FEDERAL RESERVE BANK of ATLANTA

Transcription

1 FEDERAL RESERVE BANK of ATLANTA A Dscrete Choce Model of Dvdend Renvestment Plans: Classfcaton and Predcton Thomas P. Boehm and Ramon P. DeGennaro Workng Paper October 2007 WORKING PAPER SERIES

2 FEDERAL RESERVE BANK of ATLANTA WORKING PAPER SERIES A Dscrete Choce Model of Dvdend Renvestment Plans: Classfcaton and Predcton Thomas P. Boehm and Ramon P. DeGennaro Workng Paper October 2007 Abstract: We study 852 companes wth dvdend renvestment plans n 1999 matched by total assets to 852 companes wthout such plans. We use dscrete choce methods to predct the classfcaton of these companes. We nterpret the msclassfed companes as beng lkely to swtch ther plan status. That s, f a frm s fnancal data suggest that a company should have had a dvdend renvestment plan n 1999 but dd not, then we expect that t would be more lkely to nsttute a plan than the other companes n the sample. Conversely, f t dd have a plan but the fnancal data suggest that t should not, then we expect that the company would be more lkely to drop the plan. We use data from 2004 to explore ths conjecture and fnd evdence supportng t. Our model s an economcally and statstcally relable predctor of changes n plan status. We also dentfy whch varables have the most nfluence on a company s decson whether or not to offer a plan. JEL classfcaton: G20, G29, G35 Key words: dvdend renvestment, dscrete choce, clusterng Ramon P. DeGennaro gratefully acknowledges the support of a summer research grant from the Unversty of Tennessee s Fnance Department. The authors thank partcpants at a sesson at the 2007 meetngs of the Assocaton of Prvate Enterprse Educaton for helpful comments. The vews expressed here are the authors and not necessarly those of the Federal Reserve Bank of Atlanta or the Federal Reserve System. Any remanng errors are the authors responsblty. Please address questons regardng content to Ramon P. DeGennaro, Vstng Scholar, Federal Reserve Bank of Atlanta, and SunTrust Professor of Fnance, Fnance Department, Unversty of Tennessee, 423 Stokely Management Center, Knoxvlle, TN 37996, , rdegenna@utk.edu, or Thomas P. Boehm, AmSouth Scholar and Professor of Fnance, Fnance Department, Unversty of Tennessee, 430 Stokely Management Center, Knoxvlle, TN , , tboehm@utk.edu. Federal Reserve Bank of Atlanta workng papers, ncludng revsed versons, are avalable on the Atlanta Fed s Web ste at Clck Publcatons and then Workng Papers. Use the WebScrber Servce (at to receve e-mal notfcatons about new papers.

3 A Dscrete Choce Model of Dvdend Renvestment Plans: Classfcaton and Predcton 1. Introducton Dvdend renvestment plans (DRIPs) allow nvestors to renvest ther dvdends n addtonal shares of the same stock that pad the dvdend. 1 Prevous research suggests that frms that offer such plans dffer from those that do not n systematc ways (DeGennaro, 2003). Is t possble to use fnancal data to determne whether a frm wll or wll not offer a plan? And s t possble to take the next step and predct whch frms wll or wll not offer plans n later years? The answer to both s yes. The fnancal characterstcs of companes that offer DRIPs do dffer from those that do not, and fnancal trats respond qucker than management can decde to add or drop a DRIP and mplement that decson. Ths has mmedate manageral mplcatons as well as potental wealth effects for nvestors. We also fnd evdence suggestng that frms nsttute DRIPs to nsulate management from control challenges. Our paper s therefore dfferent n substance and n sprt from prevous research on DRIPs. For example, some researchers have studed the value of specfc plan terms to nvestors. Two mportant examples are Dammon and Spatt (1992), who calculate the value of an opton mplct n the share-purchase terms of certan DRIPs, and Scholes and Wolfson (1989), who analyze and report the success of ther efforts to explot a prce dscount provson ncorporated n other plans. Another strand of research studes the stock prce of companes that announce plans (e.g. Dubofsky and Berman (1988), Perumpral, Keown and Pnkerton (1991) and Dhllon, Lasser and Ramrez (1992)). Stll others have explored the nteracton of DRIPs and the tax code. Chang and Nchols (1992), for example, nvestgate whether Internal Revenue Code 1 For a thorough descrpton of these plans and a dscusson of why frms offer them, see DeGennaro (2003) and the references theren.

4 Secton 305(e) affects qualfyng utltes. They study the cost of equty captal, leverage ratos, stock prce reactons and partcpaton rates for DRIPs around the tme of the changes n the tax code. Todd and Doman (1997) conduct a survey to relate plan characterstcs to shareholder partcpaton rates. To our knowledge, though, no research has attempted to predct whether or not a company wll have a DRIP. DRIPs and a more general class of nvestments, Drect Investment Plans, allow nvestors to avod nvestment channels typcally used n the past, such as securtes brokers. A DRIP s a mechansm that permts shareholders to renvest ther dvdends n addtonal shares automatcally. Brokers are not nvolved unless they are agents of the plan admnstrator. If the frm does not restrct ts plan to current shareholders, then the plan s also what s called a Drect Investment Plan, sometmes known as a Super DRIP. Transactons costs are typcally much lower than when usng tradtonal brokerage accounts. For example, share purchases are often executed free of charge and sales usually cost just a few cents per share. DRIPs are not a dfferent class of securty, such as swaps or optons. They are smply a new way of sellng tradtonal equty securtes. The prvleges and oblgatons of equty ownershp are unchanged. For example, DRIP nvestors receve the usual malngs and retan all votng rghts. Tax mplcatons of share ownershp are unaffected, and stock splts are handled exactly as f the nvestor were usng a tradtonal brokerage account. Our analyss uses a sample of 852 matched pars of frms. The frms are matched on total asset sze n the base year of observaton, Each par of frms contans one company that had a DRIP n 1999 and one that dd not. We ft a logstc probablty model to the 1999 data to determne emprcally whch specfc frm characterstcs have an mpact on whether or not frms have a DRIP. Based on ths model we predct whether or not a frm wll change ts DRIP status - 1 -

5 by In general, we fnd that the dvdend yeld and several varables capturng a frm s ablty to pay dvdends, the extent of manageral entrenchment, and ndustry dfferentals are sgnfcant predctors of whether or not a frm has a DRIP. We are able to predct successfully whch frms change ther DRIP status based on these parameter estmates and frm nformaton on the sgnfcant factors. From the perspectve of the fnancal economst, these data provde nformaton that may let us determne the lkelhood that companes wthout plans wll adopt one. Gven the results of Dubofsky and Berman (1988), Perumpral, Keown and Pnkerton (1991) and Dhllon, Lasser and Ramrez (1992), the ablty to predct such an adopton before the margnal nvestor can do so represents a potentally proftable tradng opportunty. The manageral mplcatons are even more mportant: companes that admnster drect nvestment plans that seek new customers can dentfy frms most lkely to be nterested n purchasng ther servces. The reverse s also possble: they can dentfy whch companes are lkely to abandon ther plans, helpng plan admnstrators concentrate resources on customers that are at greatest rsk to become former customers. Predctng changes n plan terms may also be possble. The paper proceeds as follows. Secton 2 descrbes our data and outlnes our method. Secton 3 reports summary statstcs and unvarate results. Secton 4 contans the logstc results and Secton 5 provdes a summary. 2. Data and Methodology Data are from The Gude to Dvdend Renvestment Plans (1999, 2004) and the Compustat and CRSP databases. We begn wth 852 frms wth avalable data n 1999 that offered DRIPs. Because DRIP frms tend to be much larger n terms of total assets than companes wthout such plans (DeGennaro, 2003), we match these 852 DRIP companes to a - 2 -

6 sample of frms wthout such plans, for a total of 1704 companes. We use total assets n 1999 as our matchng varable. Snce the dependent varable n our analyss s dscrete (1 = company had a DRIP at a partcular tme, 0 otherwse) Ordnary Least Squares regresson s napproprate for two reasons. Frst, because the regresson analyss s lnear, t s qute possble to estmate probabltes n the sample that are outsde the (0,1) nterval. In addton, the error terms n such a regresson would be heteroskedastc. To avod these problems we use a maxmum lkelhood logt model. Ths s the standard way to handle these problems. The logt model has the followng form: P DRIP = 1/ (1 + e -X β ), (1) where: P DRIP = the probablty the frm has a DRIP, X = a set of varables hypotheszed to nfluence P DRIP, β = a set of coeffcents whch represent the estmated mpact of X on P DRIP, X β = β 0 + X 1 β 1 + X 2 β X n β n. Classfcaton methods have a long hstory of productve uses n busness and fnance. These methods nclude both models that use contnuous varables and those that use dscrete choce varables. Dscrete choce models are probably more common. One form of dscrete choce model s cluster analyss. Shaffer (1991), for example, studes federal depost nsurance fundng and consders ts nfluence on taxpayers. Multnomal logt, another dscrete choce approach, has been used at least as far back as Holman and Marley (n Luce and Suppes, 1965). More recent varatons nclude the nested logt model of Ben-Akva (1973), whch s desgned to handle correlatons among alternatves. Calhoun and Deng (2000) use multnomal logt models to study loan termnatons. 3. Sample statstcs and Unvarate Results - 3 -

7 Table 1 presents the number of frms n each category parttoned by year and by plan status. In 1999 we have a sze-matched sample of 852 companes, one of each par offerng a DRIP and one not offerng a DRIP, for a total of 1704 companes. By 2004 the sample has shrunk consderably. Only 916 reman n the sample. 2 Of these, 465 have a plan and 451 do not. Of course, ths masks movement across groups. Table 2 shows that most companes mantaned ther plan status, ether retanng a plan fve years later (387 of 916) or not offerng a plan n ether year (394 of 916). A moderate number do change ther plan status, though. A total of 71 companes, or about 7.75%, added a plan wthn the fve years and 64 of the 916, or 6.99%, dropped ther plans. Factors Affectng the Lkelhood of Havng a DRIP Table 3 lsts the ndependent varables ncluded n the analyss. It also shows whether we expect companes that offer DRIPs to have hgher or lower values n unvarate tests. Excludng total assets (the matchng varable) and the categorcal ndustry varables that we nclude as controls, our varables fall nto four categores. These rely on DeGennaro (2003). We call varables n the frst category fundamental varables, not n the sense of fundamental economc value, but rather because ther economc meanngs are fundamentally changed by a renvestment plan. These are the payout rato and the dvdend yeld. Consder two companes whch are dentcal except that one has a plan and one does not. Suppose that the optmal dvdend yeld s 4% for both. The company wthout a DRIP smply pays a 4% yeld. The DRIP company, though, cannot expect plan partcpants to retan all of ther dvdends. The DRIP company must offer a hgher explct yeld to have an effectve yeld of 4%. The same reasonng apples to the 2 Frms leave the sample for the usual reasons: Merger or other corporate combnatons along wth delstng due to fnancal dstress head the lst

8 payout rato. All else equal, we expect DRIP companes to have hgher explct payout ratos and dvdend yelds. We call varables n the second category maturty varables because they dstngush mature companes from growth companes. In our sample these nclude net sales, net proft margn, the debt rato, the market-to-book rato, and the prce/earnngs rato (measured at fscal year-end). Mature companes tend to pay hgher dvdends (Smth and Watts 1992 and Barclay, Smth and Watts 1995). Although hgher dvdends are not drectly lnked to the probablty of havng a DRIP, to the extent that the dvdend yeld and payout rato fal to proxy completely for maturty, these varables could have predctve power. In unvarate tests the frst three of these varables should be margnally hgher for DRIP companes and the last two, margnally negatve (DeGennaro 2003). 3 We call varables n the thrd group ablty varables because they control for the ablty to pay dvdends all else equal, frms that earn more can pay more. These varables are aftertax return on assets (ROA), after-tax return on common equty (ROE) and earnngs per share (EPS). Although the ablty to pay dvdends s not drectly related to the probablty of havng a DRIP, the dvdend yeld tself s a strong predctor of havng a plan. What do these ablty varables offer that the yeld tself does not? To the extent that the ablty to pay dvdends affects the dvdend yeld n the future, these varables contan nformaton about future dvdends that s not captured by the current yeld. Therefore, the ablty varables could provde ndrect nformaton about the lkelhood of havng a DRIP n the future. The fourth group controls for manageral entrenchment. DeGennaro (2003) speculates that one reason for the exstence of DRIPs s that they can nsulate management from threats to 3 Research and development expense s another obvous choce. We do not use t because t has the most mssng values by far. In almost all cases, ncludng t reduces the sample sze by more than half

9 ther control. Four varables ft ths category: The number of common shareholders, the number of common shares outstandng, the number of common shares traded, and the number of employees. Frst, f management worres about retanng ts control, then t prefers a dffuse shareholder base wth many small shareholders. If they bother to vote ther shares at all, these nvestors are lkely to vote wth management. Second, as long as small shareholders do not get a large enough poston to become actvst shareholders, management wants them to have more votng shares. Thrd, these small shareholders tend to trade less frequently. Fnally, because employees jobs are often at rsk durng corporate reorganzatons, employees have ncentves to support current management n takeover battles. Ths means that management wants employees to be shareholders, too, so companes wth many employees are more lkely to nsttute a DRIP (sometmes plans have features that are qute favorable to employees and are sometmes even restrcted to employees). DRIP companes, therefore, should tend to have hgher values for all of these varables except for the number of shares traded, whch should be lower for DRIP frms. Table 4 presents sample statstcs for all 1704 companes. As s to be expected, all observatons have mssng observatons for certan varables. Stll, for the sample of 1704 companes n 1999, we have upwards of 1300 observatons for all varables. Most have more than 1550 observatons. Almost all observatons on all varables le wthn a reasonable range. Exceptons occur for certan ratos wth denomnators near zero. For example, Compustat defnes the Payout Rato as essentally the dollar amount of dvdends pad to common shareholders dvded by earnngs. Because earnngs can be near zero, ratos can be large n absolute value. Even these cases, though, are relatvely rare. Results by DRIP Status - 6 -

10 Because we have two tme perods and two classes of DRIP status (plan or no plan), we have four possble pars to sgnfy plan status through tme. A company can have a plan n both perods, drop a plan, add a plan, or have no plan n ether perod. Table 5 reports t-tests of dfferences across these four groups usng 1999 data. We report the results of sx combnatons of plan status. The frst column contans the results of t-tests comparng companes that dd not have a plan n ether year to companes that dd have a plan n both years. We would expect these companes to be dfferent, and they are. Sx t-tests are sgnfcant, and the sgns of all sx are consstent wth our predctons. In addton, all eght of the nsgnfcant tests have the correct sgn. These strong results make sense, because companes that had a plan n both years and those that had no plan n ether year are the most dstnct groups n Table 5. The second column contans the results of companes that dd not have a plan n ether year versus companes that added a plan by To the extent that the fnancal data from 1999 foreshadow ths change n plan status, we would expect these classes of companes to dffer, and they do. Settng asde the matchng varable, Total Assets, seven of the 14 t-tests are statstcally sgnfcant. Sx of these are correctly sgned: The dvdend yeld, net sales, the debt rato, the number of common shareholders, the number of common shares outstandng, and the number of employees are sgnfcantly negatve, meanng that companes whch added plans have hgher means, whch s consstent wth Table 3. The number of common shares traded, though, has a negatve sgn, meanng that companes that nsttute DRIPs have more shares traded. All other sgns are as predcted except for Market-To-Book, P/E at Fscal Year End, and EPS, and t s hard to worry about t-ratos that are under 0.3 n absolute value. The thrd column of Table 5 contans the results of t-tests comparng companes that dd not have a plan n ether year to companes that had a plan n 1999 and dropped t by To - 7 -

11 the extent that the fnancal data from 1999 completely foreshadow ths change n plan status, we would expect these classes of companes to be somewhat smlar even as early as They are. Only two t-tests are statstcally sgnfcant and both have the expected sgns: The dvdend yeld and the number of common shareholders. Consstent wth havng a plan n 1999, frms that would later drop ther plans have hgher dvdend yelds and more common shareholders than companes that had no plan n ether year. Of the 12 nsgnfcant estmates, 10 have the predcted sgn. The fourth column compares companes that dropped a plan to those that added a plan. It s hard to make predctons about these tests, because all of the companes swtched plan status. To the extent that these companes fnancal statements reflect ether ther 1999 status or foreshadow ther future status, then we mght expect sgnfcant dfferences. However, the expected sgns of the tests depend on whch of those two cases droppng or addng a plan -- domnates. The only two tests that are sgnfcant are the debt rato and the number of common shares traded. The ffth column of Table 5 contans the results of t-tests comparng companes that dd not have a plan n 1999 but added one by 2004 to companes that had a plan n both years. To the extent that fnancal data from 1999 foreshadow ths change to havng the same plan status, we would expect these classes of companes to be smlar. In contrast, f they have not fully adjusted, then they wll dffer. In fact, fve t-tests (not countng total assets) are sgnfcant. Two of these fve (ROA and common shares traded) have sgns that are consstent wth the 1999 plan status and the other three (net sales, debt rato and common shares outstandng) are not. To the extent that the nne nsgnfcant coeffcents convey useful nformaton, they support the economc reasonng underpnnng Table 3 for the 1999 data: seven have the expected sgns

12 The sxth column of Table 5 contans the results of t-tests comparng companes that had a plan n 1999 and dropped t by 2004 to companes that had a plan n both years. If fnancal statements antcpate ths change, then we would expect to fnd dfferences, and n three cases, we do. All three (net proft margn, debt rato, and ROA) are consstent wth the predctons n Table 3. These results are also consstent wth the nterpretaton that some of these companes dropped ther plans because they could no longer afford to pay dvdends. Clearly, the fnancal statements of companes that have DRIPs dffer from those that do not. For our purposes, the pont s that these dfferences hold promse for parttonng the data usng logt regressons and for predctng DRIP status n the future. 4. Logt Results Table 3 and Table 5 show that certan frm-specfc varables systematcally dffer between DRIP frms and no-drip frms. DeGennaro (2003) shows that DRIP frms cluster by ndustry. Based on ths nformaton we estmate ths logt regresson: DRIP = β + β TA + β PR + β DY + β NS + β PM + β ROE + β EPS β CS β CSout β DR + β MB + β PE + β ROA + β CST + β Emp + β Industry + ε j 8 9 Where the subscrpt sgnfes the company and: DRIP = 1 f company has a DRIP; else 0 ROA = after-tax return on assets TA = total assets ($MM) ROE = after-tax return on common equty PR = payout rato EPS = earnngs per share DY = dvdend yeld CS = number of common shareholders NS = net sales ($MM) CSout = number of common shares outstandng PM = net proft margn CST = number of common shares traded DR = debt rato Emp = number of employees MB = market-to-book rato 22 β Industry 16 j = seven one-dgt SIC codes PE = prce/earnngs rato ε = logstcally dstrbuted..d. error term. The SIC categores ncluded are: (1) Mnng ol producton and consumpton, (2) Materals and food processng, (3) Manufacturng, (4) Transportaton utltes and waste dsposal, - 9 -

13 (5) Wholesale and retal actvty, (6) Fnancal servces, (7) Other servces, and (8) Other mscellaneous. It s mportant to note that the dummy varable that we exclude from the regresson, Other servces, ncludes medcal legal, socal and accountng servces. 4 The frst column of Table 6 (Model 1) contans the results usng 1999 data. The top number n each cell s the logt coeffcent estmate and asymptotc p-values are n parenthess below. Model 1 s performance s reasonable but less than stellar. Total assets the matchng varable s nsgnfcant as expected and fve of the seven ndustry effects are sgnfcant. Of the other 14 varables, three are sgnfcant at the 5% level or better and two just mss, wth p- values under All of these fve sgnfcant coeffcents have the expected sgns. Of the remanng nne varables, sx have the expected sgns. How can we best nterpret these results? Analyzng each group of varables s a good startng pont. Three of the varable groups conform qute well to our predctons n Table 3. For example, the two fundamental varables work well. The dvdend yeld s correctly sgned and very sgnfcant, and the payout rato s correctly sgned though nsgnfcant. Two of the three ablty varables are also correctly sgned and sgnfcant, and the ncorrectly sgned coeffcent (ROE) s zero to three decmal places. The lkely explanaton s that ROE s hghly correlated wth another varable or combnaton of varables, makng t dffcult to separate ther contrbutons to explanng varaton. Two of the management entrenchment varables, common shares outstandng and common shares traded, are correctly sgned and sgnfcant and the other two are at least correctly sgned. None of the maturty varables s sgnfcant, though. In unvarate analyss, we have followed DeGennaro (2003) and argued that DRIP companes are more mature, leadng to hgher sales and margns, and lower research and development expenses. Mature companes also tend to carry more debt, and because they have relatvely few growth opportuntes they tend to have 4 There are so few n ths category that they would not have provded meanngful results

14 lower market-to-book and prce/earnngs ratos. These arguments lose force n a multvarate analyss that ncludes dvdend yelds and payout ratos. Dvdend yelds and payout ratos should be hgher for more mature companes. But these reported values do not ncluded renvestments after renvestments, the effectve values are lower. If we matched companes by reported dvdend yelds and payout ratos, then DRIP companes would very lkely be less mature, because ther effectve values are lower. Ths suggests that although dvdend yelds and payout ratos are postvely correlated wth DRIP status, there s no obvous reason to predct that other trats of mature companes are correlated wth status once we control for dvdend yelds and payout ratos. To the extent that dvdend yelds and payout ratos fal to proxy completely for maturty, then the other maturty varables may be margnally postve, but Model 1 shows that these varables can safely be elmnated. If we drop the maturty varables from the multvarate analyss, along wth ROE, then we obtan Model 2. The results are n the second column of Table 6. Model 2 gves away vrtually nothng compared to Model 1. The pseudo R 2 of s the same to the second decmal place, we actually gan two observatons, and the coeffcent estmates are remarkably smlar. These estmates are much more precse n Model 2, though. Frst, pursts wll note that the coeffcents on EPS and the number of common shares outstandng are now sgnfcant at well below the 5% level. In addton, the p-value for the estmate on the number of employees drops from above 15% to a statstcally sgnfcant 2.8%. A lkelhood-rato test formally rejects Model 1 n favor of Model 2. In short, the data support Model 2 much better than Model

15 Model 2 correctly classfes companes wth DRIPs slghtly better than companes wthout them. For DRIP frms the correct classfcaton rate s 79.33% and for no-drip frms the rate s 73.54%. Overall, the rate of correct classfcatons s 76.73%. The Impact of Independent Varable Groups on the Lkelhood of Havng a DRIP Table 7 presents two other ways to gan nsght about the mplcatons of Model 2. Panel A reports results for companes wth no DRIP n 1999 and Panel B reports results for companes wth a DRIP n The top part of each panel reports the mean predcted probablty of havng a DRIP for companes that had no plans n 2004 (second column) and for companes that dd have them n 2004 (thrd column). The second part of each panel reports the contrbuton of each of the four varable subgroups (fundamental, ablty, manageral entrenchment, and ndustry effects) to the dfference n the those mean predcted probabltes. Panel A shows that for frms wth no DRIP n 1999 the predcted probablty of havng a DRIP s substantally dfferent dependng on plan status n The 464 companes that dd not have a drp n 2004 had a predcted mean probablty of havng a DRIP of 34.73%; whereas the 50 companes that dd have a DRIP n 2004 had a mean lkelhood of havng a DRIP of 64.26%. Ths dfference s not only statstcally sgnfcant (t-statstc of 7.43) but also represents a percentage pont dfference. We nterpret ths s evdence that the model s pckng up factors n the fnancal statements that foreshadow the change n DRIP status. Ths dfference n these predcted lkelhoods of havng a DRIP derves from the dfferences n mean values for the ndependent varables rather than the estmated coeffcents n the model. For example, gven the postve coeffcent on the payout rato, a hgher payout rato mples that there s a hgher probablty of havng a DRIP. Thus t s easy to calculate the mpact of each varable group on ths dfference between the 34.73% average lkelhood and the

16 64.26% average lkelhood of havng a DRIP. In the second part of Panel A n Table 7 we see that dfferences n fundamental varables (16.40 percentage ponts) are the largest component of ths dfferental. If the mean values for the fundamental varables (payout rato and dvdend yeld) for no DRIP frms n 2004 are ncreased to the level of frms that have a DRIP n 2004, ther predcted lkelhood of havng a DRIP ncreases percentage ponts. Ths would ncrease the predcted probablty of havng a DRIP for ths group by almost half (a 47.23% ncrease over the 34.73% probablty for frms addng a DRIP by 2004). Of the remanng varable groups, manageral entrenchment varables clearly have the largest mpact, 6.44 percentage ponts (whch s 18.54% of the 34.73% probablty of frms addng a DRIP by 2004). The changes assocated wth ablty and ndustral effects varables are relatvely small (3.56 and 3.08 percentage ponts respectvely). The mportance of manageral entrenchment varables n the decson to add a DRIP merts menton because entrenched management has been lnked to lower frm value. Bebchuk and Cohen (2005), for example, show that staggered boards (probably the most mportant governance arrangement that nsulates managers from dsmssal) are assocated wth lower corporate value. Ryngaert (1988) fnds smlar (though weaker) results for poson plls. Future research would do well to explore whether companes that nsttute a DRIP to entrench management suffer stock prce declnes whle those that do so for other reasons do not. Ths mght explan the conflctng evdence researchers have found concernng the stock prce reacton around the announcement that a company wll nsttute a DRIP. For example, Dubofsky and Berman (1988) and Perumpral, Keown and Pnkerton (1991) fnd evdence of postve abnormal returns when companes announce that they wll nsttute a DRIP whle Dhllon, Lasser and Ramrez (1992) fnd evdence of losses. Peterson, Peterson and Moore (1987) fnd mxed

17 results. Perhaps these latter studes had hgher proportons of frms that nsttuted plans for reasons of corporate control. Estmatng abnormal returns after controllng for the reason for nsttutng the DRIP s lkely to be frutful. The results for frms that dd have a DRIP n 1999 are n Panel B of Table 7. Agan, the mean predcted probablty of havng a DRIP s sgnfcantly hgher for those frms that retan a DRIP n 2004 (thrd column) compared to those that dropped ther DRIP (second column). The dfference s 74.64% versus 70.54%, wth a t-statstc of Unlke the companes that have a DRIP n 1999, though, the varables controllng for the ablty to pay dvdends drve the dfference. The 2.01 percentage pont nfluence s almost double that of any other category of varables. Ths suggests that frms choose to add or drop DRIPS for substantally dfferent reasons. Companes that add DRIPs tend to have hgher payout ratos and dvdend yelds, and hgher levels of varables related to manageral entrenchment. In contrast, companes that drop DRIPS do not earn as much and may even need to tap captal markets to rase funds. If a frm does want to reduce dvdend payments, then a DRIP works aganst ths. To see ths, suppose that a frm has a dvdend yeld of 5% but because of ts DRIP, ts effectve yeld s 1%. In the face of the dvdend cut to say, 2% and poor fnancal performance, some nvestors wll probably stop renvestng. Other nvestors who were renvestng 4% to acheve an effectve yeld of 1% mght renvest only half of the reduced dvdend n order to retan ther 1% effectve yeld. From the frm s perspectve these nvestor responses reduce the effect of the dvdend cut. Because operatng a plan entals costs, companes may fnd t smpler just to elmnate the DRIP entrely. How Well Can We Predct Changes n Plan Status? Consder companes that the logstc model msclassfes; ether the model says that they should have a plan and they do not, or t says that they should not have a plan and they do. How

18 do we nterpret ths? One way s to conclude that the model smply fals n such cases. An alternatve nterpretaton s that fnancal statements contan nformaton about future plan status as well as current plan status. Under ths nterpretaton, msclassfed companes are more lkely to swtch ther plan status. That s, f the frm s fnancal data suggest that a company should have had a dvdend renvestment plan n 1999 but t dd not, then we expect that t would be more lkely to nsttute a plan than the other companes n the sample. Conversely, f t dd have a plan but the fnancal data suggest that t should not, then we expect that the company would be more lkely to drop the plan. Put dfferently, msclassfcatons n 1999 nclude both predctons of changes n plan status as well as classfcaton errors. Do the data support ths nterpretaton? The short answer s yes. We conduct a twopronged experment. In the frst part, we consder companes that have a DRIP n 1999 and explore whether or not they drop t by In the second part, we consder the companes that do not have a DRIP n 1999 and explore whether they add one by These two groups of frms should have dfferent fnancal characterstcs. To perform the frst part of ths experment we partton the 629 companes that had a DRIP n 1999 nto those that the logt model correctly classfes and those that t msclassfes. We then compute the proporton of each group that dropped ther plans by 2004 and test the dfference between them. If the 1999 fnancal statements contan nformaton about future plan status, then the companes that the model msclassfes as not havng a plan n 1999 should drop ther plans sgnfcantly more often. In the second part of ths experment we partton the 514 companes that had a DRIP n 1999 nto those that the logt model correctly classfes and those that t msclassfes. We compute the proporton of each group that added a plan by 2004 and test the dfference between them. If the

19 1999 fnancal statements contan nformaton about future plan status, then the companes that the model msclassfes as havng a plan n 1999 should add plans sgnfcantly more often. Table 8 reports the results of ths experment. The top part of Table 8 contans a transton matrx that parttons the 629 companes that had DRIPs n 1999 nto two groups: 130 companes that are predcted not to have a DRIP, and 499 companes that are predcted to have a DRIP. Consderng actual changes n DRIP status across these two groups between 1999 and 2004, a sgnfcantly hgher proporton of those companes predcted not to have a DRIP (tstatstc of 3.513), n fact do not have a DRIP n 2004 (33.85% versus 21.84%) as compared to those frms predcted to have a DRIP. Thus the predcted probabltes generated from ths model do allow us to dstngush between those frms that would be more lkely to actually termnate ther program over the next fve years and those that would not. In the bottom secton of Table 8 a smlar experment s conducted for those frms that dd not have a DRIP n The results of ths experment are even more mpressve than those for frms that ntally had a DRIP. Specfcally, of the 514 companes that dd not have a DRIP n 1999, 378 frms were predcted not to have a DRIP. Of ths group only 17 (4.5%) added a DRIP by Ths s a sgnfcantly lower proporton (t-statstc of -8.95) than that of the 136 frms that the model predcted would have a DRIP. For these companes 24.26% actually added a DRIP by Once agan, the probabltes calculated based on our logt model allow us to dstngush between frms that mght be expected to adopt a DRIP over a fve year observaton wndow and those that would not. 5. Summary Most research on dvdend renvestment plans has focused on stock-prce responses to the announcements of such plans or attempted to value certan plan features. To date, lttle research

20 has attempted to determne whch type of frm adopts a plan, and none has attempted to predct whether companes wll have a plan n the future. Ths paper flls that vod. We use dscrete choce methods to predct the classfcaton of 1704 companes: A sample of 852 companes wth dvdend renvestment plans n 1999 matched by total assets to a sample of 852 companes wthout such plans. We develop a logt model that successfully classfes almost 77% of companes yet uses only readly avalable contemporaneous fnancal data. Our analyss demonstrates that n addton to the dvdend yeld, varables measurng ablty to pay dvdends, manageral entrenchment, and ndustry dfferentals all have a sgnfcant nfluence on the lkelhood that a frm has a DRIP. In addton, by nterpretng the msclassfed companes as those beng lkely to swtch ther plan status, we can then test whether the model can predct changes n plan status. The underlyng premse s that a company s current fnancal data contan nformaton not only about whether a company currently has a dvdend renvestment plan, but also that they contan nformaton about future plan status. We use data from 1999 and 2004 to explore ths conjecture. We fnd that our model can predct changes n plan status even as much as fve years n the future. Our results are mportant for at least three reasons. Frst, the ablty to predct changes n plan status before the margnal nvestor can do so represents a potentally proftable tradng opportunty (Dubofsky and Berman, 1988 and Perumpral, Keown and Pnkerton, 1991). Second, companes that admnster drect nvestment plans that seek new customers can produce a lst of frms most lkely to be nterested n purchasng ther servces, thus savng tme and resources. The reverse s also possble: we can mprove our predctons of whch companes are lkely to abandon ther plans, and plan admnstrators can mprove ther predctons about whch customers are at greatest rsk to become former customers. Fnally, we fnd that varables

21 controllng for manageral entrenchment are hghly correlated wth the decson to nsttute a dvdend renvestment plan. Ths s the frst emprcal evdence that dvdend renvestment plans mght serve to nsulate management from outsde control

22 References Barclay, Mchael J, Clfford W. Smth, Jr. and Ross L. Watts. The Determnants of Corporate Leverage and Dvdend Polces, Journal of Appled Corporate Fnance 7, Wnter, 1995, Bebchuk, Lucan A. and Alma Cohen. The Costs of Entrenched Boards, Journal of Fnancal Economcs 78 (2005), Ben-Akva, M. E. (1973). Structure of Passenger Travel Demand Models. Ph.D. thess, Department of Cvl Engneerng, MIT, Cambrdge, MA. Calhoun, Charles A. and Yongheng Deng (2000). A Dynamc Analyss of Fxed and Adjustable- Rate Mortgage Termnatons, The Journal of Real Estate Fnance and Economcs 24, No. 1 & 2, Chang, Otto H. and Donald R. Nchols. Tax Incentves and Captal Structures: The Case of the Dvdend Renvestment Plan, Journal of Accountng Research 30, No. 1, (1992), Dammon, Robert M. and Chester S. Spatt. An Opton-Theoretc Approach to the Valuaton of Dvdend Renvestment and Voluntary Purchase Plans, The Journal of Fnance 47, No. 1. (1992), DeGennaro, Ramon P. (2003). Drect Investments: A Prmer, Economc Revew 88, Number 1, Dhllon, Upnder S., Denns J. Lasser and Gabrel G. Ramrez. Dvdend Renvestment Plans: An Emprcal Analyss, Revew of Quanttatve Fnance and Accountng, (1992), Dubofsky, D. and Berman, L. (1988), The Effect of Dscount Dvdend Renvestment Plan Announcements on Equty Value, Akron Busness and Economc Revew 19, Gude to Dvdend Renvestment Plans (1999). Temper of the Tmes Communcatons, Inc. Gude to Dvdend Renvestment Plans (2004). Temper of the Tmes Communcatons, Inc. Luce, R. D. and Suppes, P. (1965). Preference, Utlty and Subjectve Probablty. In R. D. Luce, R. R. Bush, and E. Galanter, edtors, Handbook of Mathematcal Psychology, New York, J. Wley and Sons. Perumpral, Shaln, Arthur J. Keown and John Pnkerton. Market Reacton to the Formulaton of Automatc Dvdend Renvestment Plans, Revew of Busness and Economc Research 26, 1991,

23 Peterson, Pamela P., Davd R. Peterson and Norman H. Moore. The Adopton of New-Issue Dvdend Renvestment Plans and Shareholder Wealth. The Fnancal Revew 22, (1987), Ryngaert, Mchael. The Effect of Poson Pll Securtes on Shareholder Wealth, Journal of Fnancal Economcs 20 (1988), Scholes, Myron S. and Mark A. Wolfson. Decentralzed Investment Bankng: the Case of Dscount Dvdend-Renvestment and Stock-Purchase Plans, Journal of Fnancal Economcs 23 (1989), Shaffer, Sherrll (1991). Aggregate Depost Insurance Fundng and Taxpayer Balouts, Journal of Bankng and Fnance 15, Smth, Clfford W., Jr. and Ross L. Watts. The Investment Opportunty Set and Corporate Fnancng, Dvdend, and Compensaton Polces, Journal of Fnancal Economcs 32, (1992), Todd, Janet M. and Dale L. Doman. Partcpatons Rates of Dvdend Renvestment Plans: Dfferences Between Utlty and Nonutlty Frms, Revew of Fnancal Economcs 6 (1997),

24 Table 1 Number of frms by year and plan status Number of Companes Year wth Dvdend Renvestment Plans wthout Dvdend Renvestment Plans Total Table 2 Plan status of the 916 survvng frms n 1999 and 2004 Plan Status n Years 1999 and 2004 Number of companes Percent of total Nether 1999 nor % Not 1999 but % 1999 but not % Both 1999 and % Total Survvng Companes %

25 Table 3 Expected relaton between fnancal statement data for companes wth DRIPs compared to companes wthout DRIPs (unvarate tests) Varable Total Assets Fundamental Varables Payout Rato Dvdend Yeld Maturty Varables Net Sales Net Proft Margn Debt Rato Market-To-Book Rato P/E at Fscal Year End Ablty Varables After Tax Return on Total Assets After Tax Return on Common Equty Earnngs Per Share Manageral Entrenchment Varables Number of Common Shareholders Number of Common Shares Outstandng Number of Common Shares Traded Number of Employees Companes wth DRIPs tend to have hgher or lower values? N/A (matchng varable) Hgher Hgher Hgher Hgher Hgher Lower Lower Hgher Hgher Hgher Hgher Hgher Lower Hgher

26 Table 4 Sample Statstcs, 1999 Data, 1704 Frms n Operaton n 1999 Varable N Mean Std Dev Mn. Max. Total Assets ($MM) ,648 47, ,167 Fundamental Varables Payout Rato (%) Dvdend Yeld (%) Maturty Varables Net Sales ($MM) , ,215 Net Proft Margn (%) Debt Rato Market To Book P/E at Fscal Year End Ablty Varables After Tax Return on Total Assets (%) After Tax Return on Common Equty (%) Earnngs Per Share ($) Manageral Entrenchment Varables Common Shareholders (M) Common Shares Outstandng (MM) Common Shares Traded (MM/yr.) Employees (M) Industry Effects Varables Mnng, Ol, and Constructon Materals Processng Manufacturng Transportaton, Utltes, and Waste Dsposal Wholesale and Retal Fnancal Servces Other Servces Medcal, Legal, Management Accountng Servces Other Mscellaneous

27 Table 5 T-tests: Survvng Frms, 1999 Data Varable NN v YY NN v NY NN v YN NY v YN NY v YY YN v YY Total Assets ($MM) * * 0.89 Fundamental Varables Payout Rato (%) -2.45* Dvdend Yeld (%) -10.1** -3.30** -4.68** Maturty Varables Net Sales ($MM) -2.09* -4.51** * 0.01 Net Proft Margn (%) -2.81** ** Debt Rato * ** 2.32* -2.27* Market To Book P/E at Fscal Year End Ablty Varables After Tax Return On Total Assets (%) -4.67** * -1.99* After Tax Return on Common Equty (%) Earnngs Per Share ($) Manageral Entrenchment Varables Common Shareholders (M) -2.70** -3.32** -3.17** Common Shares Outstandng (MM) ** * 0.80 Common Shares Traded (MM/yr.) * * 2.60** Employees (M) ** Degrees of freedom for the t-tests range between these numbers: 662, , 456 t-test code: NN = No DRIP n ether 1999 or 2004 NY = No DRIP n 1999 but DRIP n 2004 YN = DRIP n 1999 but no DRIP n 2004 YY = DRIP n both 1999 and 2004 The two pars of letters represent the groups n a t-test for equal means. For example, the statstcs n the column headed NN v NY report the results of t-tests for the sample of frms that had no DRIP n ether 1999 or 2004 versus the sample that dd not have a DRIP n 1999 but dd have a DRIP n A postve test statstc means that the frst-named class has the hgher mean. * ndcates sgnfcance at the 5% level. ** ndcates sgnfcance at the 1% level. 323, , , ,

28 Table 6 Logt Results, 1999 Data DRIP = β + β TA + β PR + β DY + β NS + β PM + β DR + β MB + β PE β10roe + β11eps + β12cs + β13csout + β14cst + β15emp + β j Industry + ε 16 Varable Model 1 Model 2 Intercept ** ** (<.0001) (<.0001) Total Assets (MM$) (0.321) Fundamental Varables Payout Rato (0.221) (0.179) Dvdend Yeld ** ** (<.0001) (<.0001) Maturty Varables Net Sales ($MM) (0.867) Net Proft Margn (%) (0.658) Debt Rato (0.124) Market To Book (0.412) P/E at Fscal Year End (0.954) Ablty Varables After Tax Return On Total Assets (%) * 0.031* (0.016) (0.024) After Tax Return on Common Equty (%) Earnngs Per Share ($) Manageral Entrenchment Varables Common Shareholders (M) Common Shares Outstandng (MM) Common Shares Traded (MM/yr.) Employees (M) Industry Effects Varables Mnng, Ol, and Constructon 5 6 (0.960) (0.058) (0.948) ( * (0.025) (0.156) * (0.023) * (0.019) (0.926) * (0.016) * (0.015) * (0.028) * (0.029) + β ROA

29 Materals Processng Manufacturng Transportaton, Utltes, and Waste Dsposal Wholesale and Retal Fnancal Servces Other Mscellaneous ** (<.0001) ** (<.0001) ** (0.005) * (0.016) (0.302) (0.271) ** (<.0001) ** (<.0001) ** (0.004) * (0.016) (0.126) (0.280) Pseudo R Number of observatons Asymptotc p-values n parentheses. The dependent varable s unty f a frm had a DRIP n 1999 and zero f t dd not. * ndcates sgnfcance at the 5% level. ** ndcates sgnfcance at the 1% level. Proporton of frms correctly classfed: 76.73%

30 Table 7 Mean Probabltes of Drp and Impact of Varable Group Changes 1999 Data Panel A: Companes wth No Drp n 1999 No Drp n 2004 Drp n 2004 Predcted Probablty of havng a DRIP 34.73% 64.26% Number of Observatons t-statstc for Dfference n Means 7.43** Change n No Drp versus Drp n 2004 Pr(Drp n '04 No Drp n '99) Percentage Pont % of Total Fundamental Varables % Ablty Varables % Manageral Entrenchment Varables % Industral Effects Varables % Panel B: Companes wth Drp n 1999 No Drp n 2004 Drp n 2004 Predcted Probablty of havng a DRIP 70.54% 74.64% Number of Observatons t-statstc for Dfference n Means 2.68** Change n No Drp versus Drp n 2004 Pr(Drp n '04 Drp n '99) Percentage Pont % of Total Fundamental Varables % Ablty Varables % Manageral Entrenchment Varables % Industral Effects Varables % the ndvdual percentage pont changes do not sum exactly to the total probablty dfferental because of the non-lnear (logt) form of the probablty calculatons. ** ndcates sgnfcance at the 1% level. Panel A reports results for companes wth no DRIP n 1999 and Panel B reports results for companes wth a DRIP n The top part of each panel reports the mean predcted probablty of havng a DRIP for companes that had no plans n 2004 (second column) and for companes that dd have them n 2004 (thrd column). The second part of each panel reports the contrbuton of each of the four varable subgroups (fundamental, ablty, manageral entrenchment, and ndustry effects) to the dfference n the those mean predcted probabltes

31 Table 8 Transton Matrx usng 1999 data to predct DRIP status n 2004 Companes That Had a DRIP n 1999 Predcted not to have DRIP n 1999 Predcted to have DRIP n 1999 Total Number of Observatons Number that had DRIP n Proporton that stll had DRIP n % 78.76% Number that no longer had DRIP n (.e. transton correctly predcted) 106 (.e. transton not predcted) 150 Proporton that no longer had DRIP n % 21.84% t statstc for Dfference n Proportons: 3.513** Companes That Dd Not Have a DRIP n 1999 Predcted not to have DRIP n 1999 Predcted to have DRIP n 1999 Total Number of Observatons Number that Dd Not Have DRIP n Proporton that dd not have DRIP n % 75.74% Number that had DRIP n 2004 (.e. transton not predcted) (.e. transton correctly predcted) Proporton that added DRIP by % % t statstc for Dfference n Proportons: ** ** ndcates sgnfcance at the 1% level. The top secton of the table reports the transton data for the 629 companes that had a DRIP n The logt model predcts that 130 of these companes should not have a DRIP. Alternatvely, the model predcts that 499 frms should have a DRIP. Consderng actual changes n DRIP status by 2004, a sgnfcantly hgher (t-statstc of 3.513) porton of frms predcted not to have a DRIP n fact do not have one n 2004 (33.85% versus 21.84%) as compared to frms predcted to have one. The bottom secton of the table conducts a comparable experment for companes that dd not have a DRIP n Of the 514 companes that dd not have a DRIP n 1999, 378 were predcted by the logt model not to have a DRIP, whle 136 were predcted to have a DRIP. Agan, consderng actual changes n DRIP status by 2004, a sgnfcantly lower proporton (t-statstc of ) of frms predcted not to have a DRIP added a DRIP n 2004 (4.50% versus 24.26%) as compared to frms predcted to have one