Risk-Neutral Pricing Versus Real-World Stock Forecasting
Summary
The discussion distinguishes risk-neutral valuation from forecasting returns for investment decisions. Under a risk-neutral measure, discounted asset prices are martingales, so a simulation can estimate a discounted expectation used for pricing claims. For an ordinary traded stock, however, the market price already supplies its current value; simulating a risk-neutral terminal stock value does not provide an independent estimate for choosing between investments.
The answer emphasizes that risk-neutral probabilities are constructed for arbitrage-free valuation and are not intended to represent real-world likelihoods. Portfolio choices instead require beliefs about outcomes under the physical measure, along with the investor’s objectives and risk preferences. The exchange does not fully derive the measure change or address details such as dividends, stochastic rates, or numeraire choices, so its explanation is conceptual rather than a complete pricing recipe.
Key ideas
- Risk-neutral expectations support arbitrage-free valuation of contingent claims.
- Risk-neutral probabilities are pricing tools and are not forecasts of actual returns.
- A traded stock’s current market price is already its value, so risk-neutral simulation does not identify a superior investment.
- Portfolio selection requires real-world return expectations and consideration of investor objectives and risk.
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Full text
# Risk neutral modelling of a stock
# Risk neutral modelling of a stock
Suppose a stock $S$ follows $$dS(t) = \alpha(t)S(t)dt + \sigma(t)S(t)dW(t),$$ where $W(t)$ is a Brownian motion under $P$. Also suppose there is a short rate process $r(t)$. My question would be is it possible to price a stock using the risk-neutral framework, i.e. can I say $$S(t) = E^{Q}[e^{-\int_t^Tr(s)ds}S(T) \mid \mathcal{F}_t]$$ for some $T$? More specifically, say I am currently at time $t=0$ and I simulate $S$ under $Q$ (basically change the drift from $\alpha(t)$ to $r(t)$) $N$ times up to time $T$ and I want to compute what would be a price $S(t)$ for some integer $t > 0$. Can I just average $S(T)$ over $N$ and discount up to time $t$?
If this is a valid approach furthermore assume that I computed $S(t+1)$ using the same method and I am about to decide in which security to invest for a $t+1$ horizon. Then, the rate of return, $S(t+1)/S(t)-1,$ is $r(t)$. For any other stock, say $\tilde S$ with different drift but the same diffusion, the rate of return under risk neutral measure would be again $r(t)$ and obviously this simulation would not give me useful information for my investment decision. I could however model both of them under $P$ and then pick the one that has higher expected return. Could you elaborate why risk neutral modelling does not work for portfolio choice problem?
## Answer by Sanjay (score 3, accepted)
https://quant.stackexchange.com/a/44306
The risk neutral measure is used to price assets (e.g. derivatives) and not to base your investment decisions on.
In the first part of you question your simulation gives you the Risk-Neutral expectation of the stock at time $T$. If you want the expectation at time $t$, then why don't you just simulate from time 0 up to time $t$? (I might have misunderstood the question)
For the second part of your question: Risk-Neutral Measure is constructed such that there is no Arbitrage opportunities in the market
Could you elaborate why risk neutral modelling does not work for portfolio choice problem? That question you have (more or less) already answered yourself. $Q$ probabilities are not the "real-world" probabilities and the purpose of $Q$ is not to forecast the development of the stock
Furthermore, it is redundant to "price" a stock under $Q$ because the fair value of the stock is always given by the market price of the stock.
Here is two good links: https://www.arpm.co/lab/about-quantitative-finance.html
https://www.quora.com/Quantitative-Finance-How-would-you-explain-risk-neutral-probabilities-to-a-layman/answer/Joseph-Wang-9Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.