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Four Kinds of Market Inefficiencies

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Producing trading ideas may be a process that is frustrating, particularly if there is not a structured framework for it. In order to help us with our process of ideas generation, we break algorithmic trading ideas down into four primary categories. These four categories essentially are different kinds of market inefficiencies. Inefficiencies include the core trading strategy building blocks.

1. Market Inefficiencies

There are four market inefficiencies:

  • Market Microstructure
  • Macroeconomics
  • Statistics
  • Fundamentals

Note that those four types aren’t mutually exclusive. Strategies may include more than one kind.

Table of Contents:

          1. Market Inefficiencies
          2. Comprehending Market Inefficiencies
            1. Fundamentals
            2. Algorithmic Trading
            3. Macroeconomics
            4. Market Microstructure

2. Comprehending Market Inefficiencies


The word fundamentals commonly is related to equities. In this instance, we use fundamentals to define the value that stems from the intrinsic composition of any asset. Those involve oil field discoveries, earnings releases, as well as expected corporate bond downgrades.

Fundamental inefficiencies/opportunities examples:

  • Overstated earnings within an organization’s yearly report.
  • Major breakthrough within clean energy technology that disrupts traditional oil sectors.
  • Death of a star CEO.

Algorithmic trading strategy example:

We’ll develop an algorithm which predicts company earnings based upon publicly available data. Let us say we are looking at a timber logging business.

We’ll input data concerning revenue and cost like quantity of land deforested, amount of new contracts secured, quantity of new equipment bought, market price of timber, overhead costs and labor etc. An algorithm will output the expected earnings of an organization for the quarter and we’ll compare this with analysts’ earnings predictions. If there’s a substantial discrepancy, we’ll put in a position prior to the earnings release and then exit the position afterward.

We’ll do this for thousands of businesses to reduce the variance in our performance.


If broken down to first principles, strategies based upon statistics pretty much state, ‘This market move is unusually small or big for this condition in the market.’ How unusual this is thought to be is determined by how the asset has historically performed. Past performance, of course, might not be the right predictor for the future. Therefore, we must think prudently and critically while implementing strategies, including statistical inefficiencies. Many strategies, including statistics, employ either co-integration, correlation/prediction or mean-reversion.

Statistical inefficiencies/opportunities examples:

  • Group of stocks within the same industry and country predicted to behave in a likewise fashion within specific periods.
  • Corporate bond rating that’s correlated with the price of stock of some of its main customers.
  • Prediction of timber company share pricing change within earnings release utilizing publicly available data.
  • Stock A’s price that’s correlated with the amount of tweets concerning its impending earnings disaster.

Algorithmic trading strategy example:

Group of stocks within the utilities field in the same country that has a likewise corporate composition and market capitalization are predicted to behave within the same way within a quiet period.

Therefore, as stock X’s price diverges from the remainder in a statistical way, we predict the relative difference in cost between stock X and the remainder to converge eight times out of ten. We’ll put in a trade in order to exploit this.


Macroeconomic inefficiencies include market inefficiencies stemming from macroeconomic events. These types of events involve central bank activities/announcements, economic policy shifts, government corruption, as well as multi-country trade agreements.

Macroeconomic inefficiencies/opportunities examples:

  • Lagging reaction from certain derivatives following Non-Farm Payrolls.
  • Economic sanction impact asset groups and certain industries.
  • Expectations of tightening or easing policies.

Algorithmic trading strategy example:

A high-frequency corporation utilizes powerful computers and fast connections to obtain data upon country A’s central bank rate cut determination. Then they buy the country’s bond futures prior to anybody else.

Market Microstructure

It’s the underlying mechanisms which allow trading within the financial marketplaces.

You might be thinking why market microstructure is thought to be a value source. Though market microstructure tends to handle trading infrastructure instead of the assets themselves, it’ll create multiple opportunities which traders may exploit. Usually, market microstructure inefficiencies include exploiting infrastructural flaws or analyzing additional market players.

Market microstructural inefficiencies/opportunities examples:

  • A Fund wants to purchase a massive quantity of stock A. They’ll enter the trades in a way which unintentionally signals their goal. Other marketplace players catch on and begin to buy the stock, as well.
  • Spoofing the details of the market order book in order to mislead additional traders.
  • Out queuing additional players within futures rollover inside a First In, First Out exchange.

Algorithmic trading strategy example:

When analyzing a limit order book, we’ll see a pattern of queuing which usually indicates that a purchaser wishes to long large quantity of US 10 Year futures. We’ll design an algorithm in order to identify these types of patterns and accordingly trade.

Beginning the Process of Idea Generation

Besides having a thought process that is structured for finding trading ideas, we require two other elements in the process of idea generation.

Firstly, we must know which kinds of strategies are appropriate for a trader. Factors to think about involve risk profile, trading capital, and programming competency.

Secondly, we require a framework to vet trading ideas, as well as choose promising ones for the backtesting phase.


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