The widespread availability of behavioral data has led to the development of
data-driven personalized pricing algorithms: sellers attempt to maximize their
revenue by estimating the consumer's willingness-to-pay and pricing
accordingly. Our objective is to develop algorithms that protect consumer
interests against personalized pricing schemes. In this paper, we consider a
consumer who learns more and more about a potential purchase across time, while
simultaneously revealing more and more information about herself to a potential
seller. We formalize a strategic consumer's purchasing decision when
interacting with a seller who uses personalized pricing algorithms, and
contextualize this problem among the existing literature in optimal stopping
time theory and computational finance. We provide an algorithm that consumers
can use to protect their own interests against personalized pricing algorithms.
This algorithmic stopping method uses sample paths to train estimates of the
optimal stopping time. To the best of our knowledge, this is one of the first
works that provides computational methods for the consumer to maximize her
utility when decision making under surveillance. We demonstrate the efficacy of
the algorithmic stopping method using a numerical simulation, where the seller
uses a Kalman filter to approximate the consumer's valuation and sets prices
based on myopic expected revenue maximization. Compared to a myopic purchasing
strategy, we demonstrate increased payoffs for the consumer in expectation.

针对个性化定价情况，提出了一种消费者保护策略 —— 利用基于样本路径训练最优停止时间的算法停止决策，实现对决策的最大效益，同时也是首个提供了消费者在监控行为下利用计算方法最大化效益的方法之一。

个性化定价的消费者保护：一种停止时间方法

Protecting Consumers Against Personalized Pricing: A Stopping Time  Approach

The availability of data on digital traces is growing to unprecedented sizes,
but inferring actionable knowledge from large-scale data is far from being
trivial. This is especially important for computational finance, where digital
traces of human behavior offer a great potential to drive trading strategies.
We contribute to this by providing a consistent approach that integrates
various datasources in the design of algorithmic traders. This allows us to
derive insights into the principles behind the profitability of our trading
strategies. We illustrate our approach through the analysis of Bitcoin, a
cryptocurrency known for its large price fluctuations. In our analysis, we
include economic signals of volume and price of exchange for USD, adoption of
the Bitcoin technology, and transaction volume of Bitcoin. We add social
signals related to information search, word of mouth volume, emotional valence,
and opinion polarization as expressed in tweets related to Bitcoin for more
than 3 years. Our analysis reveals that increases in opinion polarization and
exchange volume precede rising Bitcoin prices, and that emotional valence
precedes opinion polarization and rising exchange volumes. We apply these
insights to design algorithmic trading strategies for Bitcoin, reaching very
high profits in less than a year. We verify this high profitability with robust
statistical methods that take into account risk and trading costs, confirming
the long-standing hypothesis that trading based social media sentiment has the
potential to yield positive returns on investment.

通过整合计算机金融、比特币、算法交易、社交媒体情感和盈利能力方面的知识，我们成功地设计了算法交易策略，并证实了基于社交媒体情感的交易能够带来高盈利。

比特币的社交信号与算法交易

Social signals and algorithmic trading of Bitcoin

The use of robo-readers to analyze news texts is an emerging technology trend
in computational finance. In recent research, a substantial effort has been
invested to develop sophisticated financial polarity-lexicons that can be used
to investigate how financial sentiments relate to future company performance.
However, based on experience from other fields, where sentiment analysis is
commonly applied, it is well-known that the overall semantic orientation of a
sentence may differ from the prior polarity of individual words. The objective
of this article is to investigate how semantic orientations can be better
detected in financial and economic news by accommodating the overall
phrase-structure information and domain-specific use of language. Our three
main contributions are: (1) establishment of a human-annotated finance
phrase-bank, which can be used as benchmark for training and evaluating
alternative models; (2) presentation of a technique to enhance financial
lexicons with attributes that help to identify expected direction of events
that affect overall sentiment; (3) development of a linearized phrase-structure
model for detecting contextual semantic orientations in financial and economic
news texts. The relevance of the newly added lexicon features and the benefit
of using the proposed learning-algorithm are demonstrated in a comparative
study against previously used general sentiment models as well as the popular
word frequency models used in recent financial studies. The proposed framework
is parsimonious and avoids the explosion in feature-space caused by the use of
conventional n-gram features.

本文主要研究如何利用人工智能技术分析经济新闻中的情感倾向，提出一种基于人工标注金融短语库、同时考虑上下文结构信息和特征属性的线性短语结构模型，并与现有情感分析模型进行比较。

好债务还是坏债务：检测经济文本中的语义倾向性

Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts

The implied volatility surface (IVS) is a fundamental building block in
computational finance. We provide a survey of methodologies for constructing
such surfaces. We also discuss various topics which can influence the
successful construction of IVS in practice: arbitrage-free conditions in both
strike and time, how to perform extrapolation outside the core region, choice
of calibrating functional and selection of numerical optimization algorithms,
volatility surface dynamics and asymptotics.

本文综述了构建隐含波动率面的多种方法，同时讨论了多个实际构建过程中的问题，如无套利条件、拟合函数选择和数值优化算法等。