Allfineworld > Analytical Compounded Return Explorer

GBM log-growth per year (Normal); Total multiplier (Lognormal)

Inputs

Example: 7 means 7% average simple return per year; GBM drift uses ln(1+r_arith_simple).
Chart inputs; the table grid uses ranges below.
GBM assumption: per-year log-growth g ~ N( ln(1+r_arith_simple) − ½σ², σ² ). Over T years, ln(X) ~ N( (ln(1+r_arith_simple) − ½σ²)·T, σ²·T ).

Overview

Median total multiplier (T)
Mean total multiplier (T)
Prob gain (>0%)
Prob lose half (≤ -50%)
Prob > arithmetic mean
Zero-σ annual r (comp.)
Zero-σ annual r (simple)
Multiplier threshold at 80%
Annual distribution (GBM, Normal) ?
Total multiplier PDF (Lognormal GBM)

Median Return Scenario Analysis (rows: σ, cols: r)

Kelly Criterion analysis
Kelly fraction f*
Median (K vs 100%)
Mean (K vs 100%)
Prob gain (K vs 100%)
Prob ≤ −50% (K vs 100%)
P80 threshold (K vs 100%)
Expected log-growth/yr (K vs 100%)
Portfolio multiplier PDF: Kelly vs 100% risky (r=0)

Grid (rows: σ, cols: r) — Kelly fraction

Derivation (with risk-free):
Let a = ln(1+r) be the risky asset log-return per year, σ its volatility. Let a_f = ln(1+r_free) be the risk-free log-return per year. If fraction f is invested in the risky asset and (1−f) in risk-free, the portfolio per-year log-growth is:
G(f) = a_f + f(a − a_f) − ½ f² σ².
Maximize G(f): dG/df = (a − a_f) − f σ² = 0 ⇒ f* = (a − a_f)/σ² (cap or clamp if needed). The Kelly curve and grid here use this f* and recompute all values when r_free changes.
Utility Maximization Analysis (CRRA)
CRRA f* vs Kelly vs 100%
Median (U vs K vs 100%)
Mean (U vs K vs 100%)
Prob gain (U vs K vs 100%)
P80 threshold (U vs K vs 100%)
Prob ≤ −50% (U vs K vs 100%)
Zero-σ annual r (comp.)
Zero-σ annual r (simple)
CRRA utility (Utility vs multiplier x)
Total multiplier PDF: 100% risky (dashed), Kelly (dashed), Max-Utility (solid)

Grid (rows: σ, cols: r) — Max-Utility fraction

CRRA utility: U(W) = (W^{1−γ} − 1)/(1−γ); for γ=1, U(W)=ln W. Under GBM, W_T is lognormal. For a split f in risky and 1−f in risk-free with per-year logs a, a_f and volatility σ, terminal ln(W_T) ~ N(m_p, v_p). Expected utility is E[U(W_T)] = (exp((1−γ)m_p + ½(1−γ)^2 v_p) − 1)/(1−γ); when γ=1, E[ln W_T] = m_p. Optimal fraction: f*_CRRA = (a − a_f)/(γ σ²) (capped if needed). γ=1 recovers Kelly; γ=0 gives risk-agnostic linear utility.