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In addition to achieving a high prediction accuracy that outperforms several benchmarks (by up to 13%), our proposed model is also interpretable, which allows us to identify meaningful events driving the price fluctuations. We found that the events frequently associated with major fluctuations in the price include “natural,” “hike,” “policy,” and “elections,” all of which are known drivers of price change. We used a corpus containing about 1.6 million news articles of a major Indian newspaper spanning 15 years and daily prices of four crops (onion, potato, rice, and wheat) in India to perform this study. Our proposed approach is flexible and can be used to predict other time series data, such as disease incidence levels or macroeconomic indicators, that are also influenced by real-world events. Managerial implications: Firms can leverage price forecasts from our system to design inventory and procurement policies in the face of uncertain commodity prices. Commodity merchants can also use the forecasts to design optimal storage policies for physical trading of commodities when prices are volatile. 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引用次数: 0
摘要
问题的定义:近来,商品价格大幅波动,给商品加工和商品贸易公司带来了外生风险因素。准确的商品价格预测可以帮助企业利用数据驱动的采购政策,将潜在的价格波动纳入财务和运营对冲决策。然而,由于波动剧烈,仅凭历史价格不足以获得合理的预测。方法/结果:基于商品价格受现实世界事件驱动的假设,我们提出了一种方法,它能自动从新闻报道中提取事件,并利用基于神经网络的预测模型将其与价格数据相结合,从而预测价格。我们提出的模型不仅预测准确率高,超过了多个基准(高达 13%),而且还具有可解释性,使我们能够识别驱动价格波动的有意义事件。我们发现,经常与价格大幅波动相关的事件包括 "自然"、"加息"、"政策 "和 "选举",所有这些都是已知的价格变化驱动因素。我们使用了一个语料库,该语料库包含印度一家主要报纸 15 年来的约 160 万篇新闻报道,以及印度四种农作物(洋葱、马铃薯、大米和小麦)的每日价格。我们提出的方法非常灵活,可用于预测其他时间序列数据,如同样受现实世界事件影响的疾病发病率水平或宏观经济指标。管理意义:面对不确定的商品价格,企业可以利用我们系统的价格预测来设计库存和采购政策。当价格波动时,商品商家也可以利用预测结果来设计商品实物交易的最佳存储政策。我们的研究成果还能对政策制定者产生重大影响,他们可以利用即将发生的价格变化和相关事件的信息来减轻价格冲击的负面影响:本文已被《制造业与市场》(Manufacturing & Service Operations Management)杂志的《运营管理前沿》(Frontiers in Operations Initiative)收录:在线附录见 https://doi.org/10.1287/msom.2022.0641 。
Frontiers: News Event-Driven Forecasting of Commodity Prices
Problem definition: Commodity prices have exhibited significant volatility in recent times, which poses an exogenous risk factor for commodity-processing and commodity-trading firms. Accurate commodity price forecasts can help firms leverage data-driven procurement policies that incorporate the underlying price volatility for financial and operational hedging decisions. However, historical prices alone are insufficient to obtain reasonable forecasts because of the extreme volatility. Methodology/results: Building on the hypothesis that commodity prices are driven by real-world events, we propose a method that automatically extracts events from news articles and combines them with price data using a neural network-based predictive model to forecast prices. In addition to achieving a high prediction accuracy that outperforms several benchmarks (by up to 13%), our proposed model is also interpretable, which allows us to identify meaningful events driving the price fluctuations. We found that the events frequently associated with major fluctuations in the price include “natural,” “hike,” “policy,” and “elections,” all of which are known drivers of price change. We used a corpus containing about 1.6 million news articles of a major Indian newspaper spanning 15 years and daily prices of four crops (onion, potato, rice, and wheat) in India to perform this study. Our proposed approach is flexible and can be used to predict other time series data, such as disease incidence levels or macroeconomic indicators, that are also influenced by real-world events. Managerial implications: Firms can leverage price forecasts from our system to design inventory and procurement policies in the face of uncertain commodity prices. Commodity merchants can also use the forecasts to design optimal storage policies for physical trading of commodities when prices are volatile. Our findings can also significantly impact policymakers, who can leverage the information of impending price changes and associated events to mitigate the negative effects of price shocks.History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0641 .