Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE) have emerged as promising approaches for solving portfolio optimization tasks. However, the practical scalability of these methods remains a challenge due to the inherent noise and limitations of Noisy Intermediate-Scale Quantum (NISQ) devices. In this paper, we present Q-PORT (Quantum Portfolio Optimization with Resource-efficient Encoding and Scalability Analysis), a systematic study on the trade-offs between quantum circuit depth, stock encoding strategies, and scalability in quantum portfolio optimization. We investigate the impact of multi-qubit representations per stock and multi-stock encodings per qubit while varying circuit repetitions and Ansatz types. Our experimental results indicate that increasing qubits per stock offers negligible precision gains compared to classical Mean-Variance Optimization (MVO), while encoding multiple stocks per qubit significantly improves efficiency with minimal precision loss. These findings provide a new pathway toward resource-efficient and scalable quantum portfolio optimization, paving the way for near-term financial applications in quantum computing.