Managing Editor’s Letter

david rowe Reprints Manager and Advertising Director In the lead article in this issue, Dilip B. Madan and Yazid M. Sharaiha explain the linkages that may exist between automated trading and financial markets, both in theoretical and practical contexts. As they explain in their article, “Machine Trading: Theory, Advances, and Applications,” dynamic contributions to trading are evaluated over some investment horizon using covariations between asset position and price changes. They compare machine learning strategies based on a Gaussian process regression with a least squares regression. The authors generalize these two regression methods by invoking conservative valuation schemes, leading to the study of conservative conditional expectations modeled by distorted expectations which, in turn, lead to the development of distorted least squares and distorted Gaussian process regression as the associated estimation or prediction schemes. The authors analyze trading strategies based on these four regression methods—Gaussian process regression, distorted Gaussian process regression, least squares regression, and distorted least squares— using a common database of securities and set of factor (predictor) variables. The regression methods are used to trigger entry into trades, with exits conducted on a comparable basis for all of them. Trading strategies are executed for nine sectors of the US economy using 14 different predictive factor sets. Results reported by the authors indicate improvements are made by Gaussian process regression over the least squares regression, and distorted least squares regression, with the distorted regression also favorably affecting the drawdowns. Many financial models and applications rely on the ability to quickly determine which hidden state a new time-series observation belongs to. In “Greedy Online Classification of Persistent Market States Using Realized Intraday Volatility Features,” Peter Nystrup, Petter N. Kolm, and Erik Lindström develop a greedy online classifier for classifying time-series data without the need to parse historical data. While it is difficult to maintain persistence when classifying observations online, their classification methodology is based on clustering temporal features and explicitly penalizing jumps between states with a fixed-cost regularization term. A series of simulations show that the new classifier results in a higher accuracy and increased robustness to misspecification than the correctly specified maximum likelihood estimator. Hyperparameter optimization has become increasingly important as the number of machine-learning approaches to portfolio selection continues to increase. In “Hyperparameter Optimization for Portfolio Selection,” Peter Nystrup, Erik Lindström, and Henrik Madsen propose a systematic approach to hyperparameter optimization by leveraging recent advances in automated machine learning and multi-objective optimization. They establish a connection between forecast uncertainty b y gu es t o n Ja nu ar y 30 , 2 02 1. C op yr ig ht 2 02 0 Pa ge an t M ed ia L td .