Posted on 2 Comments

Trading – Man vs. Machines: Part 1 of a Continuing Series “How Algorithm Trading can supercharge your Trading Profit”

Trading Room – Tue 13 Nov 2018

 

Part 1 – Introduction: When humans and machines collide

 

Algorithm trading has taken the world by storm and it is time traders and traditional fund managers start to take notice.  Disruptive technologies are reshaping traditional business models and in the field of investment, algorithm trading is a potent force that is changing the dynamics of the industry and how it operates.

Institutional Investor’s (an international finance publisher) 17th-annual Hedge Fund 100 ranking of the 100 largest hedge fund firms in the world as of year-end 2017 finds that the four biggest hedge fund firms — and five of the six biggest — on its ranking rely largely or fully on quantitative strategies using computers to make their investment decisions and have continued to attract assets.

The value of assets managed by the quants alone passed USD1 trillion in 2018.

According to a published annual survey by Institutional Investor’s Alpha magazine, the 25 best-paid hedge fund managers are mainly “quants”, or quantitative hedge fund managers where eight of the ten top earners on Alpha’s list fall into the category, and half of the 25 richest of the year are quants.

These are quants who rely exclusively on computer models such as algorithmic models to tell them when and what to buy and sell.

In a financial world which is characterized by ever-increasing data, the speed of interaction and rapidly rising volatility, it may be time conventional funds and even retail investors get on the bandwagon before they lose their competitive edge by a bigger margin.

There are different complexity levels to the algorithm trading chain where at the benign end, investors and traders can take the selection of trades of an algorithm program and key in manually like a normal trader in real time while others may choose to let the machine take over the trade operations completely utilizing the concept of speed and frequency such as in high-frequency trading (HTF) while execution of traders takes only nanoseconds.

These levels of complexities unfortunately also gave rise to many snake oil sellers (traders and educators) peddling their trading methods on “algorithm” to naïve investors and calling their products all sorts of nice-sounding names playing on the algorithm trend such as “mechanical”, “automated” and “robo-trades”.

Most of the time, they are a re-hash of top products in the market used by top investment firms and are a cheap fake copycat (pirated) and are useful for a few trades until the losses start mounting and the capital runs out. You can read more about their perils to naïve traders and investors in this article by us – The mistake of relying on past successes.

Even in top investment and asset management firms, there are already issues of correlating markets, style rotation, fundamental market shifts, and insufficient liquidity. There are also the concerns of algorithm models using the same old re-hash stand-alone technical or statistical methods and just doing a “re-branding” without any new innovative or value-added factors.

Hence, whether you are a retail investor or an institutional fund manager, the risk is that not all algorithm trading systems are born equal.

Innovation or the identification of new or unique model factors of the algorithm trading system is hence the most important criteria for you to assess whether the algorithm trading system you adopt will improve your portfolio returns or god-forbid erode it so do your due diligence beforehand.

 

Traders and investors should hence know that as with any new disruptive technologies and advances in human mankind, there are always success and pitfalls that befall such events. And algorithm trading is really no exception.

That said, big data analytics is a disruptive force in financial investing due to the exponential growth in asset-price-relevant information. Investment funds are investing heavily in technology and risk management tools, adding a ‘quant’ element to augment their existing investment process.

These quantitative methods such as algorithm trading help with timing, sizing, and risk management of the funds’ portfolio.

 

Traditional methods of equity investing and trading are now being reshaped by massive advances in technology and data sciences. At the same time, clients and traders’ preferences are also shifting, focusing not just on outcomes but on how both performance and fees impact value.

It’s not a matter of the analysis or volume of trades that count but rather its performance and fees that matters in today’s world of investment. Asset managers and traders who simply use the same techniques and tools from the past will limit their ability to generate alpha (outperforming the benchmark) and deliver on client expectations.

With artificial intelligence and other advances in technologies that brings big data analytics and automation to reshape human mankind, is algorithm trading some of the science fiction or re-hashed marketing tool or is it here to stay as an evolution in the world of investment and trading?

 

What should you as an investor, trader or fund manager do with algorithm trading?

But we are running ahead of time. To understand the future, we have to revisit the past as they say, without understanding history, you will never be bound by its past successes and be doomed to repeat its past failures.

In the upcoming Part 2 of this continuing series, we first go back to 1997 to an astounding event that may have forever change the thinking of human mankind against machines including us at the Malacca Securities Quantitative Trading and Analytics Division.

 

Join and network with us at mPower Algorithm and mPower Trading and learn more about us and financial technologies and the world of big data analytics and algorithm trading.

 

2 thoughts on “Trading – Man vs. Machines: Part 1 of a Continuing Series “How Algorithm Trading can supercharge your Trading Profit”

  1. […] can read Part 1 here on Introduction: When humans and machines collide. You can read Part 2 here on Man vs. Machine: Kasparov vs. Deep […]

Comments are closed.