Financial Conglomerates and Chinese Walls

Andrew Tuch is Associate Professor of Law at Washington University School of Law.

In my paper, Financial Conglomerates and Chinese Walls, which was recently made available on SSRN, I examine the effectiveness of Chinese walls, or information barriers, in preventing financial conglomerates from misusing non-public information in their trading and other activities. In recent years, empirical evidence has shown that financial conglomerates’ Chinese walls fail in important contexts, allowing firms to trade using non-public information they garner from their clients. Nevertheless, Chinese walls continue to have the legal effect of allowing financial conglomerates to discharge the otherwise incompatible client duties they owe under agency law. These incompatible duties arise due to the inflexible application of agency law and to financial conglomerates’ organizational structure, under which firms act for numerous clients across a broad and diverse range of financial activities, accumulating vast quantities of non-public information in doing so. As agents, firms are duty-bound to disclose material information in their possession to clients, and yet to do so is to breach duties of confidence owed to other clients. Chinese walls help financial conglomerates to reconcile their otherwise incompatible duties.

In this paper, I discuss the phenomenon of failing Chinese walls, explain why it occurs, and propose a regulatory solution. By examining Chinese walls, the article contributes to the long-running debate in legal and financial economic literature on the merits of Chinese walls in addressing the regulatory challenges of financial conglomeration. Business and economic scholars have focused on establishing the existence of the phenomenon of failing Chinese walls, but apparently without considering regulatory reforms. To date, legal scholars have questioned the effectiveness of Chinese walls and suggested denying them legal effect, but apparently not considered how to detect and prove their failure and thereby increase incentives for firms to bolster their effectiveness. This article contributes to these strands of literature primarily by explaining why Chinese walls fail and suggesting how regulators can address those failures.

The article argues that Chinese walls fail due to market and regulatory deficiencies. First, while financial conglomerates’ clients may have strong incentives to police their advisors’ Chinese walls, some harm caused by failing Chinese walls falls on third parties. Importantly, failing Chinese walls (and informed trading that results) may cause third-party harm, that is, market-wide harm, in the form of reduced liquidity and increased volatility. Firms’ clients have no incentive to discipline firms for such harm. Other possible market explanations for the evidence of failing Chinese walls include agency costs at client corporations and the lack of sophistication by some clients for certain types of transactions (such as M&A), in which they rarely engage.

Regulatory deficiencies also explain the empirical evidence of failing Chinese walls. In particular, as regulators acknowledge, the failure of Chinese walls is difficult to detect and prove. Problems of detection arise because the spread of information, and of non-public information in particular, rarely leaves a trace. Furthermore, any witnesses are themselves likely to be collaborators in the wrongdoing. Martin Lipton regards the difficulty of discovering the misuse of non-public information as “the greatest shortcoming of the Chinese Wall approach.”

Problems of proof arise because suspicious trading can be explained not as the misuse of non-public information, but by various benign rationales. In particular, suspicious trading may be the result of coincidence or the superior trading skill or intellect of the traders involved. Disproving these explanations can be extraordinarily difficult, especially since direct evidence of information flows is seldom available, leaving regulators to rely on circumstantial evidence to prove wrongdoing. Such evidence often cannot credibly rule out benign explanations for suspicious trading.

Having explained why Chinese walls fail, the paper turns to consider the effect of the recently-adopted Volcker Rule on the phenomenon under investigation. Although primarily directed to promoting financial stability, the rule will likely reduce the harms associated with failing Chinese walls, the paper argues. Prior to the Volcker Rule’s imposition, proprietary trading was a key driver of financial conglomerates’ revenue, creating powerful incentives for firms and for their employees. These incentives operated in opposition to measures such as Chinese walls designed to limit informational advantages to traders. To the extent the Volcker Rule removes these incentives, it will have desirable effects.

However, the paper argues that the Volcker Rule’s force will turn ultimately on its scope of application. Definitional ambiguities and strong resistance by firms also promises to reduce the rule’s benefits, as do the potential breadth of its exemptions. While difficult to predict, these limits are likely to substantially diminish the rule’s potential benefits in mitigating the harms of failing Chinese walls. Even after the Volcker Rule’s implementation, the problem of failing Chinese walls will require regulatory attention.

Against this backdrop, the paper proposes a regulatory solution to bolster the effectiveness of Chinese walls. Drawing on recent literature in forensic finance, the proposal relies on the capacity of statistical analysis to rule out benign explanations—such as coincidence, superior intellect or trading skill—that might otherwise explain financial conglomerates’ fortuitous trading results. Credibly ruling out these explanations as implausible would, in particular contexts, allow regulators and markets to conclude that a firm’s superior trading results could only be explained as the result of informational advantage—and thus the failure of its Chinese walls. Enforcement action would follow.

The underlying methodology involves calculating various trading returns for a given financial conglomerate and then comparing them. The first return is that earned by a financial conglomerate over a certain time period from trading in stocks when the firm (or, more specifically, a unit within it) possessed non-public information. The return would be calculated for particular “information events,” such as when the firm trades in stock of a principal in a merger or acquisition while also advising one of the principals to the deal. Second, the return would then be compared against three benchmark returns calculated for the same time period, making adjustments to each return for market-wide movements. The benchmarks are the firm’s trading return from the rest of its portfolio (that is, on trading stocks of companies about which it lacked non-public client information); the trading return comparable firms earned when trading stocks about which the firm possessed non-public information; and the trading return the firm earned in stocks about which it had non-public information—but at times it lacks such information.

The comparison with benchmarks allows benign rationales for superior trading returns by a firm in possession of non-public information to be ruled out as implausible. Assume that the first return is positive, while each of the three benchmarks is not statistically different from zero. One could dismiss the alternative explanation of superior intellect or trading skill, since the firm failed to achieve above-market performance with the rest of its portfolio. Similarly, one could dismiss the possibility that the firm’s superior results relate to peculiarities of the companies in question, since the firm failed to achieve above-market performance when trading in the same stocks (about which the firm held non-public information). Finally, one could rule out coincidence as a possible explanation, since the firm failed to achieve above-market performance when trading in the same stock but at times it lacked non-public information. [1] The only plausible explanation for the above-market returns would be the use of non-public information in the firm’s possession and thus the failure of its Chinese walls.

The strategy would have dual prongs. The first would compel financial conglomerates to publicly disclose quantitative metrics—comprising the financial return and benchmarks referred to above—from which the failure of Chinese walls could be inferred statistically. By mandating such disclosure, the proposal would harness market forces, creating incentives for clients to discipline financial conglomerates’ wrongdoing. The second prong would empower regulators to fine financial conglomerates that fail to rebut an inference that their Chinese walls failed. By imposing potential liability, the proposal recognizes the inadequacy of market forces to constrain financial conglomerates, which arises because of the third party harm failing Chinese walls may cause. The strategy would thus address the reasons for failing Chinese walls.

The paper discusses the strategy’s limitations, but argues that it nevertheless holds promise in addressing the regulatory challenges of financial conglomeration. The strategy would not verify whether Chinese walls failed in a specific transaction; while firm-specific, the required analysis necessarily applies across repeated transactions. It would not detect all systemic failures of Chinese walls, since it may be applied only to particular contexts where financial conglomerates are known to possess non-public information. It might give rise to “statistical dueling” among experts and even “gaming” by financial conglomerates themselves.

While the proposed use of statistical analysis to identify failing Chinese walls is novel, the use of such analysis in other fields to provide legal proof is not. The law often turns to statistical analysis to provide proof in contexts where direct evidence of wrongdoing is seldom available, such as in the context of systemic employment discrimination. The proposed strategy represents a further use of statistical analysis to provide legal proof.

The full paper is available for download here.


[1] The particular explanation ruled out is that companies the financial conglomerate advises (about which it has non-public information) are those about which it happens to have special insights that lead to superior trading performance.
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