Forecasting Sales: A Model and Some Evidence from the Retail Industry. Russell Lundholm Sarah McVay Taylor Randall

Transcription

1 Forecasing Sales: A odel and Some Evidence from he eail Indusry ussell Lundholm Sarah cvay aylor andall

2 Why forecas financial saemens? Seems obvious, bu wo common criicisms: Who cares, can we can look a he analys forecass? Surely in he real world analyss use much more sophisicaed models han we could ever imagine.

3 Why forecas sales? he sales forecas is he mos imporan forecas in mos financial forecasing applicaions combine wih margin o ge income forecas combine wih urnover o ge asse forecas A % change in he sales growh esimae can change he equiy value esimae by abou 5%. ur goal is o develop a reasonably general sales-forecasing model show how o esimae i on a reail firm es is predicive abiliy in-sample and ou-of-sample compare i o analys forecass

4 Wha needs o be modeled? how do we mix ogeher beliefs abou he number of new sales-generaing unis, and he growh in sales from exising unis? Half of he puzzle - # of sores - is commonly esimaed/disclosed by he firm. wha does his imply abou he sales forecas? Comp growh raes are more complicaed han you migh have hough

5 he odel sales raes / sore = he average sales/sore rae for he new sores in year, (annualized in resuls) = he average sales/sore rae for he mid sores in year, and = he average sales/sore rae for old sores in year. Sales =

6 he number of sores and comps 4 new new 3 new 2 4 mid 3 mid 5 new 5 mid 0 new 0 mid 0 old 5 6 old old close Comparable sore sales growh is he growh in sales from sores ha where open a he beginning of he prior year and are currenly sill open.

7 he algebra of comps recall ha = - - D so same sores are in numeraor and denominaor noe ha comps are sensiive o he movemen of sores from mid o old (if he raes are differen) if each period hen D C ) ( = = C =

8 Using he comp growh o clean up he esimaion we know he # of sores of each ype Sales = bu he raes are unknown. However, we know somehing abou he evoluion of he raes over ime. he rick in he esimaion is o accoun for he changes in pas raes as par of he independen variable so ha we can esimae he mos recen raes as parameers.

9 he odel somehing we can esimae? Sales = ( ) ( ) ( ) observe all he # of sores and conrol for he changing raes over ime using he comps (plus some assumpions). Esimae where is mos recen year in daa. Esimae by firm over ime. Esimae in changes o conrol for non-saionariy.

10 odel afer he end of firs year, sore is fully maure all raes evolve a he rae (C ) adjusing he number of beginning sores down change in sores and raes on oldmid change in sores and raes on new know ha = ( C ) assume ha = and = ( C ) Sales ) ( ) ( = C C Sales = ) ( ) ( ) ( ) (

11 odel is nicer han i looks! everyhing in brackes is known so can esimae he raes. change in # of sores plus comp effec in curren year bring prior year daa o year basis by deflaing wih hisorical comps i i i i C C C C Sales = = = τ τ τ τ τ τ τ τ τ τ τ ) ( ) ( ) ( ) ( ) )( (

12 odel 2 = k = ( C )Q, where Q = k D mid raes and new raes differ by proporion k all raes evolve a he rae ( C ) Q = if k > (so Q>) hen prolonged new sore honeymoon period. if k < (so Q<) hen prolonged ime o mauriy.

13 models 3-6 model 3: % Sales -τ = (C -τ )(G -τ )- assumes = = model 4: Sales -τ = Sales -τ- SG j where SG j is hisorical sales growh for ha decile (issim/penman 200) model 5: Sales -τ = β( oal # sores) ε model 6: Sales -τ = γ exising sores γ 2 new sores ε

14 he Sample 87 reail firms (036 firm-years) wih a leas 6 years of daa on sales number of sores a year end sores opened during he year sores closed during he year comparable sore growh rae for he year expeced number of sore openings/closings for he following year

15 Summary egression esuls (esimaed firm-by-firm)

16 In-Sample comparison of models

17 examples old rae new rae k 2 Sarbucks k= 98.9 Whole Foods k= 97.5 Walmar k=

18 building an ou-of-sample forecas need he company forecas of nex year s # of new sores (already know old and mid) need an esimae of he old rae and new rae based on pas daa (use pas 5 years) need an esimae of nex year s comp afer much searching, bes model is simply C.02 = b*(c.02).

19 ou-of-sample resuls

20 model versus analys forecass ibes model median IBES analys absolue forecas error is 3.20% of sales median model absolue forecas error is 3.79% of sales

21 incremenal value relaive o analys forecass

22 eview of Accouning Sudies Financial Saemen Analysis and Valuaion: Forecasing Firm and Indusry Fundamenals cober 22-23, 200 endoza College of Business he Universiy of ore Dame Submission Deadline: ay 5, 200