Thousands of financial academic research papers are published every year. People at universities and research centers try to shed light on the functioning of the global financial system. And some of them (out of curiosity or simply because of money) try to understand just a subset of the global financial system - financial markets.
They are looking for ways to beat it.
Are they successful in their quest? Did they discover something which we can use in the real trading world?
These are the questions we are trying to find answers to.
Our mission at Quantpedia.com is to help traders crack financial academic research and find new ideas for algorithmic trading strategies. We use a great number of finance research resources from all over the world. We go through these sources and search for new interesting articles and papers for quantitative trading strategies. Each article is evaluated based on the strategy’s implementability, backtest length period and overall soundness. If the strategy passes the selection criteria, it is then categorized and included into our database.
But is it really possible to find strategies in academic papers with an added value?
There are a lot of misconceptions floating around the internet about financial academic research such as:
- One popular misconception says that academics are torn away from reality and study only theoretical problems.
- Another popular one asks the question that if academics are so smart then why do they work at academia and do not manage money.
The truth is often far away from these prejudices.
Academics are usually very curious and smart people. They can sometimes examine problems just out of pure curiosity. Or examine problems which don't have practical applications. But they are often very well motivated to study practical problems. Their motivation can be simple – professional pride, career advance or possibility of an offer from big players in the asset management industry to start managing external money based on a unique alpha/factor/strategy that they have found.
There also exists a lot of professionals out of the hedge/mutual fund industry who are well-known for their academic work. After 2008, a lot of hedge funds have become much more transparent and have started showing how they manage client’s money. Research published by these companies demonstrates their competency to the external world and helps in attracting new clients. And we can get inspired by their work and use it in our trading too.
Published strategies no longer work because of ... ?!
One common critique of financial academic research is that the factors/strategies that are found and published no longer work. As other players learn about them, they arbitrage all the alpha that was available before the strategies are published.
Research shows that this is again not entirely true.
There really is a performance decay after a new trading strategy is published. However, statistically speaking, abnormal alpha still remains even several years after a strategy becomes public.
There are multiple reasons for that:
- Limits to arbitrage
- Reluctant players in financial markets (money pours into new strategy slower than what is usually expected)
- Bad timing of new strategy/factor (a lot of new money can start to trade any particular strategy and can cause a crash in the strategy’s profitability and higher spreads afterward for players who remain committed).
Blind spots.
Academic research often examines mainly the most popular asset classes and types of strategies. Therefore there is an above average number of papers related to stock picking strategies. Knowledge of the quant research space can help find sources of unique alpha – strategies on asset classes which are less known and therefore could be less crowded and more profitable in the future.
An Algorithmic Trading Strategy is a very vague term. Historically, it covered all strategies - from fast intraday HFT strategies up to a long-term systematic investment strategy like systematic value. In our webinar hosted by Quantpedia and QuantInsti, we explored the universe of Quantitative Investment Strategies. We demonstrated how Quantpedia classifies quant strategies and tried to find blind spots – types of strategies which are not very well covered by academic research and therefore, can offer better performance. We also highlighted some examples of lesser-known strategies derived from the academic papers. We also took a look at some of the common issues related to the implementation of these strategies and illustrate ways to avoid them.
Access the webinar recording here: Classification of Quantitative Trading Strategies
