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on Risk Management |
| By: | Cruz, Lizelle Ann |
| Abstract: | Systemic risk remains a key concern for financial authorities, especially in emerging economies where traditional, low‑frequency balance sheet indicators often lag changing conditions. This study develops a high‑frequency Systemic Risk Sentiment Index (SRSI) for the Philippines using news headlines from 2011–2025 and an ensemble of domain‑specific financial sentiment models. Results show that negative sentiment is mainly driven by external‑sector developments, market volatility, and equity‑related news, with surges aligning with global and domestic stress episodes. Empirical tests indicate only modest predictive power for domestic equity returns, and misclassifications highlight challenges in capturing nuances of Philippine financial reporting. Overall, the SRSI is best viewed as a responsive, real‑time barometer that complements conventional systemic risk measures. |
| Keywords: | Systemic Risk, Early Warning Indicators, Sentiment Analysis, Machine Learning, Large Language Model |
| JEL: | C43 C55 E44 G14 |
| Date: | 2026–03–02 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128944 |
| By: | Lathrop, John; Dikmen, Irem; Soane, Emma; Aven, Terje |
| Abstract: | The Applied Risk Management Specialty Group of the Society for Risk Analysis (SRA) identified a need to define, characterize, and improve risk analysis quality, specifically its quality in supporting risk management. To address that need, they drew on prior research and experience to develop the Risk Analysis Quality Test, the RAQT, a list of 76 questions, each asking if a risk analysis satisfies an aspect of risk analysis quality. The RAQT is both a definition of risk analysis quality, and a “spotter” of shortfalls, providing a language with which to describe then address possible shortfalls. The 76 questions were compiled by a working group based on risk science knowledge and on shortfalls they had observed in practice. With this study, we demonstrate that, simply by defining an explicit process to characterize risk analysis quality, the RAQT can improve risk analysis quality on several levels. We describe applications of the RAQT at each of three levels: 1) to evaluate risk analysis reporting within a large project; 2) to critique and suggest improvements for describing risk; and 3) more strategically, as a basis for orienting an organizational culture around awareness and management of risk. Finally, we discuss the implications of our study. This paper contributes to the risk analysis body of knowledge and practice by demonstrating the critical role of risk analysis quality assessment to identify shortfalls in risk characterization and communication, and the role of organizational culture in shaping how effectively risk analyses can guide risk management. |
| Keywords: | risk analysis quality; risk analysis; risk management; risk culture; risk communication |
| JEL: | G32 |
| Date: | 2024–12–31 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:126088 |
| By: | Nag, Arindam |
| Abstract: | This paper investigates whether artificial intelligence amplifies systemic risk in equity markets using daily data spanning February 2023 to December 2025, comprising 721 observations across the CBOE Volatility Index, S&P 500 and NASDAQ Composite returns, abnormal trading volume, and the Amihud illiquidity ratio. Employing descriptive statistical analysis, an event study framework, OLS regression with Newey-West HAC-corrected standard errors, and a six-lag Vector Autoregression, the results provide evidence broadly consistent with systemic risk amplification through the liquidity withdrawal channel. The regression results indicate that market illiquidity, as measured by the Amihud ratio, is a statistically significant predictor of volatility (coefficient = 1, 144, 957; p |
| Keywords: | Artificial Intelligence, Algorithmic Trading, Systemic Risk, Market Volatility, Financial Stability, Liquidity Risk |
| JEL: | G0 G10 G14 G18 G3 G33 O33 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128853 |
| By: | Pegoraro, Stefano (University of Notre Dame) |
| Abstract: | In a continuous-time model, a risk-neutral decision-maker chooses the volatility of a state variable and is terminated when the variable falls below a threshold. I provide economically interpretable conditions under which the decision-maker becomes risk averse endogenously and minimizes volatility near termination, even if she faces myopic incentives to gamble for resurrection. The conditions introduce forward-looking incentives to preserve economic rents. I show these conditions are met in a wide range of apparently unrelated models, thus identifying forward-looking rents as a unifying economic mechanism behind endogenous risk aversion. I also provide conditions for the decision-maker to become risk loving endogenously. |
| Date: | 2024–10–09 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:9tcjz_v1 |
| By: | Yuxuan Zhao; Sijia Chen; Ningxin Su |
| Abstract: | LLMs have shown strong performance across diverse financial tasks, yet portfolio management (PM), a critical financial decision-making task, remains poorly benchmarked. Existing benchmarks exhibit two main gaps: they ignore cross-asset correlation structures, thereby failing to distinguish genuinely diversified portfolios from concentrated ones, and fail to evaluate the complete PM decision pipeline in real-world scenarios. We introduce PortBench, a benchmark spanning six heterogeneous asset classes over ten years. PortBench consists of two complementary layers: a static QA dataset of 6, 269 correlation-based questions across seven task templates, and a dynamic five-stage allocation pipeline that mirrors the full PM decision cycle. To evaluate these layers, we introduce two dedicated metrics: a dual-layer correlation score that measures whether proposed portfolios exploit inter-class hedging and avoid intra-class concentration, and CEPS, a metric that quantifies how reasoning errors compound across pipeline stages. We further assess strategy robustness and investor alignment under three historical stress regimes and risk profiles. Evaluating ten frontier LLMs, we find that despite strong performance on static financial QA, 90\% of model-profile combinations fail to outperform a basic equal-weight allocation, and models that satisfy every procedural constraint still suffer catastrophic drawdowns under stress. Our source code is available at \href{https://github.com/AgenticFinLab/portbench}{this https URL}. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.27887 |
| By: | Friederike Niepmann; Leslie Sheng Shen; Joshua Walker |
| Abstract: | Geopolitical risk has emerged as a central driver of global financial markets, with episodes such as Russia's invasion of Ukraine and recent conflicts in the Middle East triggering sharp movements in asset prices and increases in market volatility. But not all industries are exposed to such shocks in the same way (Caldara and Iacoviello 2022; Culver, Niepmann, and Shen 2025). |
| Date: | 2026–06–02 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfn:103377 |
| By: | Rauf, Saima; Audi, Marc; Ali, Amjad |
| Abstract: | The research investigates how banks manage liquidity risk through their loan quality assessment and asset quality evaluation, which they regulate using board member characteristics. The research employed panel data from 2011 to 2023 to examine variable relationships through fixed effects and random effects regression models. The results show that nonperforming loans create adverse effects on banks' liquidity risk management abilities because their loan quality decreases, which harms their liquidity management capabilities. The relationship between asset quality and liquidity risk management shows a positive correlation because banking institutions with better asset structures achieve superior financial stability and more effective liquidity management systems. The research outcomes show that board governance characteristics interact with financial risk indicators to create effects that determine the results of liquidity risk management. The study demonstrates that governance mechanisms function as vital elements that determine banks' credit risk management strategies and their approaches to handling liquidity challenges. Board members who exercise proper oversight will enhance monitoring systems, which will promote responsible financial choices that protect institutional resilience. The research findings demonstrate that banks must develop stronger governance structures while they need to implement effective credit risk management methods. The financial system will achieve greater stability through better board supervision, which will lead to improved loan portfolio monitoring and safer lending practices. Banking institutions need these measures to build their resilience, along with maintaining their operational capacity during economic downturns. |
| Keywords: | Liquidity Risk Management, Loan Quality, Asset Quality, Governance |
| JEL: | G21 G34 M41 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128920 |
| By: | Kopytov, Alexandr; Taschereau-Dumouchel, Mathieu; Xu, Zebang |
| Abstract: | We propose a model in which risk, at both the micro and macro levels, is endogenous and driven by incentives. In the model, each firm chooses the mean and variance of its productivity process, as well as how it covaries with the productivity of other firms. Aggregate risk arises when firms select productivity processes that are correlated with one another. The theory predicts that firms with larger sales and lower markups are less volatile and less correlated with aggregate productivity. We find support for these predictions in the data. Through their impact on risk-taking decisions, distortions such as taxes and markups can make GDP more volatile in equilibrium. In a calibrated version of the model, removing distortions significantly reduces GDP volatility. |
| Keywords: | risk, uncertainty, endogenous risk |
| JEL: | E32 D81 C67 D57 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:341039 |
| By: | Friederike Niepmann; Leslie Sheng Shen; Joshua Walker |
| Abstract: | Geopolitical risk has emerged as a central driver of global financial markets, with episodes such as Russia’s invasion of Ukraine and recent conflicts in the Middle East triggering sharp movements in asset prices and increases in market volatility. This brief examines how geopolitical risk affects U.S. bank valuations and which institutions are most vulnerable. Through cross-border lending, foreign subsidiaries, and trading activities, banks face multifaceted exposure to geopolitical risk that can affect their profitability via credit losses, disrupted funding markets, and altered fee income. Banks’ valuations, in turn, influence their funding costs and capital-raising capacity, ultimately affecting credit supply to the real economy. And if geopolitical risk affects some banks more than others, it may create uneven vulnerabilities within the financial system, which would have implications for financial stability. |
| Keywords: | geopolitical risk; Bank valuation; cross-border lending |
| JEL: | G12 G14 G21 |
| Date: | 2026–06–02 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedbcq:103352 |
| By: | Ji, Zihao; Wang, Guan; Hu, Chenxi; Zhang, Hongru |
| Abstract: | This paper examines the non-linear transmission of external Economic Policy Uncertainty to the volatility of Macau gaming stocks. By combining a TVP-VAR-DY framework with XGBoost and SHAP machine learning attribution, we map the dynamic risk spillovers originating from Mainland China, Hong Kong, and the United States. The empirical results reveal a dual risk topology: regional regulatory shocks manifest as transient, high-intensity pulses (episodic), whereas global systemic uncertainty exhibits a chronic structural persistence that determines the baseline volatility regime. Furthermore, our machine learning analysis reveals that firm resilience depends on specific micro-financial foundations. We find that operating profitability effectively buffers external shocks, whereas financial leverage significantly amplifies them. The study uncovers precise structural tipping points, identifying a sharp increase in risk sensitivity when the debt-to-asset ratio exceeds 80% and determining an optimal liquidity buffer at approximately 15% cash-to-assets. These findings challenge traditional linear assumptions and support the adoption of threshold-based macro-prudential regulations for the sector. |
| Keywords: | Economic Policy Uncertainty (EPU), Macau Gaming Industry, XGBoost, Non-linear Thresholds, Financial Accelerator |
| JEL: | C45 G15 G32 L83 |
| Date: | 2026–01–20 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128532 |
| By: | Wu, Yining (UBS Investment Bank) |
| Abstract: | This paper develops a state-dependent version of Prospect Theory in which loss aversion, reference adaptation, and probability weighting vary with resource buffers, attention constraints, and boundary-risk context. Prospect Theory is usually treated as a descriptive account of deviations from expected-utility rationality. This paper proposes a finite-capacity bridge model: bounded agents operating under scarcity, limited attention, finite memory, and finite update capacity should exhibit state-dependent risk preferences. The model introduces a vector-valued resource buffer, perceived distance to a constraint threshold, event-class-dependent probability weighting, and a nudge-bandwidth model. It predicts that loss aversion should rise under scarcity and downside exposure, reference baselines should adapt more slowly under depletion, and probability weighting should become more distorted for low-capacity agents and boundary-relevant rare events. The paper includes deterministic synthetic simulations and a replication package, but does not fit human-subject data. It is intended as a theoretical working paper and empirical identification framework for future tests of state-dependent Prospect Theory. |
| Date: | 2026–05–19 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:pj43k_v1 |
| By: | Mdhlalose, Dickson |
| Abstract: | The relationships among different investment types in South Africa, and how these shift with the overall economic environment, are the focus of this research, as is what this means for managing the risk of investment collections. Using a Markov-Switching Vector Autoregressive (MS-VAR) method, we observe how the Johannesburg Stock Exchange (JSE) All Share Index, South African government bonds, gold priced in South African rand, listed property, and the USD/ZAR exchange rate move together in both rising (bull) and falling (bear) markets from January 2000 to December 2024, with 300 months of data. During bear markets, the way investments' returns relate to each other increases considerably, which supports the idea of contagion and, in effect, lowers the number of genuinely separate investments in a portfolio by roughly 50% compared to bull markets. South African government bonds do not protect investments during bear market periods, which is typical for a developing nation with growing government finance issues and a series of credit rating downgrades. |
| Keywords: | Markov-switching VAR, Regime-dependent correlations, Safe haven assets, Portfolio risk management, Dynamic asset allocation |
| JEL: | C32 G11 G15 G01 O55 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:341030 |
| By: | Rahul Fernandes; Travis Desell |
| Abstract: | Portfolio optimization in real-world financial markets is notoriously difficult due to non-stationarity, noisy data, and high transaction costs. Standard predict-then-optimize methods first forecast returns and then solve for weights, compounding prediction errors and often failing under regime shifts. We propose an end-to-end framework that directly optimizes differentiable surrogates of key financial metrics - Sharpe ratio, Omega ratio, Conditional Value-at-Risk (CVaR), and Risk Parity - allowing neural networks to learn portfolio weights via backpropagation. Our expanding-window walk-forward procedure, applied to 50 S&P 500 stocks from 2007 to 2023, incorporates realistic bid-ask spread costs and rebalances quarterly. On the challenging out-of-sample test period (2022-2023), the best model - an AttentionLSTM with the Omega-CVaR-RiskParity loss - achieves an annualized Sharpe of 0.29 and a total compounded return of +7.86%, while the S&P 500 delivers -4.52% total return and an annualized Sharpe of -0.02. This outperforms the S&P 500 by 12.38 percentage points (a relative improvement of over 270%), while keeping tail risk (CVaR) nearly unchanged. The framework consistently outperforms the equal-weight portfolio, S&P 500, and traditional methods (MVP, HRP, NCO), demonstrating that embedding financial objectives directly into model training yields robust, economically meaningful outperformance even in adverse market conditions. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.28853 |
| By: | Tirupam Goel; Ulf Lewrick; Isha Agarwal |
| Abstract: | We model a stablecoin issuer that optimises capital, cash and bond holdings under persistent stablecoin flows. Absent regulation, the issuer holds little capital and favours interest-bearing but less-liquid bonds over cash. This exposes coin-holders to default risks and poses systemic spillovers via price impact of bond fire-sales. How can regulation mitigate these risks? We consider capital and liquidity thresholds as usable buffers. They can be breached in stress but discipline issuers by triggering additional redemptions, thus endogenising stablecoin flows. The thresholds work through asymmetric channels. While the liquidity threshold only raises cash holdings, the capital threshold increases both capital and cash. Both thresholds mitigate default and spillover risks, suggesting they are substitutes. However, they are complements for regulators targeting both risks. Using stablecoin flows and US Treasury market depth, we calibrate a two-way mapping that enables regulators to recover capital-liquidity threshold combinations implied by chosen risk targets (and vice-versa). |
| Keywords: | capital regulation, liquidity regulation, stablecoins, crypto, money market funds, financial stability, buffer usability |
| JEL: | G2 G28 C6 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1355 |
| By: | Edward P. Herbst; Scott R. Konzem; Cristina Scofield |
| Abstract: | We comprehensively document 1, 265 Federal Reserve staff alternative scenarios presented to the Federal Open Market Committee in publicly released materials from 1968 to 2020. Scenarios grew in frequency and sophistication, typically spanning a range of outcomes around the baseline. We construct a taxonomy with six categories: aggregate demand, aggregate supply, external risks, financial conditions, fiscal policy, and expectation shifts. Staff qualitative risk assessments complemented the scenario composition. Comparing scenario forecasts to realized outcomes, the most accurate scenarios often anticipated major macroeconomic developments even when magnitudes were missed, revealing the value and limits of scenario analysis for central bank risk management. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:103343 |
| By: | Andres Azqueta-Gavaldon; Borja Ureta |
| Abstract: | We introduce CausalAlpha, an open-source framework that constructs a high-frequency Geopolitical Risk (GPR) index from Telegram OSINT channels using natural language processing, and applies causal discovery methods to identify the directed causal structure between geopolitical uncertainty and financial market variables. Unlike standard sentiment indices or Granger-causality approaches, CausalAlpha employs the Peter-Clark (PC) algorithm to recover the directed acyclic graph (DAG) of causal dependencies between five category-specific GPR indicators and a set of financial variables spanning commodity prices, equity indices, and credit instruments, estimated across four DAG specifications and three significance levels with 500 block-bootstrap resamples. Two findings emerge as globally robust across all DAG specifications at alpha = 0.10: political instability and energy media coverage independently and causally precede conflict coverage, establishing conflict as the primary causal sink of geopolitical narrative escalation in real-time OSINT channels. At the strictest significance level (alpha = 0.05), conflict coverage causally precedes energy sector equity returns (delta XLE), consistent with geopolitical escalation transmitting to energy markets. A Structural VAR on the core macro panel confirms that dynamic transmission from geopolitical NLP signals to financial market prices is statistically weak at daily frequency, suggesting that geopolitical news signals operate primarily within the media narrative system. The framework is deployed as a production application on Google Cloud Run with automated data collection and index construction, representing a step toward real-time macrofinancial risk monitoring using OSINT. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.07049 |
| By: | Ji, Zihao; Zhang, Mengchen; Wang, Guan; Zhang, Hongru |
| Abstract: | Does replacing hard debt-to-income (DTI) limits with risk-adjusted pricing improve household welfare? We develop a heterogeneous agent life-cycle model incorporating behavioral flow disutility, endogenous credit menus, and regime-switching income risk. Simulating a "Double Trigger" crisis, we uncover a Solvency Paradox: price-based regulation eliminates immediate credit rationing but imposes risk premia that erode liquidity buffers, generating a 53.7% cumulative default rate among marginal borrowers exceeding the counterfactual exclusion rate under quantity limits. Welfare consequences are starkly regressive: the "marginal middle class" suffers 6.7% consumption-equivalent losses while wealthy households gain 2.1%. Our policy comparison reveals a state-contingent hierarchy: forbearance efficiently resolves transitory liquidity shocks, while principal reduction is necessary for persistent solvency crises. State-contingent contracts (Shared Responsibility Mortgages) achieve intermediate efficacy with superior dynamic stability. Marginal credit expansions are dominated across all simulated shock scenarios. Optimal macro-prudential design requires severing the link between income shocks and debt service burdens. |
| Keywords: | Macroprudential Policy, Mortgage Default, Risk-Based Pricing, Liquidity Constraints, Household Heterogeneity |
| JEL: | E44 G21 G28 R21 |
| Date: | 2026–02–10 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128531 |
| By: | Serrano, Enil; Gardner, Grant; Biram, Hunter |
| Abstract: | Farmers face growing production and price risks that traditional Farm Service Agency (FSA) programs and crop insurance only partially address. This article reviews existing decision tools and introduces the Crop Insurance Decision Maker (CIDM), a web-based platform that uses stochastic simulations to project expected net revenues across insurance products and coverage levels. By incorporating farmer risk preferences and county-level data, CIDM improves transparency and supports more informed enrollment in FSA and crop insurance programs. Case studies demonstrate how CIDM enhances risk management decisions, balancing income stability with financial resilience. |
| Keywords: | Crop Production/Industries, Risk and Uncertainty |
| Date: | 2026–05–28 |
| URL: | https://d.repec.org/n?u=RePEc:ags:ukynea:402737 |
| By: | Miquel Noguer i Alonso |
| Abstract: | Practitioners allocate capital with forecast-light rules such as equal weight, inverse volatility, risk parity, HRP, and return-adjusted HRP (RA-HRP). This paper develops \emph{Heuristic Portfolio Optimization} (HPO): an information-restricted projection of the Markowitz/tangency solution onto a stable rule class. The implied-return principle, $\mathbf{w}$ is maximum-Sharpe iff $\mathbf{\mu}_e \propto \mathbf{\Sigma}\mathbf{w}$, gives closed-form optimality sets for leading heuristics and exposes the Schur-complement substitutions behind HRP. For RA-HRP, we introduce fixed-tree cluster-Sharpe recursion, unit-free HRP--RA-HRP interpolation, tangency conditions, conditional-risk splits, and pathwise/KL decompositions of weight distortion. First-order Sharpe calculus expresses the marginal value of return information as nodewise alphas against HRP and yields a linear KL trust budget. We formalize generic HPO maps, define the implied-return defect, prove that it equals squared Sharpe inefficiency, characterize tree-HPO coincidence by nodewise mass ratios, and give a bias--variance decomposition for estimated rules. Finally, HPO is embedded into Reinforcement Learning Portfolio Optimization (RLPO): every HPO map induces a deterministic stationary policy; static HPO is the $\gamma=0$ no-friction face of the Bellman problem; RA-HRP supplies a hierarchical policy prior; and dynamic improvement is warranted when continuation value exceeds myopic HPO defect plus frictions. A performance-difference identity prices the myopic value gap, gives an $\varepsilon/(1-\gamma)$ myopia bound, and identifies nodewise alphas as policy-gradient coordinates of the hierarchical actor. Thus HPO is the static optimality layer and RLPO the dynamic control layer. The conditions are GRS-testable, extend to mean--CVaR and expected utility under ellipticity, and become Kelly-growth conditions in diffusion limits. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.12612 |
| By: | Majlesi, Kaveh (Monash University, Lund University, IZA and CEPR); Molin, Elin (Lund University, UCFS, CED and KWC); Roth, Paula (Stockholm School of Economics, UCFS and IFN) |
| Abstract: | We study how fatal and nonfatal health shocks affect households’ ability to meet their financial obligations. We find that fatal shocks substantially increase the likelihood of default and that housing wealth plays a key role as a self-insurance mechanism. Surviving spouses who experience the largest income losses are more likely to sell their homes, and those without housing wealth face a sharply higher risk of debt collection. In the most financially vulnerable families, these shocks even generate intergenerational spillovers. In contrast, nonfatal health shocks lead to only modest increases in default risk. Taken together, our findings suggest that strengthening survivors’ benefits for households with limited resources could improve welfare across generations. |
| Keywords: | Financial Distress; Health shocks; Household Debt; Household Saving; Intergenerational Transmission |
| JEL: | D14 G22 G51 I12 |
| Date: | 2026–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:ifauwp:2026_011 |
| By: | Alex Leung; Rex Zhang; Kentaroh Toyoda; SiewMei Loh |
| Abstract: | AI losses that arise through an insured organization's generative or agentic AI system require state reconstruction, not merely event reconstruction, because the relevant state changes as the system reasons, retrieves, calls tools, and acts. The relevant question is not only what loss occurred, but what the system was allowed to do, what it actually did, and whether that reconstructed loss can support insurance claim recovery. This paper addresses losses in which the insured's AI system is in the causal chain, including externally triggered failures such as prompt injection, retrieval-augmented generation (RAG) poisoning, malicious tool output, credential misuse, and data poisoning. Specifically, this paper introduces CER, a use-case-level diagnostic for AI residual risk transfer. C (control boundary) asks whether the system had an enforceable operating envelope. E (evidence reconstruction) asks whether the system state and causal chain can be reconstructed from retained artifacts. R (insurance response) asks whether the reconstructed loss is insured: whether insurance coverage is available in the market and placed for the insured, together with the proof needed to support insurance claim recovery. The paper makes three contributions: it defines the AI-specific reconstruction problem, operationalizes that problem through CER, and specifies claim-grade evidence for AI reconstruction. Public examples include the reported PocketOS and Replit agentic database-deletion incidents and Moffatt v. Air Canada as an adjudicated output/reliance case. Keywords: AI systems; CER framework; residual risk transfer; agentic AI; generative AI; AI insurance; evidence reconstruction. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.03777 |
| By: | Fang, Meng |
| Abstract: | This technical note starts from a deliberately strict axiom: at the chosen short horizon, returns are not forecastable (a no-forecast stance). Under this axiom, the key decision is not “what to predict, ” but “how portfolio weights are updated after prices move.” We parameterize weight-updating rules by a single feedback exponent β, spanning constant-weight equal-weight discipline, buy-and-hold drift, and procyclical “reverse rebalancing” (chasing recent winners and selling recent losers). Using a discrete-time log-wealth identity, we decompose relative log growth into (i) an exposure/drift component, (ii) a concavity (Jensen/AM–GM) component generated by contrarian rebalancing, and (iii) implementation losses from turnover, convex market impact, taxes, and non-tradability constraints. Reverse rebalancing forfeits the concavity component and can be interpreted as paying a “volatility tax” for convex behavior when no forecasting edge exists. We connect the mechanism to high-quality evidence on the robustness of naive 1/n rules (DeMiguel, Garlappi, and Uppal, 2009) and to decompositions showing that a large share of equal-weight outperformance is attributable to rebalancing itself rather than purely to size tilt (Plyakha, Uppal, and Vilkov, 2012). Finally, we outline a reproducible U.S.–China roadmap (e.g., RSP–SPY; CSI 500 equal-weight vs cap-weight) and highlight market-specific failure modes, especially China A-shares’ price limits and suspensions. The note’s message is pedagogical but operational: for ordinary investors, disciplined rules can constitute a practical form of “alpha” by systematically avoiding self-inflicted convexity losses. Archived version (Zenodo DOI): 10.5281/zenodo.18638385 |
| Keywords: | reverse rebalancing; volatility tax; equal-weight rebalancing; 1/n rule; Jensen gap; concavity; volatility harvesting; stochastic portfolio theory; log wealth; estimation error; mean-variance optimization; transaction costs; market impact; non-ergodicity; A-shares; price limits |
| JEL: | G11 G12 G14 |
| Date: | 2026–02–14 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128048 |