# Predicting Fraud by Investment Managers

After completing this reading, one should be able to:

• Explain the use and effectiveness of information disclosed by investment advisors in fraud prediction.
• Discuss the barriers and the costs incurred in implementing fraud prediction methods.
• Discuss ways to improve the ability of investors to use disclosed data to predict fraud.

## Introduction

Investment fraud refers to a wide range of deceptive practices used by managers to get money from unsuspecting investors. They may use false or deeply misleading information to convince the investor to make buy/sell decisions.

There are a few signs of investment fraud:

1. A promise to pay extremely high (or guaranteed) returns with little or no risk.
2. High-pressure tactics to convince the investor to act. The investor may be presented with a time-limited offer because all the scammer wants is their money before moving on to other victims.
3. The individuals pitching the ideas may not even be registered to sell investments or act on behalf of well-known investment firms.
4. A source may pretend to be in possession of insider information that could help the investor reap big within a very short time.

Fraud tends to fall in a number of categories: ponzi schemes, pyramid schemes, pump-and-dump schemes, promissory notes, and pre-IPO investment scams.

In recent times, the U.S. Securities and Exchange Commission has intensified efforts to uncover fraudulent activities among investment managers. This includes strong pursuit of insider trading claims, high whistleblower payouts, and expanded scrutiny of all asset valuation methodologies. However, investment fraud still remains a big problem, and stronger, more innovative tactics are needed to curb its steady rise.

In this chapter, we’re going to see the barriers and the costs incurred in implementing fraud prediction methods and see how information disclosed by investment managers can be used as yardstick to predict fraud.

## The Use and Effectiveness of Information Disclosed by Investment Advisors in Fraud Prediction

Disclosure refers to the timely release of all information that could reasonably be expected to influence an investor’s decision. It encompasses both positive and negative news as well as operational elements that impact business at an investment firm.

### The Need for Disclosure

In 2008, Bernie Madoff, founder of Madoff Investment Securities LLC, was charged with securities fraud. He went on to admit that the multibillion-dollar wealth management branch of his firm was essentially a Ponzi scheme.

In a Ponzi scheme, investment managers promise investors high rates of return which are actually never generated. Instead, money received from later investors is handed over to the unsuspecting earlier investors as returns. As such, a Ponzi scheme will continue running as long as there are new investors willing to invest. And as long as the older investors receive the “high returns,” getting money from new investors is hardly a problem.

In the Madoff case, prosecutors estimated the size of the fraud to be $64.8 billion. This figure was arrived at by scrutinizing funds in the accounts of Madoff’s 4,800 clients as of November 30, 2008. Madoff was later sentenced to 150 years in prison with restitution of$170 billion.

In the aftermath of the Madoff scandal, there was a concerted effort by both law makers and the general public to scale up and tighten the regulation of investment advisors.

The U.S. Securities and Exchange Commission (SEC) has since responded by proposing a raft of regulatory changes. Perhaps under pressure to act quickly, the proposed changes have been criticized as being tailor-made to the specific characteristics of the Madoff fraud. The general feeling is that the SEC appears to have conducted very little review of the existing regulatory system. In particular, the changes scarcely mention disclosure. Instead, the SEC appears to have emphasized on the scaling up of public enforcement of securities laws, such as through SEC examinations of investment advisors.

## Can Disclosure be an Effective Tool to Predict Fraud?

To answer this question, some research was done:

• Form ADVs were gathered from registered investment advisors from August 2001 through July 2006. In total, the study involved 13,853 advisors controlling about \$32 trillion in assets. Form ADV contains information on conflicts of interest as well as past regulatory and legal violations.
• SEC files were scrutinized to identify all cases in which investment advisors defrauded their clients from August 2001 through July 2010.
• This information was used to test whether the information investment advisors disclose in their Form ADV filings can be used to predict fraud.

The research results revealed that disclosures related to past regulatory violations, conflicts of interest, and monitoring all significantly predict fraud. To be precise, if an investor avoids 5% of the firms with the highest ex ante fraud risk – as measured through their ADV filings – then they are able to avoid 29% of frauds and over 40% of the dollar losses from fraud.

### Types of Disclosures

Let’s now see how important the information contained in form ADV is in predicting investment fraud.

Disclosures on past regulatory violations and past criminal or civil incidents have statistically significant fraud prediction power. The simplest explanation of this finding is that past problems, even if only minor, may be indicative of long-term, deeply entrenched issues. These could be either poor internal controls or unethical behavior among senior managers. In addition, the probability of regular impromptu SEC examinations is higher for firms with past violations.  With more frequent examinations, the probability of fraud detection increases.

Referral Fees show a significant positive relation with future fraud. Fraudulent firms would be relatively more willing to pay referral fees because fraud increases the marginal profit per every dollar under management.

Having an economic interest in client transactions significantly increases the probability of fraud. Having an investment manager who takes the opposite side in transactions with clients may signal a clear conflict of interest and increase the likelihood of fraud.

Firms with dealers/brokers as affiliates have significantly higher rates of fraud. Use of an affiliated brokerage removes external oversight and can help firms to commit fraud.

Other variables that show significant ability to predict fraud include:

• Logarithm of average account size – larger investors are associated with fewer subsequent frauds. Such investors are likely to have sophisticated financial positions and a deep understanding of the markets. This discourages fraud because it’s likely to be detected.
• Percent client agent – firms whose clients include a high proportion of agents (e.g., pension managers investing on behalf of employees) are significantly more likely to commit fraud. Unlike principals, agents do not bear the full cost of a fraud. As a result, they can be swayed through gifts or kickbacks.

The following variables show no significant ability to predict fraud:

• Soft Dollars – payments made by managers out of their clients’ accounts to purchase research that helps them make investment decisions – do not significantly predict fraud.
• Custody – where the firm has custody of the client’s cash or securities.

## The Barriers and the Costs Incurred in Implementing Fraud Prediction Methods

Now that we’ve established that fraud is indeed predictable, let’s examine the barriers and costs that are incurred when trying to implement the predictive methods that rely on Form ADV data.

### 1. Absence of historical Form ADV filings

Although the SEC discloses each investment firm’s current Form ADV filing, investors do not have access to historical filings. This reduces the investor’s ability to predict fraud.

Research seems to strongly suggest that a prediction model that relies on all prior Form ADV filings performs better than a model that only relies on a cross-section of ADV filings at a particular point in time. Although both models are found to perform reasonably well, the cross-sectional model is only able to predict 25.9% of frauds, compared to 31.4% for the panel model that uses data submitted in all prior years.

### 2. Provision of Form ADV filings in a format not amenable to statistical analysis

An investor’s ability to predict fraud is strongly impacted by the format of the disclosure data. The data has to be provided in a format that’s easy to read and interpret to ensure that it benefits as many investors as possible. For the longest time, however, this wasn’t the case.

Historically, Form ADV filings were shared as HTML documents, and investors had to decode them to extract the important pieces of data for detailed statistical analysis. This scared off some investors who had no idea how to go about the extraction process. For those that attempted to extract the data nonetheless, probably by enlisting skilled IT personnel, the cost ended up outweighing the benefits, forcing the investors to give up somewhere along the way.

In 2008, the SEC heeded calls for the provision of historical Form ADV data in a standardized format. This has substantially increased investors’ ability to put the information to good use.

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