Presented by CEO Ambre Soubiran at Consensus: Distributed on May 14th
On May 14, we had the unique opportunity to present at Consensus: Distributed alongside skew. and Delphi Digital, hosted by CoinDesk’s Galen Moore. Our presentation focused on cryptoasset market liquidity, specifically focusing on liquidity measures derived from order book data. Below, you will find a summary of this presentation.
The Kaiko team has always placed a special emphasis on developing and improving our order book data product. Over the years, we have noticed that cryptocurrency market researchers, traders, analysts, and other data providers rely heavily on trade data as an indicator of a particular asset’s liquidity.
Trade data, in particular trading volume, is a notoriously unwieldy measure in cryptocurrency markets. Since 2018, most professional exchanges have implemented measures to counter against wash trading and misreported volume data, but the practice of inflating trading volumes still exists across a subset of exchanges.
Ultimately, we realized that trade data is not sufficient for fully understanding a market’s liquidity, not only because it is prone to manipulation, but because it ignores an important aspect of the concept of liquidity.
Liquidity is defined as the degree to which an asset can be quickly bought or sold on a marketplace at stable prices. When only looking at trade volume as an indicator of liquidity, we ignore a crucial aspect of this concept which is price stability.
Trading volume alone will not indicate how easy it is to exchange an asset at stable prices because we are unable to simulate how larger market orders will affect the price. For example, if a large market sell order causes a significant price movement that doesn’t necessarily reflect the asset’s intrinsic value, then this asset’s order book can be considered illiquid. No matter how high the trading volume for this asset, if the order book is unable to absorb a large market order without a significant price movement, then this asset is not liquid when considering the full definition of the term.
Thus, it is essential that order book data be a component in liquidity analyses, as this data type allows for the study of price stability.
By measuring market depth at different price levels, price slippage for simulated order sizes, and changes in bid/ask spreads (all measures derived from order book data), we can better understand how easy it is to exchange an asset at stable, market-driven prices.
Measuring Liquidity: Spread and Market Depth
We will demonstrate how two measures derived from raw order book data can be used to measure a market’s liquidity.
Bid/Ask Spread: The difference between the highest price a buyer is willing to pay for an asset and the lowest price a seller is willing to accept. Generally, the narrower the spread the more liquid the market. To learn more about spread, read our in-depth analysis here.
Market Depth: The quantity of bids and asks placed on an order book by market makers. Generally, the higher the quantity of bids and asks, the more liquid the market.
Spreads: Comparing Market’s Across Exchanges
By comparing the same currency pair across multiple exchanges, or different currency pairs trading on the same exchange, traders can make decisions that take into account the liquidity of individual markets.
Some exchanges are more liquid than others, and we can observe this by comparing the same market across multiple exchanges. In our Research Factsheet (updated every week), we chart the spread across 8 top exchanges for 3 high-volume markets: btc/usd, eth/usd, and xrp/usd.
We can observe that spreads vary depending on the exchange. Spreads tend to follow wider market trends, thus changes in the price of an asset will result in wider or narrower spreads.
For example, we can observe that xrp/usd trading pairs tend to have higher average spread than btc/usd, which can be explained by the fact that these markets are not as liquid as btc/usd. Itbit also experiences more volatile spreads, which can be explained by the lesser liquidity on this exchange compared to an exchange like Coinbase.
Spreads: Altcoin Markets
Altcoin markets tend to be less liquid than BTC or ETH, with varying liquidity across pairs and exchanges. We chart the spread for 3 altcoin markets on Coinbase: algo-usd, dash-usd, eos-usd.
We compare the spreads to the trading volume on Coinbase to show how the two measures can be used in combination. The asset with the highest average hourly trading volume had the narrowest spreads, and vice versa.
By comparing a trading pair’s spreads with volume, we show how higher volumes and tighter spreads are correlated. In natural, unmanipulated markets, higher volumes indicate that markets are more liquid, which would be reflected with tighter spreads. By selecting three altcoin pairs that have slightly different trading activity, we show how this correlation holds up.
Spreads: Derivatives Markets
We can also demonstrate how spread is an accurate indicator of a market’s liquidity by comparing contracts of various expiries. Generally, derivatives contracts closer to expiry tend to be more liquid and have narrower spreads.
Again using trading volume as another indicator of liquidity, we can observe that there is a perfect correlation between the future expiry date, the volume traded, and the spread. This is the best example of market forces at work, showing how higher volume contracts are almost always the contracts closest to expiry (in this case, the June contract). Spreads are correlated with the volume traded, which is why the June contract has the tightest spreads, and the December contract has the widest spreads (and barely any volume).
Market depth is closely associated with an asset’s liquidity, and is a factor in determining the spread. Market depth determines the ease by which an asset can be exchanged at stable prices. Market’s with deep order books are better able to support large market orders with little impact to the price, thus are considered more liquid. When the price of an asset changes, market depth is impacted as market makers readjust their positions.
We can observe how market depth for the same trading pair differs across exchanges. Some exchanges have deeper order books, which means it will require larger market orders to cause a price change. For example, we can observe that market depth on Gemini is far less than depth on Bitfinex. Gemini is a lower-volume exchange, thus it makes sense that market depth and trading volume is correlated in this case.
Market depth on these 4 exchanges declined as a price movement occurred, which is natural behavior as market makers take time to readjust their positions.
By studying patterns in market depth, one can determine whether a market has natural liquidity or not. For example, if market depth at a certain price level is not impacted by a large market order or a sudden change in the price, then this could indicate some form of manipulation. Natural behavior of order books can be determined by studying the impact of market orders on depth or by charting market depth at various price levels.
Order book data provides additional insights into a market’s liquidity not immediately apparent from trade data. We show how spreads and market depth reveal order book liquidity by comparing these measures to trading volume on reputable exchanges. Generally, there should be a correlation between the three measures in natural markets: higher volumes = tighter spreads = deeper order books.
Of course, during price movements, spreads tend to widen and depth falls as market makers readjust their positions, which indicates a decline in liquidity despite large trading volumes being present. Order book data during a price crash, in particular, reveals the importance of order book liquidity by showing how volume and depth de-correlate.
Ultimately, measures derived from order book data should be an essential component of a trader or researcher’s toolbox when studying market liquidity.
Written by Clara Medalie.