Introduction To Market Making & High Frequency Trading Strategies

Introduction to Market Making & High Frequency Trading Strategies

By Nitin Thapar & Milind Paradkar

Global Financial Market has witnessed a seismic change over the last two-three decades. Rapid advancement in technology, increase in the number of trading venues, increased market participation and high trading volumes, have all made markets little more complex than before.

Edward Leshik and Jane Cralle, the authors of the book, “An Introduction to Algorithmic Trading” have put down this complexity in the following words, “In order of complexity the Markets rank a good 4th after the Cosmos, Human Brain, and Human Immune System.”

Market participants play an important role in today’s ever-changing markets. A key market participant in an exchange’s trading structure is the Market Maker. This article covers the Who, What and How of Market Making and an introduction to high frequency algorithmic trading strategies.

Introduction to Market Makers – Who are they?

Market makers are agents who stand ready to buy and sell securities in the financial markets. The rest of the market participants are therefore always guaranteed counterparty for their transactions.

Market makers are known by different names. On the London Stock Exchange, market makers are called jobbers, while on the New York Stock Exchange they are now known as Designated Market Makers (formerly known as ‘specialists’). Find below the names of DMMs on the NYSE.

DMMs on the NYSE

Source: www.nyse.com

who are market makers

Market Makers operate in the markets for the following instruments:

  • Futures
  • Options
  • Exchange-traded products (ETPs)

Traditional market makers are usually under contractual arrangements with the stock exchange and are incentivized to achieve benchmark quoting requirements. High frequency traders can also act as market makers and play a vital part in the overall ecosystem.

Market makers can choose to have the following quoting benchmarks:

  • make a market on a continuous basis
  • make a market in response to quote requests
  • make a market both on a continuous basis and in response to quote requests

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How do they earn profits?

Market Makers profit by charging higher offer prices than bid prices. The difference is called the ‘spread’. The spread compensates the market makers for the risk inherited in such trades. The risk is the price movement against the market makers trading position.

The market maker may purchase 1000 shares of IBM for $100 each (the ask price) and then offer to sell them to a buyer at $100.05 (the bid price). The difference between the ask and bid price is only $.05, but by trading millions of shares a day, he manages to pocket a significant chunk of change to offset his risk.

what do market makers do

Risks in Market Making

As mentioned above, the primary risk a Market Maker can face is a decline in the value of a security after it has been purchased from a seller and before it’s sold to a buyer.

Market Makers are always counterparties to trades done by informed traders and in case of any volatility in the market; the Market Makers are often stuck with wrong positions.

market makers manages to pocket a significant chunk of change to offset their risk

Another fatal risk for a Market Maker is to not have the latest information. The Market Makers can survive by managing risks only if it is possible for them to receive & respond to information quickly.

fatal risk for market maker

Strong markets need Market Makers and to have Market Makers it should be possible for them to survive & succeed without big losses.

How automated trading enables market making?

To be efficient, market makers should be able to adjust their quotes immediately in response to market events.

These events could be

  • changes in prices of financial instruments,
  • trading positions accumulated by the market maker

Automated systems are more efficient than human beings in detecting & responding to such events. Therefore they can handle their risks better.

automated system risk handling

Since automated systems can handle their risks better, therefore they offer better quotes for others.

Faster response time

Pricing of derivatives that enable investors to hedge often involve time consuming mathematical calculations. While humans can take minutes, automated systems are can do these calculations in microseconds. The response time is therefore much faster.

Faster response time

Scalability

Human traders can only track activities in a few instruments, while automated systems can do thousands simultaneously. The same trader using an automated trading system provides liquidity in significantly more financial instruments simultaneously.

Scalability

Availability

Machines don’t have to take breaks. Automated market making systems are always active.

Availability

What is the impact of algorithmic market makers on markets?

Price Volatility

Difference between prices of consecutive trades done against a human market maker will be much higher than those done against an automated market maker, asset price volatility therefore reduces.

Price Volatility

Impact Cost

With automation rendering market making easy, order books have become thick. Execution price for even big orders are close to fair price, Impact cost & volatility is thus lower.

Impact Cost

The overall impact of algorithmic Market Making can be summed up as mentioned below

impact of algorithmic Market Making

HFT Strategies and their types

HFT Strategies and their types

High frequency trading firms use different types of HFT strategies and the end objectives & underlying philosophies of each vary. Some of the important types of HFT strategies are explained below.

Order flow prediction HFT strategies

HFT order flow prediction strategies try to predict the orders of large players in advance by various means then take trading positions ahead of them and then lock in the profits as a result of subsequent price impact from trades of these large players.

Execution HFT Strategies

Execution HFT strategies seek to execute the large orders of various institutional players without causing a significant price impact. These include:

VWAP (Volume-Weighted Average Price) strategy – this strategy is used to execute large orders at a better average price. It is the ratio of the value traded to the total volume traded over a time period

TWAP (Time-Weighted Average Price) strategy – this strategy is used for buying or selling large blocks of shares without affecting the price.

Liquidity Provisioning – Market Making strategies

HFT market-makers are required to first establish a quote and keep updating it continuously in response to other order submissions or cancellations. This continuous updating of the quote can be based on the type of the model followed by the HFT market-maker. (Inventory based model or Information based model). In the process, the HFT market-makers tend to submit and cancel a large number of orders for each transaction.

Automated HFT Arbitrage strategies

HFT arbitrage strategies try to capture small profits when a price differential results between two similar instruments. Index arbitrage can be considered as an example of the same. The price movement between S&P 500 futures and SPY (an ETF that tracks the S&P 500 index) should move in line with each other. If the price movement differs then the index arbitragers would immediately come into the picture and try to capture profits through arbitrage using their automated HFT strategies. To do effectively, the HFT arbitrage strategies require rapid execution to profit quickly from the mispricing before other participants jump in.

Apart from the ones discussed above, there are other HFT strategies like rebate arbitrage strategies which seek to earn the rebates offered by exchanges, HFT strategies based on low latency news feeds, and other like Iceberg and Sniffer which are used to detect and react to other traders trying to hide large block trades.

Next Step

Learn the basics of  XGBoost machine learning model, along with a sample of XGBoost stock forecasting model using the “xgboost” package in R programming.

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