From econo­mics, we all know that a higher inte­rest rate policy can be used as a tool to coun­ter infla­tion. The busi­ness press frequently reports that higher inte­rest rates are making corpo­rate tran­sac­tions more diffi­cult. Howe­ver, a closer look reve­als a diffe­ren­tia­ted picture. The yield on ten-year German govern­ment bonds was just over 6% for the previous 20 years before the finan­cial market crisis in 2008/2009. And please believe me — I’ve been doing this for more than 20 years, too: even then, there were very, very sensi­ble M&A tran­sac­tions. Against this back­drop, the current inte­rest rate level is still favorable over a longer obser­va­tion period. Certainly, the inte­rest rate level has an influence on the company valua­tion via the cost of capital.

Rising inte­rest rates lead, cete­ris pari­bus, to lower company values and prices. In my opinion, howe­ver, the decline in the number of tran­sac­tions is due less to rising inte­rest rates than to a signi­fi­cant increase in uncer­tainty on the part of buyers. This uncer­tainty is trig­ge­red by the sum of the events of the last three years. First, with the COVID pande­mic, we had to survive a situa­tion that no entre­pre­neur had ever expe­ri­en­ced before in his entre­pre­neu­rial life. When this crisis seemed to have been over­come, the upward trend was abruptly inter­rupted by the Russia-Ukraine war. In my opinion, there is no end in sight here and ther­e­fore no fore­cast can be made about energy and raw mate­rial price deve­lo­p­ments. A few years ago there was the buzzword VUCA
^[1]
; in this uncer­tainty, a common reflex is to “keep powder dry”, i.e. not to spend exis­ting finan­cial resour­ces on corpo­rate tran­sac­tions, but to keep them within the company to finance opera­tio­nal challenges.

Another factor that we have not had too many problems with in the past is the tech­ni­cal hand­ling of infla­tion in busi­ness valua­tion. The basis for an income-based busi­ness valua­tion is the plan­ning calcu­la­tion of a company. This plan­ning calcu­la­tion on a nomi­nal basis must also take into account infla­tion-rela­ted price effects in the mode­ling of the income state­ment and balance sheet. In addi­tion, the so-called termi­nal value, i.e. the infi­nite conti­nua­tion value in the finan­cial mathe­ma­ti­cal valua­tion calcu­la­tion, is an important influen­cing factor. This termi­nal value is deter­mi­ned with the aid of a perpe­tual growth rate. The factors influen­cing this growth rate are infla­tion, growth and reten­tion. In the past, these complex inter­re­la­ted factors were mapped at 1% growth rate in the vast majo­rity of cases. This para­me­ter, which is some­ti­mes assu­med to be rather typi­cal, must now be recon­side­red in every evalua­tion. Again, the busi­ness valua­tion process beco­mes rather more complex. All of the above factors add another element of uncer­tainty to the valua­tion of subhems. Over­all, this increa­ses the entre­pre­neu­rial risk and as stated above — many entre­pre­neurs hesi­tate when making acqui­si­tion decisions.

[1] Acro­nym refer­ring to “vola­ti­lity”, “uncer­tainty”, “comple­xity” and “ambi­guity”.

In addi­tion to the subjec­tively percei­ved incre­asingly expen­sive shop­ping at the weekend, infla­tion is also seen when looking at stock port­fo­lios. The DAX and other indi­ces reach record levels. Normally, this should be a sign of a good economy and bubbling corpo­rate profits. Howe­ver, this is curr­ently not the case, so that in my view these highs ulti­m­ately repre­sent a further sign of infla­tion. Stocks, as real assets adjust to price increa­ses and, in parti­cu­lar, listed compa­nies with pricing power are natu­rally also in a posi­tion to be able to compen­sate for infla­tion effects on both the income and expense side. Against this back­drop, the highs on the stock market are largely due to infla­tion as much as to other factors.

I’ve gotten into the habit of judging new tech­no­lo­gies by their serving function.

The topic of arti­fi­cial intel­li­gence, which is curr­ently being conju­ga­ted up and down in all media, has long since found its way into ever­y­day M&A. Howe­ver, in my opinion, the term arti­fi­cial intel­li­gence is often used too super­fi­ci­ally and in many cases simply means cleverly formu­la­ted algo­rithms that make certain digi­tiza­tion-capa­ble proces­ses run faster. — This still has nothing to do with AI. If you’ll pardon the expres­sion, it’s not as if we haven’t been doing complex data­base queries, scena­rio calcu­la­ti­ons, Monte Carlo simu­la­ti­ons, and the like. We have been doing that for a very, very long time. Of course, appli­ca­ti­ons are incre­asingly being used that can extract tables from exis­ting docu­ments in due dili­gence proces­ses, for exam­ple, or even create them, as in the case of lease over­views, but at the moment I can’t see any real influence on the outcome of corpo­rate tran­sac­tions. In tech­ni­cal tran­sac­tion support, digi­tiza­tion — with or without AI — can ensure effi­ci­ency and robust processes.

Howe­ver, arti­fi­cial intel­li­gence can only ever be as good as the defi­ni­tion of the input para­me­ters. Howe­ver, many influen­cing factors cannot be para­me­ter­i­zed or are still subject to intrans­pa­rency. For exam­ple, busi­ness leaders will not provide their corpo­rate stra­te­gies en détail as input para­me­ters to “feed” AI systems. In recent years, there have been many attempts to estab­lish match­ma­king plat­forms. These plat­forms were suppo­sed to bring buyers and sellers toge­ther; so far with — let me put it chari­ta­bly — mode­rate success. For deca­des, the M&A indus­try has had data­ba­ses that clas­sify compa­nies based on indus­try codes. Now it seems rela­tively simple to super­im­pose these indus­try codes and thus iden­tify compa­nies that should be stra­te­gi­cally rele­vant to each other. If, in addi­tion, one balan­ces finan­cial circum­s­tances accor­ding to the motto “who can afford whom,” it is obvious that such match­ma­king plat­forms should repre­sent added value. In prac­tice, howe­ver, there are a large number of other influen­cing factors that are not recor­ded in such data­ba­ses. This can be, for exam­ple, the speed of indus­try conso­li­da­tion, the stra­te­gic fit of product port­fo­lios, inno­va­tions or the like. No busi­ness leader is curr­ently willing to para­me­ter­ize their busi­ness stra­tegy in detail and thus create the basis to define the input para­me­ters for AI flaw­lessly. From there, conside­ra­ti­ons of stra­te­gic fit will conti­nue to be the preserve of humans. Trust and empa­thy “still matters”.

About Arnd Allert

After ten years in corpo­rate banking at Deut­sche Bank and in a manage­ment posi­tion in steel trading, from 2000 mana­ging direc­tor of an M&A consul­ting firm for corpo­rate tran­sac­tions. Foun­der and mana­ging part­ner of Allert & Co. since 2003. Seve­ral advi­sory board and board of direc­tors manda­tes. — Many years of expe­ri­ence as a lectu­rer at univer­si­ties on the topics of “Corpo­rate Finance”, “Mergers and Acqui­si­ti­ons” and “Nego­tia­tion Strategies”. 
CVA, Certi­fied Valua­tion Analyst. Graduate of Harvard Law School’s PON Program on Negotiation.

Author of the book “Successfully Nego­tia­ting M&A Tran­sac­tions in Small and Medium-Sized Busi­nesses” and “Distres­sed M&A”. Co-author with: “Modern Restruc­tu­ring Manage­ment” and “Selling Compa­nies in Crisis”.