Despite the efficient market hypothesis, many studies suggest the existence
of inefficiencies in the stock market, leading to the development of techniques
to gain above-market returns, known as alpha. Systematic trading has undergone
significant advances in recent decades, with deep learning emerging as a
powerful tool for analyzing and predicting market behavior. In this paper, we
propose a model inspired by professional traders that look at stock prices of
the previous 600 days and predicts whether the stock price rises or falls by a
certain percentage within the next D days. Our model, called DeepStock, uses
Resnet's skip connections and logits to increase the probability of a model in
a trading scheme. We test our model on both the Korean and US stock markets and
achieve a profit of N\% on Korea market, which is M\% above the market return,
and profit of A\% on US market, which is B\% above the market return.

通过使用深度学习技术进行系统交易，该文构建了一种名为 DeepStock 的模型，能够根据过去 600 天的股票价格预测未来 D 天内股价的涨跌幅度，在韩国和美国的股市中比市场平均水平的收益分别高出 N% 和 A%。

深度股票：基于深度学习的训练和交易方案

Deep Stock: training and trading scheme using deep learning

We propose a method for creating a matte -- the per-pixel foreground color
and alpha -- of a person by taking photos or videos in an everyday setting with
a handheld camera. Most existing matting methods require a green screen
background or a manually created trimap to produce a good matte. Automatic,
trimap-free methods are appearing, but are not of comparable quality. In our
trimap free approach, we ask the user to take an additional photo of the
background without the subject at the time of capture. This step requires a
small amount of foresight but is far less time-consuming than creating a
trimap. We train a deep network with an adversarial loss to predict the matte.
We first train a matting network with supervised loss on ground truth data with
synthetic composites. To bridge the domain gap to real imagery with no
labeling, we train another matting network guided by the first network and by a
discriminator that judges the quality of composites. We demonstrate results on
a wide variety of photos and videos and show significant improvement over the
state of the art.

该论文提出了一种使用手持相机在日常环境中通过拍摄照片或视频来创建人物的 alpha 通道。论文使用基于深度学习的对抗性损失训练了两个抠图网络，能够有效地自动抠图，而无需使用绿幕或手动抠图，相比已有的抠图方法取得了显著的改进。

背景抠像：让世界成为你的绿幕

Background Matting: The World is Your Green Screen

We designed a machine learning algorithm that identifies patterns between ESG
profiles and financial performances for companies in a large investment
universe. The algorithm consists of regularly updated sets of rules that map
regions into the high-dimensional space of ESG features to excess return
predictions. The final aggregated predictions are transformed into scores which
allow us to design simple strategies that screen the investment universe for
stocks with positive scores. By linking the ESG features with financial
performances in a non-linear way, our strategy based upon our machine learning
algorithm turns out to be an efficient stock picking tool, which outperforms
classic strategies that screen stocks according to their ESG ratings, as the
popular best-in-class approach. Our paper brings new ideas in the growing field
of financial literature that investigates the links between ESG behavior and
the economy. We show indeed that there is clearly some form of alpha in the ESG
profile of a company, but that this alpha can be accessed only with powerful,
non-linear techniques such as machine learning.

本文基于机器学习算法，通过将 ESG 特征与金融绩效进行非线性关联，找出了一种有效的股票筛选方法，其能力超越传统的 ESG 评分筛选方法，揭示了 ESG 特征与金融经济之间的链接及股票筛选的新思路，证明了只有机器学习这样某些非线性的技术才能挖掘企业 ESG 的 Alpha。