Exploring The Possibilities of Investing in Artificial Intelligence : A comprehensive analysis of NQROBO index performance

Abstract

This thesis investigates the potential of beating the market index for an investor by investing in Artificial Intelligence (AI). We have analysed the performance of Nasdaq CTA Artificial Intelligence & Robotics (NQROBO) from January 2018 to August 2023, comparing it to the Nasdaq Composite (NASDAQ) and S&P 500. We have simulated the behaviour of an openminded investor who uses simple prediction models to forecast returns. We have tried to make this simulation as realistic as possible using minimal hindsight. Our thesis is based on three analyses: a historical analysis evaluating NQROBO’s performance, a pseudo-out-of-sample forecasting performance analysis exploring how an investor in real time utilising a forecasting tool would perform, and lastly, an optimal relative weighting analysis of NQROBO, based on the pseudo-out-of-sample analysis. The historical analysis revealed that NQROBO outperformed the market from 2020 through 2022. It also uncovered that the Alpha was primarily positive from 2020 to early 2022, before turning negative in 2022. The Beta was lower than the market until 2022 before increasing sharply and stabilising at 1,1. Regarding the Fama French Factors, we identified the market as a consistent driver for returns. HML, RMW and CMA fluctuating greatly, being mostly negative, suggesting that NQROBO performs best when the market favours growth-oriented firms with an aggressive investment strategy. Indicating that the index has the potential of outperforming the market over certain periods if the market conditions are favourable. Furthermore, the pseudo-out-of-sample forecasting performance analysis showed that portfolios utilising Sharpe Ratio, RMSE and Hybrid RMSE weighting could outperform the market, if rebalancing daily. Suggesting that potential gains of investing in NQROBO is short lived. Lastly, our optimal relative weighting analysis of NQROBO’s shows that a highly dynamic weight allocation that is rebalanced frequently is beneficial. Enabling the portfolio to capture short-term gains and beating the market index over the period. The findings suggest that investing in AI offer the potential of beating the market index if done flexibly.