The case is about the Monetta Financial Services Company, an investment house Essay Example

The case is about the Monetta Financial Services Company, an investment house Essay Example The case is about the Monetta Financial Services Company, an investment house Essay The case is about the Monetta Financial Services Company, an investment house Essay The case is about the Monetta Financial Services Company, an investment house. The company has been charged by the Securities and Exchange Commission (SEC) of United States that it knowingly allocated hot IPOs to its own Directors and trustees instead of to its mutual fund clients. The case explains the process of issuing of the Initial Public Offerings (IPOs) in the United Statess capital market in addition to describing the critical role played by the investment bank or underwriters. The case highlights how the underwriters carry out the due diligence of the company, writes the prospectus and file the all important documents with the SEC. The case ends with the series of stock market data for IPOs in which Monetta participated and require the students to draft the brief for SEC enabling it to make a case against the company. Brief for SEC In order to draft a brief for the SEC that will help SEC to make its case against Monetta Financial Services, Inc. it is imperative to describe here the methodology and set of techniques that will be used to build the case. Two major arguments will be used to establish that Monetta willingly and knowingly distributed hot IPOs to its directors. These are mathematical / statistical arguments using standard descriptive statistics and legal arguments based on the SEC Act. Both arguments will hopefully proof beyond reasonable doubt that Monetta acted with ill faith and deceitful intent. Statistical Analysis To perform the statistical analysis we need to separate the IPOs that were allocated to Directors with the ones allocated to the Fund clients in order to show that IPOs allocated to Directors have higher returns with low risk as compared to IPOs allocated to Fund clients in addition to comparing these figures with the overall 50 IPOs in which Monetta participated. Using four data series i.e Ret-Close, Ret-Open, Flipping Ratio and Mid-to-Offer, we will calculate descriptive statistical figures for each set of IPOs (Directors and Fund clients). Return-to-Open Data Series The return-to-open is defined as the change in price of IPO from offering price to the opening day trade price. The higher percentage change from offering to open day trade price represents that IPO is hot. Using the statistical data in Exhibit 1, following can be inferred: * The IPOs allocated to directors have a mean 34.2% with a standard deviation (read: risk) of 16.7%. As compared to IPOs allocated to fund clients where the mean appreciation in the price is 22.7% with a standard deviation of 19.3%. It clearly indicates that IPOs allocated to directors have higher returns with low risk attached to them. * Similarly, minimum and maximum price appreciation for the IPOs allocated to directors was 12.5% and 68.8% respectively. While minimum and maximum price for IPOs allocated to fund clients were 0% and 69.4% that represents that the range is much wider for IPOs allocated to fund clients. * Comparing both percentages with the overall percentages shows that IPOs allocated to directors appreciates 9% more as compared to 3% for IPOs allocated to clients on day 1. * Other statistics such as sample variance and skewness also lead us to believe that Monetta allocated hot IPOs to its directors and cold IPOs to fund. Flipping Ratio Data Series The term Flipping ratio indicates block of 10,000 shares sold on the day 1 of IPOs trading. It reflects whether the investor consider the share to yield long-term gains. If the flipping ratio is low which means that the investor consider it best IPOs in terms of long-term investment. Based on this ratio and using the series of statistical analysis in Exhibit 2, we conclude that: * For the period in question, the average Flipping ratio was 19.0% for the IPOs allocated to director/both, 29.5% allocated to fund client, and 26.7% for all IPOs. * Another important measure that proves that Monetta allocated best IPOs to its directors is Median. The median for IPOs allocated to directors was 17.4% as compared to 24.3% for IPOs allocated to fund clients. The 17.4% is again lower in comparison with the median for all IPOs that were 23%. * This above data proves that well informed investors do not flip the hottest IPOs because on the average these are the best long-term investment. Mid-to-Offer Data Series The Mid-to-Offer is a good indicator for the hot IPOs. It highlights the change in IPOs issue price from the mid-point of the filing range to the offering price. The higher change in the percentage of Mid-to-Offer price from its initial filing range proves that shares are most likely to be above average performers in the secondary market trading. Based on the calculation as shown in Exhibit 3, we see that: * The Mid-to-Offer change for 13 IPOs allocated to directors was 21.9%, while for 37 IPOs allocated to fund clients the Mid-to-Offer change was 10.1%. Since the information of changes in Mid-to-Offer price is available before the trading of share begins, therefore any well informed investor can easily deduct the level of demand or in other word interest level of potential buyers. * The range (max and min) is especially a good measure to determine the Mid-to-Offer change. Looking at the ranges for IPOs allocated to director which was from 0% to 60% and IPOs allocated to fund clients which was -27.3% to 80, it is safe to conclude that Monetta knowingly allocated best IPOs to its directors because of the reason stated above. The change in the price for IPOs allocated to directors does not fall below 0%., while for 10 IPOs out of 37 IPOs allocated to fund clients the Mid-to-Offer price becomes negative. All of the above analysis leads us to believe that there is statistical significant inference that Monetta deliberately, willfully and consistently allocated IPOs that had, or appeared to have had, the highest probability of earning the best returns with the minimum possible risk. The management of Monetta knew well in advance about the chances of any particular IPO of giving highest return because of there knowledge about that IPO, since they attend all of the meetings organized by the underwriters to market their IPOs. Also, these statistics proves that there is a high probability that the result did not occur by chance. Similarly, individual analysis of Return-to-Open, and Flipping Ratio produce positively correlated results as shown in Exhibit 4. As shown the correlation for IPOs allocated is -0.675 as compared to -0.684 for IPOs allocated to clients. Legal Analysis The statistical analysis proved beyond doubt that Monetta distributed IPOs to its directors that were hot, therefore violated a number of SEC laws and its fiduciary responsibilities. The Monetta management was fiduciaries of the company. A fiduciary duty is a duty to act for someone elses benefit, while subordinating ones personal interests to that of the other person1. By deliberately and willfully allocating hot IPOs to its directors the management of Monetta did not upheld the highest standard of duty implied by law. More specifically, the Monetta management violated following SEC laws2: Section 17(a) of Securities Act and Section 10(b) of Exchange Act and Rule 10b-5 Section 17(a) of Securities Act and Section 10(b) of Exchange Act and Rule 10b-5 prohibits any person in the offer, purchase, and sale of any security in interstate commerce or by use of the mails: (1) to employ any device, scheme, or artifice to defraud, or (2) to obtain money of any untrue statement of material fact or any omission of a material fact necessary so as not to mislead, or (3) to engage in any transaction, practice, or course of business which would operate as fraud or deceit upon the purchase. The statistical analysis proves that Monetta willfully violated the antifraud provisions of the securities statutes because each knowingly or recklessly omitted to inform Fund shareholders and prospective shareholders of the conflict of interest caused by the allocation to the directors of hot IPOs by Monetta. Section 206 (1) and Section 206 (2) of the Advisors Act It also proved the Monetta willfully violated Section 206 (1) and Section (2) of the Advisors Act by not disclosing to the Funds, the Funds clients and possible investors the allocation of hot IPOs to certain directors. Assumptions The set of assumptions that I have made in establishing the case against Monetta is as follows: * The Monetta management attended all the presentations conducted by underwriters on behalf of its clients. * The Monetta knew special information such as subscription ratio for all the IPOs and deliberately did not disclose this information to its fund clients. * The Monetta management are composed of experienced professionals that can predict with relatively high accuracy when the opening trading price of an IPO in the secondary market likely will be greater that the offering price even before the issuing of IPO Exhibit 1 Open-to-Return Data Series IPOs (Directors/Both) Mean 0.342384615 Standard Error 0.046326951 Median 0.292 Standard Deviation 0.167034197 Sample Variance 0.027900423 Kurtosis 0.852885034 Skewness 1.186083281 Range 0.563 Minimum 0.125 Maximum 0.688 Count 13 IPOs IPOs (Fund Clients) Mean 0.227162162 Standard Error 0.031890967 Median 0.177 Standard Deviation 0.193985182 Sample Variance 0.037630251 Kurtosis -0.346461682 Skewness 0.745853834 Range 0.694 Minimum 0 Maximum 0.694 Count 37 IPOs IPOs (Overall) Mean 0.25712 Standard Error 0.027234517 Median 0.242 Standard Deviation 0.192577113 Sample Variance 0.037085944 Kurtosis -0.298852966 Skewness 0.632488126 Range 0.694 Minimum 0 Maximum 0.694 Count 50 Exhibit 2 Flipping Ratio Data Series IPOs (Directors/Both) Mean 0.190307692 Standard Error 0.01577989 Median 0.174 Standard Deviation 0.056895203 Sample Variance 0.003237064 Kurtosis 0.383093378 Skewness 0.17173002 Range 0.221 Minimum 0.081 Maximum 0.302 Count 13 IPOs IPOs (Fund Clients) Mean 0.295216216 Standard Error 0.02760873 Median 0.243 Standard Deviation 0.167937345 Sample Variance 0.028202952 Kurtosis 0.96144109 Skewness 1.165747672 Range 0.703 Minimum 0.085 Maximum 0.788 Count 37 IPOs IPOs (Overall) Mean 0.26794 Standard Error 0.021759603 Median 0.2305 Standard Deviation 0.15386363 Sample Variance 0.023674017 Kurtosis 2.143649271 Skewness 1.500169594 Range 0.707 Minimum 0.081 Maximum 0.788 Count 50 Exhibit 3 Mid-to-Offer Data Series IPOs (Directors/Both) Mean 21.9 Standard Error 4.429027213 Median 18.2 Standard Deviation 15.96908472 Sample Variance 255.0116667 Kurtosis 1.375573908 Skewness 1.058585971 Range 60 Minimum 0 Maximum 60 Count 13 IPOs (Fund Clients) Mean 10.11351351 Standard Error 3.405602085 Median 9.1 Standard Deviation 20.71546875 Sample Variance 429.1306456 Kurtosis 2.888275781 Skewness 1.22609158 Range 107.3 Minimum -27.3 Maximum 80 Count 37 IPOs (Overall) Mean 13.178 Standard Error 2.846068954 Median 11.2 Standard Deviation 20.12474657 Sample Variance 405.0054245 Kurtosis 1.920960104 Skewness 0.940388102 Range 107.3 Minimum -27.3 Maximum 80 Count 50 Exhibit 4 Correlation Coefficient for IPOs allocated to Directors/Both ISSUE PORTFOLIO Ret-Open Flipping Mid-Offer (%) Powersoft Director 0.688 0.081 33.3 Wall Data Both 0.25 0.186 33.3 Parallan Computer Both 0.125 0.302 9.1 BHC Financial Both 0.268 0.174 0 3DO Director 0.233 0.153 36.4 Catalyst Semiconductor Director 0.205 0.242 10 Auspex Systems Director 0.292 0.245 9.1 Papa Johns International Both 0.385 0.17 18.2 Sunglass Hut International Both 0.363 0.215 21.2 BroadBand Technologies Both 0.431 0.163 12.5 Cyrix Director 0.234 0.15 14.3 Wonderware Both 0.321 0.239 27.3 NetManage Both 0.656 0.154 60 Correlation Coefficient using the Return-to-Open and Flipping Data Series is as follows: =CORREL(Ret-Open, Flipping) returns -0.675. The negative signs show that there exists inverse relationship between appreciations of share price on day 1 with the amount of shares sold in the block of 10,000 termed as Flipping. Exhibit 5 Correlation Coefficient for IPOs allocated to Fund Clients ISSUE PORTFOLIO Ret-Open Flipping Mid-Offer (%) A Pea in the Pod Fund 0.021 0.561 -14.3 Actel Fund 0.184 0.229 0 Allied Holdings Fund 0.018 0.124 0 ANTEC Fund 0.333 0.232 20 Avid Technology Fund 0.25 0.2 25 Broadcasting Partners Fund 0.276 0.117 11.5 Cobra Golf Fund 0.524 0.228 10.5 Coca-Cola FEMSA Fund 0.098 0.273 13.9 Community Health Computing Fund 0.1 0.395 -9.1 Cornerstone Imaging Fund 0.318 0.149 18.9 CTL Credit Fund 0 0.424 0 Envirotest Systems Fund 0.078 0.407 6.7 Gupta Fund 0.694 0.13 80 HomeTown Buffet Fund 0.4 0.237 50 Inco Homes Corp Fund 0 0.607 -9.1 Intuit Fund 0.375 0.119 25 Key Technology Fund 0.056 0.434 -18.2 Kurzweil Applied Intelligence Fund 0.225 0.315 -9.1 The data provided here is truncated to save space. The figure below is for all 37 IPOs. Correlation Coefficient using the Return-to-Open and Flipping Data Series is as follows: =CORREL(Ret-Open, Flipping) returns -0.684. The negative signs show that there exists inverse relationship between appreciations of share price on day 1 with the amount of shares sold in the block of 10,000 termed as Flipping. 1 Securities and Exchange Commission of US website 2 Securities Regulation by David L. Ratner, 3rd Edition and SECLAW.com website

8 Ways to Tell If a Website Is Reliable

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