Adaptive Investment Strategies For Periodic Environments

TL;DR

This paper introduces an adaptive investment strategy tailored for environments with periodic returns, comparing it against reference and technical analysis strategies through computer simulations to evaluate performance under various conditions.

Contribution

It proposes a novel adaptive investment approach for periodic environments and systematically compares it with baseline strategies using simulated data.

Findings

The adaptive strategy outperforms zero-knowledge and complete-knowledge benchmarks in certain periodic settings.

Performance varies with the periodicity and noise level of the return on investment.

Parameter tuning is crucial for fair comparison of strategies.

Abstract

In this paper, we present an adaptive investment strategy for environments with periodic returns on investment. In our approach, we consider an investment model where the agent decides at every time step the proportion of wealth to invest in a risky asset, keeping the rest of the budget in a risk-free asset. Every investment is evaluated in the market via a stylized return on investment function (RoI), which is modeled by a stochastic process with unknown periodicities and levels of noise. For comparison reasons, we present two reference strategies which represent the case of agents with zero-knowledge and complete-knowledge of the dynamics of the returns. We consider also an investment strategy based on technical analysis to forecast the next return by fitting a trend line to previous received returns. To account for the performance of the different strategies, we perform some computer experiments to calculate the average budget that can be obtained with them over a certain number of time steps. To assure for fair comparisons, we first tune the parameters of each strategy. Afterwards, we compare the performance of these strategies for RoIs with different periodicities and levels of noise.