It is often said that the greatest enemy of a businessman is himself. Behavioral biases have the tendency to throw out rational trading strategies out of speech as concerns in the event of loss, fear of disappearance, or even control over-confidence – ultimately put into crisis. Fortunately, technology has reached a stage where impulsive decision-making humans can be replaced by unimpeded and emotionally neutral business plots. And some believe that they are the future of finance.
Conquering cognitive bias: a quantitative approach
When evaluating an investment, traders use several strategies to better identify entry and exit opportunities. They have qualitative and quantitative analysis. The latter involves statistical modeling on technical aspects such as volatility and historical performance, while the former concerns data analysis related to company management, income, competitive advantage, and other such subjective information.
Per 2020 PwC-Ellwood Crypto Hedge Fund ReportHowever, it is the quantitative approach that stands as an obvious favorite among crypto fund managers. According to the report’s survey, a significant 48% of respondents claimed to use a quantitative strategy. And the reasoning behind this is absolutely clear. It boils down to eliminating all cognitive biases – something that is very prevalent in business. This doubles for the crypto market, where volatility rules the king.
In addition, given the data-centric characteristics of the cryptocurrency market (multitude of trading venues, transaction volume, fees, market capitalization, etc.), quantitative analysts can dig deeper, which is typically the case in traditional financial assets. – Providing more scope for calculativity and prediction.
Regardless of how sophisticated a trader’s analytical skills are, cognitive bias sometimes represents a danger that exists.
There have been many studies into the impact of cognitive bias in trading – and several strategies attempting to overcome it. Behavioral finance – a subfield of behavioral economics – argues that psychological effects are the sole cause of market irregularities, such as price crash and parabolic upside movements.
A study by researchers at MIT Sloan School of Management investigated Emotional reaction to trading performance. The report concluded that extreme emotional reactions are detrimental to trader returns, particularly during times of volatility and crisis.
However, a separate, almost antitheical school of behavioral finance, known as modern portfolio theory (MPT), believes the market is efficient and traders are perfectly rational.
Neither behavioral finance nor MPT is completely correct, but neither is completely wrong. Like the yin and yang of investment, these two approaches are equivalent to each other, providing traders a comfortable and realistic middle ground.
However, it is the MPT’s approach to portfolio construction that actually stands out as a strategy for avoiding behavioral bias, specifically in favor of avoiding loss gains bias, that is, losses over potential gains. The MPT argues that diversification among multiple assets can maximize returns regardless of the risk-return profile of individual assets. In other words: Do not put all your eggs in one basket. This method develops the bias of avoiding losses by compensating for the risk through a pair of unrelated assets. And it is one of the strategic tools in the trading bot arsenal.
Human Researchers vs Bot Trading
Trading bots, which come in both analyst and advisory varieties, are designed to take on the roles of traditional research advisors and analysts, and often the above strategies (particularly quantitative analysis and in order to achieve their user goals) Diversification). A typical robo Advisor Will create a basket of data based on the customer’s risk profile, while Robo Analysts SEC filings and data will be released in the annual company report. But it is their ability to counter the cognitive bias between volatile, stressful and high-pressure market conditions that cut these bots above the rest. And they have already proven themselves superior to their human counterparts as a result.
In December 2019, Indiana University researchers Over 76,000 research reports were evaluated Issued by Rabbo-Analysts over 15 years old. As it turns out, Robo has performed better than human analysts, providing a 5% higher profit margin.
But not all robo analysts and advisors are created equal. This year, researchers measured performance 20 German B2C Robo-AdvisorsEstimated from May 2019 to March 2020 – a time frame that coincided with the bull market in 2019 and the onset and fall of the coronovirus epidemic. The disparity between bots was tremendous, with the top robo advisor limiting the downdrafts to just 3.8%, and a impressive 14-point double-digit fall in March, with a 14-point point increase over the rest of the average. Did, which brought an average year. Up to now 9.8% for hedge funds.
The main difference between the top performer and others was its strategic approach. Based on traditional measures of risk, rather than specific portfolio constructions, the top performer accurately measured what traders fear: longer time to lose money and recover from those losses. By factoring in quantitative analysis and behavioral finance, the top performer was able to read the market, outperforming both robo advisors and human-driven funds.
It is no surprise that major banks are turning to automated researchers. Last year, Goldman Sachs announced Our own robo-advisory service. Although the launch of Coronovirus is delayed until 2021, the market for Rabo Advisors has not slowed, with growth in usage Between 50 and 30% From Q4 2019 to Q1 2020.
But given its data-rich and risk-related landscape, the crypto market is where the Robo analysis will actually deliver.
This article was originally published by Anton Altement On TechtalksA publication, which examines technology trends, how they affect the way we live and do business, and the problems they solve. But we also discuss the bad side of technology, the deep implications of new technology, and the things we need. You can read the original article here.
Published December 29, 2020 – 11:00 UTC