Książki o AI dla bankowości inwestycyjnej

A typical pitch book includes a situation overview, a company profile, trading comparables, precedent transactions, a valuation summary (often shown as a "football field" chart), a list of potential buyers or investors, and a process timeline. Most of the analyst hours go into data gathering, spreadsheet work and making slides look right. That makes it a natural target for automation. Some of that automation already existed before generative AI. Office add-ins such as Macabacus and UpSlide link Excel models to PowerPoint and enforce formatting. They are mainly productivity tools, not generative models. Generative AI adds drafting: profiles, industry overviews and first-pass commentary, plus assistants built into Office tools and internal assistants that some large banks have rolled out for employees. The accuracy review stays because comps are full of definitional choices. Enterprise value has to be built consistently: equity value plus debt, preferred stock and noncontrolling interests, minus cash. Diluted shares usually use the treasury stock method. Companies with different fiscal year-ends need calendarization so their multiples cover the same period. LTM and forward figures can't be mixed, and one-off items need consistent adjustments. A model that pulls a headline EBITDA from a press release may be using the company's own adjusted definition, which isn't comparable to peers'. Choosing the peer set, deciding which outliers to exclude and making those adjustments are judgment calls a senior banker has to defend in front of a client. A common misconception is that AI will eliminate junior banking roles. The more realistic change is less time formatting and more time checking and interpreting. Deal code names, client confidentiality and information barriers still govern what data can go into which tools.