Flash Crashes and securities taxes
4 min read . Updated: 25 Jan 2017, 04:22 AM IST
PremiumAlgorithms generate fake liquidity that is there when you do not need it and disappears when you do
At 8.32pm on 10 May 2010, Central European Time, I stepped up to a lectern to speak about financial stability to an audience of international officials and academics at the grand President Wilson Hotel in Geneva, Switzerland. As I began talking, heads started dropping one by one, transfixed to the screens of their smartphones. I try to give short presentations to maximize attention but no one seemed to be paying the blind bit of notice. When I came to a close, 30 minutes later, heads lifted to their previous position, punch drunk. In just 30 minutes, traders wiped almost $1 trillion off the value of US stocks, before putting it back. That is how I experienced the first Flash Crash.
Politicians, prosecutors and journalists believe in the Bad Apple Theory of financial crises. After the Flash Crash, they set off in hot pursuit of evil-doers. Five years later, their chase ended at the door of a modest semi-detached house in a forlorn part of west London where a mostly unheard of 36-year-old man, Navinder Singh Sarao, traded stock futures off the Internet. It’s always a foreigner.
The scale of the allegations against one man in a semi in Hounslow armed with an Internet cable does not sit well with claims made at meetings of international regulators, attended by the most senior officials, that they have collectively delivered a more robust and resilient financial system.
The sterling fell 10% in 10 minutes while Sarao was lying on his bed in Wandsworth Prison at 4.13am on 7 October 2016. Flash Crashes have not only become more frequent in recent years, but they are also occurring in markets where stability is both expected and critical to the smooth function of market economies. Europe’s interest-rate benchmark, the market for German government debt, experienced a Flash Crash on 15 January 2015. More financial contracts derive their price from developments in the US treasury market than from any other market, and even this market proved susceptible to a Flash Crash on 15 October 2014.
To understand what is happening, why and what to do about it requires an understanding of market functioning. Financial market liquidity needs diversity. It requires someone to be willing to buy when others are selling because they have a different view of the value of the securities than the sellers or a different time horizon, or some other difference.
Buying when others are selling requires capital to absorb short-term losses in anticipation of longer-term gains. Consequently, long-term savers like pension funds and life insurers who are largely unleveraged investors, along with well-capitalized banks, are the natural suppliers of financial liquidity in a crisis.
Yet, in an example of unjoined-up policymaking, bank regulators have acted as if insurers were better providers of liquidity than banks and insurance regulators have acted as if the opposite is the case. Just as international bank regulators are making global banks hold three times more capital against the risks of their trading positions than before the financial crisis, regulators of life insurers and pension funds are basing new capital adequacy requirements on the short-term volatility of their assets. This rule makes it more costly for life insurers and pension funds to buy assets that have just crashed, even though that is exactly the right time for them to do so. The result of these new capital rules on international financial markets is that both banks and long-term savers have withdrawn from buying when others are selling, especially when markets become more volatile.
In New York and London, algorithmic traders, not life insurers and pension funds, have moved into the space previously occupied by bank traders. The defining feature of “algos" is not their Bengaluru-written algorithms but the fact that they have slim levels of capital and can dynamically change their trading strategies. In quiet times when markets are range-bound, algorithmic traders are contrarians: buying when the market is falling and selling when the market is rising. Their behaviour adds to market liquidity and makes bank traders redundant. When the market starts to trend, and bank traders stand back, the algos switch into trading faster or more aggressively in the same direction of the trend. Selling more or before others will drain liquidity and create a Flash Crash. Algos generate fake liquidity that is there when you do not need it and disappears when you do. We need to find a systematic way of curbing fake liquidity.
One way of doing so is through the securities transaction taxes universally hated by brokers, exchanges and finance professors. Perversely, these taxes will benefit them in the long run because they will make markets more durable, more inclusive, better suited to their customers, and offering more meaningful liquidity. What we have learnt in practice, if not in finance classrooms, is that market structure matters.
International and local bank regulators were right to demand that banks hold more capital against the risks in their trading books than before, but they were wrong not to consider who could and should take their place in providing market liquidity.
It is also clear that we are still trying to curb systemic risks by addressing individual risks one at a time and not thinking about the system as a whole. That won’t work.
Avinash Persaud is non-executive chairman of Elara Capital Plc. and emeritus professor of Gresham College.
