M6 competition

100,000

submissions

50+

countries

$300,000

prize money

The M6 Forecasting Competition took place from February 2022 to February 2023, which brought together a diverse range of participants from around the globe to forecast financial (stock and ETF) prices and explore the relationship between forecast accuracy and investment returns.

From February 2022 to February 2023, the M6 Competition brought together a diverse range of participants from around the globe to forecast financial (stock and ETF) prices and explore the relationship between forecast accuracy and investment returns.

The M6 Competition provided a valuable platform for forecasting experts, researchers, and practitioners to showcase their skills, exchange knowledge and techniques, contribute to the advancement of the field and compete for the 300,000USD in prize money generously provided by the M6 sponsors (listed below).

M6 competition aim

The aim of the M6 Competition was similar to the previous five: that is to empirically identify the most appropriate way of forecasting financial (stock and ETF) prices as well as to investigate the connection between the accuracy of such forecasts and the associated returns on investment. Its purpose was to shed new light on the EMH (Efficient Market Hypothesis) by explaining the poor performance of professionally managed funds, as well as the exceptional achievements of the likes of Warren Buffet, Peter Lynch and George Soros as well as celebrated firms including Blackstone, Bridgewater Associates and Renaissance Technologies. An objective of the M6 competition was to learn as much as possible about the factors producing above average financial returns and their relation to accurate forecasting while explaining deviations from the EMH and why they occur.

The efficient market hypothesis (EMH) posits that share prices reflect all relevant information, which implies that consistent outperformance of the market is not feasible. The EMH is supported by empirical evidence, including the yearly “Active/Passive Barometer” Morningstar study which regularly finds that active, professional investment managers do not beat, on average, random stock selections. On the other hand, legendary investors like Warren Buffett, Peter Lynch and George Soros, among others, as well as celebrated firms including Blackstone, Bridgewater Associates, Renaissance Technologies, DE Shaw and many others have achieved phenomenal results over long periods of time, amassing returns impossible to justify by mere chance, and casting doubts about the validity of the EMH. It was the express purpose of the M6 competition to empirically investigate this paradox and to shed new light on the EMH by explaining the poor performance of active funds, as well as the exceptional performance of the likes of Warren Buffet, whose fund has achieved an average annual return of 20.0% since 1965, almost double that of S&P500’s 10.2% annual gain during that period.

The M6 competition intended to determine if above average financial returns are achieved by one or a combination of the following factors:

  • The ability to accurately forecast overall market returns, or those of individual stocks/ETFs.
  • The ability to properly model market or individual stocks/ETF uncertainty.
  • The ability to combine forecast accuracy and uncertainty with (portfolio) investment decisions when investing in various stocks and ETFs.
  • The ability to use judgement when forecasting and investing in order to “beat” the market.
  • The importance of a consistent investment strategy.

The importance of other factors, including judgmental and model-based prediction and investment decision biases and inefficiencies that can be exploited to achieve above average returns, for example.

The M6 competition, was similar to the previous five Makridakis competitions in its focus on forecasts of stock price (returns) and risk. However, this iteration of the competition focuses equally on investment decisions made based on the use of said forecasts. Competition inputs made by participants were designed to empirically allow the testing of the factors that most affect financial returns. This was done by requiring participants to forecast and create investment decisions from a universe of 50 S&P500 stocks and 50 international ETFs, covering a variety of asset categories and countries. The forecasting and investing “duathlon” was designed to attract participation from financial experts, data scientists, economists and other interested parties. Incentives for participants included monetary prizes for best forecasting performance and for highest (risk adjusted) investment returns. The M6 competition was live, lasting for twelve months, which started in February 2022, and ended a year later in 2023. It consisted of a single month trial run and 12 rolling origins for participants who provided their submissions and were evaluated when the actual data became available.

Given the strong interest in financial forecasting, the M6 competition received substantial coverage not only from the forecasting community and its journals but also from the public and the mass media. The objective of the M6 competition was to learn as much as possible about the factors affecting financial returns and to explain deviations from the EMH and why they occur.

M6 competition sponsors

Platinum Sponsor

Gold Sponsor

Diamond Sponsor

The competition’s success highlights the critical role of accurate forecasting and prediction in our rapidly changing world, and its significance has been recognized by industry leaders such as Google, Meta, J.P. Morgan, International Institute of Forecasters, Kinaxis, Intech, causaLens, SaS, ForecastPro, Rutgers University, Erasmus School of Economics Rotterdam, University of Nicosia, NTUA, and INSEAD , who provided financial or academic support.

The findings of the M6 Competition were presented and discussed at THE FUTURE OF FORECASTING & THE M6 COMPETITION conference in New York on 6 & 7 November 2023, an event organised by the University of Nicosia and the International Institute of Forecasters.