<p>Nonlinear financial models may exhibit oscillations and chaos, yielding volatility in variables such as interest rate, investment demand, and price index. Many studies analyze such dynamics qualitatively, yet fewer demonstrate when simple linear feedback can reliably suppress chaos and what its practical limits are (e.g., partial measurements, uncertainties, and noise). We study a three-state financial model and ask: can linear state-feedback, designed around a nonzero operating point, stabilize the system in a chaotic parameter regime? What are the tuning and robustness considerations? We linearize about a target equilibrium, design full-state feedback via pole placement (PP) and linear quadratic regulation (LQR), and provide Lyapunov-based local stability certificates together with robustness and measurement-noise bounds. We also document an observer-based realization under partial measurements. In simulations, both PP and LQR stabilize the system locally; LQR converges faster but demands higher peak control effort. An observer-based controller with only one measured state accurately estimates the states but fails to stabilize the nonlinear plant at the intended equilibrium, highlighting limits of linear separation in this setting. In this study, we provide a concise design/tuning recipe for PP and LQR around a nonzero operating point, a constructive Lyapunov-based stability analysis and a practical procedure to estimate the region of attraction, the robustness and ISS bounds, and an explicit demonstration of an observer-based limitation in the nonlinear regime.</p>