There are many types of investing. Quantitative analysis attempts to makes use of data to make more systematic, rules-based investing decisions. Data has taken centre stage in the new digital era and its use is growing at an exponential rate. It is estimated that there is 2.7 zettabytes of data in the world today, that’s a lot of information. Quants attempt to extract intelligence from this data to make smarter asset allocation decisions. There are various strands of quantitative investing, however, the underlying concept remains constant; exploit data to produce outperformance.
One type of quantitative investing – factor investing involves selecting stocks based on the characteristics which best explain returns. The benefits of factor investing include risk management, diversification and outperformance. There are multiple top level or meta-factors – value, technical, quality, low volatility, size and dividend. We will explain the economic reasoning behind each of these factors in a later post. They are grounded in academic research and their impressive performance has been studied on data as far back as 1801.  At a basic level, factors are data points derived from price or financial line statement data. This data is released on a quarterly or yearly interval.
We create single or multi-factor strategies depending on our goals. Factor based strategies generally have medium to long-term holding periods. These strategies require less rebalancing.
Similar to factor investing, smart beta strategies are a low cost method of systematic investing, used to manage passive funds such ETFs. Rather than using market capitalisation to weight the positions in a portfolio, factors can be used. Its benefits include the potential for an improved risk adjusted return while being a transparent and simple fund type.
Arbitrage is the trading of assets with the purpose of profiting from price inconsistency between exchanges. It is a result of market inefficiencies.
Statistical arbitrage (Stat Arb) is a statistical approach to trading assets. It is also known as pairs trading, which depends on trading correlated assets. It involves observing the relationship between two stocks and buying or selling whenever the relationship deviates.
There are three steps in this process –
- Select a pair of assets that move in unison.
- Wait for prices to diverge beyond a threshold.
- Sell or short the winner.
- Buy the loser.
- Reverse the positions when the prices converge.
Say, for example, that stock A and stock B are within the same industry and highly correlated. The stocks have become uncorrelated recently and an arbitrage opportunity has appeared. A trader would go long (buy) the underperforming asset and short (sell) the over-performing one to profit on the mean reversion which is statistically likely to occur. The final, optional step is to reverse the positions once the stocks converge.
Mean reversion is a simple yet powerful concept within investing which states that an asset or stock will revert back to it’s historical average. Stat Arb utilises mean reversion in predicting the future of an assets price by assuming it will revert back to its previous association. Stat Arb strategies trade at high frequencies and generally seek a small return on vast quantities of assets.
Within Stat Arb, there are various other methods that traders use in order to make profits, including event driven, convertible and volatility arbitrage, and high frequency trading.
Event-driven quant strategies involve capitalising on the short term mis-pricing brought about by corporate events. These include earning calls, mergers and acquisitions, capital restructuring and regulatory changes applicable to a given stock.
For example, company A announces that it is acquiring company B. Company B sees an increase in stock price. The trader sells company A and buys company B. strategy would be to take advantage of a merger are within the same industry and highly correlated.
High Frequency Trading
HFT was made famous or even infamous by Micheal Lewis’s book Flash Boys. Firms front-run orders to profit a marginal percentage from a high number of trades. Speed is of the essence. HFT utilises co-location, low-latency and high processing speeds to action trades faster. Placing high-tech infrastructure close to (or in the same building as) the exchanges provides an edge. These techniques allow them to implement various arbitration strategies (price differences between exchanges or markets) at the microsecond frequency.
Systematic Global Macro
As the name suggests, this form of investing applies a quantitative model to global financial data in order to drive return. Assets held include equities, bonds, currency, and commodities. It has the benefit of providing high levels of diversification. Strategies in this form of quant fund will focus on assets with higher levels of either value, dividend or momentum.