Risk-neutral return distributions as investment opportunities
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- Option data is used to rank investment opportunities in stocks.
- We avoid zero risk-neutral excess returns by focusing attention on the distribution of the next price movement.
- Investment opportunities are valued via the theory of acceptable risks.
- Fast valuation is achieved using saddle point programming.
Methods for assessing investment-value-based risk-neutral distributions are developed, and nonzero mean returns are extracted by focusing on the return from the next n moves. These distributions are vacuous for many of the traditional limit laws fitted to option prices. New finite-activity models that synthesize option prices are employed to extract such distributions from option data. The resulting mean returns are risk adjusted to determine a conservative and positive financial valuation using convex sets of risk acceptability, as described in financial finance valuation theory and monetary utility theory. Fast algorithms are developed for determining scale-optimized valuations. Indexes of value are created by formulating a geometric weighted average of the daily extracted indexes. The indexes are used in a variety of trading strategies and are observed to improve performance metrics.
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