Risk management strategies for finding universal portfolios

  title={Risk management strategies for finding universal portfolios},
  author={Esther Mohr and Robert Dochow},
  journal={Annals of Operations Research},
We consider Cover’s universal portfolio and the problem of risk management in a distribution-free setting when learning from experts. We aim to find optimal portfolios without modelling the financial market at the outset. Although it exists, the price distribution of the constituent assets is neither known nor given as part of the input. We consider the portfolio selection problem from the perspective of online algorithms that process input piece-by-piece in a serial fashion. Under the minimax… 

A nonlinear optimisation model for constructing minimal drawdown portfolios

In this paper we consider the problem of minimising drawdown in a portfolio of financial assets. Here drawdown represents the relative opportunity cost of the single best missed trading opportunity

Long and Short Term Risk Control for Online Portfolio Selection

The experimental results of the six datasets demonstrate that the performance of LSTR is better than the online portfolio selection algorithms with risk control and those without risk control.

Online Portfolio Selection with Long-Short Term Forecasting

This work considers an online portfolio selection problem with reward and risk criteria. We use short-term historical data to forecast the reward term, reflecting the current market trend. We use

Wealth Flow Model: Online Portfolio Selection Based on Learning Wealth Flow Matrices

A wealth flow model is proposed to learn wealth flow matrices and maximize portfolio wealth simultaneously, which achieves the Pareto improvements in terms of multiple performance indicators and the steady growth of wealth over the state-of-the-art algorithms.

Knowledge Science, Engineering and Management: 13th International Conference, KSEM 2020, Hangzhou, China, August 28–30, 2020, Proceedings, Part II

A novel Multi-Agent Trajectory-ranked Reward EXtrapolation framework (MATREX), which adopts inverse reinforcement learning to infer demonstrators’ cooperative intention in the environment with high-dimensional state-action space and can effectively surpass the demonstrators and obtain the same level reward as the ground truth quickly and stably.

Reversion strategy for online portfolio selection with transaction costs

By exploiting the mean reversion property of stock prices, a portfolio selection strategy named 'mean reversion strategy with transaction costs (MRTC)' is proposed, which outperforms the existing state-of-the-art ones when taking transaction costs into account.



Universal portfolio selection

A general approach to designing investment strategies in which, instead of making statistical or other assumptions about the market, natural assumptions of computability are made about possible investment strategies; this approach leads to natural extensions of the notion of Kolmogorov complexity.

Risk-Adjusted On-line Portfolio Selection

This work presents a novel risk-adjusted portfolio selection algorithm (RAPS), which incorporates the ‘trading risk’ in terms of the maximum possible loss and shows that RAPS performs provably ‘as well as’ the Universal Portfolio (UP) in the worst-case.

A Minimax Portfolio Selection Rule with Linear Programming Solution

A new principle for choosing portfolios based on historical returns data is introduced; the optimal portfolio based on this principle is the solution to a simple linear programming problem. This

On the Competitive Theory and Practice of Portfolio Selection (Extended Abstract)

A mixture of both theoretical and experimental results are presented, including a more detalied study of the performance of existing and new algorithms with respect to a standard sequence of historical data cited in many studies.

A Linear Programming Approximation for the General Portfolio Analysis Problem

  • W. Sharpe
  • Economics
    Journal of Financial and Quantitative Analysis
  • 1971
Almost twenty years ago, Markowitz [4] first suggested that portfolio selection be regarded as a parametric quadratic programming problem. Risk is stated in terms of the predicted variance of

Portfolio Selection Problem with Minimax Type Risk Function

A minimax risk criterion is considered, which aims to restrict the standard deviation for each of the available stocks so that the corresponding portfolio optimization problem is formulated as a linear program and can be implemented easily.

Adaptive universal portfolios

It is shown that Cover's universal portfolio is equivalent to a Bayes estimator of the optimal growth portfolio, and an empirical study is carried out over a range of exchange traded funds over a 5 year period, which exhibits the enhanced early performance generated by the adaptive universal portfolio.

On‐Line Portfolio Selection Using Multiplicative Updates

We present an on‐line investment algorithm that achieves almost the same wealth as the best constant‐rebalanced portfolio determined in hindsight from the actual market outcomes. The algorithm