What can we learn from Chess?

Chess is not only a game. It mirrors life in many ways. Chess forces us to make decisions in a short period of time under pressure. it requires us to evaluate, strategise, and use creative solutions to win the game.

Gary Kasparov was one of the most celebrated chess grandmasters and a dominant world champion of chess.  In his time, man or machine could not beat him.  In 1985, Gary Kasparov played 32 simultaneous blitz games against different chess engines. He said out of the 32 matches there was 1 match he struggled with because it was modeled after him. Gary won all 32 matches.

Things changed quickly 12 years later. Machines became more powerful and the cost of computing came down by leaps and bounds. In 1997, Gary played against IBM’s Deep Blue and lost. This was the first time a computer had defeted the world chess champion. Since then, computer’s have mastered other games like GO.

In recent years, Gary reflected that the use of computers could greatly enhance the performance of amateur chess players. The chess world, faced cultural challenges among old school players who were unwilling or unable to adopt the use of chess machines. He explained in his book Deep Thinking that highlighted the strengths of both the Human and Machine.


In investment, there are 2 opposing approaches: 1) human  and the 2) quantitative algorithmic investing. The human camp relies on fundamental or technical analysts pouring through countless financial statements or charts . As humans, we are all subjected to various behavioural biases in our thinking and can succumb to them. These include fear, greed, loss aversion or anchored to our old beliefs when the facts have changed.

Quantitative algorithmic investing. They rely on software engineers / mathematicians to create various algorithms to trade the markets. Sometimes they use the term “Black Box” under the guise of proprietary knowhow. Clients, stakeholders and increasingly the software developers themselves are clueless about the steps taken in the model.

At 8VantEdge we want to overcome the cultural inertia that human equity analysts have to adopting quantitative methods. We believe we should harness the algorithms to mitigate behavioural biases, and also speed up analysis. We shall rely on human judgement to review unstructured data, or to make qualitative assessments such as the evidence for a competitive moat. We will set algorithms to manage portfolio sizing and risk management.  Most importantly the algorithm will mitigate our behavioural biases.

“We aim to improve our clients’ investment returns and experience using the best of both human and machines. “

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