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Prediction markets have gained adoption as on-chain mechanisms for aggregating information, with platforms such as Polymarket demonstrating demand for stablecoin-denominated markets. However, denominating in non-interest-bearing stablecoins introduces inefficiencies: participants face opportunity costs relative to the fiat risk-free rate, and Bitcoin holders in particular lose exposure to BTC appreciation when conver…
We derive explicit recursive formulas for Target Close (TC) and Implementation Shortfall (IS) in the Almgren-Chriss framework. We explain how to compute the optimal starting and stopping times for IS and TC, respectively, given a minimum trading size. We also show how to add a minimum participation rate constraint (Percentage of Volume, PVol) for both TC and IS. We also study an alternative set of risk measures for t…
This paper studies Relief-Gated Relative Rotation (RGRR), a two-ETF rule that allocates between QQQ and DIA by mapping screened relative and macro states into a continuous QQQ weight. RGRR is economic rather than mechanical: it rotates between a growth-heavy sleeve and a Dow/value-heavy sleeve only when QQQ-DIA relative states are confirmed by rate, volatility, credit, or broad-market relief conditions. Candidate mai…
Blandhol (2025) estimates that wealth-tax-induced emigration from Norway reduces long-run GDP by 1.3%. Dansk Industri scaled this figure to argue that a Danish wealth tax would cost billions - a claim central to the 2026 Danish election campaign. We develop a social contagion model in which the emigration rate depends on a visibility-weighted fraction of prior emigrants, producing tipping-point dynamics. Embedding th…
Global Business Services (GBS) have emerged as a "living laboratory" for the Twin Transition of Green and Digital Transformation, as multinational corporations (MNCs) face increasing pressure to harmonize digital efficiency with environmental stewardship. Aiming to derive a socio-technical framework, this paper synthesizes Technology Roadmapping (TRM) with the International Telecommunication Union (ITU) ICT-centric i…
This study investigates the inherently random structures of dry bulk shipping networks, often likened to a taxi service, and identifies the underlying trade dynamics that contribute to this randomness within individual cargo sub-networks. By analysing micro-level trade flow data from 2015 to 2023, we explore the evolution of dry commodity networks, including grain, coal, and iron ore, and suggest that the Giant Stron…
In this article, we consider a risk process to model the capital of a household. Our work focuses on the analysis of the trapping time of such a process, where trapping occurs when a household's capital level falls into the poverty area. A function analogous to the classical Gerber-Shiu function is introduced, which incorporates information on the trapping time, the capital surplus immediately before trapping and the…
We present a machine learning (ML)-assisted framework bridging manifold learning, neural networks, Gaussian processes, and Equation-Free multiscale modeling, for (a) detecting tipping points in the emergent behavior of complex systems, and (b) characterizing probabilities of rare events (here, catastrophic shifts) near them. Our illustrative example is an event-driven, stochastic agent-based model (ABM) describing th…
Designing dynamic portfolio insurance strategies under market conditions switching between two or more regimes is a challenging task in financial economics. Recently, a promising approach employing the value-at-risk (VaR) measure to assign weights to risky and riskless assets has been proposed in [Jiang C., Ma Y. and An Y. "The effectiveness of the VaR-based portfolio insurance strategy: An empirical analysis" , Inte…
Recently, there are many trials to apply reinforcement learning in asset allocation for earning more stable profits. In this paper, we compare performance between several reinforcement learning algorithms - actor-only, actor-critic and PPO models. Furthermore, we analyze each models' character and then introduce the advanced algorithm, so called Reward clipping model. It seems that the Reward Clipping model is better…
This article studies deep neural network expression rates for optimal stopping problems of discrete-time Markov processes on high-dimensional state spaces. A general framework is established in which the value function and continuation value of an optimal stopping problem can be approximated with error at most $\varepsilon$ by a deep ReLU neural network of size at most $κd^{\mathfrak{q}} \varepsilon^{-\mathfrak{r}}$.…
We analyze the systemic risk for disjoint and overlapping groups (e.g., central clearing counterparties (CCP)) by proposing new models with realistic game features. Specifically, we generalize the systemic risk measure proposed in [F. Biagini, J.-P. Fouque, M. Frittelli, and T. Meyer-Brandis, Finance and Stochastics, 24(2020), 513--564] by allowing individual banks to choose their preferred groups instead of being as…
Managing unemployment is one of the key issues in social policies. Unemployment insurance schemes are designed to cushion the financial and morale blow of loss of job but also to encourage the unemployed to seek new jobs more pro-actively due to the continuous reduction of benefit payments. In the present paper, a simple model of unemployment insurance is proposed with a focus on optimality of the individual's entry …
We solve the problem of optimal stopping of a Brownian motion subject to the constraint that the stopping time's distribution is a given measure consisting of finitely-many atoms. In particular, we show that this problem can be converted to a finite sequence of state-constrained optimal control problems with additional states corresponding to the conditional probability of stopping at each possible terminal time. The…
Much research has been conducted arguing that tipping points at which complex systems experience phase transitions are difficult to identify. To test the existence of tipping points in financial markets, based on the alternating offer strategic model we propose a network of bargaining agents who mutually either cooperate or where the feedback mechanism between trading and price dynamics is driven by an external "hidd…
Equivalences are known between problems of singular stochastic control (SSC) with convex performance criteria and related questions of optimal stopping, see for example Karatzas and Shreve [SIAM J. Control Optim. 22 (1984)]. The aim of this paper is to investigate how far connections of this type generalise to a non convex problem of purchasing electricity. Where the classical equivalence breaks down we provide alter…
The aim of this work is to explore the possible types of phenomena that simple macroeconomic Agent-Based models (ABM) can reproduce. We propose a methodology, inspired by statistical physics, that characterizes a model through its 'phase diagram' in the space of parameters. Our first motivation is to understand the large macro-economic fluctuations observed in the 'Mark I' ABM. Our major finding is the generic existe…
We present a methodology for obtaining explicit solutions to infinite time horizon optimal stopping problems involving general, one-dimensional, Itô diffusions, payoff functions that need not be smooth and state-dependent discounting. This is done within a framework based on dynamic programming techniques employing variational inequalities and links to the probabilistic approaches employing $r$-excessive functions an…
Early stopping treats training time as a capacity knob: halt when a validation metric stops improving so the model does not wander into overfit.
A neural network is a layered function approximator: units compute a weighted sum, apply a nonlinearity, and pass the result forward so the whole stack can learn a mapping from inputs to outputs.
Regularization is any constraint that trades train fit for expected live error: weight decay, dropout, early stopping, data augmentation, or a simpler hypothesis class.
Baltic Dry Index tracks shipping rates for dry bulk commodities and offers a real-economy signal on trade flows, freight conditions, and industrial demand.
Core PCE Inflation — The Fed's preferred inflation gauge, stripping volatile food and energy components.
PPI measures prices from the seller’s side of the pipeline — an upstream inflation print that may or may not pass through to CPI.
John Law’s Mississippi Company and Banque Royale fused monetary expansion, colonial equity, and French public finance until 1720 — a state-run bubble that ended in a paper-money collapse.
Baltic Dry Index tracks shipping rates for dry bulk commodities and offers a real-economy signal on trade flows, freight conditions, and industrial demand.
Core PCE Inflation — The Fed's preferred inflation gauge, stripping volatile food and energy components.
Early stopping treats training time as a capacity knob: halt when a validation metric stops improving so the model does not wander into overfit.
John Law’s Mississippi Company and Banque Royale fused monetary expansion, colonial equity, and French public finance until 1720 — a state-run bubble that ended in a paper-money collapse.
A neural network is a layered function approximator: units compute a weighted sum, apply a nonlinearity, and pass the result forward so the whole stack can learn a mapping from inputs to outputs.
PPI measures prices from the seller’s side of the pipeline — an upstream inflation print that may or may not pass through to CPI.
Regularization is any constraint that trades train fit for expected live error: weight decay, dropout, early stopping, data augmentation, or a simpler hypothesis class.