Part three of the series titled, “Understanding Liquidity”
So far we have been able to know what liquidity is and how it is measured. In this third part of the series, we will consider information (both public and private). The flow of information is closely related to liquidity, which is formalized in Kyle’s model and ongoing research. Asymmetries in the inflow of information disrupt market efficiency and dry up liquidity. Information is valuable, and demand for it is high. The rise of algorithmic trading has fueled the competition for information. Simply put, lunch is eaten by whoever sits first at the table.
Market data, the live streaming of trade-related data, is valuable information, encompassing, among other things, prices, bid-ask quotes and market volume. Market data is typically presented as a time-stamped collection of trades and quotes, showing sizes and prices. Investors most often consult real-time market data to make timely trading decisions and use historical data for various types of technical analysis. Market-data-related services account for a significant portion of exchanges’ revenues — for example, 20 percent for the Johannesburg Stock exchange (JSE) and 11 percent for the Nairobi Securities exchange(NSE). Every few days, markets generate data equivalent in volume
Market-adjusted liquidity uses the residual of a regression of the asset’s return on the return of the market (thus purging it from systematic risk) to determine the intrinsic liquidity of the asset. The smaller γ2 is, the lower the impact of trading volume is on the variability of the asset’s price — meaning the asset is liquid. The lower the coefficient, the more breadth the market has. No single measure unequivocally measures tightness, immediacy, depth, breadth and resiliency. However, from the perspective of practitioners, it is possible to cluster securities based on tradability difficulty, which depends on many factors but mostly on volatility, spread, price, queue size, volumes and so forth.
Fair access to market data allows all market participants to trade based on the same information. Some exchanges, however, sell premium data services that allow customers to receive data feeds slightly faster than others in the market. One of these is the Nairobi & Johannesburg stock exchanges, which costs a multiple of regular market data-distribution fees.
Other participants attempt to reduce transmission time by finding the shortest geodesic line of communication and employing microwave products and lasers; they believe more timely information gives them a competitive edge. This is not a new phenomenon. Since markets have existed, success has demanded acting relatively quickly based on the most up-to-date information. During the 1850s, Paul Reuter used pigeons to relay stock prices between Aachen and Brussels, shaving off hours of news propagation time. (The telegraph eventually replaced the pigeon).
Since financial markets began to go electronic in the 1970s, competition has driven down both bid-ask spreads and commissions by more than 90 percent, significantly reducing costs for investors. Though this is clearly beneficial to investors, the complexity of navigating the environment has also greatly increased. There has been an increase of electronic brokers that use algorithms designed to efficiently navigate the complexity of electronic markets.
Although financial markets are highly regulated, there remains an element of the survival of the fittest as multiple parties compete for scarce resources — liquidity at favorable prices or queue priority. Recent advances in technology make such competition even fiercer. Broker creativity appears infinite, given the algo names, but they are all trying to sell pretty much the same idea, which can be roughly summed up as “eat or be eaten.”
Many Investment professionals and financial researchers ask why the need of having so many liquidity measures and dimensions. This is attributed to the fact that these methods reveal often-conflicting information in periods of stress, and that can be difficult to interpret. These underlying dimensions and measures allow us to glimpse the complex dynamics of liquidity and illiquidity.
Financial markets behave quite differently under stress than they do in more stable periods. With a continuous flow of new information, the spread and the turnover of an instrument are nearly constant and the price adjusts smoothly. During periods of stress, however, the positive correlation between volume and volatility found in many empirical studies may cease to exist. We can differentiate between two types of stress events: first, high volatility combined with high turnover, which generally is good for market makers because they can widen the spread but easily unload their positions; second, high volatility combined with low turnover, which is bad for market makers. Moreover, transaction costs that are important in normal circumstances may become insignificant compared with expected losses or gains in periods of stress. In short, illiquidity is a symptom rather than a cause. Liquidity has an exact definition, but it can suddenly and unexpectedly appear or disappear based on shifting market conditions, and it is mainly governed by flows of supply and demand.