Arbeitsbuch zur Finanzwirtschaft für Anfänger

Fischer, Edwin O.; Keber, Christian; Maringer, Dietmar

doi:10.1524/9783486799583

“…The time horizons considered for financial forecasts and plans usually range from 1 month to several years (Roos et al 2019;Fischer 2009). The choice of time horizon depends on company-specific circumstances and objectives; for instance, stockmarket listed companies typically put additional weight on quarterly figures.…”

Section: The Role Of Fpandamentioning

confidence: 99%

Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls

Wasserbacher

¹

,

Spindler²

2021

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This article is an introduction to machine learning for financial forecasting, planning and analysis (FP&A). Machine learning appears well suited to support FP&A with the highly automated extraction of information from large amounts of data. However, because most traditional machine learning techniques focus on forecasting (prediction), we discuss the particular care that must be taken to avoid the pitfalls of using them for planning and resource allocation (causal inference). While the naive application of machine learning usually fails in this context, the recently developed double machine learning framework can address causal questions of interest. We review the current literature on machine learning in FP&A and illustrate in a simulation study how machine learning can be used for both forecasting and planning. We also investigate how forecasting and planning improve as the number of data points increases.

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“…The time horizons considered for financial forecasts and plans usually range from one month to several years [43,24]. The choice of time horizon depends on company-specific circumstances and objectives.…”

mentioning

confidence: 99%

Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls

Wasserbacher¹,

Spindler²

2021

Preprint

1

0

View full text Add to dashboard Cite

This article is an introduction to machine learning for financial forecasting, planning and analysis (FP&A). Machine learning appears well suited to support FP&A with the highly automated extraction of information from large amounts of data. However, because most traditional machine learning techniques focus on forecasting (prediction), we discuss the particular care that must be taken to avoid the pitfalls of using them for planning and resource allocation (causal inference). While the naive application of machine learning usually fails in this context, the recently developed double machine learning framework can address causal questions of interest. We review the current literature on machine learning in FP&A and illustrate in a simulation study how machine learning can be used for both forecasting and planning. We also investigate how forecasting and planning improve as the number of data points increases.

show abstract

Production Portfolio Theory II—First Steps Towards a General Portfolio Theory and Numerical Examplifications

Heiden,

Tonino-Heiden

2024

Lecture Notes in Networks and Systems

2

0

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Arbeitsbuch zur Finanzwirtschaft für Anfänger

Cited by 5 publications

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Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls

Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls

Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls

Production Portfolio Theory II—First Steps Towards a General Portfolio Theory and Numerical Examplifications

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