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.
Deep Dive into Adaptive Investment Strategies For Periodic Environments.
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
Finding a proper investment strategy is a problem that has been addressed by many researchers from different areas. In economy, this problem usually concerns the behavior that an investor should follow in order to maximize the profits under an uncertain environment. To this end, researchers usually investigate the relation between methods for optimization under uncertainty, the different preferences of an investor and the amount of information available from the environment. For this, different measures of risk aversion have been proposed together with the classification of investors by their behavior towards risk (e.g. risk-averse, risk-neutral or risk-seeking behaviors), see [3]. Many researchers have been also concerned in finding different manners to control the risk-exposure. Many of the proposed methods are based on decision-making and utility theory and are addressed to scenarios where the investor can choose between investing in a risky or a risk-free asset, see [4,18,19,20]. And other researchers have extended this to the problem of portfolio diversification, where more than one risky asset is considered, see [26,27,28].
On the other hand, many researchers have used different machine learning methods to find good investment strategies in different type of stochastic environments. For example, in [25] the authors use neural networks to find patterns from financial time series, where the main goal is to find changes in volatility. And in [11], the authors propose the use of a risk-sensitive reinforcement learning algorithm to find the most proper policy for controlling under constraints and applied it to the control of a feed tank with stochastic inflows. Other techniques from machine learning that are frequently used for investment decision problems are those based on evolutionary computation. For example, those using genetic programming and genetic algorithms for portfolio management, inducing rules for bankruptcy prediction, and assigning credit scoring, see [6]. Some investment strategies based on genetic programming techniques usually lead to profitable trading strategies, however, they usually find strategies which are difficult to understand and sometimes they cannot be funded [17,33,36,37]. Even though investment strategies that are based on genetic algorithms may be also difficult to abstract and to explain, we believe that they are more natural and understandable than those using genetic programming techniques [7]. However, many of these approaches are applied to environments that are stationary; this means that some of them cannot be directly applied to changing environments. In the literature, there are some researches which have investigated the use of genetic algorithms in changing environments [5,14]. However, to our knowledge, they have not been applied specifically to the problem of controlling the proportion of investment in periodic environments.
On the other hand, the typical scenario to study investment strategies is to let an agent choose between betting in a lottery or receiving a constant amount of money [2]. This simple scenario is usually extended to different type of investment models where investors are commonly referred to as agents and the complexity of the investment models may differ considerably, see [8,22,24]. In some of these models the amount of money that the agents invest in the market is assumed to be proportional to their budget, this assumption is also called investment fraction or investment proportion. Researchers investigate in these models from the optimal investment strategies to the different properties that emerge in the artificial market, see [27,28]. Interestingly, if the market is simply treated as a random variable and the proportion of investment is fixed to a constant value, then it has been shown that eventually the agent looses all its money in the course of time [31,38]. In order to avoid bankruptcy, the agent may have an income, [21,34,38], or a budget-barrier may be assumed [23]. Some researchers have investigated different strategies to control the proportion of investment in this type of models for different scenarios [28,32]. On the other hand, some other authors use different artificial market models to compare the performance of agents with zero-intelligence and rational agents [9,12].
This paper may also draw interest on the research area of pattern recognition of time series. In particular, for the cases when there is no prior knowledge of the existance of a periodic signal or of its characteristics, see [1,39]. Note that with some small proper changes on the proposed adaptive algorithm, a useful algorithm could be proposed for the detection and measurement of periodic signal in time series.
In this paper, we propose a new approach based on evolution for finding investment strategies in periodic environments. This paper is organized as follows. Section 2 describes the investment model where the agent decides at every time step the
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