Is it sport over for human monetary analysts?

It is often said that a trader’s worst enemy is himself. Behavioral biases tend to upset otherwise rational trading strategies, as fears of loss aversion, fear of missing out, or even overconfidence take control and ultimately endanger portfolios. Fortunately, technology has advanced so far that impulsive people who make decisions can be replaced by infallible and emotionally neutral trading bots. And some believe that they are the future of finance.

Overcoming Cognitive Prejudice: A Quantitative Approach

When evaluating an investment, traders use different strategies to better identify entry and exit opportunities. Below is the qualitative and quantitative analysis. The latter involves statistical modeling of technical aspects such as volatility and historical performance, while the former involves data analysis relating to corporate governance, earnings, competitive advantage and other such subjective information.

As of 2020 PwC-Elwood Crypto Hedge Fund Report, however, it is the quantitative approach that is considered a clear favorite with crypto fund managers. According to the report’s survey, 48% of respondents said they used a quantitative strategy. And the reasons for this are perfectly clear. It all boils down to removing cognitive biases – something that is all too common in retail. This applies twice to the crypto market, where volatility is in charge.

In addition, given the data-centric characteristics of the cryptocurrency market (the multitude of trading venues, transaction volumes, fees, market capitalization, etc.), quantitative analysts can dig deeper than would be the case with traditional financial assets – and offer further scope for predictability and prediction.

No matter how refined a trader’s analytical skills may be, cognitive bias is a pervasive threat.

There have been several studies of the influence of cognitive prejudice in retailing – and as many tactics attempting to overcome them. Behavioral finance – a branch of behavioral economics – argues that psychological influence is the only cause of market irregularities such as price crashes and parabolic upward movements.

A study conducted by researchers at the MIT Sloan School of Management examined the emotional reactivity to trading performance. The report concluded that extreme emotional responses hurt trader returns, especially during times of volatility and times of crisis.

Another, almost contradicting, mindset about behavioral finance known as Modern Portfolio Theory (MPT), however, assumes that the market is efficient and traders are completely rational.

Neither Behavioral Finance nor MPT are entirely correct, but neither are they entirely wrong. Like the yin and yang of investing, these two approaches balance each other out, offering traders a comfortable and realistic middle ground.

However, it is MPT’s approach to portfolio construction that stands out as a strategy for avoiding behavioral bias, in particular loss reversal bias, ie it prefers avoiding losses over potential gains. MPT argues that diversification between multiple assets can maximize returns despite the risk-reward profile of individual assets. In other words, don’t put all your eggs in one basket. This approach circumvents loss aversion bias by offsetting risk by pairing uncorrelated assets. And it’s just one of the strategic tools in the arsenal of the trade bot.

Trading bots against human researchers

Trading bots, offered in both analyst and advisor variants, are intended to take on the traditional roles of research advisor and analyst and often use a mix of the above strategies (particularly quantitative analysis and diversification) to achieve their users’ goals. A typical robo Consultant will create a data basket based on the customer’s risk profile, while robo Analysts will deal with SEC filings and data disclosed in annual company reports. But it is their ability to tackle cognitive biases in volatile, stressful, and high pressure market situations that set these bots apart. And they have already proven that they outperform their human counterparts as a result.

In December 2019, Indiana University researchers evaluated over 76,000 research reports issued over 15 years by a number of robo-analysts. As it turned out, the robo-buy recommendations outperformed human analysts’ recommendations, granting 5% higher profit margins.

But not all robo-analysts and consultants are created equal. This year researchers measured the performance of 20 German B2C robo-advisors, rated from May 2019 to March 2020 – a period that happened to coincide with both a bull market in 2019 and the outbreak and fallout of the coronavirus pandemic. The differences between the bots were huge: the top robo-advisor limited the downside to just -3.8% and outperformed the rest by an average of 14 basis points – a pretty impressive feat given the market-wide double-digit collapse in March that saw an average year Previous losses of 9.8% for hedge funds.

The main difference between the top performer and the others was his strategic approach. Instead of typical portfolio constructions based on traditional risk measurements, the top performer measured exactly what traders fear: losing money and taking a long time to recover from those losses. By incorporating quantitative analysis and behavioral finance, the top performer was able to read the market and outperform both robo-advisors and human-run funds.

So it’s no wonder that big banks are increasingly turning to automated researchers. Last year Goldman announced Sachs its own robo-advisory service. While the start of the coronavirus is delayed until 2021, the market for robo-advisors has not slowed as usage has increased between 50 and 30% from the fourth quarter of 2019 to the first quarter of 2020.

Because of its data-rich and high-risk landscape, the robo market is going to get really good results in the crypto market.

This article was originally published by Anton Altement on TechTalks, a publication that examines technology trends, how they affect the way we live and do business, and what problems they solve. But we also discuss the evil side of technology, the darker effects of the new technology, and what to look out for. You can read the original article here.

Published on December 29, 2020 – 11:00 UTC

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