A Novel Online Portfolio Selection Based on Pattern Matching and ESG Factors

In modern finance, social investment portfolios have attracted the attention of researchers, investors, and practitioners. Regarding the long-term nature of this investment, the selection of the portfolios for a single period should be reconsidered as On-Line portfolio selection (OLPS) which focuses on the allocation of portfolios over multiple periods to maximize the expected growth rate of the portfolio. Besides common factors such as return of investment, many investors are willing to invest on assets complying with sustainability requirements. In this study, an OLPS strategy is developed which considers Environmental, Social, and Governance (ESG) factors in addition to return and risk. Due to the diversity of constructed portfolios, different assets are first clustered based on their mutual information (MI). Then, a novel pattern-matching approach is implemented on the clustered assets that not only considers the amount of profitability of previous windows, but also finds the optimal length and number of windows. After predicting the last groups of windows based on the pattern matching, superior assets in terms of return and Sharpe ratio in each cluster are chosen and the final portfolios are established regarding two scenarios; (i) a Mean-Variance (MV) strategy, and (ii) a developed MV strategy which considers ESG factors besides return and risk.The presented approaches are compared with several well-known benchmarks on four different datasets (i.e. 100 selective assets from S&P 500 index, S&P 500, Nikkei 225, and Dow Jones). The results indicate the superiority of the approach based on simple MV strategy over others in metrics such as Sharpe Ratio and Deflated Sharpe. Approaches containing ESG factors also show profit and can be considered a long-term investment opportunity by investors who seek more ESG-based decisions