Table 2 Relationship between the independent variable \(\beta _{1} \in (0, 1]\) and the response variables using a simple linear regression model for SEPO-\(\ell _{0}\) with \(r=0.1\)
From: Proximal linearized method for sparse equity portfolio optimization with minimum transaction cost
Response variable | Estimate for intercept | Estimate for coefficient | Standard error for coefficient | p-value for coefficient | R-squared |
|---|---|---|---|---|---|
Expected return, \(y_{1}\) | 0.6514 | −0.3396 | 0.0383 | 2.0737e–0.5 | 0.9076 |
Variance risk, \(y_{2}\) | 3.1246 | −2.2371 | 0.2992 | 7.0894e–0.5 | 0.8748 |
Sparsity ratio, \(y_{3}\) | 0.6013 | −0.2679 | 0.0796 | 9.8657e–0.5 | 0.5859 |