This paper fundamentally reformulates economic and financial theory to
include electronic currencies. The valuation of the electronic currencies will
be based on macroeconomic theory and the fundamental equation of monetary
policy, not the microeconomic theory of discounted cash flows. The view of
electronic currency as a transactional equity associated with tangible assets
of a sub-economy will be developed, in contrast to the view of stock as an
equity associated mostly with intangible assets of a sub-economy. The view will
be developed of the electronic currency management firm as an entity
responsible for coordinated monetary (electronic currency supply and value
stabilization) and fiscal (investment and operational) policies of a
substantial (for liquidity of the electronic currency) sub-economy. The risk
model used in the valuations and the decision-making will not be the
ubiquitous, yet inappropriate, exponential risk model that leads to discount
rates, but will be multi time scale models that capture the true risk. The
decision-making will be approached from the perspective of true systems control
based on a system response function given by the multi scale risk model and
system controllers that utilize the Deep Reinforcement Learning, Generative
Pretrained Transformers, and other methods of Artificial Intelligence
(DRL/GPT/AI). Finally, the sub-economy will be viewed as a nonlinear complex
physical system with both stable equilibriums that are associated with
short-term exploitation, and unstable equilibriums that need to be stabilized
with active nonlinear control based on the multi scale system response
functions and DRL/GPT/AI.

这篇论文基本重构了经济与金融理论，以包括电子货币。电子货币的估值将基于宏观经济理论和货币政策的基本方程，而不是基于微观经济理论的折现现金流量。该论文开发了将电子货币视为与次经济体的有形资产相关的交易性权益的观点，与将股票视为主要与次经济体的无形资产相关的权益的观点相对立。论文还将发展电子货币管理公司的观点，将其视为负责协调次经济体的货币（电子货币供应和价值稳定）和财政（投资和运营）政策的实体，为了电子货币的流动性。该论文使用的风险模型和决策制定方法将不是普遍存在且不适宜的指数风险模型导致的折现率，而是捕捉真实风险的多时间尺度模型。论文将从真实系统控制的视角来探讨决策制定，该视角由多尺度风险模型给出的系统响应函数和利用深度强化学习、生成预训练转换和其他人工智能方法（DRL/GPT/AI）的系统控制器来实现。最后，将次经济体视为一个非线性复杂物理系统，既有与短期开发相关的稳定平衡态，也有需要基于多尺度系统响应函数和 DRL/GPT/AI 进行主动非线性控制以实现稳定的不稳定平衡态。

货币的一种新的经济和金融理论

A new economic and financial theory of money

Monetary policy pronouncements by Federal Open Market Committee (FOMC) are a
major driver of financial market returns. We construct the largest tokenized
and annotated dataset of FOMC speeches, meeting minutes, and press conference
transcripts in order to understand how monetary policy influences financial
markets. In this study, we develop a novel task of hawkish-dovish
classification and benchmark various pre-trained language models on the
proposed dataset. Using the best-performing model (RoBERTa-large), we construct
a measure of monetary policy stance for the FOMC document release days. To
evaluate the constructed measure, we study its impact on the treasury market,
stock market, and macroeconomic indicators. Our dataset, models, and code are
publicly available on Huggingface and GitHub under CC BY-NC 4.0 license.

本研究通过构建 FOMC 讲话、会议记录和新闻发布会文本的最大化标记数据集，研究了货币政策如何影响金融市场；发展了新的鹰派和鸽派分类任务，并在所提出的数据集上进行了基准测试；使用 RoBERTa-large 最佳模型构建了 FOMC 文件发布日的货币政策立场的度量，并以国债市场、股市和宏观经济指标为评估指标。

万亿美元的话语：一个新的金融数据集、任务和市场分析

Trillion Dollar Words: A New Financial Dataset, Task & Market Analysis

The Federal Reserve System (the Fed) plays a significant role in affecting
monetary policy and financial conditions worldwide. Although it is important to
analyse the Fed's communications to extract useful information, it is generally
long-form and complex due to the ambiguous and esoteric nature of content. In
this paper, we present FedNLP, an interpretable multi-component Natural
Language Processing system to decode Federal Reserve communications. This
system is designed for end-users to explore how NLP techniques can assist their
holistic understanding of the Fed's communications with NO coding. Behind the
scenes, FedNLP uses multiple NLP models from traditional machine learning
algorithms to deep neural network architectures in each downstream task. The
demonstration shows multiple results at once including sentiment analysis,
summary of the document, prediction of the Federal Funds Rate movement and
visualization for interpreting the prediction model's result.

本文介绍了 FedNLP，一个可解释性多组件自然语言处理系统，用于解码联邦储备委员会的沟通，用于情感分析、文档摘要、联邦基金利率预测和可视化结果解释。