Designing an optimum portfolio for allocating suitable weights to its
constituent assets so that the return and risk associated with the portfolio
are optimized is a computationally hard problem. The seminal work of Markowitz
that attempted to solve the problem by estimating the future returns of the
stocks is found to perform sub-optimally on real-world stock market data. This
is because the estimation task becomes extremely challenging due to the
stochastic and volatile nature of stock prices. This work illustrates three
approaches to portfolio design minimizing the risk, optimizing the risk, and
assigning equal weights to the stocks of a portfolio. Thirteen critical sectors
listed on the National Stock Exchange (NSE) of India are first chosen. Three
portfolios are designed following the above approaches choosing the top ten
stocks from each sector based on their free-float market capitalization. The
portfolios are designed using the historical prices of the stocks from Jan 1,
2017, to Dec 31, 2022. The portfolios are evaluated on the stock price data
from Jan 1, 2022, to Dec 31, 2022. The performances of the portfolios are
compared, and the portfolio yielding the higher return for each sector is
identified.

为了分配适当的权重给组成资产的优选投资组合，从历史股价数据中基于三种方法设计和评估投资组合，比较每个部门的投资组合收益以识别出业绩较高的投资组合。

印度股票的等权组合和最优风险组合的绩效评估

Performance Evaluation of Equal-Weight Portfolio and Optimum Risk  Portfolio on Indian Stocks

Analyzing trends across industries is critical to maintaining a healthy and
stable economy. Previous research has mainly analyzed official statistics,
which are more accurate but not necessarily real-time. In this paper, we
propose a method for analyzing industry trends using stock market data. The
difficulty of this task is that the raw data is relatively noisy, which affects
the accuracy of statistical analysis. In addition, textual data for industry
analysis needs to be better understood through language models. For this
reason, we introduce the method of industry trend analysis from two
perspectives of explicit analysis and implicit analysis. For the explicit
analysis, we introduce a hierarchical data (industry and listed company)
analysis method to reduce the impact of noise. For implicit analysis, we
further pre-train GPT-2 to analyze industry trends with current affairs
background as input, making full use of the knowledge learned in the
pre-training corpus. We conduct experiments based on the proposed method and
achieve good industry trend analysis results.

本文提出了一种利用股票市场数据进行产业趋势分析的方法，并从显式分析和隐式分析两个方面引入了产业趋势分析方法，即层次数据分析和基于 GPT-2 预训练模型的分析，最终取得了良好的产业趋势分析结果。

基于分层金融数据的行业趋势分析 2.0

FQP 2.0: Industry Trend Analysis via Hierarchical Financial Data

We propose an approach to generate realistic and high-fidelity stock market
data based on generative adversarial networks (GANs). Our Stock-GAN model
employs a conditional Wasserstein GAN to capture history dependence of orders.
The generator design includes specially crafted aspects including components
that approximate the market's auction mechanism, augmenting the order history
with order-book constructions to improve the generation task. We perform an
ablation study to verify the usefulness of aspects of our network structure. We
provide a mathematical characterization of distribution learned by the
generator. We also propose statistics to measure the quality of generated
orders. We test our approach with synthetic and actual market data, compare to
many baseline generative models, and find the generated data to be close to
real data.

使用生成对抗网络，提出一种生成逼真高保真的股票市场数据的方法，通过使用条件 Wasserstein GAN 来捕捉订单历史依赖性，生成器设计包括近似市场拍卖机制的组件以及运用订单簿构建增强订单历史的生成任务，提供学习生成器分布的数学特征，并提出统计方法来衡量生成订单的质量，结果显示其生成数据与真实数据相似。