


































http://journals.sfu.ca/abr  ADVANCES IN BUSINESS RESEARCH 
2014, Volume 5, pages 16-32 

 
 

16 

 

Capital Structure over the Life Cycle 

Chase Parker DeHan 
University of South Carolina, Upstate 

 
Whether firms in highly innovative industries — those with high risk, yet higher 
potential return — will be more likely to raise funds through stock markets than firms 
in mature industries is investigated within the context of the trade-off theory of capital 
structure using the product life-cycle as a theoretical framework.  Empirically, the 
relationship of innovative activity to equity issuance is tested by regressing patent 
activity (as a proxy for innovation) on the ratio of funds raised through the stock 
market to total funds raised.  The results are statistically and economically meaningful. 
 
Keywords: corporate finance, capital structure, life cycle, trade-off theory 

 

Introduction 

One aspect of capital structure literature that does not receive attention is that firms have 
different capital requirements over their life cycle.  Firms’ external capital decisions are hypothesized 
to change depending upon where they fall within the innovation life cycle. In the beginning of the life 
cycle, firms are more innovative and those in innovative industries will be more likely to raise funds 
through the stock market than mature firms that are further into the life cycle.   

Banks ration credit, limiting the amount of funds to newer industries with higher levels or risk, 
even though there is a high potential return.  Even in the presence of a high risk premium, banks have 
asymmetric returns in high risk/return industries as they are exposed to losing their entire investment 
in the event of default while returns are limited to a fixed interest rate.  

At the beginning of the innovation life cycle, the industries emerging are classified as being 
radically innovative because the products or processes they are promoting are radical departures from 
others currently on the market.  Innovations tend to be clustered in the beginning of the innovation 
life cycle (Keklik, 2003), while competition intensifies as the product begins production.  After the 
weaker firms begin to drop out, successful firms become more attractive for debt financing.  These 
supply constraints imposed by lenders do not reduce firms’ requirements for capital.  These radically 
innovative firms still have large capital requirements in spite of the inability to obtain the debt 
instruments mature industries typically can access. To avoid extinction, these firms will continue to 
seek capital, having a larger portion of equity to debt in their capital structure. 

The trade-off theory (Kraus & Litzenberger, 1973) posits that firms will balance the tax 
advantages of debt with the increased probabilities of bankruptcy as they become more leveraged.   
Theoretically, an optimal level of leverage maximizes the firm’s value, thus taking on more debt than 
the optimal will result in a lower valuation.  The life cycle theory argues that radically innovative 
industries are more likely to default, which will lead to a lower optimal level of leverage.  Since 
innovative firms have additional capital requirements above the optimal leverage ratio, they will be 
forced to raise the additional funds through equity/stock markets.   

Firms in innovative industries can be empirically shown to have a higher likelihood of raising 
funds through the stock market, controlling for portfolio returns and other market conditions.  While 
the data do not allow for a precise positioning of firms within the life cycle, the empirical results 

http://journals.sfu.ca/abr


DeHan 

17 
 

support of the theory that firms at the beginning of the innovation life cycle will be more likely to 
raise funds through equity than firms at the end of the life cycle.  This is performed using patents as 
a proxy for innovation, operating on the assumption that more innovation occurs in the early stages 
of the life cycle. Probit estimation methods with clustered standard errors are used to estimate this 
relationship between innovation and firm choice as to whether debt or equity financing is used.  The 
results are straightforward; significant evidence suggests that the more innovative a firm is, the more 
likely it is to use the stock market than the bond market to raise funds when seeking external capital.   

 

Financing 

In the earlier states of the product life cycle — when there are high levels of innovation — a 
company will not be able to raise capital through debt markets and are more likely to rely on equity 
markets for external capital. This is primarily a supply constraint because the level of risk to lenders is 
too high given the expected return.  Lenders, while they may be able to see the venture as being 
profitable, have a limited upside in the form of an interest rate, yet risk losing their entire investment 
if the firm fails.  In order to raise funds, these firms in new and innovative industries are likely have a 
higher reliance on equity in order to compensate investors for the additional risk. 

Innovation is defined as any improvement over old products, processes, or ideas.  The 
innovations earlier in the life cycle are larger departures from those currently seen, while those towards 
the end do not have much of an impact.  Established firms that are constantly innovating are treated 
in the same manner as brand new firms if the radically innovative activities consume a large portion 
of their activities.  The main distinguishing feature is that established firms may have more retained 
earnings to fall back on. Under the assumption that firms will use internal funds before seeking 
external capital, firms entering new industries will exhaust their retained earnings, and will be more 
likely to raise funds through equity than firms still engaged in an old industry.  The relevant factor of 
the above is how large a percentage of activity the radically new technology consumes of a company.  
If a massive company like General Electric begins operating a radically new innovation, it may only 
be a small portion of their portfolio and would not have much influence on their external capital 
needs.  On the other hand, a small company shifting their entire focus toward a radical innovation 
would be treated similarly to a brand new firm.  Regardless of whether a new firm or established firm 
is engaged in the radically innovative activity, their external capital decisions will be similar if the 
innovations pertain to a large percentage of their activity.   

Traditional theory suggests that increases in the interest rate are compensation for holding 
risk.  Charging higher interest rates on loans for riskiness is a common practice.  However, high risks 
like those found in new industries would require interest rates that would be higher than normal rates.  
Merton (1974) presented the first comprehensive theory on the risk structure of interest rates, finding 
that bonds are more difficult to price when there is significant risk of default.  Therefore, investors 
find these bonds less desirable in spite of the higher interest rates received.  Not only do firms in new 
industries have higher probabilities of default, they also have large amounts of intangible capital that 
is difficult to price.  When an asset is difficult to price, investors will discount the price.  Investors will 
not be satisfied with the current risk premium and either i) demand a higher interest rate or ii) not 
lend at all.   
 While difficulties in pricing risk do exist, once interest rates rise above a point, banks will no 
longer lend funds out of fear of adverse selection where the only firms accepting high interest rate 
loans will be bad risks.  Stiglitz and Weiss (1981) conceptualized this with the introduction of a theory 
of credit rationing.   Basic economic theory posits that market equilibrium is where supply meets 
demand; as prices rise, suppliers will produce more.  However, debt markets act differently with 
investors retaining their excess funds rather than lending them out at higher interest rates.  Lenders 



CAPITAL STRUCTURE OVER THE LIFE CYCLE 

18 

 

care about two things: risk and return; as risk increases, the banks are induced to loan at a higher 
interest rate in order to compensate for the additional risk.  The problem arises when interest rates 
rise as the rate increase itself may influence the riskiness of the project.  Increased rates make debt 
service more expensive, tightening margins and threatening the likelihood of repayment.  Lenders, 
therefore, are aware of the lemons problems where those who would borrow at the highest interest 
rates may be worse risks; the interest rates would be able to act as a screening device, keeping bad 
investments out.  Banks may interpret the radical industries’ willingness to accept high interest rates 
as a signal of poor investments, thereby rationing credit. 

Barnhart and Dwyer (2012) find that firms in new industries have a much higher volatility in 
their returns, but have a much higher expected return compared to the rest of the market.  Their 
findings indicate that a small number of companies generate outstanding stock market returns while 
a high number of firms failed.  Bankers are aware of the high potential return, but fear volatility.  The 
firm’s need for capital allows speculative investors to reap the benefits of higher risk through the stock 
market, allowing for the diversifying investment while delivering the necessary capital to innovative 
firms.  Into the third stage, the innovations are not substantial departures from previous innovations.  
This does not lend itself to high rates of return, yet presents lower risk to investors.  At this time, firms 
still have capital requirements, but now have the ability to raise capital through debt and retained 
earnings to expand production.  These options allow more flexibility in choosing the lowest cost of 
capital as they can attract bank financing.  The difference from stage two in relation to bank financing 
is that the firms are able to receive a lower interest rate because of a low, measurable risk.  Firms will 
choose the cheapest way to raise the necessary capital and with interest rates lower than the cost of 
equity, the firms will choose significantly more debt financing.   
 

Capital Structure 

 As the first widely accepted theory of capital structure, Modigliani and Miller (1958) showed 
that the value of the firm is not affected by how the firm is financed.  The theory is that firms will 
raise external capital through whichever avenue is the least expensive, bringing the most capital at the 
lowest cost.  The implication is that  the underlying capital structure does not matter.  Whether firms 
raise funds through equity or debt and how they pay dividends is irrelevant to firm value, and that 
firms will be indifferent to their capital structure.  Modigliani and Miller reached this irrelevance 
proposition under some crucial assumptions: perfect capital markets in the absence of taxes, 
bankruptcy costs, asymmetric information, adverse selection, and agency costs.   

Arguing that taxes are an important factor in how firms finance themselves, Kraus and 
Litzenberger (1973) introduced the trade-off theory of capital structure. The mix of financing depends 
on the tax savings and the states in which a firm would become insolvent. The significant tax 
advantages for firms are gained by issuing debt that far outweigh any of the costs incurred by investors’ 
personal taxes.  Paying interest on outstanding debt is tax deductible and lowers the cost to service 
the debt.  On the dark side of leverage, Kraus and Litzenberger introduce bankruptcy costs into 
consideration.  Modigliani and Miller (1958) assumed that firm value does not depend on how certain 
they are to repay their debt obligations; the value of a firm is not affected by its leverage since 
bankruptcy penalties do not exist in perfect capital markets.   However, under the trade-off theory, as 
the leverage ratio increases, the value of a firm begins to fall because of the increased probability of 
becoming insolvent.   
 An illustrative, stylized model of the trade-off theory is shown in Figure 1.  The total value of 
the firm on the vertical axis is plotted against the leverage ratio, debt to assets (D/A) on the horizontal  
 

Figure 2 



DeHan 

19 
 

 

 

 

 

 

 

 

 

 

Figure 1. Trade-off theory 

axis.  The intercept, �̂�, is the value of a firm with no leverage and funded purely by equity or retained 
earnings.  When a firm is at this point, it is not maximizing the value of the firm as borrowing would  

allow the firm to pursue additional profit opportunities.  𝐹𝑉𝑛𝑑 represents firm value, with the nd 
subscript indicating no default that shows what a firm’s value would be in the absence of any costs 
associated with increased leverage.  With no chance of default, a firm could theoretically borrow 
unlimited funds, invest them, and watch the value of the firm grow.  The flip side of the leverage is 
that while leverage can multiply profit rates, it can also multiply losses.  The more leverage a firm 
maintains, the higher the probability a shock or poor investment will leave the firm insolvent.   

 𝐹𝑉′ is the firm value that incorporates bankruptcy costs and additional borrowing costs 
imposed by lenders to cover a higher risk of default.  Eventually, the costs of increased leverage 

outweigh the benefits, causing the firm’s value to drop.  𝐹𝑉′ would be close in value with 𝐹𝑉𝑛𝑑 up 

until a point, 𝐷/𝐴′, because at low levels of leverage, the probability of default is negligible.  Firms, as 
profit maximizing entities, will attempt to maximize their value and select the optimal leverage ratio 

at 𝐷/𝐴∗ where the marginal benefits of debt equal the marginal costs of bankruptcy.  At leverage 

levels between 𝐷/𝐴′ and 𝐷/𝐴∗, the costs of leverage are non-negligible, but increasing at an increasing 

rate.  Until 𝐷/𝐴∗ is reached, the benefits still exceed the costs; at leverage levels greater than 𝐷/𝐴∗, 
bankruptcy costs exceed the tax benefits.  If a firm has additional profitable opportunities and is in 
need of more capital than it would receive at the optimal leverage ratio, the firm would then turn to 
equity markets.  The trade-off theory maintains that firms will reach their optimal leverage ratio in 
order to maximize their value.  The only circumstances where they would utilize equity financing is if 
capital in excess of the optimal level was needed. 
 Adjusting the level of bankruptcy costs and tax benefits has important implications for the 
trade-off theory of capital structure. When the probability of default is altered, the trade-off theory 
predicts a change in the optimal level of leverage.  Figure 2 illustrates how changing the probability of 
default for an innovative industry will alter the optimal leverage ratio. Two types of firms are assumed: 
one in a new industry and the other in an old industry.  The distinction between the two types of firms 
is that the firm in the new industry has a higher probability of default, yet higher potential return due 
to a product in the beginning of the life cycle.  This is contrasted by the firm in the old industry that  



CAPITAL STRUCTURE OVER THE LIFE CYCLE 

20 

 

 

 

 

 

 

 

 

 

 

Figure 2. Trade-off with new industries 

has a standardized product and concrete forecasts of future profits.  Both firms enjoy the same tax 

benefits of debt and will both have a no default value curve at 𝐹𝑉𝑛𝑑 and an unlevered value at the 

intercept, �̂�.  Incorporating different probabilities of default gives two separate FV functions, 𝐹𝑉𝑜 

and 𝐹𝑉𝑛, where the subscripts respectively indicate old and new industries.  𝐹𝑉𝑛, with a higher 

probability of default, pushes the optimal leverage ratio, 𝐷/𝐴𝑜
∗ , to the left as the bankruptcy costs 

become greater than the tax benefits at a lower leverage ratio.  Firms in radically new industries still 
have significant profit opportunities and will turn to equity markets for the large amounts of capital 
they require.  Firms in old industries, on the other hand, do not have as many profit opportunities or 
capital requirements above their optimal leverage ratio and are less likely to seek equity financing.  
Firms in stagnant industries with few profitable growth opportunities are not expected to even reach 
the optimal leverage ratio as the tax benefits of borrowing are insignificant compared to profitable 
investments. 

Note that increasing the costs of bankruptcy also lead to a lower market value of the firm; 𝑉𝑛 

is the market value for new industries while 𝑉𝑜 shows the higher market value for old, established 
industries.  The market value of new firms has to be lower in order to compensate investors for the 
increased risk and should be associated with higher expected returns.  The firms will seek to bolster 
available debt funds with equity.  Hsu (2009) proposes that technological innovations increase returns 
on stocks, finding that firms with more technological innovations (as proxied by patenting activity) 
are typically assigned higher risk premiums, in keeping with market valuations for firms in new 

industries as shown by 𝑉𝑛.  New and radically innovative industries are associated with higher volatility 
and higher probabilities of default; therefore, they should also have higher expected returns.  Empirical 
results show that firms involved in innovative industries have a higher likelihood of failure (Eisdorfer 
& Hsu, 2011) and leverage and volatility have an inverse relationship (Bradley, Jarrell, & Kim, 1984).  
These results are consistent with the reduced leverage ratio and valuation due to increased bankruptcy 
costs. 

To restate, firms in the earlier stages of the innovation life cycle are hypothesized to be more 
likely to raise funds through the stock market than firms in the later stages.  In the earlier stages of the 
innovation life cycle, when firms are engaged in radically new technologies, they will have high capital 



DeHan 

21 
 

requirements and be a questionable risk for lenders.  Because of the limited upside in the form of an 
interest rate and a reasonable likelihood of default, debt will be less of an option; these highly 
innovative firms are then left with equity as a more viable option.  Due to data limitations on 
pinpointing where firms fall within the innovation life cycle, it is also difficult to track firms’ specific 
financing choices over the life cycle.  Because of this, empirically testing the direct relationship 
between firms’ position in the life cycle and their stock/bond choice is not possible.  However, testing 
whether firms engaged in highly innovative industries are more likely to raise funds through the stock 
market versus the bond market is possible.  Since more innovative activity will occur at the beginning 
of the life cycle, this will be an acceptable way to show support for the idea that firms in the earlier 
stages of the life cycle will be more likely to raise capital through the stock market. 
 

Empirical Methodology 

 The empirical section presents an unbalanced panel of stock/bond issuance, patent activity, 
and various control variables for the time period 1970 to 1992. 1970 was selected as the beginning of 
the time period because of the reliability of data on stock and bond issuance before that time is 
questionable.  Having the observed time period stop at the end of 1992 was to exclude the wave of 
stock offerings in the mid-1990s that could skew the results.  As most observers are aware, the mid-
1990s saw an incredible number of technology companies issue IPOs.  Since most of these companies 
were in brand new industries, this time period was marked predominately by firms in new industries 
issuing equity.   

The setup of the empirical models is performed following the basic specifications of Choe, 
Masulis, & Nanda (1993).  In this case, the dependent variable is firm level stock market decisions, 
with the important explanatory variable being industry patenting while controlling for other industry, 
and market conditions.  Considering the two types of financing in each time period, firms had an option 
of financing their operations by using debt or equity.  The dichotomous relationship of choosing to 
raise funds through equity or debt financing makes using a simple linear regression a potentially 
hazardous method of estimation.  For this reason, a probit model is used to estimate the probability 
of raising funds through the stock market relative to bond financing based around the simplified 
regression equation: 

 

𝑃(𝑆𝑡𝑜𝑐𝑘 𝑀𝑎𝑟𝑘𝑒𝑡)𝑓,𝑖,𝑡 =  𝛽0 + 𝛽1𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝑖,𝑡 + 𝛽2𝑀𝑎𝑟𝑘𝑒𝑡𝑡 + 𝜀𝑡,𝑖.𝑡                              (1) 

 

Where 𝑃(𝑆𝑡𝑜𝑐𝑘 𝑀𝑎𝑟𝑘𝑒𝑡)𝑓,𝑖,𝑡 is the probability of firms raising capital through the stock 

market, and is defined at firm level, f, in i industry in the time period t.  Since the instances where firms 
are seeking external capital are of interest, and only two options exist, bond and stock, the probabilities 

of raising funds through the bond market, 𝑃(𝐵𝑜𝑛𝑑 𝑀𝑎𝑟𝑘𝑒𝑡)𝑓,𝑖,𝑡, must be equal to 

(1𝑚𝑃(𝑆𝑡𝑜𝑐𝑘 𝑀𝑎𝑟𝑘𝑒𝑡)𝑓,𝑖,𝑡).  𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝑖,𝑡 represents the industry level variables for all firms, f, within 

the industry, i.  The industry level contains the important explanatory variable of industry 
innovation/patenting activity.  An additional industry-level control of returns will also be included.  

Various time variant market conditions are controlled with 𝑀𝑎𝑟𝑘𝑒𝑡𝑡; within this are a number of 
market factors traditionally in the literature and discussed in detail in the section below (Complete data 
descriptions can be found in Table 1).   

 
 
 

  



CAPITAL STRUCTURE OVER THE LIFE CYCLE 

22 

 

Table 1 
Data Description 
Var 
Name 

Description Mean Std 
Dev 

S/B Ratio Ratio of stock issuance to bond issuance for all public issuance 1970-1992.  
Computed as: total proceeds of stock issuance to proceeds from bond issuance 
plus stock issuance for each 6 month period.  Binomial variable equal to one when 
the firm raises funds through stock market issuance and zero when through the 
bond market.  Bond classifications included:  Asset Backed, Convertible, High-
Yield Corporate, Investment Grade Corporate, and Mortgage Backed.   
Source: Thomson Reuters SDC Database 

n/a n/a 

PGrant Granted patents per 2 digitover The six month time periods.   
Source: NBER Patent database, Hall, Jaffe, and Trajtenberg (2001) 

1.838 2.336 

Time Time factor; used to control for the time trend of stock market issuance.  Calculated 
as time period minus 1969, resulting in time periods ranging between 1 – 22.5 

n/a n/a 

Ind. 
Return 

Industry returns computed as a market return average 2-digit SIC industries over 
the six month time periods.  Includes returns on all publicly held stocks in the 
United States listed on the NYSE, AMEX, and NASDAQ stock exchanges. Values 
listed as percentage. 
Source: CRSP US Stock Database.  

0.037 0.257 

Bus. 
Cycle 

Dummy Business Cycle Variable equal to one when economy is in expansion and 
zero when a contraction. 
Source: NBER Business Cycle Dating Committee 

n/a n/a 

10 
YrTBill 

Average 10 year T-Bill rate over each 6 month period and acts as the long run 
interest rate 
Source: WRDS 

8.94 1.98 

RF Risk Free Rate (1 Month T-Bill), averaged over the six month time periods. 
Source: Fama French, WRDS 

0.0059 0.0018 

Mkt. 
Return 

Average market (S&P 500) return over the 6 month period. 
Source: S&P 500 

0.0095 0.0113 

MKTRF Excess return on the market as measured by a value weighted return of all securities 
minus the rate of return on one month T-Bills.  Averaged over the 6 month period. 
Source: Fama French, WRDS 

0.006 0.021 

 
Because of the possible presence of intra-industry and time correlations in the error terms, clustering 
standard errors is important. The data include industry and time variant effects.  Heterogeneity bias is 
treated by removing the inter-industry effects and time (year) effects.  As such, the standard errors are 
clustered at the industry/time level.  Within the dataset, there were 25,064 instances of firms raising 
capital across 71 industries and 46 time periods; clustering at the industry/time level results in a 
maximum possible 3,266 observations.  However, some periods have no fundraising activity for a 
specific industry, leaving 2,106 total independent observations.   
 
Data 
 The dependent variable is this model is the ratio of stock market financing to the total amount 
of financing received in each time period.  The dataset incorporates all stock and bond issuance 
occurrences in the United States between 1970 and 1992 as reported in the Thomson Reuters SDC 
Database.  The dependent variable, SB.Ratio is given as: 
 

𝑆𝐵. 𝑅𝑎𝑡𝑖𝑜𝑡 =  
𝑆𝑡𝑜𝑐𝑘 𝑀𝑎𝑟𝑘𝑒𝑡 𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑛𝑔𝑡

(𝑆𝑡𝑜𝑐𝑘 𝑀𝑎𝑟𝑘𝑒𝑡 𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑛𝑔𝑡+𝐵𝑜𝑛𝑑 𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑛𝑔𝑡)
                  (2) 

 
Fund raising activity has been organized into 6 month time periods in order to gather a 

complete picture of their total external fund raising activity.  Firms may have large capital requirements 



DeHan 

23 
 

and raise funds from numerous sources in a relatively short period.  Each time period is organized as 
January through June and July through December, for a total of two time periods in each year between 
1970 and the end of 1992 for a total of 46 time periods.   For example, when t=1970, it indicates the 
first 6 months of 1970; t=1970.5 indicates July through December of 1970; etc.  These time 
designations are used for every variable in this model; firm fundraising, industry patenting, and market 
considerations are all factored over the 6-month time periods. 

The value of the dependent variable for a firm that raises capital only through the bond market 
would equal zero, while a firm that raised all their funds through the stock market would equal one.  
As can be seen from firm’s actions in capital markets, it was unusual for a firm to raise capital through 
both stock and bond issuance in the same time period.  Of the 25,153 fund raising instances, only 89 
firms raised capital through both the bond and stock market in one of the 6-month time periods.  
These 89 instances present an interesting anomaly as the only plausible explanation as to why a firm 
would seek both bond and stock market issuance in the same time period would be that the 
costs/benefits were exactly identical.  This rare occurrence, 0.3%, demonstrates that firms are unlikely 
to seek funds through both avenues simultaneously.  These are dropped from the analysis to allow for 
the use of binomial regressions, leaving 25,064 instances of firm level fund raising.  This leads to a 
simpler dependent variable, where: 

 

𝑆𝐵. 𝑅𝑎𝑡𝑖𝑜𝑓,𝑖,𝑡 = {
1, 𝑓𝑢𝑛𝑑 𝑟𝑎𝑖𝑠𝑖𝑛𝑔 = 𝑠𝑡𝑜𝑐𝑘 𝑚𝑎𝑟𝑘𝑒𝑡
0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

                            (3) 

 
 Figure 3 displays the distribution of how many times a firm enters the market for external 
capital.  The horizontal axis displays the number of time a firm appears in the sample while the vertical 
axis is the number of firms that are included in each group.  Of the 12,131 firms that raised capital 
through either the bond or stock market in the time period the vast majority, 8,571, only went to the 
market a single time.  Firms that raised capital less than five times between 1970 and 1992 represent 
93.5 % of the observed firms.  The large number of firms with limited observations precludes the 
ability to use a dynamic panel or fixed effects model.  For example, the use of a fixed effects model 
results in the dropping of 96.7% of observations. 
 
Industry Level Variables 

These data include all patents issued by the United States Patent and Trademark Office 
(USPTO) and compiled by Hall, Jaffe, and Trajtenberg (2001).  The relationship between where a firm 
is in the life cycle is as follows: stock market issuance correlates with patenting, innovative industries 
patent more, and more innovation occurs at the beginning of the innovation life cycle.  The empirical 
estimations of this paper are focused specifically on the relationship between patents and stock market 
issuance.  The patent data used in this analysis includes only utility patents granted in the United States 
as these are issued for the invention of “any new and useful process, machine, manufacture, or 
composition of matter, or a new and useful improvement thereof…” (Patent Laws and Regulations, 
2000, p. E-25) and are generally referred to as patents for invention.  This data set excludes plant and 
design patents as these patents cannot be considered a radical departure from previous innovations.  

Industry level patenting is important; path-breaking innovations generated by innovation 
come in clusters, providing more opportunities for innovation by others in the industry.  While one 
firm in an industry might be the leader in patenting activity, a large number of other firms will attempt 
to bring a similar product to market.  Industries with higher levels of patenting will contain firms with 
higher levels of innovation.  Information as to which firms are going to survive and succeed is not 
known with any level of certainty, but accurate forecasts of the success of the wider industry is known 
by most investors. Pastor and Veronesi (2005) found that during technological revolutions investors  



CAPITAL STRUCTURE OVER THE LIFE CYCLE 

24 

 

  

 

Figure 3. Fund-raising 

will diversify their investments among many in the industry.  This is done because some will be 
winners, others losers, and cannot be known a priori who will be the winners. Patent data from the 
USPTO are classified by internal codes that are not relatable to other variables.  Hall et al. (2001) were 
able to relate the internal USPTO codes to industries and other outside factors through a 3-digit US 
Patent class.  This US Patent class is assigned to the appropriate 2-digit SIC industry codes; the broader 
2-digit codes were chosen because of the necessity to incorporate the spillover effects on closely 
related industries.  Due to the way assignments are referenced by the USPTO, some patents were 
referenced in a number of industries.  When this occurred, patents with applicability in multiple 
industries were included with the total count of patents for each industry they referenced; this results 
in total patent counts being overestimated.  During the time period, the total amount of actual patents 
was just under 1.9 million while my assignments resulted in just over six million assigned patents.  This 
is the optimal practice as there are innovations that have a wider application, and attempting to select 
a single industry for these would result in subjective assessments of the data.  Patents are reported 
according to the year they were received; the time periods, however, are every 6 months.  To reconcile 
this, the way the patents are assigned was to place the half of the yearly number of patents in the first 
half of the year (e.g., 1970, 1971, etc.) and then assign the second half of the year as the average of the 
two surrounding time periods (e.g., 1970.5 = (1970 + 1971)/2).  Assigning patents in the manner 
allows for continuous patenting data.   
 Following the market timing literature (Baker & Wurgler, 2002), when stock values are higher, 
firms are more likely to raise funds through equity.  While market returns are also included, industry 
returns are also necessary to control for since individual industries do not necessarily correlate with 
the wider market.  One of the main considerations for the inclusion of industry controls is that if an 
asset price bubble is emerging in a specific industry, the valuations an innovative firm would receive 
could be greatly overvalued.  These high valuations could be an easy decision for a firm to raise capital 
through equity.  Time variant stock returns for industries are included to pick up the industry level 
variation that could be overlooked by returns from the entire market.  As industry returns rise, more 
stock issuances are expected because of the higher valuations associated with the industry. 
 Industry returns data are from the Center for Research in Security Prices (CRSP) and 
incorporates all publicly listed stocks in the United States on NYSE, AMEX, and NASDAQ 
exchanges.  This index was then computed as a market cap-weighted price index according to the 2-

8571

1579
1190

472 247 54 18
0

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2000

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7000

8000

9000

1 2 5 10 20 30 More

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o
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s

Number of Instances a Firm Raises External Capital

Frequency



DeHan 

25 
 

digit SIC codes.  This computation is accomplished by computing the return accrued from the overall 
industry.  The returns over the period were computed as a simple percentage change from the 
beginning of the period to the end of the time period based on the cumulative industry prices of 
publicly traded stock.  Some of the smaller industries with less external capital raised, have no publicly 
traded stocks.  This was the case for 59 of the fund raising instances; these without any corresponding 
public stocks were discarded from the analysis.   
 
Market Control Variables 

In order to control for hypotheses proposed by market timing theorists, a number of variables 
are used to proxy for market conditions.  These are time variant and include the business cycle, interest 
rates, market return, and other constructed variables commonly used in the literature.  The business 
cycle plays a key role in the fundraising decisions surrounding firms due to its effects on both investor 
and company expectations.  The expected impact of the business cycle on SB.Ratio is ambiguous.  It 
could be negative since when the economy is in an expansion, equity tends to receive a higher market 
value, leading to more stock issuance relative to bonds.  It could also be positive since in times of 
economic expansion lenders typically assign lower probabilities of default and bankruptcy.  Supporting 
this idea, Choe, et al. (1993) found evidence that common stock offerings are positively correlated 
with the business cycle.  With a positive correlation, supply constraints placed on innovative firms will 
be lessened and firms will raise more funds through the stock market. 

The business cycle is controlled for using the official estimates of US Business Cycle 
Expansions and Contractions as released by the National Bureau of Economic Research (NBER).  
The NBER defines a contraction as significant declines in a number of factors: real GDP, real income, 
employment, industrial production, and wholesale-retail sales. The subjectivity of these measurements 
is used alongside the traditional definition of a recession as two consecutive quarters of decline in real 
GDP in order to more accurately date the peaks and troughs in the changes of economic activity. 1   
The time between the trough and the peak was considered to be a time of economic expansion, while 
contraction was the time following a peak until the trough was reached again.  A simplistic dummy 
variable is used with expansions equal to one and contractions equal to zero.  Since the exact peak and 
trough are not likely to be assigned on exactly January 1 or July 1, they are going to fall somewhere 
within the 6 month time periods.  For this reason, the 6 month time periods that included a peak were 
assigned the expansion value of one, while those including the trough received the contraction value 
of zero.   

To account for changes in interest rates, the rates on 1-month and 10-year treasury bills are 
used.  The reasoning for including interest rates is that there is a positive relationship between interest 
rates and costs to service debt; when these costs rise, firms should be more likely to look to equity 
financing since the costs of stock market financing have become less expensive relative to debt.  The 
coefficients attached to the different interest rates are expected to be positive.  The short-term and 
long-term are both used in order to be inclusive of the decisions firms may make.  While long-and 
short-term interest rates typically move together, the long-term interest rates are expected to have 
more of an impact on stock market issuance since most future projects are based off long term debt 
contracts.   

The other factor of stock/bond issuance is the current market return; if the market seems to 
offer a higher return on stocks, investors will pay more for a stock offering; the higher valuation means 
the firm has a higher likelihood of being overvalued and, therefore, is going to be more likely to raise 
funds through the stock market.  This variable, Mkt Return, is measured as the weighted average equity 

                                                           
1 For a complete description of how the NBER dates the business cycle, please see the most recent NBER announcement, 
dated 09/20/2010. 



CAPITAL STRUCTURE OVER THE LIFE CYCLE 

26 

 

return of the Standard & Poor’s 500 Index over the 6-month time periods.  The S&P 500 is used 
because it is one of the most followed indexes of equity returns, and its diversity makes it an indicator 
of the health of the United States economy.  The expected coefficient of market return should be 
positive; when the stock market is booming and there are high returns, firms’ value is increasing and 
will be more likely to raise funds through equity issuance.   

An additional variable used for determining relative returns to both investors and firms is 
MKTRF, which is the difference between the market return and the risk free rate.  The measurement 
of market return under this variable differs from the variable Mkt Return in that it is the value-weight 
return of all firms in the CRSP database incorporated in the United States and listed on the NYSE, 
AMEX, or NASDAQ, rather than the narrower S&P 500.  A positive relationship is expected between 
MKTRF and SB.Ratio.  The intuition is that when MKTRF is higher, equity is receiving a greater return 
relative to debt, and there is a reasonable likelihood that equity is overvalued by the market.  Perhaps 
one of the most important single variables, this difference is a direct test of the difference between 
returns to equity and debt.   
 Table 2 shows the total number of stock and bond issuances by year along with a simplified 
ratio of instances of stock financing to total financing activities; this is computed as IPO/(Bond + 
IPO).  The bond market has significantly more activity than the stock market, with minimal activity 
from the mid to late 1970s.  The total dollar value of the bond market issuances is substantially larger 
than the fundraising used through stock issuance; the ratio of funds raised through the stock market 
is in the column labeled SB.Ratio.  Figure 4 shows the total number of fundraising instances graphically 
across time and Figure 5 displays the total dollar value of the fundraising activities.   
 

Results 

In every estimation, evidence is found in support of my hypothesis that innovative firms are 
more likely to pursue financing from the stock market relative to the bond market.  The first of these 
cohorts directly tests the market timing variables to control for market circumstances and is shown in 
Table 3.  Industry returns are added and presented in Table 4.  Each table provides coefficients, 
standard errors, statistical significance, and the marginal effects at the median for each variable, along 
with variance inflation factors (VIF) testing for multicollinearity. 
 Testing the hypothesis that more innovative firms are more likely to raise funds through stock 
market issuance uses the number of patents granted (PGrant) as a proxy for innovation.  As such, as 
more patents are granted, firms are expected to be more likely to raise funds through equity issuance.  
PGrant enters into every estimation highly significant, effectively showing that firms in industries with 
large amounts of innovative activity will be more likely to raise funds through the stock market than 
those in industries without technological innovations.  PGrant was significant at least at the 0.1% level 
in every estimation.  The marginal effects did not vary much, and a one standard deviation increase 
from the median2 in granted patents ranges between a 1.27 and 1.62 % increase in probability of raising 
funds through the stock market with an average of 1.42 %. Considering that the probability of raising 
funds through the stock market remained around 8.4 %, a one standard deviation change from patent 
grant’s median of 1.42 % is substantial.  Regardless of which controls were added to the model, there 
were only small changes in coefficients, p-values, or marginal effects observed for PGrant, resulting in 
strong support for my hypothesis 
 

  

                                                           
2 The median was used rather than the mean due to a skewed distribution of patents.  Computing from the mean would 

create an upward bias and overstate the effect of patents. 



DeHan 

27 
 

Table 2 
Data Summary 

 IPO Bond IPO/Total  IPO ($ Mil)   Bond ($ Mil)  SB.Ratio 

1970 16 187 0.079                 48              9,397  0.005 

1970.5 8 240 0.032                 25            13,778  0.002 

1971 22 251 0.081              109            13,686  0.008 

1971.5 21 184 0.102              104              9,505  0.011 

1972 34 225 0.131              118            10,336  0.011 

1972.5 27 157 0.147              158              8,829  0.017 

1973 14 140 0.091              727              7,118  0.085 

1973.5 2 125 0.016                   6              8,491  0.001 

1974 1 169 0.006                   2            13,170  0.000 

1974.5 0 168 0.000                  -              14,414  0.000 

1975 0 252 0.000                  -              22,073  0.000 

1975.5 1 183 0.005                 17            11,961  0.001 

1976 6 181 0.032                 72            16,374  0.004 

1976.5 4 166 0.024                 66            13,050  0.005 

1977 5 147 0.033                 80            12,747  0.006 

1977.5 2 163 0.012                   5            12,111  0.000 

1978 1 154 0.006                 26            10,947  0.002 

1978.5 3 122 0.024                 12              9,302  0.001 

1979 6 129 0.044                 38            13,077  0.003 

1979.5 7 138 0.048                 43            12,213  0.004 

1980 8 206 0.037                 63            21,710  0.003 

1980.5 31 163 0.160              398            15,067  0.025 

1981 40 423 0.086              360            25,063  0.014 

1981.5 33 441 0.070              360            22,637  0.015 

1982 8 402 0.020                 52            20,109  0.003 

1982.5 15 582 0.025              228            42,632  0.005 

1983 57 552 0.094           1,553            38,240  0.038 

1983.5 121 507 0.193           1,894            33,857  0.050 

1984 40 422 0.087              673            34,487  0.019 

1984.5 35 629 0.053              730            60,809  0.012 

1985 36 684 0.050              692            58,270  0.012 

1985.5 58 911 0.060           2,638            92,647  0.027 

1986 83 931 0.082           2,573          124,712  0.020 

1986.5 130 1087 0.107           5,764          148,183  0.036 

1987 108 1023 0.095           8,652          127,891  0.060 

1987.5 88 982 0.082           4,489          131,663  0.032 

1988 57 1072 0.050           7,185          164,414  0.040 

1988.5 55 1148 0.046           4,851          168,924  0.027 

1989 54 1016 0.050           4,057          188,098  0.021 

1989.5 67 1088 0.058           4,521          203,971  0.021 

1990 66 857 0.072           4,870          160,651  0.029 

1990.5 31 796 0.037           1,902          149,075  0.012 

1991 76 878 0.080           4,063          227,327  0.017 

1991.5 132 896 0.128           8,075          229,081  0.033 

1992 160 951 0.144         11,220          334,536  0.031 

1992.5 137 1089 0.112           6,246          307,170  0.020 

Total 1906 23217 0.076         89,767      3,373,805  0.026 

 

 



CAPITAL STRUCTURE OVER THE LIFE CYCLE 

28 

 

 

Figure 4. Bond and IPO activity over time: Total instances 

 

 

 

 

 

Figure 5. Bond and IPO activity over time: Total dollar proceeds 

 

0

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DeHan 

29 
 

 

Table 3  
Base model 

Ratio 1 2 3 4 5 6 7 8 

PGrant 0.046 0.046 0.045 0.045 0.046 0.045 0.044 0.046 
 0.009*** 0.009*** 0.009*** 0.009*** 0.009*** 0.009*** 0.009*** 0.009*** 

  1.47% 1.38% 1.38% 1.31% 1.29% 1.35% 1.35% 1.38% 

Bus.Cycle  -0.110 -0.097 -0.135 -0.197 -0.140 -0.098 -0.146 
  0.067 0.066 0.070' 0.072** 0.068* 0.066 0.068* 

    -1.55% -1.36% -1.85% -2.74% -2.00% -1.36% -2.11% 

10Yr T-
Bill   -0.015 0.055 0.077   -0.008 

   0.014 0.028* 0.028**   0.014 
      -0.38% 1.33% 1.83%     -0.20% 

RF    -88.419 -108.048 -40.408 -40.492  
    29.0** 29.53*** 14.99** 14.47**  

        -2.01% -2.38% -0.96% -0.97%   

Mkt 
Return     10.766 9.145  8.981 

     2.46*** 2.43***  2.41*** 
          1.45% 1.33%   1.31% 

Constant -1.526 -1.446 -1.323 -1.397 -1.550 -1.281 -1.217 1.441 
  0.039*** 0.067*** 0.143*** 0.144*** .149*** .120*** 0.118*** 0.148*** 

Pseudo R2 0.01 0.01 0.01 0.01 0.02 0.02 0.01 0.01 

Mean VIF 1.00 1.00 1.03 2.25 2.07 1.03 1.00 1.06 
Bolded value is coefficient, second is SE with statistical significance, third is 1 StDev change 

 *** significant at 0.1%      
 ** sig at 1%       
 * sig at 5%       
  'at 10%        

 
Table 4:  
Base plus industry returns 

Ratio 9 10 11 12 13 14 15 16 

PGrant 0.047 0.046 0.045 0.045 0.046 0.044 0.044 0.045 
 0.009*** 0.009*** 0.009*** 0.009*** 0.009*** 0.009*** 0.009*** 0.009*** 

  1.47% 1.39% 1.39% 1.31% 1.27% 1.33% 1.35% 1.36% 

Ind.Return 0.058 0.029 0.023 0.032 -0.135 -0.141 0.019 -0.140 
 0.066 0.071 0.073 0.068 0.127 0.128 0.073 0.132 

  0.20% 0.10% 0.08% 0.10% -0.41% -0.46% 0.06% -0.46% 

Bus.Cycle  -0.107 -0.095 -0.133 -0.215 -0.159 -0.096 -0.165 
  0.068 0.067 0.070' 0.075** 0.071* 0.067 0.071* 

    -1.50% -1.33% -1.81% -3.01% -2.29% -1.34% -2.41% 

10Yr T-Bill   -0.015 0.055 0.077   -0.008 
   0.014 0.028* 0.028**   0.014 

      -0.38% 1.35% 1.81%     -0.21% 

RF    -88.712 -107.734 -40.640 -40.396  
    29.01** 29.37*** 15.01** 14.49**  

        -2.02% -2.36% -0.96% -0.97%   

Mkt Return     11.690 10.125  9.971 
     2.677*** 2.656***  2.663*** 

          1.57% 1.46%   1.45% 

Constant -1.529 -1.449 -1.327 -1.404 -1.538 -1.270 -1.220 -1.430 
  0.040*** 0.068*** 0.144*** 0.145*** 0.149*** 0.120*** 0.119*** 0.147*** 

Pseudo R2 0.01 0.01 0.01 0.01 0.02 0.02 0.01 0.01 

Mean VIF 1.00 1.02 1.04 2.01 1.95 1.08 1.02 1.11 
Bolded value is coefficient, second is SE with statistical significance, third is 1 StDev change 

 *** significant at 0.1%      
 ** sig at 1%       
 * sig at 5%       
  'at 10%        



CAPITAL STRUCTURE OVER THE LIFE CYCLE 

30 

 

Base Model 
 The basic model estimating the effects of innovation on stock market issuance is based 

around the market timing literature with results presented in Table 3. Controls used in this model 
include estimating the influence of the business cycle (Bus.cycle), short-term and long-term interest rates 
(RF and 10.YrT-Bills respectively), and return on the market (Mkt.Ret).  Because of the relationship 
between short-and long-term interest rates and the perceived importance of market returns, model 
selection includes a number of combinations. 

The important, explanatory variable, PGrant, did not face much variation within the results 
from the models included in the first estimations.  Results from these estimations show coefficients 
for innovation (PGrant) in a tight band from 0.044 to 0.046, with statistical significance at the 0.1% 
level in every estimation.  Where this becomes important is in determining the changes in the 
likelihood of raising funds through the stock market relative to the bond market.  An increase in one 
standard deviation change in patenting activity from the median will result in a higher likelihood of 
raising funds through the stock market between 1.29 and 1.47 %.  The differences between the least 
innovative industries are a significant number of patents; the difference between the most and least 
patenting industries results in the most innovative being 14.25% more likely to raise funds through 
stock market issuance.   
 The business cycle (Bus.Cycle) plays an important role in the decisions firms make when they 
are seeking external capital.  Bus.Cycle enters significantly (at the 5% level) into three out of seven 
regressions it is included in, with one of those being significant at the 1% level.  Computing the  change 
in the likelihood of raising stock versus bonds on the models where Bus.Cycle is significant, a discrete 
change results in a change in the likelihood ratio between -2.00 and -2.74%.  Effectively this means 
that during expansions, firms are at least 2% more likely to raise funds through the bond market; the 
intuitive explanation for this is that lenders assign a lower probability of default when the economy is 
expanding and will ration credit less. 

The inclusion of the two interest rates (10YrT-Bill and RF) provided some surprising results.  
Where the traditional expectation is that increases in the interest rate (in both the short and long term 
rates) will lead to higher borrowing costs for the firm, creating an incentive to seek equity financing, 
the opposite is seen.  Sign flipping can be viewed on 10YrT-Bill when short-term interest rates (RF) 
are included, yet are still statistically significant in every model it is included in.  The interaction of 
these two interests rates are expectedly related and show some collinearity between them.  On the two 
models where they are both included (5 and 6), the mean VIF jumps to 2.25 and 2.07.  Using the rule 
of thumb that VIF values above 5 needs to be reevaluated, these models would be acceptable.  
However, individual VIF values for 10YrT-Bill and RF are both around 3.5 and tolerance values below 
0.3, meaning that these variables show some collinearity.  Even though these two variables are collinear 
with each other, no adjustments to the model are made as they are not collinear with or change the 
variable of interest, PGrant.  If an attempt is made to quantify the individual impact of these variables 
out of collinear estimations, confidence in the estimates would rise.  As the impact of these interest 
rates is not being qualified, but rather they are only used to control for external factors, it is acceptable 
to leave the model as is, report the results, and acknowledge the limitations.  This decision was made 
because the inclusion of the variables provides a better fit as both interest rates can influence the costs 
incurred by firms when seeking capital. 

RF displays strongly negative coefficient values between -40.408 and -108.048 and they are 
significant at the 1% level.   The one standard deviation change in the likelihood ratio from over 2% 
when 10YrT-Bills are included drops to under 1% when it is excluded.  This negative coefficient 
indicates that as the risk free rate increases, investors will require a higher return on any other 
investments since the risk premium is indexed off the risk free rate.  These higher returns must come 
from a lower valuation assigned to firms looking for capital through equity issuance.  The strength of 



DeHan 

31 
 

the coefficient indicates that the effects on the costs of debt service are far outweighed by investor 
requirements for equity returns. 

The relationship between 10YrT-Bill and RF shows the possibility of joint effects.  The 
interesting thing to note is that the only time the coefficients attached to T-Bills are statistically 
significant is when both variables are included in the same model.  By itself, X has a negative, 
insignificant coefficient while the inclusion of Y turns it into being positive and statistically significant.  
The coefficient for Y also changes as X are both in the same model as Y’s coefficient increases in 
magnitude.  A possible explanation of this phenomenon is that financing decisions are sensitive to the 
spread between the interest rates.  These coefficients support the notion that as the spread widens 
between interest rates, firms would be more likely to raise funds through the stock market.  The 
intuition is that since investments are risk adjusted to the risk free rate, as the spread widens between 
the variables, long-term debt becomes more expensive relative to other avenues of financing.  Under 
the assumptions of the market time theorists, this could influence a shift away from debt to equity as 
firms attempt to maximize their value. 

Past market returns (Mkt Return) are strongly associated with stock market issuance.  The 
average coefficient of 9.8 is in keeping with the predictions of theory.  As the market return rises, 
firms’ valuations rise with it; at higher valuations, firms will be more likely to raise funds through 
equity issuance.  Highly statistically significant at the 0.1% level, this variable has relatively high 
marginal effects, with a one standard deviation change from the median changing the likelihood of 
raising funds through the stock market between 1.31 and 1.45%.  Even controlling for interest rates, 
high past market returns are a significant indicator that firms will be more likely to issue equity. 

 
Industry Returns Cohort 
 The second cohort of models on the determination of the stock/bond choice includes industry 
controls (Ind.Returns), with model selection being identical to the first cohort; results are reported in 
Table 4.  Controlling for industry returns should pick up any industry factors that went excluded in 
the first cohort of models as those controls were purely based off the market.  There are only minor 
changes for PGrant and the other control variables; this variation is subtle enough to not question the 
results from the first models. 
 According to VIF tests, the only models with elevated VIF levels are 13 and 14, which include 
RF and 10YrT-Bills as the collinear factors, discussed in the base model cohort section.  Including the 
industry return control variable should pick up any inter-industry variation.  The results from this 
variable are insignificant in every model.  Some differences among industries were expected to drive 
any debt/equity decisions.  The dominance of market returns seem to point to the wider market as 
being more important in determining capital decisions than industry returns. 
 

Conclusion 

 In answering the question of when firms seek equity financing over debt financing when 
searching for external capital, it was hypothesized that radically innovative firms at the beginning of 
the innovation life cycle will be more likely to look to the stock market than firms at the end of the 
life cycle with less innovative activity.  The primary contribution of this paper was linking this life 
cycle to firm capital structure and decisions firms make when attempting to raise external capital 
through the bond and stock channels.   
 By using patent activity as a proxy for innovative activity, statistically significant evidence 
suggests that innovative firms are more likely to raise capital through equity issuance than firms 
without innovative activity.  These results were robust while controlling for industry returns and a 
variety of market factors, including short-and long-run interest rates, returns on the market and 



CAPITAL STRUCTURE OVER THE LIFE CYCLE 

32 

 

business cycle factors.  The impact of patents appeared as a much stronger predictor of whether a 
firm will seek stock market or bond market financing than some of the other variables found 
prominently in the literature.  A limitation of this study is the inability to pinpoint where in the 
innovation life cycle firms are when they are seeking external capital.  This is an avenue in which future 
research would be able to contribute; the determination of where the transition points occur and at 
what point firms begin searching for equity financing through the stock market would be of significant 
value.  The other path in expanding this research is to formalize the relationship of the innovation life 
cycle to models of capital structure. 
 

References 

Baker, M., & Wurgler, J. (2002). Market timing and capital structure. Journal of Finance, 57, 1–32. 
Barclay, M., & Smith, C. (1999), The capital structure puzzle: Another look at the evidence. Journal of 

Applied Corporate Finance, 12, 8–20 
Barnhart, C., & Dwyer, G. (2012). Returns to investors in stocks in new industries. Economic Inquiry, 

50, 1031–1049. 
Bradley, M., Jarrell, G., & Kim, E. H. (1984). Optimal financial policy and firm valuation. Journal of 

Finance, 39, 593–607. 
Choe, H., Masulis, R., & Nanda, V. (1993). Common stock offerings across the business cycle: 

Theory and evidence. Journal of Empirical Finance, 1, 3–31. 
Eisdorfer, A., & Hsu, P. H. (2011). Innovate to survive: The effect of technology competition on 

corporate bankruptcy. Financial Management, 40(4), 1087-1117. 
Hall, B., Jaffe, A., & Trajtenberg, M. (2001). The NBER patent citations data file: Lessons, insights, 

and methodological tools. NBER Working Paper 8498. 
Hsu, P. H. (2009). Technological innovations and aggregate risk premiums. Journal of Financial 

Economics, 94, 264-279. 
Keklik, M. (2003). Schumpeter, Innovation and Growth: Long-cycle Dynamics in the Post-WWII American 

Manufacturing Industries.  Burlington, VT: Ashgate Publishing Company. 
Kraus, A., & Litzenberger, R. (1973). A state preference model of optimal financial leverage. Journal 

of Finance, 28, 911–922. 
Merton, R. (1974). On the pricing of corporate debt: The risk structure of interest rates. Journal of 

Finance, 29, 449–470. 
Modigliani, F., & Miller, M. (1958). The cost of capital, corporate finance and the theory of 

investment. American Economic Review, 48, 261–297. 
Pastor, L., & Veronesi, P. (2005). Technological revolutions and stock prices. Retrieved from: 

http://www.nber.org/papers/w11876. 
Patent Laws and Regulations. (2000) Title 35, U.S. Code. Retrieved from: 

http://www.uspto.gov/web/offices/dcom/olia/35amend2.pdf  
Stiglitz, J., & Weiss, A. (1981). Credit rationing in markets with imperfect information. American 

Economic Review, 71, 393-410. 
 

 

Chase Parker DeHan is an Assistant Professor of Finance at the University of South Carolina 
Upstate.  His research primarily highlights the financing of innovation.  Dr. DeHan can be reached at 
cdehan@uscupstate.edu  

http://www.uspto.gov/web/offices/dcom/olia/35amend2.pdf
mailto:cdehan@uscupstate.edu

