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on Financial Markets |
| By: | Bryan T. Kelly; Yinan Su; Chao Zhang |
| Abstract: | Can we explain asset price movements even with ex post observations of their drivers? Reinterpreting Roll’s (1988) AFA Presidential Address, “R2, ” as a modern empirical exercise, we show that conventional macroeconomic, cash-flow, expectations, and flow variables deliver out-of-sample R2 values that are often negative and otherwise below 10% for monthly U.S. equity returns and are unavailable at the daily frequency. We use generative AI to extract newspaper-stated reasons from Wall Street Journal market summaries and filter out price and return language. These reasons explain around 30–40% of daily and monthly returns out of sample. Their content maps to interpretable themes, including corporate news, macroeconomic conditions, monetary policy, sentiment, and geopolitics. |
| JEL: | C45 C49 G1 G14 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35885 |
| By: | Daiya Mita (Nomura Asset Management Co, ltd.,); Akihiko Takahashi (School of Interdisciplinary Mathematical Sciences, Meiji University /Center for Advanced Research in Finance (CARF), The University of Tokyo) |
| Abstract: | This study proposes a novel equity investment strategy that effectively integrates artificial intelligence techniques, multi factor models and financial technical indicators. In investment-related finance research, there exists a serious problem that many actively managed funds fail to outperform simple index buy-and-hold strategies. Our objective is to address this issue by designing a strategy that not only delivers high investment performance but also maintains interpretability, an often-overlooked aspect in complex models based on artificial intelligence. Specifically, as an equity long (buying) strategy, this paper extends a Five Factor model, a well-known finance model for its theoretical grounding in financial economics and its high accountability, to predict future returns by using a gradient boosting machine and a state space model. In addition, an index futures short (selling) strategy for downside hedging is developed with IF-THEN rules and three technical indicators. To the best of our knowledge, this is the first study to develop an equity investment framework based on a new, more predictable Five Factor model, enhanced by artificial intelligence techniques and technical indicators. Finally, empirical analysis is conducted using the data on the Japanese stock market from 1/04/2010 to 12/29/2023 and shows that the proposed strategy outperforms not only the baseline buy-and-hold strategy, but also typical mutual funds for the Japanese equities. Furthermore, statistical tests for Sharpe and Sortino ratios reveal that while the baseline strategy fails to reject the null hypothesis, particularly in the most recent period, our proposed strategy exhibits statistically significant performance across all subperiods, underscoring its robustness. |
| Date: | 2026–10–23 |
| URL: | https://d.repec.org/n?u=RePEc:cfi:fseres:cf632 |
| By: | Pedram Ebadollahyvahed; Nneka Umeorah |
| Abstract: | Cryptocurrencies trade continuously while major traditional markets close over the weekend. We examine whether weekend cryptocurrency volatility predicts Monday variance in equity, commodity and fixed-income markets. Using daily range-based variance measures for Bitcoin, Ether, XRP, the S&P 500, gold and a US Treasury ETF over 2017-2026, we augment heterogeneous autoregressive models with a common weekend cryptocurrency factor. Predictive content is weak before 2024 but significant thereafter. In the post-2024 sample, a one-standard-deviation increase in the factor is associated with 27-54% higher fitted Monday variance. Adding the factor reduces out-of-sample QLIKE losses by 11-22%, although statistical evidence varies across markets. Friday cryptocurrency volatility does not reproduce the result, and coin-specific variation adds no significant predictive information beyond the common factor. An endogenous procedure estimates a break in late 2023, during growing anticipation of US spot-Bitcoin ETF approval. However, ETF-flow interactions provide no evidence of amplification through greater measured Monday flow activity, leaving the mechanism unresolved. The findings suggest that calendar-specific volatility measures reveal predictive information that aggregate connectedness measures may obscure. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.35702 |
| By: | Christian Conrad; Julius Schoelkopf; Michael Weber; Frank Brueckbauer |
| Abstract: | Professional forecasters' heterogeneous narratives about how inflation affects stock markets in 2023 rationalize their high disagreement regarding quantitative expectations for 12-month-ahead inflation and stock returns in December 2022. Professional forecasters causally update their return expectations in heterogeneous directions in response to information about the inflation outlook, depending on the narrative they entertain. Moreover, the narratives also affect their asset allocation in a hypothetical portfolio-choice experiment. Providing common signals does not necessarily lead to convergence of beliefs if agents subscribe to different narratives. |
| Keywords: | narratives, expectation formation, inflation, beliefs, monetary policy, subjective models, RCT, inflation expectations, return expectations |
| JEL: | D83 D84 E31 E44 E52 E71 G11 G12 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_13076 |
| By: | Javier Ojea Ferreiro; Andrea Ugolini; Juan C. Reboredo |
| Abstract: | Artificial intelligence (AI) has a double-edged relationship with financial stability: it may improve efficiency and risk management, while also creating new vulnerabilities and amplification channels. This paper studies whether market-perceived exposure to AI is reflected in the tails of financial-sector returns, and whether this relationship depends on macroeconomic conditions. We develop a framework that decomposes financial-sector tail outcomes across contemporaneous AI and macroeconomic scenarios, and identifies whether the AI-related episodes are primarily associated with extreme gains or losses. Using daily equity returns for financial sectors in Canada, the United States, Europe, the United Kingdom, and China, we document substantial heterogeneity. Financial-sector-specific scenarios dominate tail outcomes across markets, while the contribution of AI-related scenarios to return asymmetry differs substantially across countries. AI-sector scenarios contribute to downside asymmetry in Europe, China, and especially the United Kingdom, but make only a limited contribution in Canada and have the smallest tail weights in the United States. China is the only market exhibiting positive average skewness, driven by the financial and systemic scenarios rather than by AI-related outcomes. Overall, AI-related market movements are neither uniformly stabilizing nor destabilizing; their financial-stability interpretation depends on the market considered and on whether AI-sector tail events occur in isolation or jointly with broader macroeconomic movements. |
| Keywords: | Financial system; Financial institutions and intermediation; Financial stability and systemic risk; Models and tools; Econometric, statistical and computational methods; Structural challenges; Digitalization and productivity |
| JEL: | C C5 C58 G G0 G01 G2 G21 G3 G32 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:bca:bocawp:26-40 |
| By: | Daniel Marcel te Kaat; Jiancong Liu; Alexander Raabe |
| Abstract: | Why do stock markets appear resilient to geopolitical risk? Using ï¬ rm-level measures of geopolitical risk exposure and data on investment funds' holdings, we show how funds help swiftly restore valuations compressed during bouts of heightened geopolitical risk. More intense geopolitical shocks strengthen this stabilizing role. Declines in valuations of exposed but otherwise sound ï¬ rms raise expected returns, attracting funds to acquire these assets at depressed prices, consistent with a search for yield mechanism. This behavior is more pronounced among funds with greater risk-taking capacity and weaker past performance. Funds' tilt toward exposed stocks increases portfolio concentration, partly offset by higher cash buffers. In contrast, ultimate investors withdraw from exposed funds. The results are not driven by any particular industry - including defense or dual-use production - and are robust to a rich set of controls, ï¬ xed effects, and placebo tests. |
| Keywords: | geopolitical risk, investment funds, portfolio reallocations, stock markets |
| JEL: | F3 F5 G1 G3 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2026-88 |
| By: | Nicholas C. Barberis |
| Abstract: | Behavioral finance seeks to improve our understanding of markets, investors, and firms by adopting a psychologically realistic description of how people think. While behavioral finance ideas have been part of human discourse for decades if not centuries, the modern research effort in the field began in the 1980s. Forty years later, behavioral finance is a vibrant area of financial economics. In this article, I explain why by reviewing some of its recent progress. I focus on three topics – beliefs about future earnings, beliefs about future returns, and prospect theory – and on their applications to understanding asset prices and investor behavior. I close by commenting on some new directions in the field – cognitive foundations, non-traditional decision processes, and artificial intelligence. |
| JEL: | G11 G12 G14 G40 G50 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35959 |