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1.1 Background of study

Intangible asset size increase and its impact on business growth during the last 20 years provide an intriguing area for investigation. Strong market incentives for analysts to supply value-added information for high-intangible organisations are implied by the growing significance of intangible assets and the lack of clear information regarding the contribution of intangible to profitability. According to Gold finger (1974), the development and management of intangible assets, rather than the production of physical things, is the current source of economic value and prosperity1.

Forecasting profitability for companies with a high concentration of intangible assets is becoming more and more challenging.

Using two sets of stocks, Chan, Louis K.C., Lakonishok, and Sougiannis [1999] hypothesised that businesses with a high R&D intensity had a unique impact on returns. R&D-intensive firms often outperform stocks with little to no R&D within the group of growth stocks. Similar findings come from their naive examination of how advertising affects returns. They offered proof that R&D intensity has a favourable correlation with return volatility2.

In order to increase revenues, the pharmaceutical business spends billions of dollars each year on intangibles. Investors are therefore naturally curious about whether intangible investments and costs really increase shareholder value. Heiens, Richard A.; McGrath's study,

Four intangibles—advertising, research and development (R&D), goodwill, and other intangibles—are examined to determine their influence on market-adjusted holding period returns in Leanne C. and Leach, Robert T.'s 2008 study (HPR). Their findings seem to suggest that, among these factors, advertising really seems to have a significant and advantageous influence on HPR.3

It has been noted that the stock market behaviour of so-called "knowledge corporations" usually differs from that of fundamental sectors. Additionally, there is some data that suggests a relationship between a company's intangible assets and its share market value [Amir and Lev 1996, Lev 1997, Lev and Zarowin 1998].
The necessity for understanding how businesses produce and manage their intangible assets and how intangible assets effect company share prices has expanded as a result of the growing relevance of intangible assets to investors, analysts, and shareholders.

1.2 Statement of the problem

This finance assignment help - thesis seeks to provide an answer to the question:

What is the impact of the intensity of firm’s intangible assets on the volatility of their stock prices?

1.3 Purpose of the study

This research attempts to

• An outline of the problems with intangible assets.

• After presenting an introduction, various types and definitions of intangible assets, and evaluating the effect of intangible asset intensity assets in a company on the erratic nature of the company's share prices will be carried out.

1.4 Significance of the study

The relevance and usefulness of increased study on intangible assets is further supported by the rising contribution of intangible assets to firm value development in recent years.

The research will be helpful to shareholders, analysts, and investors who are curious in how a company's intangible asset size impacts the volatility of its stock price.

The impact of intangible asset intensity on stock prices will also be discussed and subsequent research on the basis of this study.

1.5 Limitation of the study

The manufacturers of basic medicines, food goods and beverages, information technology, and manufacturers of basic metals are the four industrial groupings whose listed businesses provided the data for this research. These companies had consolidated balance sheets for between eight and ten years. As a consequence, the conclusions we draw are based entirely on the data utilised in this research.

1.6 Layout of the study

There are five chapters in the research. The backdrop of the research, the issue statement, the objective of the investigation, its importance, and its limitations are the main topics of the first chapter. An overview of intangible assets is provided in Chapter 2. That serves as a definition, categorization, and method of value. The study design and a review of earlier studies in this field are covered in chapter three. In chapter four, the data sample and empirical findings are presented, and in chapter five, the findings are summarised and conclusions are offered.


In the context of innovation, intangible assets have been widely analysed in economic literature4. On the economic nature, definition, and categorization of intangible assets, there is often no consensus.

2.1 Definition of intangible asset

For the sake of simplicity, we refer to an intangible asset as a value-adding item that is not physical in nature5.Corporate intellectual property, including goodwill and brand awareness, includes things like patents, trademarks, copyrights, and business processes. Intangible assets, in a nutshell, are noncurrent, nonphysical assets that are utilised in the functioning of the firm. Intangible assets should be valued based on their cost; they will only be placed on the balance sheet if they incurred substantial expenses during their acquisition or development. These assets will appear on the balance sheet at their cost.

2.2 Classification of intangible assets

The categorization of intangible assets is not widely recognised. However, the following six recommended intangible asset types are the most popular:

? General refers to goodwill and other things, such as beneficial connections with the government.

? Brand equity is the ability of a company to support and promote consumer demand as well as other market capabilities like advertising.

? Intellectual capital includes both intellectual property (patents, trade names, trademarks, and copyrights), which are protected by a complex body of legislation, as well as trade secrets, internally produced computer software, drawings, and other proprietary technologies.

? The assembled workforce (the relationship between the company and its employees, employee contracts, and employee training), leadership, the organization's capacity for commercially viable innovation, the organization's capacity for learning, leaseholds, franchises, licences, and mineral rights are all examples of structural capital.

? Customer equity includes distribution agreements and connections, customer lists and other customer-based intangibles, customer loyalty, and customer pleasure.

? Supplier relations, include contracts, ownership stakes in suppliers, and supplier dependability


3.1 Review of previous research

The majority of intangible assets that are taken into account are those whose expenses are high when spent, such R&D and advertising.
Lev and Sougiannis (1996) hypothesised that the excess returns either reflect stock market mispricing or serve as compensation for the increased risk associated with businesses that invest heavily in research and development. After doing a number of studies, Lev and Sougiannis (1999) come to the conclusion that the extra returns are more likely a result of increased risk.

However, later research (Lev, Sarath, and Sougiannis, 2000; and Penman and Zhang, 2002) shifted their attention from R&D intensity defined based on the estimated amount of R&D assets to change in R&D assets because observations suggest that R&D assets' absolute levels may not be what affect earnings persistence. These studies provide data supporting the claim that the market is, in part, obsessed on profits and underappreciates the effect of R&D accounting on earnings quality.

More convincing evidence for the risk explanation may be found in the conference article by Chambers, Jennings, and Thompson. They also demonstrate that R&D-intensive companies have substantial profits volatility, which is consistent with earlier results (see Chan, Lakonishok and Sougiannis, 2000)

The impact of technology progress on rising firm-specific and overall stock price volatility is highlighted in recent finance literature (Campbell et al. 2001, Shiller 2000, Pastor and Veronesi 2005).

The market worth of a corporation, its level of innovation, and its productivity have all been positively correlated, according to productivity literature.

Patents' citation weighted by R&D intensity (Griliches 1981; Pakes 1985; Hall 1993, Hall, Jaffe and Trajtenberg 2005).

The study is based on Mazzucato's empirical research (2002, 2003), which indicated that stock price volatility is greatest during the stages of an industry's life cycle when innovation is the most "competence-destroying," as judged at the industry level.

Although it has been often said that intangible assets play a significant role in economic prosperity, scholarly research has yet to adequately measure their influence (Griliches 1998). One issue is the difficulty in measuring intangible assets including R&D expenditures, marketing, advertising, and human capital. Academic studies often use either firm financials or market data. Due to a lack of information on other types of intangible investment, prior studies employing the former had a tendency to focus only on research efforts.

After the "New Economy" era, when many high-tech firms that were seen to be overpriced witnessed a significant decline in their share price, there has been greater focus on stock price volatility. Even while there hasn't been a trend rise in overall stock price volatility, this pervasive notion of the "knowledge economy" has contributed to even higher stock price volatility (Schwert 1989; 2002).

According to Shiller's research from 2000, there is more "excess volatility" at times of technological revolutions when uncertainty is at its peak because investors lack confidence in their own assessments as a result of the heightened uncertainty surrounding both technology and demand. He argues that since the efficient market model does not take into account the social process by which expectations are generated, it significantly understates stock price volatility (i.e. animal spirits, herd behaviour, bandwagon effects).

In finance models, uncertainty relates to how assumptions about a company's future growth impact its market value (Campbell, Lo and McKinley 19973)6.Technology developments are an example of real uncertainty, according to Knight (1921) and Keynes (1973), which cannot be assessed using probabilities like risk but are a significant factor in a firm's potential future growth.

Interesting insights on the connection between innovation, uncertainty, and stock price volatility may be found in Pastor and Veronesi's (2005) study. They contend that if uncertainty about a company's typical future profitability is taken into account in market valuation models, bubbles may be viewed as the result of reasonable, rather than irrational, behaviour towards future predicted growth. Thus, market value rises when there is uncertainty regarding average productivity, according to Pastor and Veronesi's (2004) finding. They develop the model to explain why technology revolutions increase the volatility and exhibit bubble-like patterns in the stock prices of innovative companies. The fundamental tenet is that when a company adopts a new technology, its stock price increases because investors anticipate that the technology would increase productivity. Because danger is unpredictable when technology is applied on a small scale, volatility also increases. The risk becomes systematic when the new technology spreads across the economy, which lowers volatility and causes a decline in stock price. The most unpredictable technologies exhibit this bubble-like behaviour the strongest.

According to Mazzucato and Tancioni's study from 2005, it is untrue that industries with higher levels of innovation are generally more volatile than those with lower levels of innovation. Instead, idiosyncratic risk and R&D intensity are found to be positively and significantly correlated at the firm level.My goal is to determine if, as suggested in the works above, the amount of excess volatility in stock prices is positively connected with greater intangible assets (innovations).

3.2 Research Design

According to prior studies, businesses purchase intangible assets for two reasons: to get new knowledge and to learn about and profit from the innovations of others (Mowery, 1983; and Cohen and Levinthal, 1989) Therefore, we anticipate that companies (Industry group) with more intangible assets would experience greater stock price volatility.

Our theory (in modified form):

Higher intangible asset firms (or industry groups) have more volatile stock prices.

We research the volatility of the firm's stock prices and the intangible assets recorded on the balance sheet

(BI) of the company (S). We estimate using the following regression model to look at the intensity of a firm's intangible assets and the volatility of stock prices:

St = α + βBIt + εt

Where St represents stock price volatility. It should be mentioned that we used price changes for the year 2006 to compute price volatility since we believed that price volatility would be constant throughout the course of the ten-year timeframe. On the company's balance sheet, BIt stands for the intensity of the yearly average recorded value of intangible assets.

A more specific version of the hypothesis is therefore given from the regression model as;

H1: β > 0

If the volatility of the stock prices is correlated with the firm's intangible intensity, it may be determined by the coefficient estimate β of the intangible variable BI.


4.1 Sample Data

The sample businesses (industry group) included in this study's test must have at least ten (10) years' worth of consolidated balance sheet data, and they must be listed on at least one stock exchange. The analysis includes 40 companies from four different industry groups and spans the years 1996 to 2006.

The necessary financial data were obtained from two secondary sources, namely the BVDEP-Amadeus database7 for the consolidated balance sheet and the ECO Win database from various stock markets for the stock prices. Due to a lack of stock price information, data from two businesses was later removed from the study.

The following industrial categories are represented by sample companies in this study: production of basic medicines, production of food and drinks, production of information technology, and production of basic metals.

Tables 1 and 2 below provide the data set and descriptive statistics for the relevant variables. It is clear that there is significant concentration in a group of companies with larger intangible assets since the mean values of BI and stock price volatility are all higher than their medians.


Table 1: Data Set of Project

4.2 Empirical Results

Both Microsoft Excel and SPSS were used to do the regression analysis. The findings from both programmes are similar and are shown in Table 3 below.

The standardised beta (β = -0.203), which also indicates that the ratio of intangible assets to total assets, BI (-.011, p=0.221), seems to be unrelated to stock price volatility. The Intangible Intensity Coefficient is not Statistically Significant, according to the data.

The R-squared is 0.041, which indicates that the model's variables can explain 4.1% of the variation in stock price volatility (St).

The resultant negative association between intangible asset intensity and stock price volatility contradicts earlier studies on the extent of intangible assets that are not recorded. At first glance, the negative sign of the beta coefficient (β) seems to point in the opposite direction of what we would anticipate. This negative association may be caused by the following factors:

? There appears to be little or no significant impact of booked intangible asset on the volatility of the firm share prices, which are driven by uncertainty and the expectation of future growth.

? One may also contend that costs for R&D and marketing (advertising) are what will ultimately be producing intangible asset, but this argument is debatable. Not when these expenses are recognised as intangible assets, often with fairly conservative/precautionary application of accounting standards, are they classified as such to have a favourable influence on the volatility of share prices. As a result, the book value of an intangible asset is never equal to its real worth.

Following the findings from the preceding analysis, an industry-wise regression is performed on the same data, with the results shown in the tables 4, 5, 6, and 7 below.

Table 4: Statistics summary of OLS regression for Basic Pharmaceutical

From table 4 above, it seems that the ratio of intangible assets to total assets, BI (0.019, p=0.215), for the production of basic medicines, is connected to stock price volatility, which is also suggested by the standardised beta (β= 0.430). This positive association backs up earlier studies that found a positive relationship between the intensity of intangible assets and the volatility of pharmaceutical company stock prices, perhaps as a result of the high degree of R&D in this sector.

The results of the regression show that there is a negative correlation between the intensity of intangible assets and the volatility of stock prices in the other three industry groups, which include the manufacture of food and beverage products, information technology, and the manufacture of basic metals, with beta values of -0.318, -0.415, and -0.348, respectively. These negative coefficients seem to be moving in the opposite direction from what we anticipated, which might be because there is the booked intangible asset has little to no effect on how volatile the firm's share prices are.



In this research, I provide a general introduction of intangible assets and investigate the relationship between the concentration of intangible assets as a percentage of book value at 38 different companies and the volatility of their stock prices.

In contrast to my original hypothesis and other research in this field, the analysis reveals a negative link between the intensity of a firm's intangible assets and the volatility of its stock prices. The fact that the statistics for intangible assets utilised in this research were book values, which are less than their real worth, and the fact that the results were averaged over a period of between 8 and 10 years may have contributed to the counterintuitive findings.

I look at the relationship between the intensity of the book value of intangible assets of four business groups and the volatility of their stock prices based on industry groupings. This hypothesis is backed by my discovery of a positive association between the book value of intangible assets for the pharmaceutical sector and the cyclicality of their stock prices. Due to the significant degree of R&D being conducted in this business, the influences of uncertainty and expectations on investor behaviour also contribute to this outcome.

The findings from the other three industry groups were consistent with the study's principal finding.

The research may serve as a starting point for a more thorough understanding of how the book value of intangible assets affects the volatility of stock prices.

Future research should take into account all intangible assets rather than only concentrating on R&D and marketing. Intangible assets in general should also be considered, since investor behaviour is a significant contributor to stock price volatility.


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