## 5 Experiment and Analysis
### Evaluation on Validated Dataset  
Upon training our models with the dataset from 2012 to 2014, we evaluated the efficacy of various hyperparameters--specifically, the window length and temperature coefficients--using the 2015 dataset as our validation set. This step was crucial to calibrate the models effectively before final testing.  
In Figure 2, the analysis of cumulative returns for the validation dataset across varying window lengths reveals the strategic influence of the chosen time frame. An intriguing periodic decline in \(P_{t}/P_{0}\) at the end of each month is observed, which may reflect routine market occurrences such as settlement cycles. The 10-day window (NSTD-10), marked in red, consistently surpasses other strategies from mid-year to year-end, indicating that a 10-day span effectively leverages historical data to inform current market predictions. Shorter windows like the 3-day (NSTD-3) and 5-day (NSTD-5) show early promise but later falter, suggesting they may be too narrow to grasp the larger cyclical movements of the market that a 10-day window encapsulates. Conversely, the longer window strategies--NSTD-30 and NSTD-50--demonstrate smoother return trajectories but do not achieve the higher returns seen with NSTD-10. This implies that while longer windows provide a broader historical context, potentially capturing more extended market cycles, they may lack the agility to capitalise on immediate market shifts. The 10-day strategy emerges as the most effective, balancing immediate market reactivity with understanding broader market movements, highlighting its efficiency for this period.  
In Figure 3, the cumulative returns for the validated dataset are mapped out across different temperature coefficients (T) on portfolio performance. The parameter T influences the dispersion of stock weights within the portfolio, with higher T values promoting diversification and lower values indicating concentration. Portfolios with T = 5000 and T = 10,000, respectively, demonstrate a marked increase in cumulative return, especially noticeable from mid-year onward, with T = 10,000 displaying a pronounced uptick as the year concludes. This trend suggests that higher temperature settings, while promoting a more balanced weight distribution across the portfolio, may also enable the capture of growth opportunities in a rising market, contributing to higher overall returns. Conversely, the T = 500 and T = 1000 settings indicate a preference for a more concentrated portfolio. While this strategy may yield higher short-term gains by focusing on a limited number of stocks, it also introduces heightened risk, as the portfolio is more vulnerable to the volatility of its few constituents. These lower T values produce more modest growth throughout the year, with occasional dips reflecting the increased risk exposure. The T = 3000 setting balances the two extremes. Portfolios with this temperature setting deliver steady, consistent growth over the year, suggesting that a moderate T value can effectively balance the benefits of diversification with the potential gains from a more focused investment strategy. Overall, the temperature parameter's calibration is crucial for managing risk and capturing growth within a portfolio.