A new economic and financial theory of money
- URL: http://arxiv.org/abs/2310.04986v7
- Date: Mon, 14 Oct 2024 14:24:17 GMT
- Title: A new economic and financial theory of money
- Authors: Michael E. Glinsky, Sharon Sievert,
- Abstract summary: The valuation of electronic currencies will be based on macroeconomic theory and the fundamental equation of monetary policy.
The view of electronic currency as a transactional equity associated with tangible assets of a sub-economy will be developed.
- Score: 0.0
- License:
- Abstract: 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 Generative Artificial Intelligence (genAI). 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 genAI.
Related papers
- Modeling Systemic Risk: A Time-Varying Nonparametric Causal Inference
Framework [27.025720728622897]
We propose a nonparametric and time-varying directed information graph (TV-DIG) framework to estimate the evolving causal structure in time series networks.
We apply this framework to identify and monitor the evolution of interconnectedness and systemic risk among major assets and industrial sectors within the financial network.
arXiv Detail & Related papers (2023-12-27T20:09:57Z) - Quantum Computational Algorithms for Derivative Pricing and Credit Risk
in a Regime Switching Economy [0.0]
We introduce a class of processes that are both realistic in terms of mimicking financial market risks as well as more amenable to potential quantum computational advantages.
We study algorithms to estimate credit risk and option pricing on a gate-based quantum computer.
arXiv Detail & Related papers (2023-11-01T20:15:59Z) - Modeling Inverse Demand Function with Explainable Dual Neural Networks [7.502222179088035]
We introduce a novel dual neural network structure that operates in two sequential stages.
The first neural network maps initial shocks to predicted asset liquidations, and the second network utilizes these liquidations to derive equilibrium prices.
Our model can accurately predict equilibrium asset prices based solely on initial shocks, while revealing a strong alignment between predicted and true liquidations.
arXiv Detail & Related papers (2023-07-26T17:41:51Z) - Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines.
We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
arXiv Detail & Related papers (2023-04-15T19:22:37Z) - Forecasting and stabilizing chaotic regimes in two macroeconomic models
via artificial intelligence technologies and control methods [0.3670422696827526]
One of the key tasks in the economy is forecasting the economic agents' expectations of the future values of economic variables.
The behavior of mathematical models can be irregular, including chaotic, which reduces their predictive power.
We study the regimes of behavior of two economic models and identify irregular dynamics in them.
arXiv Detail & Related papers (2023-02-20T11:55:15Z) - Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics
in Limit-Order Book Markets [84.90242084523565]
Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics.
By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention.
By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
arXiv Detail & Related papers (2022-03-07T18:59:54Z) - Finding General Equilibria in Many-Agent Economic Simulations Using Deep
Reinforcement Learning [72.23843557783533]
We show that deep reinforcement learning can discover stable solutions that are epsilon-Nash equilibria for a meta-game over agent types.
Our approach is more flexible and does not need unrealistic assumptions, e.g., market clearing.
We demonstrate our approach in real-business-cycle models, a representative family of DGE models, with 100 worker-consumers, 10 firms, and a government who taxes and redistributes.
arXiv Detail & Related papers (2022-01-03T17:00:17Z) - The AI Economist: Optimal Economic Policy Design via Two-level Deep
Reinforcement Learning [126.37520136341094]
We show that machine-learning-based economic simulation is a powerful policy and mechanism design framework.
The AI Economist is a two-level, deep RL framework that trains both agents and a social planner who co-adapt.
In simple one-step economies, the AI Economist recovers the optimal tax policy of economic theory.
arXiv Detail & Related papers (2021-08-05T17:42:35Z) - Solving Heterogeneous General Equilibrium Economic Models with Deep
Reinforcement Learning [0.0]
General equilibrium macroeconomic models are a core tool used by policymakers to understand a nation's economy.
We use techniques from reinforcement learning to solve such models in a way that is simple, heterogeneous, and computationally efficient.
arXiv Detail & Related papers (2021-03-31T10:55:10Z) - Adaptive Control and Regret Minimization in Linear Quadratic Gaussian
(LQG) Setting [91.43582419264763]
We propose LqgOpt, a novel reinforcement learning algorithm based on the principle of optimism in the face of uncertainty.
LqgOpt efficiently explores the system dynamics, estimates the model parameters up to their confidence interval, and deploys the controller of the most optimistic model.
arXiv Detail & Related papers (2020-03-12T19:56:38Z) - Reinforcement-Learning based Portfolio Management with Augmented Asset
Movement Prediction States [71.54651874063865]
Portfolio management (PM) aims to achieve investment goals such as maximal profits or minimal risks.
In this paper, we propose SARL, a novel State-Augmented RL framework for PM.
Our framework aims to address two unique challenges in financial PM: (1) data Heterogeneous data -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
arXiv Detail & Related papers (2020-02-09T08:10:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.