Week 8: Current Issues In Corporate Finance Flashcards
How does climate risk influence firms’ capital structure?
1) DEMAND-SIDE: FIRMS’ OPTIMAL LEVERAGE DECREASES –> firms may reduce their demand for debt financing or issue new equity to mitigate potential financial distress caused by climate-related events.
2) SUPPLY-SIDE: lenders e.g. banks & bond investors may become more cautious when lending to firms w high climate risk exposure as may perceive these firms as riskier investments –> may be less willing to provide them w same level of debt financing –> lenders may increase spreads e.g. offer loans with higher interest rates or impose stricter terms on firms w high climate risk profiles –> can lead to increased financing costs for firms facing significant climate risks i.e. higher cost of capital –> may limit firm’s long-term investment & consequent profitability.
–> BREAKDOWN OF MODIGLIANI-MILLER FRAMEWORK UNDER CLIMATE RISK –> ALTERS TRADE-OFF BETWEEN BENEFITS & COSTS OF DEBT DUE TO HIGHER RISK PROFILE & COST OF CAPITAL OF DEBT FINANCING.
What are the mechanisms behind climate risk decreasing debt in firms’ capital structure?
1) HIGHER EXPECTED DISTRESS COSTS –> climate-related events e.g. natural disasters, extreme weather events, or environmental hazards can lead to operational disruptions, asset damage (plants, property, equipment), & revenue losses –> increases likelihood of financial distress –> lenders perceive firms w higher climate risk exposure as facing greater default risk as less likely to service their debt obligations –> e.g. have lower value of assets as collateral due to asset damage –> higher financing costs i.e. spreads required to compensate.
2) Higher Operating Costs –> climate risk can increase operating costs for firms e.g. disruption in supply chains, damage to infrastructure, investments in climate resilience measures, relocation costs & increased insurance premiums –> can decrease profitability & cash flow of firm –> decreases its ability to service debt obligations –> lenders perceive firms w higher climate risk exposure as facing greater default risk & less likely to service their debt obligations –> higher financing costs i.e. spreads required to compensate.
What is the ‘crowding out’ effect of government debt?
1) INCREASE IN GOV. DEBT SUPPLY –> increased gov. borrowing leads to reduced availability of funds for private sector firms –> less capital available for private firms to borrow –> higher borrowing costs for firms i.e. interest rates due to increased competition w gov. for limited funds –> can drive up interest rates as lenders seek higher returns to lend money to private firms –> higher cost of capital for firms as more expensive to raise capital via debt issuance –> private firms reduce their leverage –> decreases private sector investment & business expansion.
2) REDUCED INVESTOR DEMAND FOR CORPORATE DEBT RELATIVE TO EQUITY –> i.e. if investors prefer to maintain relatively stable proportion of debt & equity securities in their portfolios –> crowding out exacerbated by gov. debt being better substitute for corporate debt than for equity (i.e. perceived as more safe, liquid & of higher credit quality) –> higher demand for gov bonds & other debt securities considered close substitutes over corporate debt –> investors may allocate more of their funds to gov. securities, reducing pool of funds available for private sector borrowing –> higher bond prices may result in lower bond yields (inverse relationship) –> increases expected return on gov. bonds.
–> SHOWN VIA -VE RELATIONSHIP BETWEEN GOV. DEBT & CORPORATE LEVERAGE.
How might the extent of the crowding-out effect depend on the economy’s institutional features?
1) DEPENDENCE ON BANK FINANCING –> crowding out effect stronger in countries w low bank loan dependence –> firms rely more on market-based sources of financing, e.g. corporate bonds or equity issuance –> when gov. debt supply increases, domestic investors may shift their investments towards bonds, perceiving them as close substitutes for corporate bonds –> also experience lower switching cost from debt to equity financing when gov. debt supply increases –> reduces availability of funding for corporate borrowers in the market, exacerbating crowding-out effect.
2) SIZE & LIQUIDITY OF EQUITY MKTS –> crowding out effect stronger in countries w larger & more liquid equity markets relative to their GDPs –> firms have easier access to switch from corporate debt to equity financing when gov. debt levels increase –> exacerbates crowding-out effect on corporate debt due to private firms’ greater reliance on equity financing v. corporate debt.
3) SIZE & PROFITABILITY OF FIRMS (WITHIN COUNTRIES) –> crowding out effect stronger for larger & more profitable firms as their corporate debt may be perceived as closer substitute for gov. debt –> i.e. investors may perceive this debt as having similar characteristics or risk profiles to government debt –> when gov. debt levels increase, there may be a tendency for investors to reallocate their investments towards government debt –> these firms may also have greater financial flexibility & resources w easier access to various financing options, including debt & equity mkts –> may incur lower costs when switching from debt to equity financing in response to increases in gov. debt supply.
What is the main channels through which AI can enhance firm growth?
1) PRODUCT INNOVATION
2) PROCESS INNOVATION
What is the impact of AI on product innovation in increasing firm growth?
- REDUCES COST OF PRODUCT INNOVATION IN PRODUCT PORTFOLIOS –> i.e. expansion of new product varieties increases product appeal & demand for existing
products –> top use of AI = enhancement of existing products & creation of new ones (according to executive surveys):
1) CAN FACILITATE KNOWLEDGE ACCUMULATION THROUGH EXPERIMENTATION & REDUCED COSTS OF PRODUCT INNOVATION –>
AI algorithms can quickly analyze
large datasets & learn about underlying relationships from data
–> potentially reduces uncertainty of firm experimentation in determining optimal product variety & appeal –> more efficient learning process –> higher experimentation & creation of new products (product innovation) –> EXAMPLE: AI algorithms can shorten drug development life cycle e.g. rapid turnout of Moderna’s first COVID-19 vaccine.
2) CAN INNOVATE ON QUALITY OF EXISTING PRODUCTS & SERVICES BY BUILDING AI MODELS DIRECTLY INTO PRODUCTS –> enhances appeal to customers –> EXAMPLES: AI-driven tradng platform DeepX at JPMorgan (allows for faster & cheaper
execution of trades) + “smart” machinery at Caterpillar (improves machine safety & flexibility).
3) CAN IMPROVE PRODUCT APPEAL BY HELPING FIRMS LEARN ABOUT CONSUMER PREFERENCES MORE EFFICIENTLY –> BETTER TAILOR PRODUCT & SERVICE OFFERINGS TO CONSUMERS’ TASTES & NEEDS –> i.e. when firms launch new products or expand product variety, they face
uncertainty regarding customers wants & changing customer preferences might change –> AI can analyse customer data to overcome frictions in firms’ demand accumulation processes –> EXAMPLES:, data on individual behaviors (web browsing, location history, other digital footprints) can enable better approximations of parameters entering individual demand functions than pure demographic info, leading to more heterogeneity in products tailored to customers w diff. tastes).
What is the impact of AI on process innovation & operating costs in increasing firm growth?
- REDUCES COSTS OF PROCESS INNOVATION I.E. LOWERS OPERATING COSTS BY IMPROVING FIRMS’ PRODUCTIVITY IN PRODUCING EXISTING PRODUCTS.
1) REPLACING HUMAN LABOUR FOR SOME TASKS VIA CUTTING PER-UNIT LABOUR COSTS –> reduces operating costs & increases productivity (LABOUR REPLACEMENT EFFECT NOT EMPIRICALLY OBSERVED IN FIRM GROWTH).
2) BETTER FORECASTING –> increases operational & production efficiency –> can help reduce forecast errors & optimise input decisions by firms under uncertainty where error adjustment may be costly & result in under/over-investment –> EXAMPLES: AI at JPMorgan Chase model default of non-performing loans; Caterpillar leverages AI for inventory management; UnitedHealth uses AI to support efficient medical billing.
What is the empirically observed overall effect of AI on firm growth?
1) +VE RELATIONSHIP BETWEEN FIRM’S AI INVESTMENTS & FIRM GROWTH –> industries investing more in AI experience overall increase in sales & employment –> stronger among ex-ante larger firms as AI can increase inequality by favouring large firms
w more data (crucial input to AI implementation) –> AI investments associated w increased industry concentration/mkt share–> AI investments can affect industry dynamics by reinforcing winner-take-most dynamics –> +ve feedback loop between AI investments & firm size i.e. investments concentrate among largest firms & as firms in-
vest in AI, they grow larger –> however +ve effects on AI-investing firms could be offset or even dominated by -ve spillovers to competitors within industry –> use of tech can be contractionary at aggregate level if input use declines due to uneven distribution of AI tech within industry.
2) FIRM GROWTH PRIMARILY DRIVEN BY PRODUCT INNOVATION, NOT LABOUR REPLACEMENT OR COST-CUTTING (I.E. PROCESS INNOVATION) –> allows firms to expand by creating new & more products more efficiently –> expands firm scale & overcomes capacity constraints by allowing firms to deploy more capital to produce additional products however comes w corresponding increases in costs –> in contrast to task-based model of automation suggesting specific tasks within jobs could be performed more efficiently & so replaced labour (in previous tech) –> would cut per-unit labor costs by aiding in complex decision-making processes & cognition problems leading to concerns that AI can disrupt high-skill/wage occupations.
