The Sharpe Ratio is a tool investors can use that measures the risk of an investment in relation to its return. This strategy can be incredibly detrimental to investors because it may lead them to invest in a fund that exceeds their risk tolerance without their knowledge. Window dressing is all about creating an appearance of more success than there truly is. The most obvious issue is that this practice may mislead investors and cause them to make investments they would not otherwise make.
The other problem with window dressing is that portfolio managers with poor portfolio management more often implement it. Not only is window dressing associated with worse performance, but it can also cause it. Window dressing can cause worse long-term performance due to the high rate of turnover in the portfolio, which often leads to higher trade costs. Thankfully, there are a few simple red flags to watch out for.
The best way to avoid being misled by window dressing is to review performance. Holdings may lie, but performance is a far more reliable metric. Finally, you should review portfolio turnover percentages and how often the portfolio manager buys and sells investments. High portfolio turnover is not necessarily bad, but it can be a red flag.
Not all funds with a high turnover percentage are window dressing, but almost all funds that use window dressing will have a high turnover percentage. While window dressing can occur quarterly, it is more often used at the end of the year since this is usually when more investors review reports. You may want to spend extra time studying reports highlighting year-end performance and holdings.
If you spot any window dressing red flags, the best thing to do is ask questions. Window dressing is not limited to portfolio managers and mutual funds. Companies may also use window dressing to make financial statements look better than they are. There are two main reasons companies use window dressing. The second reason is to convince a lender to allow the company to borrow money under more favorable circumstances. Window dressing at a company is similar to the window dressing of a portfolio, but it is slightly different.
Near the end of an accounting period, a company may use several different strategies to improve the appearance of financial statements. These strategies may include accounting practices impacting accounts receivable, revenue, fixed assets, cash, depreciation, expenses, etc. It may also be a slippery slope. Companies that encourage window dressing may continue using more and more manipulative accounting practices that eventually constitute fraud. Window dressing is a way of legally manipulating the reports of a portfolio manager or company to improve appearances.
Thankfully, it becomes relatively easy to spot once you know what to look for. Sign up for the newsletter to get tips and strategies I don't share anywhere else. By Leo Smigel Updated on April 13, How Window Dressing Works. Poor Performers. The CNMV provided this information exclusively to the authors for research purposes.
Therefore, the database is not available for retail and institutional investors, which means that managers could not anticipate the use of this information. Moreover, the sample is free of survivorship bias because the analysis also included funds that disappeared during the study horizon.
The holding database includes portfolio positions in stocks, bonds and other assets and excludes cash positions. All securities reported are carefully identified by the ISIN codes. The second set, also provided by the CNMV, contains daily net asset values NAV and management fees for each fund in the sample from December 1, to January 31, The analysis performed in this paper requires that all fund portfolio holdings have the corresponding daily NAV data at least during one month before and one month after the date of the report.
To achieve the goals of this paper, the daily returns of securities reported by funds are also necessary. Therefore, the daily closing price of all Spanish stocks that trade in the Continuous Market and in the New Market of Spain which are the main domestic stocks that are traded in this market are obtained from the Madrid Stock Exchange.
With regard to foreign stocks, the major leaders of European stocks are controlled i. Reuters DataLink provided the daily closing prices of these stocks. The returns of fixed-income securities are calculated using indices published by Analistas Financieros Internacionales AFI , as follows: three-year Spanish public debt index for Spanish long-term securities; Treasury bill index one-year for Spanish short-term securities; and three-year Euro public debt index for European fixed-income securities.
The returns on investments in other mutual fund units are obtained from the daily fund NAV database. The sample period for these returns spans from December 1, to January 31, The share of the fund portfolio in each type of security is reported in Table 1. All funds in the sample are Spanish domestic equity funds. Therefore, as expected, the main investment is in domestic stocks. Portfolio holdings of Spanish domestic equity funds.
This table reports the portfolio share by type of security for our sample. The assets invested by funds are classified by categories, as follows: stocks Spanish and European , fixed income Spanish long-term, Spanish short-term, and European , other mutual fund units, cash and cash equivalents, and non-controlled securities. The following data correspond to December of each year. This section describes the methodology employed in this paper to determine whether a mutual fund engages in abnormal investment strategies around quarterly disclosures.
Window-dressing practices have traditionally been tested by analyzing fund trading activity using portfolio holdings databases. Nevertheless, there is another approach that is based on the analysis of fund return anomalies around portfolio reporting dates. Meier and Schaumburg propose an approach that combines the use of both portfolio holdings and mutual fund returns.
Taking advantage of the information that each database supplies, these authors propose a test to identify window-dressed portfolios by examining divergences between the return of the reported portfolio and the observed fund return. Interim fund trades cannot be directly captured with this approach. Nevertheless, an effort is made to solve this issue by performing a daily analysis of return differences.
As a consequence, the assessment of fund management behaviour in between reporting dates can be improved. Moreover, this method has the advantage of analyzing each fund portfolio holding individually and not in an aggregate form, as is performed in other approaches employed to detect window-dressing practices.
To distinguish return patterns associated with potential window-dressing practices, the approach proposed by Meier and Schaumburg requires a benchmark to compare against the realized fund return. This benchmark is the return of the buy-and-hold strategy, which represents the return that the fund would have reached if the holdings of the disclosed portfolio were maintained for the period analyzed.
Given that window-dressing practices may imply higher trading activity just prior to reporting Meier and Schaumburg, ; Elton et al. Meier and Schaumburg analyze US domestic equity funds that must report each quarter, but their database only covers semi-annual portfolio holdings. These authors then calculate the return on the buy-and-hold strategy in an interval that starts 91 days before the reporting date and ends 91 days afterwards i.
In the Spanish market, mutual funds must report to investors quarterly, which would require a detailed analysis on a quarterly basis. However, our database of monthly portfolio holdings allows us to analyze every month. Therefore, our study overcomes the aforementioned study because it analyzes return patterns not only around disclosure portfolios quarterly mandatory reports but also around the non-disclosure portfolios. The return patterns associated with potential window-dressing practices are analyzed in an interval that starts one month before the reporting date and ends one month afterwards.
The number of trading days varies depending on the month, with a maximum of 23 trading days. The interval analyzed can be defined between d b and d a , where d b d a is the number of trading days before after the reporting date. However, the portfolio weights calculated with Eq. As this weight calculation is performed under the assumption that funds follow a buy-and-hold strategy, the following process guarantees the correct daily updating of security positions according to their appreciation.
The daily security returns are calculated from datasets of daily closing prices, previously described in the data section. From the database of daily net asset values NAV , the daily observed fund return is calculated for each fund as the relative change in NAV. However, the NAV return cannot be compared with the return of the fund portfolio holdings. The NAV return is net of the operating expenses, while the return of fund holdings does not include the subtraction corresponding to these expenses.
To solve this incompatibility, the management fees are added back to the net fund return to obtain the gross fund return. Daily returns are calculated for each fund over the period from December 1, through January 31, Therefore, a fund that exists throughout the sample period has 1, daily returns. For the entire sample of funds, a total of , daily fund returns are calculated. As mentioned above, the key to identifying window-dressed portfolios is to analyze possible significant divergences between the daily return of fund portfolio holdings Rk,tP and the daily observed fund return Rk,tF.
These returns are calculated for each fund and each reported portfolio for a period of time spanning from a month before to a month after the reporting date i. Following the approach of Meier and Schaumburg , the return difference RD between these returns for fund k on day t is calculated as:. The analysis of the RD sign i. A significant positive RD implies that the buy-and-hold return of the reported portfolio outperforms the observed fund return.
If this pattern occurs prior to the reporting date, it could indicate that the fund manipulates the portfolio by buying recently winner stocks and eliminating loser stocks. This window-dressing strategy results in a return of reported assets that is not representative of the portfolio held by the fund during the month, with the consequent difference in returns.
The window-dressing hypothesis states that fund managers are only motivated to improve the portfolio's image when they must disclose their portfolio holdings to clients. Therefore, one would expect that this trading strategy only appears before mandatory reports, which are reported quarterly for the Spanish market. This hypothesis can be verified in this study because our monthly database of portfolio holdings allows for the comparison of return patterns between disclosed and non-disclosed portfolios.
Specifically, we expect to find a higher daily RD before the reporting dates for portfolios reported quarterly than for those in other months. To identify possible RD patterns associated with window-dressing practices, a detailed analysis of the RDs of each fund reported portfolio is conducted. Taking into account that a time series of RD is created for each reported portfolio, the RD study is based on a time series analysis of daily returns.
These series elapse from d b days before to d a days after the reporting date. Regarding the methodologies for a time series analysis, several financial studies employ linear regression models OLS , assuming that the data are normally distributed, serial uncorrelated, and with constant variance. However, these assumptions are unrealistic to model some financial market variables.
In particular, for financial market returns, the changes in variance over time have been widely documented. The equation of the mean 6 is written as a function of exogenous variables with an error term, while the variance Eq. Independent of the distribution assumption, the GARCH models are typically estimated by the method of maximum likelihood.
Although the aim of this paper is to study the mean behaviour of the sample of the time series, the GARCH approach is employed to correct possible variance problems, such as heteroscedasticity and autocorrelation. To determine the order of the GARCH model, some p , q combinations are applied to find the most accurate model for our sample. Once the order for the model has been specified, we find that the most accurate conditional distribution of the error term is the GED distribution.
An advantage of the GED assumption is that it contains the normal distribution as a special case but also allows fatter and thinner tails than the ones in the normal distribution Nelson, This section focuses on the identification of filings where the reported portfolio is not informative of the actual return obtained by the fund. Once these portfolios are identified, we aim to provide evidence on two issues: first, to determine potential persistence in window-dresser funds; and second, to determine potential common characteristics in portfolios that have been manipulated.
As previously mentioned, this study only focuses on the analysis of the mean to identify significant RDs and possible fund patterns. Given that the main goal is the identification of window-dressed portfolios, it is necessary to modify the mean equation 6 to differentiate the RD patterns before and after the reporting date because this phenomenon is mainly observed prior to reporting Meier and Schaumburg, ; Elton et al.
BEF t takes the value of one for days before the reporting date and zero otherwise. In contrast, AFT t takes the value of one for days after the reporting date and zero otherwise. The results are reported in Table 2. Summary results for identified portfolios. This table shows the number of portfolios and the average daily RD before the reporting date for the entire sample and the set of portfolios identified with a significant and positive BEF t coefficient from the GARCH estimation Eqs.
This information is also presented for months that coincide with mandatory disclosure dates quarters and other months. Moreover, the difference of the average daily RD between quarters Q and other months OM is reported in the final column. This table also shows remarkable results when splitting the sample between months in which portfolios were disclosed quarters and other months. This finding suggests that the main divergence between the return of the fund portfolio holdings and the observed fund return occurs prior to mandatory reports, which supports the window-dressing hypothesis.
As expected under this hypothesis, identified portfolios reported on quarter-ends exhibit the highest average daily RD 0. This result means that these mutual funds would have earned, on average, 0. This RD is even larger in our study than in Meier and Schaumburg's study; these authors find that the median return difference is approximately 0.
Pattern of daily RD for the entire sample and identified. The boxes represent the interquartile range i. The sample consists of 6, reported portfolios in Panel A and in Panel B. Portfolios that have been identified in the previous section have positive and significant RDs before the reporting dates; however, this condition is not enough to ensure that such portfolios have been manipulated according to window-dressing strategies.
The window-dressing hypothesis states that fund managers are motivated to improve the portfolio's image when they must disclose their portfolio holdings to shareholders and clients. Therefore, one would expect this trading strategy to appear only near mandatory reports.
The goal of this section is to analyze in detail the identified portfolios to determine whether fund managers follow certain cosmetic practices around portfolio disclosure to investors. Table 3 summarizes the main results related to the identified portfolios by month.
Average daily RD for identified portfolios. By months, this table shows the number of portfolios reported and identified as well as the percentage of identified portfolios with respect to the sample. In addition, the average daily RD for identified portfolios before the reporting date is presented. Moreover, when the average daily RD before the reporting date is analyzed, Table 3 reveals that the highest RD also corresponds to portfolios reported in June and September.
In the previous section, Table 2 showed significant differences in daily RD of identified portfolios between quarter-end months and other months. Table 3 confirms former results and further details that the phenomenon is mostly driven by second and third quarter June and September.
The portfolio image at mid-year seems to be important in the Spanish industry compared to previous studies in which December is the month with higher window dressing activity. Note, however, that the frequency of our data allows us to deep in detail in other quarters rather than only the last one. This differential pattern is better illustrated in Fig.
The comparison of the daily RD for portfolios that coincide with mandatory reports Panel A with those in other months Panel B , reveals that something atypical occurs in quarterly reports. Before the reporting date, portfolios in Panel B exhibit a smaller dispersion and a median close to zero, while portfolios in Panel A show a positive RD, positive interquartile ranges, and a median of 0. However, the RD behaviour for portfolios in June and September Panels C and D is even more remarkable because they display more positive interquartile ranges than the entire set of quarterly portfolios, and their median RD before the reporting date is approximately 0.
Pattern of daily RD for identified. This figure illustrates the average daily RD for identified portfolios corresponding to quarterly mandatory reports Panel A and those in other months Panel B. Moreover, Panels C and D show RD patterns in months with the highest percentage of identified portfolios with respect to the sample, June and September.
Regarding RD behaviour after the reporting date for quarterly portfolios, Fig. This finding differs from the results obtained by Meier and Schaumburg ; they found no abnormal return differences after the reporting date, suggesting that the mutual funds might hold the reported portfolio over the next quarter.
However, our results do not support this conclusion for the Spanish mutual funds, especially in June. This month funds exhibit significantly high turnover ratio which might explain those results. This section looks for potential common features in the portfolios that have been identified as window-dressed portfolios.
Although the analysis only focuses on portfolios, one might expect certain characteristics from funds that have manipulated their portfolios. For example, one would expect some of the following patterns: funds periodically window dress their portfolios; fund management companies follow window-dressing strategies in several funds that they manage; higher levels of window dressing occur in funds with poor past performance; and some coincidences in dates, among others.
In a first review of common characteristics in the sub-sample, we find that window-dressed portfolios are quite dispersed over funds, as 95 out of funds in the sample have at least one portfolio identified as window dressed. In addition, the analysis of fund management companies for those funds with a higher percentage of window-dressed portfolios also shows a high dispersion level because a different company managed each fund. Regarding the dates of the identified portfolios, we find that each of the quarters of the sample period 29 in total has at least one window-dressed portfolio.
This fact is more interesting when it is related with the Ibex performance on those dates because these were the months of the lowest profitability during the sample period. Although our finding contradicts the results of Meier and Schaumburg , because they find that the use of window-dressing strategies is more likely in a bull market, it seems more reasonable to think that mutual funds need to engage in this type of strategies in poor performance periods, since they need to ensure that their clients are satisfied with the fund management.
This result might suggest that funds with poor past performance are more likely to manipulate their portfolios that are presented to clients. This behaviour of mutual funds is reasonable if one considers that many investors guide their decisions according to recent performance records Chevalier and Ellison, ; Sirri and Tufano, Table 4.
Past performance of window-dressed portfolios. Each quarter, mutual funds are classified into four quartiles according to their cumulative return over the past month: Winner , Medium-Winner , Medium-Loser , and Loser. The Winner quartile contains the funds with the largest returns and the Loser quartile contains the funds with the smallest returns. This table reports, for each quartile, the number of window-dressed portfolios and the average fund return over the past month. In summary, the analyses of identified portfolios that coincide with mandatory reports seem to indicate that the window-dressing practice is an isolated case within the sample of funds analyzed because these portfolios are distributed over funds and fund management companies.
Nevertheless, the manipulated portfolios seem to be clustered over bear market periods, probably as a response to poor past performance. Several studies have found evidence of the use of window-dressing practices by mutual funds by comparing portfolio holdings and analyzing their trading activity around disclosure dates. However, there are few studies in existing literature that analyze anomalies in mutual fund returns to identify these practices, and there is an even smaller number of studies that combine the analysis of observed fund returns with portfolio holdings information.
In the latter subset, none of the studies use holdings data with a higher frequency than quarterly, which could limit their conclusions. Therefore, this paper aims to extend the study of portfolio manipulation around mandatory reports by examining daily observed fund returns and monthly portfolio holdings in Spain, a relevant European fund industry. The detection of window-dressed portfolios is based on the analysis of the difference between the return of the reported fund holdings and the observed fund return.
The estimation of a GARCH model allows for the identification of a low percentage of filings that have positive RD before the reporting date and that coincide with mandatory reports. The monthly database used allows for the comparison between disclosed and undisclosed identified portfolios, showing that the average daily RD is higher for quarterly portfolios than for those in other months. In addition, the results show that June and September are the months with the highest percentage of portfolios identified with respect to the total number of reported portfolios in the sample.
The results also show that these portfolios have the highest RD before the reporting date. This finding is in accordance with expected results under the window-dressing hypothesis because those months coincide with mandatory reports. The analyses of those portfolios identified as window-dressed portfolios suggest that window dressing is not a common practice in the Spanish equity funds.
This conclusion is supported by the lesser proportion of filings identified in the extensive sample of portfolio holdings and the dispersion of these portfolios over funds and fund management companies. However, the results also suggest that funds with poor past performance are more likely to manipulate their portfolios and that window-dressed portfolios seem to be clustered over bear market periods, probably as a response to poor past performance.
Any possible errors in this article are the exclusive responsibility of the authors.. Returns for Spanish stocks are adjusted by dividends, stock splits, and seasoned equity offerings, while returns for European stocks are adjusted by dividends and stock splits.. See, for example, Fama and Lau et al. We estimate the monthly portfolio turnover to explore the intensity of mutual fund trading throughout the calendar year. These results are omitted for the sake of brevity, but this information is available upon request..
Tables with this information are not presented to avoid having large amounts of data without a significant contribution to the value of the analysis.. Inicio The Spanish Review of Financial Economics Assessment of window dressing using fund returns and portfolio holdings. ISSN: Indexed in: Scopus See more Follow us:. Discontinued publication For more information click here. Previous article Next article. Issue 2. Pages July - December Export reference.
|Irr calculation assumes reinvesting cash flows at irresolute||Compare Accounts. Nevertheless, the manipulated portfolios seem to be clustered over bear market periods, probably as a response to poor past performance. If you were to disclose the top 15 most heavily weighted sells in a portfolio, if they were sold to lock in profits, then even if a manager plans on re-buying some of these stocks, no one will know the exact date of when the manager re-buys these stocks in the next quarter, or even if he does re-buy these stocks, they will not know this information until the end of books download forex next quarter. The following data correspond to December of each year. In addition, the results show that June and September are the months with the highest percentage of portfolios identified with respect to the total number of reported portfolios in the sample. As a result of this potential manipulation, the disclosed portfolios may reveal an uninformative image of the recent management of the fund, thus rising agency problems between fund managers and investors.|
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|Window dressing illegal investing portfolio||923|
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The act of pumping up a stock the final day of the quarter or year to 'boost performance' - isn't this falling into the are of market manipulation and potential fraud or at least misleading advertising against fund investors? People have been prosecuted in the futures markets for jamming the close before expiry, so why do stock fund managers get a free pass? Either make all 'market manipulation' legal, or enforce the laws in all cases - the current selective enforcement makes no sense at all.
I agree with Cutten, and I understand what he is talking about but I checked investopedia and other sites and the CK is correct. Now I don't know what they call the eom manipulatioon of stock prices that Cutten is referring to but it's done and he has a valid point.
If you think it is unnatural, why not simply short into it? I say this tongue-in-cheek as sometimes over the Internet these things could be misinterpreted. Enforcement is a sticky wicket. You can't catch everyone and even if you could it would become very unpopular for you to do so. Better to take clear-cut egregious offenders and fry the hell out of them in a public spectacle better for regulators, better for the string pullers in industry.
I agree with Cutten in premise though, but I think it is unrealistic to enforce in this case as it is so easily masked as legitimate activity. What I find entertaining is how those guys gamed the Timber Hill market making bot, started smacking the house around, and then they get a nasty enforcement action. You would almost think they were counting cards in Nigeria or something It sure wakes one up to who the regulators work for It should be noted that such a practice is neither illegal nor unethical, and it is within the ambit of accounting practices as guided by relevant governing bodies.
Window dressing may also be done through the use of exceptional and extraordinary items. This involves including the cost and revenues that arise from normal business activity but are unusual in some way. For example, redundancy costs are normal in business , but these are exceptional items. Similarly, revenues that arise only a single time and, as such, that are unusual and unlikely to be repeated, are considered exceptional items.
If exceptional items are shown as exceptional items, this is acceptable. However, if they are shown as regular items, revenues are affected, which results in either an understatement of profits inclusion of redundancy costs or an oversetting of profits inclusion of unusual revenue. As these items do not occur due to normal business activity, they should be highlighted and included only after calculating profit before interest and tax. If such items are included as normal items, this means that regular profit is understated or overstated.
Choosing a convenient time for reporting is another way to engage in window dressing. Therefore, the manager postpones payments that should have been made in the last week before the end of the financial year. Of course, after the postponed payments are made in this case, after the date by which the balance sheets needed to be prepared , the bank balance will fall back into negative territory.
Crucial information of this kind, which is essential for determining the liquidity of the enterprise, is window-dressed by choosing a convenient time of reporting. Choosing a convenient method of depreciation is another window dressing approach that can depict a rosy picture for an enterprise.
For example, by choosing the fixed installment method of charging depreciation instead of the reducing balance method , it is possible to boost profits. As all of the above examples indicate, it is possible to engage in window dressing in diverse ways to present a rosier-than-reality picture of a business. Window dressing that is done to serve a positive purpose, without violating the principles and standards of accounting , is not considered illegal.
However, fraudulent practices that are indulged under the umbrella of window dressing are punishable under the law. All accounting professionals, account analysts, credit rating agencies, and other professional bodies are aware of window dressing. In the case of machinery and depreciation, the following questions would be pursued in a detailed examination:. The following information was extracted from the books of Goodluck Company Ltd for the year Therefore, disappointed with this operating performance, the manager decided to window dress the figures to boost the profits, the aim being to suggest to shareholders that operating efficiency is good.
The balance sheet of the company before window dressing stood as follows:. Note: Students should note that the above statement is prepared only to illustrate how window dressing helps to boost profits on paper.
They are all hidden adjustments that are known only to the accounts manager and to no one else. That is to say, these figures are not recorded at all. Note: The major drawback of this decision is that the company will have to pay corporate tax on boosted profits i. By contrast, the tax to be paid on real profits i. Window dressing is when managers in an organization take measures to make their Financial Statements appear better than they actually are.
Businesses use window dressing for diverse reasons. Here are a few: 1. To protect an enterprise from takeovers 2. To improve share valuations by posting higher profits 3. To appease shareholders by posting higher profits and, thereby, encouraging them to approve accounts without interrogation at the annual general meeting 4. To increase revenue from takeovers 5. To retain or gain lines of credit.
Window dressing. Like window dressing with funds, window-dressing a company's financial statements is legal but misleads shareholders, investors, and lenders. It. Window dressing is a short-term strategy used by companies and funds to make their financial reports and portfolios look more appealing to clients, consumers.