From left to right: Andrea Rapisarda, Alessandro Pluchino, Alessio E.Biondo and Dirk Helbing (at Lipari Summer School 2013)
Are random trading strategies more successful than technical ones?
Can they also reduce the volatility of financial markets, limiting the occurrence of bubbles and crashes?
A.E.Biondo, A.Pluchino, A.Rapisarda, D.Helbing
For a review of the main results see:
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A random investment might beat the advice of your financial advisor: this is the new and counterintuitive result of this study just published in the journal "PLOS ONE". It resulted from an interdisciplinary collaboration between an economist, Alessio Emanuele Biondo, two physicists at the University of Catania, Alessandro Pluchino and Andrea Rapisarda, and the physicist and sociologist Dirk Helbing at ETH Zurich (in the picture above, from left to right: Rapisarda, Pluchino, Biondo and Helbing).
The research analyzes the performance of financial markets. Based on the observation that some randomness ("noise") often improves the functioning of physical and biological systems, and thanks to recent findings obtained exploiting the beneficial role of noise in other socio-economic contexts, the article compares some of the most popular trading strategies with a completely random one.
As the data base for their simulations, the four researchers used 15-20 year long time series of four market indices chosen among the most representative and popular in the world (the FTSE MIB for the Italian market, the FTSE UK for the London Stock Exchange, the DAX the Frankfurt Stock Exchange and the S&P 500 index for the NYSE U.S.). On this ground the different trading algorithms were compared with a random strategy in an attempt to predict the trend of the market day by day.
The results clearly show that the performance of the random strategy in predicting the evolution of the market is, on average and in the long term, quite similar to that of traditional approaches, but its variability on a small time scale is much lower. In other words, the study shows that, if a trader bets at random, he would be exposed to lower risks of serious losses, while the long-term gains will be comparable to those of popular chart-based trading strategies.
This research is part of a broader and ongoing literature debate questioning the theory of Efficient Markets. It provides a relevant contribution to the efforts of the international scientific community to understand and mitigate major financial crises and their devastating effects. The fragility that comes from the high complexity and interconnectivity of global markets today can be much more dangerous, as it inevitably exposes all market participants, including National States, not only to large financial speculations, but also to unpredictable crisis which periodically causes serious economic losses and dramatic drops in consumption. In this context, random strategies, used not only on an individual scale but also on a global one and at the level of economic policy, may also be able to avoid herding effects among investors and, consequently, to reduce the probability of dangerous "avalanche effects" that have been among the main causes of the recent collapses of the stock markets.
More recently, building on similarities between earthquakes and extreme financial events, we used a self-organized criticality-generating model to study herding and avalanches dynamics in financial markets. We consider a community of interacting investors, distributed on a small world network, who bet on the bullish (increasing) or bearish (decreasing) behavior of the market compared to the day before, following the S&P500 historical time series.
Remarkably, we find that the size of herding-related avalanches in the community can be strongly reduced by the presence of a relatively small percentage of traders, randomly distributed inside the network, who adopt a random investment strategy. These results suggest a promising strategy to limit the size of financial bubbles and crashes. We also find that the final wealth distribution of all traders corresponds to the well-known Pareto power law, while that one of random traders only is exponential. In other words, for technical traders, the risk of losses is much greater than the probability of gains compared to those of random traders.
Finally, we realized a financial market model, characterized by self-organized criticality, that is able to generate endogenously a realistic price dynamics and to reproduce well-known stylized facts. We consider a community of heterogeneous traders, composed by chartists and fundamentalists, and focus on the role of informative pressure on market participants, showing how the spreading of information, based on a realistic imitative behavior, drives contagion and causes market fragility. In this model imitation is not intended as a change in the agent's group of origin, but is referred only to the price formation process. We introduce in the community also a variable number of random traders in order to confirm their beneficial role in stabilizing the market. Finally we also suggest some counterintuitive policy strategies able to dampen fluctuations by means of a partial reduction of information.
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INTERNATIONAL PRESS AND RELATED LINKS:
"Monkey beats man on stock market picks" by Chris Flood on Financial Time - The original article: "An evaluation of alternative equity indices" by Andrew Clare, Nick Motson and Steve Thomas, Cass Business School, London