Forecasting financial variables industry-wide : an empirical study to which extend insider are able to forecast earnings and cash flow in branch related companies

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Forecasting financial variables industry-wide

An empirical study to which extend insider are able to forecast earnings and

cash flow in branch related companies.

This thesis has studied publications about the predictability of accounting variables through individual insider purchases industry-wide. Based on this empirically gathered information, this thesis tries to draw conclusions as individual insider purchaser for the predictability of earnings. The analysed models in this thesis show that when a separation between earnings and cash flow is made, the holding returns will behave differently over time. To conclude: insider purchases in the same industry is relevant for the predictability of earnings and less relevant for cash flows.

Suzanne Groen 10003288 Bachelor Thesis

Finance & Organization

Supervisor: Shivesh Rafaél Changoer 22-08-2013

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Their trading activities are subject to regulations and restrictions. In the stock market, the trades from insiders are the most heavily analyzed activities. Because the insider trades are enormously in the stock market is it not astonishing that there is a huge amounts of empirical literature about insider trading (Cohen, 2012). Trades of insiders are visible to investors through disclosure on the Security and Exchange Commission (SEC), which means that insiders must report their trading activities to the SEC. In the years before 2001 the trades had to be registered before the tenth of the next month. From August 2002 the Sarbanes Oxley (SOX) was established and requires from insiders that they disclose their trading activity in two business days (Veenman, 2012).

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4 on prior research. Previous study from Lakonishok (2001) implies namely that insider

purchases consist of more private information than the insider sales.

Three previous studies about insider trading and the predictability of future cash flows

in the case of insider trading are relevant for this thesis. A study by Seyhun (1988) shows that

there is a positive correlation between aggregate trading activities and the return of the market

portfolio. Seyhun (1988) also argues that to a certain extent market returns remain predictable

even if the insider information has become publicly known. Conclusion is that there is not always a difference in effects of industry wide, firm specific and economy wide components.

This thesis shows evidence of predictable market returns in the case of future cash flows. This

study contributes to the study from Seyhun (1988) because this research emphasizes on both

variables earnings and cash flows. The reason for this particular research with also earnings

being tested is that cash flow brings timing and measure problems. This makes it interesting

to look at both variables instead of only cash flows. Another study by Piotroski and Roulstone

(2005), related to this thesis, states that insiders are possessors of superior information and

trade on the basis of contrarian beliefs. They found different relations for insider trades with

respect to some accounting variables. This study does not only checks book-to-market ratio’s

but also size and momentum. This gives another effect on the relation between future cash

flows and insider trades. While the separation between cash flow and earnings might have an

extra impact. The last more or less interesting study is by Veenman (2012) who concludes

that companies with more information uncertainties, insider purchase fillings trigger more

positive market reactions. Furthermore it is suggested that market reactions to purchase filings

are predictably associated with earlier earnings changes. This says something about the

persistence of accrual. Veenman’s study is focussed on all different companies whereas this

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5 the effect of future earnings information is not tested with randomly chosen companies, but is

selectively tested with branch related industry.

This paper is organized in five sections. In the second section some existing literature about

the field of this research is discussed. The third section describes the data and research

methodology that will be used. The fourth section analysis the results obtained from the

regression made. Finally the last section is the conclusion combined with some implications for further research in this topic.

2. Literature Review

Over the last decades many studies have been carried out to determine different trading

activities of insiders with private information. A considerable input was delivered by Cohen et

al (2012), who was trying to show the information flow from insiders. According to Cohen et

al (2012) decoding the information flow from insider trades, as well as some insights about

the type of information and environment, is achievable. They classified trading between

opportunistic and routine trading, by doing this they isolate the information that insiders have

about the future of the firm. Cohen et al (2012) show that ‘’routine’’ insiders are not informative about a firm’s future and so the abnormal returns are zero, while opportunistic

traders yield an abnormal return and can predict future news and events. Cohen et al (2012)

argue that insider trades can be informative to investors. This can be used to search for other

insights about insiders activities. This is possible be course insiders have to disclose their

trading activities which means that the trades are visible for all kinds of people. Related with

this was the study from Brochet (2010) which concluded that the information flow from

insider trades was as Cohen et al (2012) argued.Brochet (2010) found evidence that after the

introduction of the SOX the abnormal returns and trading volumes increased and expedited in

the case of insider purchases. Thus the SOX has brought more transparency with regard to

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6 A further important contribution to insider trading insights was made by Seyhun

(1988), who provided theoretical evidence that their generally exists a relationship between

aggregate trading activities and the return of the market portfolio. The return to the market

portfolio during the future two months is positively correlated with net aggregate insider

trading activity in a certain month. This means that insiders react with an increase in their

stock purchases prior to increases in the stock market. But the stock purchases decreases by

insiders, following increases in the stock market. Evidence for this conclusion comes from

observed mispricing by insiders and a part of that comes from economy wide changes that are

unanticipated. This explains that even after the information from insiders became public, the

future market returns still were predictable to some extent. Brochet (2010) analyzes around

the SOX regime, the information content of Form 4 filings. He found that after the SOX

regime the trading volumes and abnormal returns are significantly greater around filings of

insider stock purchases. This understanding and achievement in information about insider

activities triggered more research towards the relation between insider activities and future

market returns. For example Lakonishok (2001) who did similar research. Evidence showed

that insiders are able to predict cross-sectional returns and that especially purchases from

insiders reveal more information because selling has no predictive power.

Additionalstudiesfrom Seyhun (1998), Jaffe (1974) and Jeng et al. (2003) were built

on previous studies which proved the positive relation between trading and the returns. Their

focus was on abnormal returns over several periods. Seyhun (1998) shows that there is a

correlation between abnormal returns and the level of trading frequencies. The same applies

for Jaffe (1974); insiders trade on the basis of their extra information and on their ability to

identify mispricing in their own firms. Jeng et al. (2003) base their theory on performance

evaluation and found evidence for significant abnormal returns for insider purchases. This

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7 researchers intended to examine more detailed evidence about abnormal returns through

insider trading. They build on Seyhun’s (1988) research who made a huge step forward in the

ongoing insight about insider trading. Several papers concentrate on other aspects of insider

trading namely the type of information and how these different types are reflected into stock

prices. Studies focussed on firm-specific, industry-level and market-level information. As

Seyhun (1988) states, there is not always a difference between the effects of firm-specific and

economy wide factors due to the examined relation between aggregate insider trading and

market movements. According to King (1966) there exists a correlation between market and

industry movements with stock prices. Whereas Roll (1988) argues that a part of the stock

price is not referable to market or industry movements but has something to do with

firm-specific information. These different approaches of looking at market returns are contraire

with the positive relation that Seyhun (1988) found earlier.

Research from Piotroski (2004) extends the investigationsfrom the studies above with

synchronicity but taking the three different types of information into account. He measures

stock return synchronicity through which extent trading activities can influence the stock

prices. When industry and market wide information is in cooperated in the study there will be

more synchronicity, while the opposite will occur when only firm-specific information is

used. Piotroski (2004) found evidence for a negative relation between stock return

synchronicity and insider trades. They also examine the timing of incorporation of

industry-level and firm-specific earnings into prices on the basis of different activities of these

informed parties. Piotroski (2004) found that only the firm-specific component of future

earnings news incorporated in the price is accelerated by insider trading. A year later Piotroski

(2005) looked at various types of firm events which might influence market returns. He

examined whether there is a relation between future cash flow news and insider trades.

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8 each case. For example; insider trades are positive to future earnings performance, positive to

the book to market ratio and negative to returns. This implicates that insiders trade on the

information they have about future cash flows but only a small proportion of insider

purchases is explained by superior information about future cash flows. They also found a

stronger relationship between future earnings and insider purchases in weak information

environments. In this research Piotroski (2005) shows that this predicting of accounting

variables also could be important in the case of insider trading. Piotroski (2005) argues that

there is a lot to learn from investigating insider transactions. Not only the relation between insider trades and market reactions can be tested, as Seyhun (1988) did but also the

predictability of accounting variables can be examined (Piotroski, 2005). An example is the

more detailed paper of Ke et al. (2003) about insider trades consistency with cognition of

future earnings over a following period of earnings increases. Ke et al. (2003) argues that

insiders trade to profit because of the anticipated earnings trends. These trends are up to two

years in the future and differ for sales and purchases. Like Seyhun (1998) showed, insiders

much rather buy than sell when positive news comes out, which is described to the difference

of in formativeness.

Predicting future performance

More general research about the predictions of future cash flows and earnings is often

conducted because it seems to be an important question underlying financial reporting. Bowen

et al. (1986) examined the relation between various measures of cash flows and earnings.

They argued that earnings is not exactly a good predictive variable for future cash flows

compared with past cash flows. While Greenberg et al. (1986) had a different conclusion, they

concluded that earnings have more predictive power than cash flows. A later paper from

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9 year-end based. This difference makes the predictability of cash flows out of accruals possible

for quarterly based amounts. All of the above research was based on general predicting power

of accounting variables. This,together with taking into account the predicting power in the

situation of insider trades, is relevant for this study. Examiningthe different studies from

above together might give a reason to think of that accounting variables in case of insider

transactions is predictable industry-wide.

Last, Veenman (2012), the study which is mostly related to this paper: in how they

approach their investigation with respect to their information and data. Veenmans (2012)

results show that in situations of uncertainty about the implications of valuation of reported

earnings, the Form 4 filings provide useful and important information to investors. The

persistence of previously reported earnings can be learned by investors from the Form 4

filings. Insider purchases give also signals about private information with respect to the

valuation implications of both previous, as well as future earnings. To conclude: Veenman’s

(2012) study argues that the market reactions to insiders are a combination of future earnings

anticipation and the persistence and implications of valuations of past reported earnings.

Veenman (2012) explains this by the fact that firms with higher information uncertainty,

market reactions to Form 4 purchase filings are far more positive. Veenman (2012) uses the

earnings response coefficient model to provide evidence about resolving the problem of

earnings information uncertainty. The outcome of this model shows that with past earning

changes for purchases that have positive earnings changes, the market reactions are positively

related, while negative market reactions will occur with negative changes. This means that

concerning the implications for future earnings, investors adjust their expectations and that a

part of the reaction is a delayed reaction to previously earning news. The larger the size of

insider purchases or the relative number of shares traded, the stronger the connection. This

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10 studies. It includes all the ingredients that is needed for this paper but the contribution of this

paper is that it makes a distinction between accounting variables which might have a different

outcome because of the time problems of cash flow.

Based on the results of prior empirical research that has been done over the last decade, it is

expected that earnings will be a good variable to predict compared to cash flows.Earnings are

the net benefits of a firms operation, which is a given amount. Whereas cash flow is the

movement of money in or out a business. The cash flow amounts are daily changing which

might come from unexpected expenses or incomes. That’s why it makes it hard to predict

such movements in time. Therefore earnings might be a better variable to predict than cash

flows. And so the hypothesis to be tested is that the coefficient of B1 according to earnings

will be greater than that of B2 (B1>B2). This means that is expected that the variable earnings

has predictable influence industry-wide.

3. Data & Research Methodology

This section will formulate how the data required for this research was gathered. After that it

describes the conditions the data has to fulfil and the exact data that was selected. Finally this

section defines the methodology of this thesis.

3.1 data

The used datasets all comes from the database Wharton Research Data Services (WRDS).

From CRSP the data of adjusted returns were found, this is done as follows. Select the daily

stock file and use as firm identifier PERMNO for a later match with Thomson Reuter. Out of

this data the holding period returns will be calculated on basis of size/ book-to-market ratio

and momentum.

Insider trading data will be collected from Thomson Reuter at the insider filing data

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11 table 22. Select NCUSIP as the firm identifier which will be merged with the data from CRSP

with identifier PERMNO. This paper only takes into account insider purchases, this means

that all the other transactions will be excluded from the equation.

The dataset of COMPUSTAT will be used to find the data for earnings, cash flows and

the control variables. In this paper four control variables will be used consisting of reporting

lag, loss, tax and R&D expenses. This is done by selecting North America, fundamental

quarterly and searching for the entire database. The output will be extended with quarterly

data items and using thereby CUSIP as the firm identifier. Another remark is made here by

the fact that only the variables earnings and cash flows plus accruals will be examined as

predictable independent variables.

The data collected from those three files will be separately filtered and merged so that

they have the same identifier and are as one document in STATA. All the exact variables

from those three files are defined in Appendix A.

3.2 Sample

The period on which this study focuses is from September 2000 till August 2012. The data

gathered from WRDS was subjected to some adjustments in order to get sufficient

observations. The following insider transactions have been dropped. First the option related

sales were excluded because this would lead to misclassification of purchases and counting it

double (Veenman, 2012). Second, those not related to common stocks observations. Third, the

amended fillings. Fourth, if more than one insider trades per security date. Lastly, security

dates from mixes. The observations left over were summed up and give an insider trading file

with one observation per person per security date. Over the complete sample several filters are

2_{Header contains in normal cases the business address, name, phone and position of the insider. Table 1} contains non-derivative transaction information, it reports the number of shares bought/sold, price paid/received, transaction date and the shares remaining after the trade. Table 2 contains derivative

information and includes option exercises and option grants. The type of information, strike price, number of shares, date of expiring, date the options vest and holdings are reported in it.

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12 applied to ensure consistency of the data. Those with invalid elements and numerous missing

are deleted, likewise incomplete data where price and return data were not available on the

transaction date. Finally with merging the files together some extra observations were lost.

This procedure results in a final sample of 490,689 observations.

3.3 Methodology

In order to find out if earnings is a better variable to predict industry-wide than cash flows and

indirectly investigate how far in advance you can predict it – this is done by choosing

different periods - an equation is set up. As mentioned earlier, the focus of this thesis, for

what the model concerns, relies on Veenmans (2012) paper. For testing the hypothesis a

measure for the dependent and independent variable has to be chosen. As dependent variable,

the left hand side of the equation, the adjusted holding period return of firm i will be used.

The independent variables, right hand side of the equation, are individual insider purchases

industry-wide multiplied by earnings of firm i and the control variables. Another equation has

to be made to compare the holding period returns, the second equation has the same

dependent variable just as the control variables but another independent variable. This

equations right hand side is individual insider purchases now multiplied by cash flows plus

individual insider purchases multiplied by accruals. The equations looks as follows.

Equation 1:

{ }

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13 Equation 2: { } { }

Where holding period return (HPR) is defined as the total return received on an asset portfolio

by holding on to the portfolio for different periods. This computation is calculated later on in

this thesis. The variable Sum shares security day represents the individual insider purchases.

The sum is measured by the sum of all shares traded in a given firm at a given security date.

Further QEARN is included which is Earnings which comes from compustat described as

income before extraordinary items (IBQ) and is deflated by lagged total assets (ATQ).Cash

flows also come from compustat defined as net cash flows from operating activities

(OANCFY). Accruals were generated and calculated as income before extraordinary items

(IBQ) minus cash flows from operating activities. The paper controls for some factors that are related to insider trading and firm’s information environments. First there is controlled for

reporting delay, under the name of REPORTINGLAG computed as security date minus

transaction date. TAX is included as a dummy and defined by transaction month. TAX is one

if transmonth equals 12. Finally, the variables losses for firms (IBQ) and research and

development expense (XRDQ) were included as dummy variables. This is done be course of Veenman’s (2012) study which says that the use of Earnings for forecasting future cash flows

is complicated by those two variables3.

As mentioned before the holding period returns, which is a proxy for new information,

need to be calculated. WRDS will give the output for the daily returns. Therefore for this file

some calculations had to be made to get the correct output. In order to acquire the daily

abnormal returns, which is the characteristic adjusted returns based on size, book to market

3_{All variables are defined in Appendix A}

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14 and momentum, some computations are made. To get the abnormal returns, first stocks are

assigned to one of the Fama-French 5 x 5 portfolios. This portfolio is the result from the

returns that are adjusted, which is the intersection of five portfolios based on size and five

portfolios based on book-to-market value.4 Then, to obtain daily abnormal returns, the

portfolio returns are subtracted from individual stock returns. From these obtained adjusted

returns the holding period returns were calculated through the formula (1 + return stock i day

t) till (1 + return stock i day t-1) because different periods are used.

4. Descriptive statistics and Results

4.1 Descriptive statistics

Table 1 presents descriptive statistics of observations of insider purchases. The different time

periods for holding returns are summarized together and gives a good view of the returns

further in time. Looking at the mean values for HPR, the values will be negative over the

holding time. The first quarter of insider purchases industry-wide is associated with a negative

holding period return which become even worse over time. Although 25 percent of the

purchases in third quartile has at least a response of 0,11. According to the results of the event

study of Veenman (2012), the numbers are much higher and thus the effect is stronger. But

not all the market reactions are positive (Q1 values of CAR%), 25 percent is associated with

CAR%. The main variables sumsharesecday, oancfy and accruals are highly skewnessed as

single variables. For the regression oancfy, accruals and qearn will be multiplied by

sumsharesecday. While among the main variable qearn as percentage of lagged total assets,

the average is around 25.33 percent. Veenman’s (2012) results show that Qearn is centered

around zero, which means on average zero. This might suggest that seasonally differenced

changes in quarterly earnings plays a role. For the control variables, lossdummy is positive on

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15 average with 5.04 percent of the firms reporting a loss. Tax is positive with a percentage of

7.11 and the research and development expense is around 0.1 percent. The mean(median)

reporting delay equals 13.5(1.00) so this means that reporting occurs mostly on time. Loss and

rnd are much higher (rnd 37.6 and loss 36.0) in the study of Veenman (2012). This means

Loss and rnd have influence in the equation because they are both very significant. While in

this thesis (loss 5.04 and rnd 0.1) the positive percentage are very small.

Table 2 show the correlations based on pearson and spearman, which are pairwise and

rank correlations between the variables used in table 1. The different periods for HPR are all

positive related with insider trading industry wide(sumsharesecday), cash flows(oancfy) and

research and development expense(rnd). Tax becomes positive related as HPR reaches 100

days, on the other hand reportinglag changes over time for the different periods of HPR.

Negatively related to HPR are qearn, accruals and lossdummy. The correlations between this

study reasonable correspond with the correlations from prior research (Veenman, 2012).

Further insider trading industry-wide(sumsharesecday) is positive related to cash flows and

research and development expenditures which is consistent with expectations.

Sumsharesecday is also negatively related to the variables qearn, accruals, tax, loss and

reportinglag. Finally, the main variables qearn and cash flow are positive correlated. This is as

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16 Table 1

Descriptive statistics for insider purchases sample 2001-2011

Stats Obs. Mean Std. Dev. Q1 Median Q3

Dependent var. HPR 20 145718 .002128 .1163804 -.019321 0 .0116347 HPR 40 144269 .0008772 .1732285 -.032665 0 .0128443 HPR 60 143540 .0005033 .2207013 -.044599 0 .012351 HPR 80 142476 -.0002007 .2630922 -.054648 0 .0081604 HPR 100 140743 -.0009955 .3149383 -.065309 0 .0011011 HPR 120 139812 -.001088 .3763875 -.076148 0 0 HPR 140 138787 -.0014705 .4543483 -.085005 0 0 HPR 160 137277 -.0050226 .4694184 -.093905 0 0 HPR 180 136146 -.0066035 .4583878 -.100723 0 0 HPR 200 135049 -.0072653 .4755926 -.106321 0 0 HPR 220 133546 -.0086347 .5220964 -.112719 0 0 HPR 240 132653 -.0091062 .5781428 -.119453 0 0 Main var. sumsharesec 319160 -30095.7 1773988 -3749.99 0 2000 qearn 149142 .2533054 13.88436 0 .002195 .0152522 oancfy 133934 -69.97375 1378.562 -.949 0 0 accruals 132431 111.4022 1577.195 0 .149 6.354 Control var. lossdummy 163460 .0504099 .2187899 0 0 0 rnd 92653 .000572 .0239104 0 0 0 tax 319160 .0710678 .2569385 0 0 0 reportinglag 319160 13.50342 96.07889 0 1 4

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5_{Bold text indicates that the correlations of pearson and spearman are significant at a level of 0.05 or better} 6_{The pearson correlations are below the diagonal and spearman correlations are above the diagonal}

Correlation. 1 2 3 4 5 ₆ ₇ ₈ ₉ ₁₀ ₁₁ ₁₂ ₁₃ ₁₄ ₁₅ _{16 17}₁₈ ₁₉ ₂₀ 1HPR20 — .471 .344 .316 .292 .287 .244 .208 .204 .188 .176 .192 .028 -.138 -.137 .092 -.192 - - -.134 2HPR40 .694 — .611 .503 .495 .419 .344 .356 .320 .288 .268 .284 .072 -.166 -.166 .112 -.178 - .063 -.057 3HPR60 .527 .811 — .710 .647 .547 .503 .515 .463 .408 .395 .380 .064 -.214 -.213 .124 -.223 - - -.057 4HPR80 .445 .694 .873 — .746 .647 .555 .559 .523 .475 .432 .408 .036 -.246 -.246 .156 -.236 - - -.032 5HP100 .381 .586 .734 .863 — .766 .690 .655 .571 .531 .527 .519 .052 -.296 -.296 .144 -.286 - - -.070 6HPR120 .319 .482 .606 .723 .916 — .790 .691 .631 .583 .531 .539 .072 -.262 -.261 .132 -.261 - - -.019 7HPR140 .270 .394 .495 .604 .830 .940 — .806 .738 .706 .662 .639 .052 -.273 -.273 .216 -.263 - - -.044 8HPR160 .263 .392 .492 .601 .812 .909 .961 — .782 .687 .643 .635 .056 -.345 -.345 .232 -.339 - - -.044 9HPR180 .265 .399 .497 .601 .788 .874 .914 .960 — .802 .726 .710 .044 -.333 -.333 .204 -.312 - - -.082 10HP200 .256 .401 .500 .602 .768 .838 .867 .916 .962 — .814 .758 .068 -.269 -.269 .148 -.245 - - -.095 11HPR220 .233 .356 .443 .537 .737 .824 .875 .907 .932 .958 — .865 .079 -.293 -.293 .148 -.263 - - -.108 12HPR240 .209 .319 .402 .497 .712 .813 .864 .893 .906 .923 .964 — .056 -.293 -.293 .188 -.263 - - -.083 13sumshar .005 .003 .002 .001 .002 .003 .002 .002 -.000 -.000 -.001 -.000 — -.038 -.038 -.028 -.031 - .063 -.121 14qearn -.018 -.013 -.012 -.004 -.003 -.008 -.011 -.009 -.008 -.007 -.007 -.013 -.000 — - -.227 .893 - .063 .312 15accruals .006 -.014 -.006 -.007 -.008 -.009 -.013 -.007 -.007 -.007 -.005 -.006 -.033 -.001 — -.229 .893 - .063 .312 16oancfy .018 .011 -.000 .002 .004 .004 .006 .003 .003 .004 .005 .005 .030 .001 -.923 — -.181 - -.063 -.108 17reportl. .001 .002 -.002 -.001 -.000 -.001 .000 -.001 .000 .001 -.000 -.001 -.001 .002 .004 .012 — - .071 .277 18lossdum -.004 -.008 -.012 -.011 -.009 -.018 -.019 -.017 -.031 -.029 -.028 -.026 -.002 .006 .003 .004 .012 — - - 19 rnd -.005 .031 .005 -.001 -.002 .017 .008 .022 .032 .027 .032 .024 .016 .004 -.001 -.001 .000 .004 — - 20tax .010 -.006 -.003 -.004 .001 .008 .011 .010 .011 .009 .011 .016 -.003 .002 .038 -.000 .027 .125 -.00 —

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Table 3-6 reportthe testing results from the two different regressions. First both equations are

estimated through using the OLS regression function. Second, the option robust is used

because homoscedasticity and normality of the random error terms is not assumed. Finally a

two-tailed significance is used. Table 3 first shows the results of the basic model without the

control variables of equation 1 and table 4 shows the full model with the control variables for

equation 1 and so on for equation 2. All tables contains seven models which means the

different holding period returns as dependent variable. Some different periods are given to illustrate the course of the holding period return. For table 3 and 4 the regression coefficient

should be interpreted as the difference in holding period returns and the predictability of

earnings as industry insider purchaser.

Table 3find a strong association between insider purchases predictability of earnings

and the different periods for holding return. This shows that equation one on his own without

any control variables is significant, looking at the t-statistic. The significance is stronger if the

holding period return increases and starts at HPR80. This findings suggest that the effect of

earnings on the holding period return industry-wide is predicted well. The different period

returns show that earnings in this study will be predicted well for a bit more than eight months

in front. This strong relation between future earnings and insider purchases might indicate a

weak environment as Piotroski (2005) showed in his paper. This study also finds really small

coefficients around 0.001, which tells the slope for insider purchases in case of the

predictability of earnings at the different periods. Table 3 and 4 show similarities between the

basic model and full model but also some differences. From table 4, the full model, there is

only statistical significance from holding period return 160 at the level of 10 percent or better.

Although this model show return differences for qearn, the effect of the holding period returns

on the predictability of earnings industry-wide is predictable but from later on. This evidence

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19 earnings is predictable after nine months. Which is noteworthy because Cohen et al (2012)

suggest that the holding returns become less after nine months. If lossdummy and reportinglag

are left out of the regression the significance for the variables is similar. The effect of leaving

out those two control variables work stronger as the holding period returns are larger. Again,

the full model also show small coefficients for insider purchases times earnings around

0.0013 with some outliers of 0.0033. The coefficients of qearn are between 0.0007 and 0.002,

which is also small. The effect of leaving out lossdummy and reportinglag does not change

the coefficients by much. For the different control variables there is some significance, for tax

in the beginning periods and for reportinglag later on the holding returns. The coefficients on

the control variables suggest further that new information is increasing with firms having

research and development expenditures (rnd) which become more if the holding period return

increases. Holding period returns are lower in industry-wide firms with positive tax amounts,

increases till the holding period is at half and becomes negative at 160 days by firms having a

reporting delay.

Overall evidence show that equation one, controlled for the environments, on his own

is statistical significant which means that the effect of earnings on holding period return is

good predictable and can be predicted, as this research shows, in advance till 240 days.

Table 3 basic model equation 17

OLS regression HPR20 HPR40 HPR80 HPR120 HPR160 _HPR200 HPR240 Insider x earnings 0.39 -0.63 4.23 4.23 6.53 8.71 3.65 (0.695) (.531) (0.00) (.000) (0.00) (0.00) (.000) qearn -20.31 -0.85 3.39 2.85 4.47 6.40 2.01 (0.757) (0.397) (0.001) (0.004) (0.00) (0.00) (0.045) intercept -.68 -.23 -.42 -1.78 -1.70 -1.23 -1.29 (.497) (.82) (-.674) (.074) (.09) (.219) (.197) N 64011 63615 63264 62813 62511 62285 62059 R-squared .0003 .0001 .0001 .0002 .0002 .0001 .0002

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20 Table 4 full model with control variables equation 189

OLS regression HPR20 _HPR40 _HPR80 HPR120 HPR160 HPR200 HPR240 Insider x earnings 1.34 0.62 1.56 2.53 3.11 7.64 7.70 (0.180) (.536) (.119) (.012) (.002) (0.00) (0.00) Qearn -0.84 -0.94 3.03 1.46 2.68 5.54 -2.64 (0.399) (0.345) (0.002) (0.144) (0.007) (0.000) (0.008) Insider (sumshare) -0.56 -2.24 -2.07 -1.96 -2.65 -1.38 -1.74 (0.577) (0.025) (0.039) (0.050) (0.008) (0.166) (0.082) lossdummy - - - - rnd -.45 2.91 -.23 1.37 1.36 1.41 1.15 (.654) (.004) (.816) (.169) (.175) (.160) (.251) tax -1.95 -1.23 -1.97 -1.99 -0.86 0.04 -0.42 (.051) (.220) (.049) (.046) (.389) (.965) (.676) reportinglag 1.26 1.33 0.86 0.06 -2.08 -2.01 -1.97 (.209) (.185) (.389) (.953) (.037) (.045) (.049) intercept -0.13 0.58 0.45 -0.20 -0.11 0.23 0.22 (.897) (.57) (.652) (.841) (.912) (.817) (.828) N 62023 61868 61744 61592 61475 61375 61288 R-squared .0056 .0017 .0005 .0011 .0009 .0010 .0009 Coeff insider .0013 .0014 .0024 .0012 .0012 .0033 .0010

Table 5 and 6 reports the results from testing equation two, table 5 show the basic model and

6 the full model. For table 5 and 6 the regression coefficient should be interpreted as the

difference in holding period returns and the predictability of cash flows as industry insider

purchaser. This thesis does not find a strong association between insider purchases accounting

for cash flows and the different periods of holding returns. Although at the holding period of

20, 160 and 240 it shows a significance coefficient. Looking at accruals, there is statistical

significance at 10 percent or better between insider purchases and holding period return,

starting from holding period 100 which has a p-value of 0.062. Because these predictions are

in the same line, some values are not reported in the table. OANCFY only show a statistical

significant outcome at the holding period of 240. This findings suggest that the effect of

8

This table does not report the main coefficients of qearn, lossdummy, rnd, tax and reportinglag. To save space.

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21 holding period returns on cash flows industry-wide is not predicted well. This statement confirms earlier founding’s. Piotroski (2005) proves that only a small proportion of insider

purchases is explained by superior information about future cash flows. Those different time

periods point to predicting cash flows in advance starting at 240 days at least. The coefficients

of insider purchases accounting for cash flows and accruals are not worth mentioning here

because they are really small, they show positive small changes. If accruals and the

interaction term are excluded from the regression, the significance become even worse for the

predictability of cash flows.

From the full model, table 6,some difference in statistical significance controlling for

the environment now show up. The findings above hold for cash flows after controlling for

other factors related to insider purchases. This indicates that equation two has no predictable

power at significance levels 10 percent or better from the periods of 20 till 240 days. The

coefficient for cash flow with the control variables is around 0.0003 what is really small and

suggest a small change in the dependent variable. This is the same for accruals with an

average coefficient for the different periods of 0.0002. OANCFY coefficient is on average

0.0004, which is again small. The control variables showsome significance with reference to

the different holding period returns. The variable research and development expenditures

(rnd) is significant at holding period 40. Tax is significant at the beginning period returns and

reportinglag is significant starting at the holding period of 160 and further. The coefficients of

the control variables further points to different holding period returns. Firms with research

and development expenses facing lower holding returns in the periods from 20 till 40 and

from then it starts increasing for the different holding period returns. The holding period

returns are lower in industry-wide firms with a positive tax amount, increases till the holding

period is at half and becomes negative at 160 days by firms having a reporting delay.

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22 does not give any significance to insider times oancfy. The coefficient is far in minus so it

does not contribute to the regression to do so.

Overall evidence shows that equation two, controlled for the environment and other

factors related to insider purchases, has no statistical significance. Which only means that the

effect of cash flows and accruals on holding returns is not good predictable for the days from

20 till 240 in this paper.

Table 5 basic model equation 210

OLS regression HPR20 HPR40 HPR80 HPR120 HPR160 HPR200 HPR240 Insider x oancfy 2.17 -0.35 1.26 1.40 2.17 0.16 2.71 (.030) (.726) (.209) (.162) (.030) (.873) (.007) Insider x accruals 2.48 -0.06 1.61 3.46 2.48 2.01 3.24 (0.013) (.950) (.108) (0.001) (0.013) (.044) (.001) OANCFY 1.71 1.74 1.17 1.51 1.71 1.20 3.09 (0.087) (0.081) (.241) (.132) (0.087) (.232) (.002) intercept -1.11 0.08 -0.12 -1.22 -1.17 -0.80 -0.73 (.269) (.933) (.902) (.221) (.240) (.424) (.462) N 63452 63135 62844 62480 62224 62031 61853 R-squared .0005 .0001 .0001 .0001 .0001 .0001 .0001

Table 6 full model with control variables equation 21112

10_{This table does not report the main coefficients of insider x cash flows, insider x accruals and oancfy. This to} save space.

11

Again this table does not report the main coefficients of insider x accruals, oancfy, lossdummy, rnd, tax and reportinglag.

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23 OLS regression HPR20 HPR40 HPR80 HPR120 HPR160 HPR200 HPR240 Insider x oancfy -0.48 0.16 0.18 1.08 1.30 1.05 1.68 (0.633) (0.870) (0.857) (0.278) (0.193) (0.294) (0.093) Insider x accruals -0.44 0.13 0.21 1.14 1.36 1.11 1.73 (0.659) (0.896) (0.835) (0.253) (0.175) (0.268) (0.083) OANCFY 0.12 -0.36 -0.78 0.75 1.38 0.94 1.07 (0.904) (0.716) (0.433) (0.454) (0.167) (0.350) (0.286) lossdummy - - - - rnd -0.42 2.94 -0.22 1.40 1.37 1.41 1.16 (.673) (.003) (.827) (.161) (.170) (.157) (.248) tax -1.98 -1.24 -1.98 -2.00 -0.85 0.05 -0.39 (.048) (.217) (.048) (.045) (.398) (.958) (.695) reportinglag 1.25 1.33 0.86 0.06 -2.08 -2.01 -1.97 (.210) (.185) (.389) (.953) (.038) (.045) (.049) intercept -0.18 0.58 0.46 -0.18 -0.10 0.24 0.22 (.854) (.564) (.643) (.858) (.922) (.807) (.825) N 62016 61862 61739 61588 61472 61372 61285 R-squared .0051 .0017 .0004 .0010 .0009 .0010 .0010 Coeff insider -.0001 .0001 .0003 .0002 .0002 .0002 .0004

Finally the results from tables 4 and 6 has to be compared for answering the actual hypothesis

of B1 > B2. There can be seen a lot of similarities when it comes to the control variables in

table 4 and 6 but also differences between the market reactions in case of accounting for

earnings or cash flows.

In both cases it shows that, looking at significance, how longer the holding period is

how stronger the predictability is by insider purchases industry-wide for earnings or cash flow

in each period of time. This suggest that there might be a pattern for insider purchases

predictability as you move on the days of holding period. From equations one it shows that as

an industry insider purchaser the predictability of earnings has the biggest influence on the

regression. While for equation two the different control variables have the most influence

dependent on the holding period return. Remarkableis that the main variable for equation two

is not that strong which probably come from the fact that the environment generate more

uncertainty about future cash flows. The two equations are different in overall significance.

The equation according to earnings suggest a significance, which mean earnings is good to 12_{Also the main effects of cash flow and accruals are not reported in this table.}

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24 predict industry-wide by insider purchasers. The relation becomes even stronger when the

holding period returns is longer. Although equation two do not show any significance. This

does not correspond with prior research of predicting future cash flows in general (Willinger

and Lorek, 1996). They made a distinction between quarterly and year-end based amounts

which leads to predicting cash flows out of accruals for quarterly based amounts. This

difference in outcome might come from predicting in general and predicting in case of insider

purchases because Piotroski (2005) found a different result. Looking at the significance of the

control variables there seems to be not a lot of difference between table 4 and 6, the

coefficients are almost the same in both equations. The influence of the control variable is the

same for both equations but works stronger in equation two. Which means that equation two

is more dependent of the control variables.

The coefficientsofinsider times earnings and cash flows at the different time periods

suggest that even though the changes are really small there is a small positive change of the

dependent variable, holding period return. For equation two there might be a pattern if it

comes to insider purchases accounting for cash flows after 240 days. Whereas equation one

shows another pattern with big outliers in some time periods. Comparing the amounts of the

coefficients for earnings and cash flows it shows that the results are in line with the

hypothesis, suggesting that the value of earnings is bigger than for cash flows. This result

consistent with Greenberg et al. (1986): Theycome with the result of earnings with more

predictive power than cash flows for general accounting not specific in case of insider

purchases, which might suggest that it is related. As can be seen the values will converge at a

certain point in time because for earnings it is going downward whereas for cash flows it

looks like it is growing. Also the coefficient for the control variables are almost the same in

both equations. The two regressions indirect show the predictability of earnings and cash

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25 advance predictability. For earnings and cash flows it seems that both can be predicted for a

certain term, this model only shows till 240 days in front.

Based on the compared results there is concluded that the hypothesis is supported by

the results from table 4 and 6, that says that B1>B2 for all the different holding period returns.

5. Conclusion

This paper examines to what extent individual insider’s purchases can forecast the accounting

variables earnings and cash flows from companies in the same industry. For the entire

database and 490,689 observations in the period of September 2000 till August 2012. This

study provides preliminary evidence with respect to the predictive power of insider purchases.

So the hypothesis is not formally tested because it just looks if the coefficient of earnings is

bigger than cash flows. The conclusion is below.

At first as can be seen from regression one, is that the relation found in this research

between the holding period return and the predictability of earnings is significant. Which

means that as an industry insider purchaser, earnings have indeed predictable power.Whereas

the relation between the holding periods return and cash flow predictability is not significant.

A conspicuous remark made here is that the relation between insider purchases accounting for

cash flows and holding period return is not significant and thus is not in line with the

expectations of this paper, while prior theory came to different conclusions. The different

outcome might come fromthe controlled variables in this thesis.Final results show that there

is strong support for the hypothesis which says that as an individual insider purchaser,

earnings is better predictable in the future than cash flows. Another indirect argue that is made

in this thesis is the future predictability for a certain term. This thesis shows future

predictability till at least eight months in front. The main conclusion is B1> B2 and therefore

as an individual insider purchaser, earnings is a better predictable variable compared to cash

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26 The major difference between this study and that of Veenman (2012) is that this paper

is focused on firms in the same industry. The event-study of Veenman (2012) is different than

this study because it measures the market reaction while this thesis focusses on holding

periods return as proxy for new information. The time of this paper matched the best of that

with Veenman (2012).

From this paper also some limitations or implications are clear, as the high skewness

of some variables so the natural logarithm had to be taken, the small coefficients are not easy

to compare so standardized coefficients had to be made and the lossdummy is omitted in this

regression which means it was not taking into account. Finally, the control variables

lossdummy and reportinglag must be excluded, it will give a significance different

conclusion.

Although the find result is the one expected, there might be still a lot of research in this area

which can be gathered.

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27 - Brochet, F. 2010. Information content of insider trades before and after the Sarbanes-Oxly

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Appendix A Variable definitions

HPR = holding period return, (1 + return stock i day t) till (1 + return stock i day t-1) – 1 for days 20, 40, 80,120,160,200 and 240.

SUMSHARESECDAY = individual insider purchases, measured by the sum of all shares traded in a given firm at a given security date.

QEARN = quarterly earnings (compustat’s data item IBQ) as percentage of lagged total assets (compustat’s data item ATQ).

OANCFY = net cash flows from operating activities on year base by compustat’s database. ACCRUALS = adding together different investments or interest over a period of time, measured as income before extraordinary items (IBQ) minus cash flows from operating activities (OANCFY).

LOSS = losses of firms using the level of cash flow from operations as proxy for losses, by compustat’s database IBQ. Variable equals one if it is negative and zero otherwise.

XRDQ = research and development expense by compustat’s database. Indicator variable equal to 1 when firm reports research and developments expenditures, zero otherwise.

LAG = reporting lag, measured as the difference between the security date and the transaction date of the insider.

Forecasting financial variables industry-wide : an empirical study to which extend insider are able to forecast earnings and cash flow in branch related companies