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International Journal of Forecasting最新文献

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Subjective-probability forecasts of existential risk: Initial results from a hybrid persuasion-forecasting tournament 存在风险的主观概率预测：混合劝说-预测比赛的初步结果
IF 6.9 2区 经济学 Q1 ECONOMICS
International Journal of Forecasting
Pub Date : 2025-01-17 DOI: 10.1016/j.ijforecast.2024.11.008
Ezra Karger , Josh Rosenberg , Zachary Jacobs , Molly Hickman , Phillip E. Tetlock
A multi-stage persuasion-forecasting tournament asked specialists and generalists (“superforecasters”) to explain their probability judgments of short- and long-run existential threats to humanity. Specialists were more pessimistic, especially on long-run threats posed by artificial intelligence (AI). Despite incentives to share their best arguments during four months of discussion, neither side materially moved the other’s views. This would be puzzling if participants were Bayesian agents methodically sifting through elusive clues about distant futures but it is less puzzling if participants were boundedly rational agents searching for confirmatory evidence as the risks of embarrassing accuracy feedback receded. Consistent with the latter mechanism, strong AI-risk proponents made particularly extreme long- but not short-range forecasts and over-estimated the long-range AI-risk forecasts of others. We stress the potential of these methods to inform high-stakes debates, but we acknowledge limits on what even skilled forecasters can achieve in anticipating rare or unprecedented events.
一个多阶段的说服预测比赛要求专家和通才（“超级预测者”）解释他们对人类面临的短期和长期生存威胁的概率判断。专家们则更为悲观，尤其是对人工智能（AI）带来的长期威胁。尽管在四个月的讨论中，双方都有分享各自最佳观点的动机，但双方都没有实质性地改变对方的观点。如果参与者是系统地筛选关于遥远未来的难以捉摸的线索的贝叶斯代理，这将令人困惑；但如果参与者是有限理性的代理，随着令人尴尬的准确性反馈的风险消退，寻找确凿的证据，这就不那么令人困惑了。与后一种机制一致，强烈的人工智能风险支持者做出了特别极端的长期预测，而不是短期预测，并高估了其他人的长期人工智能风险预测。我们强调这些方法为高风险辩论提供信息的潜力，但我们承认，即使是熟练的预报员，在预测罕见或前所未有的事件方面所能取得的成就也是有限的。
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引用次数: 0
Stock return predictability in the frequency domain 股票收益在频域的可预测性
IF 6.9 2区 经济学 Q1 ECONOMICS
International Journal of Forecasting
Pub Date : 2025-01-16 DOI: 10.1016/j.ijforecast.2024.11.007
Zhifeng Dai , Fuwei Jiang , Jie Kang , Bowen Xue
This paper investigates the role of time–frequency information in dimension reduction prediction of stock returns. Using the long-term wavelet component of monthly S&P500 excess returns as supervision, we employ a machine learning method to extract the common predictive factor from prevalent macroeconomic variables and construct a new macroeconomic index aligned with stock return prediction. The macroeconomic index exhibits significant predictive power, both in and out of sample, at the market and portfolio levels. It outperforms all individual macroeconomic predictors and the factors based on higher frequency information of realized returns. Our findings demonstrate substantial economic value of the new index in asset allocation. Moreover, we also observe a complementary relation between macroeconomic index and investor sentiment. The predictive power is most pronounced during high-economic-uncertainty periods when investors are likely to underreact to fundamental signals and stems from cash flow predictability channel.
本文研究了时频信息在股票收益降维预测中的作用。以标普500指数月度超额收益的长期小波分量为监督，采用机器学习方法从主流宏观经济变量中提取共同预测因子，构建与股票收益预测相一致的新宏观经济指标。宏观经济指数在样本内外、市场和投资组合水平上均表现出显著的预测能力。它优于所有个体宏观经济预测指标和基于实现收益高频信息的因素。我们的研究结果表明，新指数在资产配置方面具有巨大的经济价值。此外，我们还观察到宏观经济指数与投资者情绪之间存在互补关系。这种预测能力在经济高度不确定性时期最为明显，此时投资者可能对基本面信号反应不足，并且源于现金流可预测渠道。
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引用次数: 0
Modeling and predicting failure in US credit unions 对美国信用合作社破产进行建模和预测
IF 6.9 2区 经济学 Q1 ECONOMICS
International Journal of Forecasting
Pub Date : 2025-01-16 DOI: 10.1016/j.ijforecast.2024.12.004
Qiao Peng , Donal McKillop , Barry Quinn , Kailong Liu
This study presents a random forest (RF)-based machine learning model to predict the liquidation of US credit unions one year in advance. The model demonstrates impressive accuracy on the test set (97.9% accuracy, with 2.0% false negatives and 8.8% false positives) when utilizing all 44 factors. Simplifying the model to only the top five factors based on feature importance analysis results in a slightly lower, but still significant, accuracy on the test set (92.2% accuracy, with 7.8% false negatives and 17.6% false positives). Comparisons with seven other classification methods verify the superiority of the RF model. This study also uses the Cox proportional-hazards model and Shapley value-based approaches to interpret key feature significance and interactions. The model provides regulators and credit unions with a valuable early warning system for potential failures, enabling corrective measures or strategic mergers to ultimately protect the National Credit Union Share Insurance Fund.
本研究提出了一个基于随机森林（RF）的机器学习模型，以提前一年预测美国信用合作社的清算。当利用所有44个因素时，该模型在测试集上显示出令人印象深刻的准确性（97.9%的准确率，2.0%的假阴性和8.8%的假阳性）。将模型简化为基于特征重要性分析的前五个因素，测试集的准确率略低，但仍然显著（准确率为92.2%，假阴性为7.8%，假阳性为17.6%）。与其他7种分类方法的比较验证了射频模型的优越性。本研究还使用Cox比例风险模型和Shapley基于值的方法来解释关键特征的重要性和相互作用。该模型为监管机构和信用合作社提供了一个有价值的潜在失败预警系统，使纠正措施或战略合并能够最终保护国家信用合作社股份保险基金。
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引用次数: 0
The decrease in confidence with forecast extremity 随着预测的极值，信心的下降
IF 6.9 2区 经济学 Q1 ECONOMICS
International Journal of Forecasting
Pub Date : 2025-01-16 DOI: 10.1016/j.ijforecast.2024.07.004
Doron Sonsino , Yefim Roth
A large panel of chief financial officers’ forecasts of the S&P 500 annual returns and four experiments suggest that forecast confidence decreases as the forecasts diverge from zero, in the positive or negative direction. This decreased confidence is reflected in longer forecast intervals, larger perceived volatility estimates, and weaker belief in the accuracy of the predictions. De Bondt’s (1993) forecast hedging intensifies with the extremity of the forecasts, but the decrease in confidence is sustained when the intervals are symmetrized. Imposing cumulative prospect theory preferences on the CFOs, permutation tests show that the decreased confidence delays the response to optimistic expectations and alleviates miscalibration, although the optimistic CFOs still discount the VIX by more than 50%. The paper thus reveals a self-corrective mechanism that partially, but far from fully, offsets the overconfidence hazards.
一大批首席财务官对标准普尔500指数（s&p 500）年回报率的预测和四项实验表明，当预测偏离零（无论是正方向还是负方向）时，预测信心就会下降。这种降低的信心反映在更长的预测间隔、更大的感知波动估计和对预测准确性的更弱的信念上。De Bondt（1993）的预测对冲随着预测的极值而加剧，但当区间对称时，置信度的下降是持续的。通过对首席财务官施加累积前景理论偏好，排列测试表明，信心的降低延迟了对乐观预期的反应，并缓解了校准错误，尽管乐观的首席财务官仍将VIX折算50%以上。因此，本文揭示了一种自我纠正机制，可以部分（但远非完全）抵消过度自信的危害。
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引用次数: 0
IF 7.1 2区 经济学 Q1 ECONOMICS
International Journal of Forecasting
Pub Date : 2025-01-01
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引用次数: 0
IF 7.1 2区 经济学 Q1 ECONOMICS
International Journal of Forecasting
Pub Date : 2025-01-01
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引用次数: 0
IF 7.1 2区 经济学 Q1 ECONOMICS
International Journal of Forecasting
Pub Date : 2025-01-01
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引用次数: 0
IF 7.1 2区 经济学 Q1 ECONOMICS
International Journal of Forecasting
Pub Date : 2025-01-01
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引用次数: 0
IF 7.1 2区 经济学 Q1 ECONOMICS
International Journal of Forecasting
Pub Date : 2025-01-01
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引用次数: 0
IF 7.1 2区 经济学 Q1 ECONOMICS
International Journal of Forecasting
Pub Date : 2025-01-01
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引用次数: 0
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