Quantitative Trading: Has Quant Trading Become Outdated?

The article was written by Jordan Klotnick, a Financial Analyst at I Know First. He graduated from Monash University with a Bachelor’s in Business – Majoring in Marketing.

Quantitative Trading

Summary 

  • What is Quantitative Trading?
  • Quant Trading vs Managed Funds
  • Artificial Intelligence
  • I Know First and Hedge Funds

What is Quantitative Trading?

Quantitative trading is trading based on quantitative analysis, which relies on mathematical computations. It also relies on number crunching, which identifies trading prospects. Financial institutions and hedge funds generally use this type of trading. These transactions are usually large and involve purchasing and selling hundreds of thousands of shares and other securities. However, this type of trading is becoming more popular amongst individual investors.

Quant Trading vs Managed Funds

The Bank of America Corp has done research to come to the conclusion that a new trend is eroding the advantage quantitative analysis provides short-term equity trading strategies.

Head of Equity and Quantitative Strategy at the Bank of America, Savita Subramanian, says there are managed future funds that use faster-acting algorithms to see trends in asset prices and volatility as trading signals. According to BarclayHedge, these have grown to represent about 10% of the hedge fund universe. They have more than $250 billion in assets under management. 

At the center of this is that there was once a reliable strategy, which is destroyed when enough traders discover its potency. In quant language the alpha is arbitraged away, it ‘decays’.

As technology advances, it becomes easier to build get-rich-quick algorithms. Some of the banks clients use three times as many pieces of predictive code, called factors, than they used 20 years ago.

“Good quantitative signals perform well in the short-term, but the decay rate is extreme,” according to researchers. “New alpha signals tend to be exploited and then quickly arbitraged away.”

Quants have issues with the way banks think. They believe that label “managed funds” does not encapsulate all quant strategies. As not all quants trade in future markets

“For trend following to achieve high returns you need to have strong trends,” Marantz said. “The actual trends that have been in the market over the last number of years haven’t been as strong as they’ve been in previous periods of times, like in bonds and currencies, for instance.”

Subramanian and her team believe quant strategies pay off over the long-term horizon through investing that marries quantitative inputs and fundamental reasoning. The focus on short-term gains over long-term reliability is having an impact on the market.

“One of today’s greatest market inefficiencies may stem from the scarcity of capital devoted toward long-term, fundamental investing,” the researchers wrote. “Our analysis shows that fundamental signals significantly improve in efficacy over longer time horizons.”

Artificial Intelligence

It used to be that if one wanted a computer to carry out an action they would have to program it to do that specific action. This took an excruciating amount of time as we would have to tell the computer to do exactly what we wanted it to do and it was unable to carry out an action, which we were not able to do or to tell it to do. Programs had no independent intelligence and could not make decisions by themselves.

In 1956, computer gaming pioneer, Arthur Samuel, wanted his computer to be able to beat him at chequers. Samuel then programmed his computer to play against itself thousands of times to the extent that the program accumulated sufficient knowledge of the game. By the 1970s, his program was proficient enough to challenge and beat the masters. Arthur Samuel is therefore credited with being the pioneer of artificial intelligence (AI).

I Know First and Hedge Funds

I Know First Algorithm predicts a growing universe of over 7,000 securities for the short, medium and long term horizons daily by applying Artificial Intelligence and Machine Learning techniques to search for patterns and relationships in large sets of historical stock market data. Through its self-learning ability and flexible multi-layered neural networks structure, the algorithm is able to learn from, adapt to and evolve together with continuously changing markets. It offers an independent, objective and unique perspective on the financial markets and doesn’t rely on any human derived assumptions or traditional theories and models that often do not hold (any more).

The results of intense learning and prediction cycles are aggregated into two indicators per time frame: signal and predictability. While predictability indicator helps to identify and focus on the most predictable assets, the signal is used to define and rank the trades and is related to the magnitude of expected return.

The applications of the algorithmic AI-based forecasts are multifold, and use empirical prices of the stock market to create daily predictions.

The scalability of the algorithmic predictive system allows I Know First to offer custom forecasting solutions to hedge funds and other financial institutions, so they can identify the best opportunities as discovered by the self-learning algorithm within the investment universe of their interest. Further, the solution can be used as a decision support system in form of an algorithmic screen integrated into client’s investment process in order to confirm or reject investment ideas before the execution.

Moreover, I Know First develops and back-tests systematic trading strategies, which are used in partnerships with, hedge funds and other asset managing entities. These strategies are rules-based and utilize algorithmic forecasting indicators mentioned above in order to rank and select the trades as well as time the execution. The type of strategies varies, including mean-reversion logic and more trend focused approaches, all generating high positive alpha while keeping beta in the 0.3-0.8 range, yielding overall high risk-adjusted returns. The strategies can be used in partnership with I Know First to launch hedge funds, mutual funds or other investment vehicles.

Conclusion

Quantitative analysis is not providing individuals with the same returns as managed funds. Due to technological advancements and more algorithms being created, the get rich quick method is decaying. Long-term investing is the safer option, however if you have a decent algorithm, the short-term can provide a good return.

Quantitive Trading