Financial Analytics in Google Sheets
David Ardia
Professor in Quantitative Methods for Finance
$$C(1+R_1)(1+R_2)\cdots(1+R_T)=C(1+R_E)^T$$
$$R_E=[(1+R_1)\cdots(1+R_T)]^{1/T} -1$$
$$m_G=[(1+R_1)\cdots(1+R_T)]^{1/T} -1$$
$\$100$ → $\$150$ → $\$75$
$50\%$ → $-50\%$
$$m_G=[(1+50\%)(1+(-50\%))]^{1/2} -1 = -0.134=-13.4\%$$
$\$100(1+(-13.4\%))(1+(-13.4\%)) = \$75$
A popular metric to infer the expected reward is the average return:
$$m_A=\frac{R_1+R_2+\ldots+R_T}{T}$$
$\$100$ → $\$150$ → $\$75$
$50\%$ → $-50\%$
$$m_A=\frac{50\% + (-50\%)}{2}=0\%$$
$\$100$ → $\$150$ → $\$75$
$50\%$ → $-50\%$
→ Returns not linked to one another!
→ Compounding effect taken into account!
Financial Analytics in Google Sheets