Algo based Investment

Algo Based Investment

Algorithms (Algos) are a set of instructions that are introduced to carry out a specific task. Algorithms are introduced to automate trading to generate profits at a frequency impossible to a human trader. The process is referred to as algorithmic trading, and it sets rules based on pricing, quantity, timing, and other mathematical models. Other variations of algorithmic trading include automated trading and black-box trading.

Algorithmic trading rules out the human (emotional) impact on trading activities. The use of sophisticated algorithms is common among institutional investors like investment banks, pension funds, and hedge funds due to the large volumes of shares they trade daily. It allows them to get the best possible price at minimal costs without significantly affecting the stock price.

Strategies for Algorithmic Trading

Any good strategy for algorithm trading must aim to improve trading revenues and cut costs of trading. The most popular strategies are arbitrage, index fund rebalancing, mean reversion, and market timing. Other strategies are scalping, transaction cost reduction, and pairs trading.`

Index Fund Rebalancing

The portfolios of index funds of mutual funds like individual retirement accounts and pension funds are regularly adjusted to reflect the new prices of the fund’s underlying assets. The “rebalancing” creates opportunities for algorithmic traders who capitalize on the expected trades depending on the number of stocks in the index fund. The trades are performed by algorithmic trading systems to allow for the best prices, low costs, and timely results.

Algos and Arbitrage

Arbitrage is the practice of taking advantage of occasional small market price discrepancies that arise in the market price of a security that is traded on two different exchanges. Purchasing a dual-listed stock at a discount in Market A and selling it at a premium in Market B offers a risk-free arbitrage opportunity to profit.

Mean Reversion

Mean reversion is a mathematical method used in stock investing, and it computes the average of a stock’s temporary high and low prices. It involves identifying the trading range for a stock and calculating its average price using analytical techniques. When the current market price lags behind the average price, the stock is considered attractive, hoping that the price will increase.

Market Timing

Strategies designed to generate alpha are considered market timing strategies, and they use a method that includes live testing, backtesting, and forward testing. Backtesting is the first stage of market timing, and it involves simulating hypothetical trades through an in-sample data period.