Sales forecasting with financial indicators and experts' input

Nikolay Osadchiy, Vishal Gaur, Sridhar Seshadri

Research output: Contribution to journalArticlepeer-review


We present a method for forecasting sales using financial market information and test this method on annual data for US public retailers. Our method is motivated by the permanent income hypothesis in economics, which states that the amount of consumer spending and the mix of spending between discretionary and necessity items depend on the returns achieved on equity portfolios held by consumers. Taking as input forecasts from other sources, such as equity analysts or time-series models, we construct a market-based forecast by augmenting the input forecast with one additional variable, lagged return on an aggregate financial market index. For this, we develop and estimate a martingale model of joint evolution of sales forecasts and the market index. We show that the market-based forecast achieves an average 15% reduction in mean absolute percentage error compared with forecasts given by equity analysts at the same time instant on out-of-sample data. We extensively analyze the performance improvement using alternative model specifications and statistics. We also show that equity analysts do not incorporate lagged financial market returns in their forecasts. Our model yields correlation coefficients between retail sales and market returns for all firms in the data set. Besides forecasting, these results can be applied in risk management and hedging.

Original languageEnglish (US)
Pages (from-to)1056-1076
Number of pages21
JournalProduction and Operations Management
Issue number5
StatePublished - Sep 1 2013
Externally publishedYes


  • finance interface
  • martingale modulated forecast evolution
  • operational hedging
  • operations
  • retail operations
  • sales forecasting

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Management of Technology and Innovation


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