I guess the concept you're looking for are martingales. These are stochastic processes which remain on their current level (in expectation!).
Ignoring some technical conditions, a stochastic process $(X_t)$ is called a martingale if for all time points $t\geq s$, $$\mathbb{E}[X_t|\mathcal{F}_s]=X_s.$$ Here, $(\mathcal{F}_s)$ refers to a filtration, the information set available at time $s$. So, given the knowledge (information) at time $s$, your best prediction for the future value $X_t$ is the current value $X_s$.
For instance, the process $(X_t)$ could model the wealth of a portfolio. This would align with your question. However, the question is whether such a portfolio really satisfies the above property. Probably it will not. Real life stock prices are not really martingales: is the best guess (expected value) for Apple's price in one year's time really today's price? Perhaps not. If you however simplify reality and assume that stock prices are simple random walks, then stock prices are indeed martingales, see here.
Derivatives pricing is obviously built upon the concept of martingales and indeed, given the absence of arbitrage strategies, there exist artifical probability measures under which discounted stock prices are indeed martingales. But the expectation in this risk-neutral world is vastly different from the real world expectations (because real world investors are risk averse).
A related concept would be the Markov property. Here, you require that the filtration $(\mathcal{F}_s)$ is generated by the random variable $X_s$, i.e. $\mathbb{E}[X_t|\mathcal{F}_s]=\mathbb{E}[X_t|\sigma(X_s)]$ for all $t\geq s$. This means, past information does not matter at all for predicting stock prices. Again, in the real world, you will find plenty of violations of the Markov property.