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The concept of discretionary portfolio management (“DPM”) is one whereby a portfolio manager makes investments on behalf of a client. The decisions regarding which investments must be made, and on what terms, are left to the portfolio manager. The client neither influences the decision-making of the portfolio manager nor does the client get involved in day-to-day investment decisions.
In the context of DPM, a question arose as to whether the possession of insider information by a client would vitiate a trade by the portfolio manager in terms of the regulations governing insider trading. This came up in a requestfor informal guidance made by HDFC Bank to the Securities and Exchange Board of India (“SEBI”). HDFC Bank set out the broad circumstances where such an issue arose. Several employees of the Bank could be in possession of unpublished price sensitive information (“UPSI”) pertaining to the Bank or other listed companies with which it deals. Hence, they would be prohibited by the SEBI (Prohibition of Insider Trading) Regulations, 2015 (the “Insider Trading Regulations”) from dealing in those securities. They would also be subject to closures of trading windows by the Bank or other listed companies. In this context, it may be necessary for such employees to make investments through other means such as mutual funds or DPM. The core issue was whether they could make investments through DPM while in possession of UPSI.
HDFC Bank’s request letter sets out details regarding the functioning of DPM, and how the client has no control or influence whatsoever on the investment decisions made by the portfolio managers. Hence, even though the clients (in this case employees) are in possession of UPSI, that ought not to matter as, decisions are taken by portfolio managers who are not privy to that information. In essence, the Bank’s case is that the information available with the clients should not be attributed to the portfolio managers, thereby rendering a wider (and arguably lenient) interpretation to the provisions of the Insider Trading Regulations.
However, in its informal guidance, SEBI refused to accept the request made by HDFC Bank, and opined that investments made by employees of the Bank who are in possession of UPSI will be in violation of the Insider Trading Regulations if portfolio managers carry out trades for them under the DPM scheme. SEBI’s reasoning is as follows:
i. Regulation 4(1) of the [Insider Trading] Regulations unambiguously states that no insider shall trade in securities that are listed or proposed to be listed on a stock exchange when in possession of unpublished price sensitive information.
ii. Further, in the explanatory notes to Regulation 4 of PIT Regulations it is mentioned that when a person who has traded in securities has been in possession of UPSI, his trades would be presumed to have been motivated by the knowledge and awareness of such information in his possession.
iii. It is therefore inferred from the above that dealing in securities, whether it is direct or indirect, is not relevant, but that any insider when in possession of UPSI should not deal in securities of the company to which the UPSI pertains. Even while dealing in such securities through a discretionary portfolio management scheme, the trades of insider shall be presumed to be motivated by the knowledge and awareness of UPSI.
On similar grounds, SEBI concluded that employees would be prohibited from undertaking any trading through DPM when the trading window is closed.
At a broad level, SEBI’s approach in the guidance is consistent with a rather strict approach adopted by the Regulations towards insider trading. As I had discussed in a recent paper(pages 5 to 8), SEBI as well as other jurisdictions such as the United Kingdom and Singapore adopt the “parity of information” approach towards insider trading whereby the focus is on whether the person trading had UPSI, and not whether that information influenced the dealing in shares or whether the person had a blameworthy state of mind. This effectively broadens the scope of the insider trading regime. Consequently, in its guidance, SEBI simply looked at whether the employees had UPSI and, if so, their actions were presumed to have motivated the trades in shares. Such a presumptive approach was put to full use by SEBI in this guidance.
This much is understandable. But, it is somewhat intriguing that SEBI used such a strict “parity of information” approach even in the scenario of trading through DPM rather than when employees (or possessors of UPSI) trade by themselves. SEBI did not place the requisite emphasis on the fact that the decisions are made by the portfolio managers independent of any UPSI that their clients in the form of employees may possess. SEBI effectively treated the UPSI in the hands of the clients as if the portfolio managers held it. If it is indeed the case that there is an opaque wall between the portfolio managers and clients in a DPM, then that conclusion is somewhat perplexing. It has the effect of expanding the scope of the insider trading prohibition when employees invest through indirect means. If investments through DPM are covered within the scope of insider trading, will it then also extend to investments through mutual funds? Although SEBI has not specifically considered mutual funds in this guidance, it is not clear whether its expansive interpretation may rein in other forms of investment management.
Background to Algorithmic Trading
Similar to most other fields, the use of technology is being optimized in trading in the stock markets. Stock trading is getting increasingly automated through use of sophisticated computer systems that operate through algorithms, which minimize human involvement and decision-making. Not only does this lead to the extensive use of technology by stock traders and investors, but it may also create imbalances in the stock markets whereby certain players can use technology to favour their own commercial interests at the cost of other similar players in the markets.
On the one hand, such algorithmic trading (or algo trading) is beneficial as it operates instantaneously based on information available in the markets, and hence makes the markets more efficient. On the other hand, it has been criticized on the ground that it creates distorted incentives in various market players that could lead to imbalances and consequently significant risks to the stock markets as well as the economy as a whole. Instances such as the flash crash that occurred in the US markets in 2010 due to erroneous order entry into the computer systems only highlight the risks of automated trading.
Interestingly, the debate surrounding algo trading achieved high intensity a couple of years ago, and was even the subject matter of a book Flash Boys: A Wall Street Revolt by Michael Lewis. In the meanwhile, regulators the world over have been consider the merits of restricting such algorithmic trading so as to mitigate its risks.
At the outset, the Discussion Paper seeks to clarify the various technical concepts relating to the topic. Algo trading is the broader concept (as discussed above), which provides greater speed to stock trading and also offers anonymity. A sub-set of algo trading is “high frequency trading (HFT)”, which uses high-speed networks and locational advantages to create trading opportunities within miniscule fractions of a second. HFT usually relies upon co-location, which in essence means that certain traders place their computer servers within close proximity of the stock exchange system such that they have access to trade information and prices a split second faster that others, which they can work to their advantage.
After introducing the concept, the Discussion Paper goes on to state:
2.5. Adoption of such advancements in technology in our market in recent years have resulted in vast majority of orders being generated through trading algorithms. Currently, more than 80% of the orders placed on most of the exchange traded products are generated by algorithms and such orders contribute to approximately 40% of the trades on the exchanges.
The use of algo trading seems quite pervasive, although it is not clear whether the Discussion Paper refers to these statistics with references to global markets in general, or whether it relates to India. If the numbers pertain to India, they look much higher that I would have thought.
The Discussion Paper then goes on to make a series of proposals in order to combat the distortive effects of algo trading. Some of them are set out below:
– Minimum Resting Time for Orders: This would ensure that there would be a minimum time between when an order is received and when it is allowed to be amended or cancelled. This is to avoid a situation of “fleeting” orders that can be prevalent in algo trading where large orders are made and then cancelled or amended in a split second, thereby distorting the market. SEBI has proposed a minimum time of 500 milliseconds before an order can be amended or cancelled. SEBI has also indicated that its approach might be unique in that such a minimum resting mechanism is not mandated by any regulator yet.
– Frequent Batch Auctions: Under this mechanism, buy and sell orders would be accumulated for a length of time, such as 100 milliseconds, at the end of which orders received during the time interval will be matched. This will obliterate any advantages that HFT players may have through co-location.
– Random Speed Bumps: This involves “introduction of randomized order processing delay of few milliseconds to orders”. This is again to nullify co-locational advantages that some players might have.
– Randomization of Orders: Under this proposal, all orders received during a predefined time period (such as 1 to 2 seconds) would be randomized in order to perform the order matching routine. Here again, any advantage of speed or location would be of no use.
A few other similar, and specific, proposals have been made in the Discussion Paper.
It is not clear what form the final set of SEBI’s regulations relating to algo trading will take, if at all. However, from nature of the proposals and the tone of the Discussion Paper, it is clear that the approach is towards imposing greater regulation on algo trading so as to bring about a level playing field in the market. In other words, the use of technology ought not to create disparities in the system (although proponents of algo trading may argue that there is nothing unfair about it). To that extent, SEBI seems to be adopting a rather strict approach towards regulating algo trading. Although I have not undertaken detailed research on the issue, it appears from the Discussion Paper and the surrounding debate that SEBI’s efforts in reining in algo trading may have gone farther than most other regulators around the world. More broadly, the idea seems to be that speed is not always beneficial, and sometimes delayed responses may be necessary and may yield better results (as Professor Frank Partnoy has argued in his book Wait: The Art and Science of Delay).
Comments on the Discussion Paper are due from the public on August 31, 2016.