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on Network Economics |
| By: | Zihan Lin; Haojie Liu; Randall R. Rojas |
| Abstract: | This study proposes a novel portfolio optimization framework that integrates statistical social network analysis with time series forecasting and risk management. Using daily stock data from the S&P 500 (2020-2024), we construct dependency networks via Vector Autoregression (VAR) and Forecast Error Variance Decomposition (FEVD), transforming influence relationships into a cost-based network. Specifically, FEVD breaks down the VAR's forecast error variance to quantify how much each stock's shocks contribute to another's uncertainty information we invert to form influence-based edge weights in our network. By applying the Minimum Spanning Tree (MST) algorithm, we extract the core inter-stock structure and identify central stocks through degree centrality. A dynamic portfolio is constructed using the top-ranked stocks, with capital allocated based on Value at Risk (VaR). To refine stock selection, we incorporate forecasts from ARIMA and Neural Network Autoregressive (NNAR) models. Trading simulations over a one-year period demonstrate that the MST-based strategies outperform a buy-and-hold benchmark, with the tuned NNAR-enhanced strategy achieving a 63.74% return versus 18.00% for the benchmark. Our results highlight the potential of combining network structures, predictive modeling, and risk metrics to improve adaptive financial decision-making. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.20039 |
| By: | Corrado Giulietti (University of Southampton); Brendon McConnell (City St George’s, University of London); Yves Zenou (Monash University) |
| Abstract: | How can crime be disrupted effectively without increasing resources? To answer this question, we develop a spatial network model of crime diffusion, using London as a case study. Moving beyond traditional hot spot policing, we identify key player neighborhoods—highly connected areas in the network. Counterfactual analysis shows that targeting top 10% of key players reduces crime by 10.7% (5.8 percentage points) more than targeting top 10% of hot spots, resulting in potential annual savings exceeding £130 million. Examining the underlying mechanisms, we find that while hot spots attract crime locally, key players facilitate its propagation across areas. |
| Keywords: | C23, D85, H50, K42 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:crm:wpaper:2525 |
| By: | Bloch, Francis (Universite Paris 1 and Paris School of Economics); Dziubinsk, Marcin (Institute of Informatics, University of Warsaw); Dutta, Bhaskar (University of Warwick and Ashoka University) |
| Abstract: | A planner wants to select one agent out of n agents on the basis of a binary characteristic that is commonly known to all agents but is not observed by the planner. Any pair of agents can either be friends or enemies or impartials of each other. An individual's most preferred outcome is that she be selected. If she is not selected, then she would prefer that a friend be selected, and if neither she herself or a friend is selected, then she would prefer that an impartial agent be selected. Finally, her least preferred outcome is that an enemy be selected. The planner wants to design a dominant strategy incentive compatible mechanism in order to be able choose a desirable agent. We derive sufficient conditions for existence of efficient and DSIC mechanisms when the planner knows the bilateral relationships between agents. We also show that if the planner does not know the network these relationships, then there is no efficient and DSIC mechanism and we compare the relative efficiency of two second-best DSIC mechanisms. Finally, we obtain sharp characterization results when the network of friends and enemies satisfies structural balance. |
| Keywords: | Peer selection ; Network, Mechanism design without money ; Dominant strategy incentive compatibility |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:wrk:warwec:1571 |
| By: | Simon Brown; Leonardo Bautista-Gomez |
| Abstract: | There have been several studies into measuring the level of decentralization in Ethereum through applying various indices to indicate the relative dominance of entities in different domains in the ecosystem. However, these indices do not capture any correlation between those different entities, that could potentially make them the subject of external coercion, or covert collusion. We propose an index that measures the relative dominance of entities based on the application of correlation factors. We posit that this approach produces a more nuanced and accurate index of decentralization. |
| Date: | 2024–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2404.02164 |
| By: | Yen Jui Chang; Wei-Ting Wang; Yun-Yuan Wang; Chen-Yu Liu; Kuan-Cheng Chen; Ching-Ray Chang |
| Abstract: | Financial markets are noisy yet contain a latent graph-theoretic structure that can be exploited for superior risk-adjusted returns. We propose a quantum stochastic walk (QSW) optimizer that embeds assets in a weighted graph: nodes represent securities while edges encode the return-covariance kernel. Portfolio weights are derived from the walk's stationary distribution. Three empirical studies support the approach. (i) For the top 100 S\&P 500 constituents over 2016-2024, six scenario portfolios calibrated on 1- and 2-year windows lift the out-of-sample Sharpe ratio by up to 27\% while cutting annual turnover from 480\% (mean-variance) to 2-90%. (ii) A $5^{4}=625$-point grid search identifies a robust sweet spot, $\alpha, \lambda\lesssim0.5$ and $\omega\in[0.2, 0.4]$, that delivers Sharpe $\approx0.97$ at $\le 5\%$ turnover and Herfindahl-Hirschman index $\sim0.01$. (iii) Repeating the full grid on 50 random 100-stock subsets of the S\&P 500 adds 31\, 350 back-tests: the best-per-draw QSW beats re-optimised mean-variance on Sharpe in 54\% of cases and always wins on trading efficiency, with median turnover 36\% versus 351\%. Overall, QSW raises the annualized Sharpe ratio by 15\% and cuts turnover by 90\% relative to classical optimisation, all while respecting the UCITS 5/10/40 rule. These results show that hybrid quantum-classical dynamics can uncover non-linear dependencies overlooked by quadratic models and offer a practical, low-cost weighting engine for themed ETFs and other systematic mandates. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.03963 |
| By: | Michael Kearns; Aaron Roth; Emily Ryu |
| Abstract: | We study a distributed learning problem in which learning agents are embedded in a directed acyclic graph (DAG). There is a fixed and arbitrary distribution over feature/label pairs, and each agent or vertex in the graph is able to directly observe only a subset of the features -- potentially a different subset for every agent. The agents learn sequentially in some order consistent with a topological sort of the DAG, committing to a model mapping observations to predictions of the real-valued label. Each agent observes the predictions of their parents in the DAG, and trains their model using both the features of the instance that they directly observe, and the predictions of their parents as additional features. We ask when this process is sufficient to achieve \emph{information aggregation}, in the sense that some agent in the DAG is able to learn a model whose error is competitive with the best model that could have been learned (in some hypothesis class) with direct access to \emph{all} features, despite the fact that no single agent in the network has such access. We give upper and lower bounds for this problem for both linear and general hypothesis classes. Our results identify the \emph{depth} of the DAG as the key parameter: information aggregation can occur over sufficiently long paths in the DAG, assuming that all of the relevant features are well represented along the path, and there are distributions over which information aggregation cannot occur even in the linear case, and even in arbitrarily large DAGs that do not have sufficient depth (such as a hub-and-spokes topology in which the spoke vertices collectively see all the features). We complement our theoretical results with a comprehensive set of experiments. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.09683 |
| By: | Lucia Illari; Nicholas J. Restrepo; Neil F. Johnson |
| Abstract: | What has been the impact of the enormous amounts of time, effort and money spent promoting pro-vaccine science from pre-COVID-19 to now? We answer this using a unique mapping of online competition between pro- and anti-vaccination views among ~100M Facebook Page members, tracking 1, 356 interconnected communities through platform interventions. Remarkably, the network's fundamental architecture shows no change: the isolation of established expertise and the symbiosis of anti and mainstream neutral communities persist. This means that even if the same time, effort and money continue to be spent, nothing will likely change. The reason for this resilience lies in "glocal" evolution: Communities blend multiple topics while bridging neighborhood-level to international scales, creating redundant pathways that transcend categorical targeting. The solution going forward is to focus on the system's network. We show how network engineering approaches can achieve opinion moderation without content removal, representing a paradigm shift from suppression towards structural interventions. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.01398 |