To study the characteristics-sorted factor model in asset pricing, we develop a bottom-up approach with state-of-the-art deep learning optimization. With an economic objective to minimize pricing errors, we train a non-reduced-form neural network using firm characteristics [inputs], and generate...
1.1 Artificial intelligence, machine learning, and deep learning First, we need to define clearly what we’re talking about when we mention AI. What are artificial intelligence, machine learning, and deep learning? How do they relate to each other?
However, these deep learning systems don't look like modern internet scale models where they have few priors and rely on massive amounts of data to refine their weights. Instead, they are still primarily algorithm solutions with heavy priors built into their architecture, with deep learning integr...
关于factor-based investing,现在传统的基于资产类的资产配置不再受欢迎,而更多的是在分配风险因子上,即不同资产的不同风险因子的配置,本质就是一个多因子模型。与传统的方法相比,分散性会更强一些。但最大的问题在于随着规模起来,它的value也会增长,最终资产可能变得太贵以至于不能outperform其他。 今天美国的hedge ...
Therefore, this study aims to investigate whether perceived TPACK could influence deep learning, and through the lens of the mediating effect of self-regulatory learning and the moderating effect of technology self-efficacy, investing influences factors in exploring how to influence deep learning and ...
Deep learning implementation and traits for medical imaging application Full size image 2Background concepts 2.1Medical imaging Deep learning in medical imaging [1] is the contemporary scope of AI which has the top breakthroughs in numerous scientific domains including computer vision [2], Natural Lang...
To address these challenges, here we introduce MATES, a deep-learning approach that accurately allocates multi-mapping reads to specific loci of TEs, utilizing context from adjacent read alignments flanking the TE locus. When applied to diverse single-cell omics datasets, MATES shows improved ...
4-3. Deep portfolio/deep factor 4-4. Deep Reinforcement Deep Reinforcement Learning in Financial Markets(2019), S. Chakraborty[pdf] Focus: It explores the usage of deep reinforcement learning algorithms to automatically generate consistently profitable, robust, uncorrelated trading signals in any genera...
Educational Data Mining (EDM) is a research field that focuses on the application of data mining, machine learning, and statistical methods to detect patterns in large collections of educational data. Different machine learning techniques have been applied in this field over the years, but it has...
The recent breakthrough of data science and deep learning make a model independent approach for hedging possible. This hedging approach known as deep hedging is a robust data-driven method able to consider market frictions as well as trading constraints without using model-computed greeks. T...