[1] LSTM ARIMA as a Hybrid Approach in Algorithmic Investment StrategiesLSTM ARIMA作为算法投资策略的一种混合方法来源:ARXIV_20240627[2] AlphaForgeAlphaForge来源:ARXIV_20240627[3] Credit Ratings信用评级来源:ARXIV_20240628
[1] LSTM ARIMA as a Hybrid Approach in Algorithmic Investment Strategies
标题:LSTM ARIMA作为算法投资策略的一种混合方法作者:Kamil Kashif, Robert Ślepaczuk来源:ARXIV_20240627Abstract : This study focuses on building an algorithmic investment strategy employing a hybrid approach that combines LSTM and ARIMA models referred to as LSTM ARIMA. This unique algorithm uses LSTM to produce final predictions but boosts the results of this RNN by adding the residuals obtained from ARIMA predictions among other inputs. The algorithm is tested across three equity indices (S&P 500,......(摘要翻译及全文见知识星球)Keywords :
[2] AlphaForge
标题:AlphaForge作者:Hao Shi, Cuicui Luo, Weili Song, Xinting Zhang, Xiang Ao来源:ARXIV_20240627Abstract : The variability and low signal to noise ratio in financial data, combined with the necessity for interpretability, make the alpha factor mining workflow a crucial component of quantitative investment. Transitioning from early manual extraction to genetic programming, the most advanced approach in this domain currently employs reinforcement learning to mine a set of combination factors with fixed weights. However, the performance......(摘要翻译及全文见知识星球)Keywords :
[3] Credit Ratings
标题:信用评级作者:Helmut Wasserbacher, Martin Spindler来源:ARXIV_20240628Abstract : Why do companies choose particular capital structures A compelling answer to this question remains elusive despite extensive research. In this article, we use double machine learning to examine the heterogeneous causal effect of credit ratings on leverage. Taking advantage of the flexibility of random forests within the double machine learning framework, we model the relationship between variables associated with leverage......(摘要翻译及全文见知识星球)Keywords :
