network,long short-term memory,and mean shift)to design and implement TAPE.Using two chronologically extracted datasets of the same four cities,we revealed important factors(e.g.,indoor equipment and high-density area)that positively or negatively affect each property’s price,and evaluated our preliminary and enhanced models.Our models achieved a Root-Mean-Square Error(RMSE)of 33.73 in Boston,20.50 in London,34.68 in Los Angeles,and 26.31 in New York City,which are comparable to an existing model that uses more features.
