BIG DATA-DRIVEN STOCHASTIC BUSINESS PLANNING AND CORPORATE VALUATION

Roberto Moro Visconti, Giuseppe Montesi, Giovanni Papiro

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review

Abstract

The research question of this paper is concerned with the investigation of the links between Internet of Things and related big data as input parameters for stochastic estimates in business planning and corporate evaluation analytics. Financial forecasts and company appraisals represent a core corporate ownership and control issue, impacting on stakeholder remuneration, information asymmetries, and other aspects. Optimal business planning and related corporate evaluations derive from an equilibrated mix of top-down and bottom-up approaches. While the former follows a traditional dirigistic methodology where companies set up their strategic goals, the latter are grass-rooted with big data-driven timely evidence. Real options can be embedded in big data-driven forecasting to make expected cash flows more flexible and resilient, improving Value for Money of the investment and reducing its risk profile. More accurate and timely big data-driven predictions reduce uncertainties and information asymmetries, making risk management easier and decreasing the cost of capital. Whereas stochastic modeling is traditionally used for budgeting and business planning, this probabilistic process is seldom nurtured by big data that can refresh forecasts in real time, improving their predictive ability. Combination of big data and stochastic estimates for corporate appraisal and governance issues represents a methodological innovation that goes beyond the traditional literature and practice.
Lingua originaleEnglish
pagine (da-a)189-204
Numero di pagine16
RivistaCORPORATE OWNERSHIP & CONTROL
Volume15
DOI
Stato di pubblicazionePubblicato - 2018

Keywords

  • Revenue Model
  • forecasting
  • free cash flow
  • real options
  • sales
  • stochastic simulation
  • valuation metrics

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