Financial accounting information acquisition
Individual investors were asked to assess the relevance of seven different information sources for making their investment decisions. (i.e., buying and selling stocks).21 The rele-vance could be rated as very low, low, moderate, high, or very high. Table 6 summarizes the response pattern. On average, the most relevant information source for individual investors is the media (i.e., newspapers, magazines, and business programs on television or the internet), followed by the annual report (including the financial statements). The other information sources in decreasing order of relevance are: advisory by banks and brokers, the interim re-port, investor relation releases, the company website, and the social circle (i.e., family and friends).
[Table 6 about here]
In Table 7, we present a subsample analysis of the average relevance of information sources for investment decisions by TRUST. For the five most important information sources, the assessed relevance is significantly greater in the high-trust subsample (TRUST ≥ median).
No significant difference is documented for the company website, while the social circle is the only information source with a significantly greater relevance in the low-trust subsample.
Thus, individual investors with high trust tend to assess most information sources as more relevant, regardless of whether these are provided by the investment targets itself or by third parties. The ranking of the information sources does not differ between the subsamples.
[Table 7 about here]
Next, we focus on the annual report as the most relevant information source that is pro-vided by an investment target. Individual investors were asked how intensively they use
21 Original question in German: Welche Informationsquellen nutzen Sie für Ihre Aktienkauf- oder -verkaufsentscheidungen und wie beurteilen Sie diese [hinsichtlich ihrer Bedeutung]?
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twelve individual components of the annual report.22 The intensity could be rated as very low, low, moderate, high, or very high. Table 8 summarizes the response pattern. On average, the three most intensively used components are the income statement, the balance sheet, and the management report, while the least intensively used components are the notes and the audi-tor’s opinion.
[Table 8 about here]
Table 9 provides the subsample analysis of the intensity of use of the annual report com-ponents by TRUST. For nine of the twelve comcom-ponents, the intensity of use is significantly greater in the high-trust subsample (TRUST ≥ median). No significant differences are docu-mented for the statement of changes in equity and for the auditor’s opinion. The remuneration report is the only component for which the intensity of use is significantly greater in the low-trust subsample. For five components, their rank differs between the subsamples.
[Table 9 about here]
Since we are especially interested in the acquisition of financial accounting information, we conduct a principal component analysis of the eight annual report components which are part of the financial statements: the income statement, the balance sheet, the management re-port, the statement of changes in equity, the cash flow statement, the segment reporting, the auditor’s opinion, and the notes. Thus, we evaluate potential redundancy between the respec-tive items because the might represent a common construct. For the analysis, we use ones as prior communality estimates and the principal axis method to extract the components. Only the first principal component displays an eigenvalue greater than 1. In line with that, a scree test suggests that only the first principal component is meaningful, which accounts for 62.3%
of the total variance in the response pattern. Since only one principal component is retained, a rotation is neither necessary nor feasible. With respect to the unrotated factor pattern, an item
22 Original question in German: Wie intensiv nutzen Sie die einzelnen Teile des Geschäftsberichts?
is said to load on the principal component if the factor loading is 0.4 or greater (Stevens 1992), which is the case for all items. Thus we label the first principal component FINACC and interpret it as the intensity of financial accounting information acquisition by individual investors. The principal component analysis in summarized in Appendix C.
Exercise of shareholder voting rights
Our second proxy for monitoring behavior is based on a questionnaire item where indi-vidual investors had to indicate the extent of their past or intended exercise of shareholder voting rights.23 Potential answers were (1) having never voted and no intention to do so, (2) having never voted but intending to delegate the voting right to a proxy for the next annual meeting, (3) having never voted but intending to visit the next annual meeting, (4) having voted by delegation to a proxy, and (5) having voted personally at an annual meeting. We construct a simple variable bound by zero and one to represent these five potential answers and label it VOTING.
Monitoring behavior and investor characteristics
We examine potential associations between the monitoring behavior of individual inves-tors, their trust in stakeholders, and other personal characteristics. Table 10 provides the vari-able distribution. As a result of the principle component analysis, FINACC and TRUST are standardized with means of zero and variances of one. With respect to VOTING, 66.5% have exercised their voting rights already, either personally or by delegation to a proxy. 13.8% in-tend to visit the next annual meeting or to authorize a proxy while the remaining 19.7% have neither voted in the past nor intend to do so with respect to the next annual meeting. We use the interaction term ECON_EDU, i.e., the level of general education conditional on having vocational training or academic education in economics or business, in the subsequent
23 Original question in German: Als Aktionär haben Sie ein Stimmrecht. Haben Sie dies im Rahmen einer Hauptversammlung (HV) bereits in Anspruch genommen oder planen Sie eine Inanspruchnahme?
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yses as an inverse proxy for the costs of monitoring. The variables %STOCKS, #FIRMS, and HORIZON are introduced in order to proxy for the stock market exposure of individual inves-tors and thus to represent the benefits of monitoring. %STOCKS is the share of wealth—
excluding the own residence—that is invested in stocks except mutual funds. The average individual investor has 29.8% of his wealth invested in stocks and 14.9% of the investors have invested more than half of their wealth in stocks. #FIRMS is the number of firms of which stocks are held. On average, stocks of eleven different firms are held by an individual investor. 3.8% hold stocks of more than 30 different firms. HORIZON represents the invest-ment horizon. 76.2% state that they are interested in the long-term formation of wealth, 20.0%
seek regular income from dividends or trading and 3.8% focus on short-term gains. The re-maining four variables are controls that we do not assume to represent costs or benefits of monitoring. MALE, AGE, and HOUSING were introduced above, already. YEARS represents the number of years having been invested in stocks except mutual funds. On average, this period amounts to 17.6 years. For the subsequent univariate and multivariate analyses, we use the natural logarithm of one plus YEARS.
[Table 10 about here]
Table 11 presents Pearson and Spearman correlations for monitoring proxies and investor characteristics. FINACC and VOTING are significantly positively correlated with TRUST and with most of the controls. HORIZON is significantly negatively correlated with both monitor-ing proxies while the Spearman correlation between VOTING and HOUSING is insignificant.
As an example, Figure 3 depicts the average levels of TRUST by the different response levels for the intensity of use of the income statement, which is the most intensively used component of FINACC. Figure 4 presents the average levels of TRUST by the different response levels for VOTING.
[Table 11 about here]
[Figure 3 about here]
[Figure 4 about here]
In order to examine potential associations between the monitoring proxies and investor characteristics in more depth, we conduct several regression analysis in Table 12. We use OLS regressions for the continuous dependent variable FINACC. In the first model, TRUST and the controls which we interpret as proxies for costs and benefits of monitoring are used as independent variables. All of them are significantly positively associated with FINACC, ex-cept HORIZON, for which we document a significant negative association. The results remain stable when we add the remaining controls in the second model. The only exemption is HORIZON, which turns insignificant. We get similar results when examining the voting be-havior. However, since VOTING is not continuous but consists of five response levels, we use logit regressions in that case.
[Table 12 about here]