大规模财务预测的多方面方法

Antony Papadimitriou, Urjitkumar Patel, Lisa Kim, G. Bang, Azadeh Nematzadeh, Xiaomo Liu
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引用次数: 3

摘要

对公司财务业绩的准确预测对资本市场的管理和分析至关重要。因此,建立一个能够产生高度可靠和稳健的财务指标预测的框架,将对市场参与者(如投资者)产生积极影响,他们可以做出更好的交易决策,并更合适地管理他们的投资组合。我们开发了一种多方面的建模方法,利用单变量和多变量模型来确定最佳表现的模型设置。通过金融时间序列的大规模实验,我们证明了该框架比专业金融分析师做出的预测更准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A multi-faceted approach to large scale financial forecasting
Accurate forecasting of a company's financial performance is critical to capital market management and analysis. Thus, building a framework that is able to produce highly reliable and robust forecasts of financial metrics provides a positive impact on market participants such as investors who can make better trading decisions and manage their portfolios more suitably. We developed a multi-faceted modeling approach which leveraged univariate and multivariate models to identify the best performing model setting. Through large scale experiments of financial time series, we demonstrate this framework can produce more accurate forecasts than those made by professional financial analysts.
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