- The report introduces a multi-cycle timing strategy for interest rates, which is constructed using shape recognition algorithms to identify support and resistance lines in interest rate trends. The strategy combines signals from short, medium, and long cycles to form composite timing views. The switching frequency for these cycles is weekly, bi-weekly, and monthly, respectively[10][23][24] - The multi-cycle timing strategy is based on the principle that when at least two cycles show downward breakthroughs of support lines and the interest rate trend is not upward, the portfolio is fully allocated to long-duration bonds. Conversely, when at least two cycles show upward breakthroughs of resistance lines and the interest rate trend is not downward, the portfolio is fully allocated to short-duration bonds. Other configurations include mixed allocations depending on the direction of the interest rate trend[23] - The strategy employs a stop-loss mechanism where the portfolio is adjusted to equal-weighted allocation if the daily excess return falls below -0.5%[23] - The backtesting results of the multi-cycle timing strategy show a long-term annualized return of 6.17% since 2007, with a maximum drawdown of 1.52% and a return-to-drawdown ratio of 2.26. Short-term results since the end of 2023 indicate an annualized return of 7.24%, a maximum drawdown of 1.55%, and a return-to-drawdown ratio of 6.21[23][24] - The strategy has consistently outperformed its benchmark, which is an equal-weighted duration strategy, with a long-term excess return of 1.65% and a short-term excess return of 2.14% since the end of 2023. The excess return-to-drawdown ratio is 1.17 for the long term and 2.29 for the short term[23][24] - Historical performance analysis reveals that the strategy achieved a 100% success rate in generating positive absolute returns and excess returns annually over the past 18 years[24] - The report also tracks the behavior of public bond funds using an improved regression model to estimate the duration and divergence of medium- to long-term pure bond funds. The latest results show that the median duration of public bond funds, including leverage, is 3.51 years, with a 4-week moving average of 3.45 years. This represents an increase of 0.13 years and 0.04 years compared to the previous week, respectively, and places the duration level at the 96.53% percentile over the past five years[6][13][14] - The divergence in public bond fund duration, measured by the cross-sectional standard deviation, is 1.55 years, which is slightly lower than the previous week and is at the 59.07% percentile over the past five years[6][14] - The yield-to-maturity (YTM) data for public bond funds, calculated similarly, shows a median YTM of 1.7%, a 4-week moving average of 1.74%, and an average of 1.79%. Compared to the previous week, the unsmoothed median YTM decreased by 4 basis points, while the smoothed data decreased by 3 basis points, indicating that institutional holdings are near historical lows[18]

Quantitative Models and Construction Methods - **Model Name**: Multi-period interest rate timing strategy **Model Construction Idea**: The model uses multi-period resonance strategies to capture interest rate trends and generate timing signals based on shape recognition algorithms[10][22] **Model Construction Process**: 1. **Signal Generation**: Utilize kernel regression algorithms to identify support and resistance lines of interest rate data. Analyze the breakthrough patterns of interest rate trends across long, medium, and short cycles[10][22] 2. **Portfolio Construction**: - If at least two cycles show downward breakthroughs and the trend is not upward, allocate fully to long-duration bonds - If at least two cycles show downward breakthroughs but the trend is upward, allocate 50% to medium-duration bonds and 50% to long-duration bonds - If at least two cycles show upward breakthroughs and the trend is not downward, allocate fully to short-duration bonds - If at least two cycles show upward breakthroughs but the trend is downward, allocate 50% to medium-duration bonds and 50% to short-duration bonds - In other cases, allocate equally across short, medium, and long durations - Stop-loss mechanism: Adjust holdings to equal-weighted allocation if daily excess returns fall below -0.5%[22] **Model Evaluation**: The strategy demonstrates strong performance with consistent positive returns and high excess return ratios over the long term[22][23] Model Backtesting Results - **Multi-period interest rate timing strategy**: - **Short-term annualized return**: 7.27%[4][22] - **Short-term maximum drawdown**: 1.56%[4][22] - **Short-term return-to-drawdown ratio**: 6.23[4][22] - **Short-term excess return**: 2.2%[4][23] - **Long-term annualized return**: 6.17%[22] - **Long-term maximum drawdown**: 1.52%[22] - **Long-term return-to-drawdown ratio**: 2.26[22] - **Long-term excess return**: 1.66%[22] - **Excess return-to-drawdown ratio**: 1.18[22] - **Annual absolute return win rate**: 100%[23] - **Annual excess return win rate**: 100%[23] Quantitative Factors and Construction Methods - **Factor Name**: Interest rate structure indicators (level, term, convexity) **Factor Construction Idea**: Transform yield-to-maturity (YTM) data of 1-10 year government bonds into structural indicators to analyze market trends from a mean-reversion perspective[7][9] **Factor Construction Process**: 1. Calculate the level structure indicator as the average YTM across maturities 2. Compute the term structure indicator as the difference between long-term and short-term YTM 3. Derive the convexity structure indicator based on the curvature of the yield curve[7][9] **Factor Evaluation**: The indicators provide insights into the current state of the interest rate market, showing low levels across all three structures[7][9] - **Factor Name**: Multi-period interest rate timing signals **Factor Construction Idea**: Use kernel regression algorithms to identify interest rate trends and generate timing signals based on breakthroughs across long, medium, and short cycles[10] **Factor Construction Process**: 1. Apply kernel regression to identify support and resistance lines for interest rate data 2. Analyze breakthrough patterns across different cycles (monthly for long-term, bi-weekly for medium-term, weekly for short-term)[10] **Factor Evaluation**: The signals are effective in capturing market trends, with the latest signals indicating a neutral-to-bearish stance[10] Factor Backtesting Results - **Interest rate structure indicators**: - **Level structure**: Current reading is 1.51%, positioned at 6%, 4%, and 2% percentiles for 3, 5, and 10-year historical perspectives, respectively[9] - **Term structure**: Current reading is 0.3%, positioned at 13%, 8%, and 10% percentiles for 3, 5, and 10-year historical perspectives, respectively[9] - **Convexity structure**: Current reading is 0.02%, positioned at 18%, 11%, and 11% percentiles for 3, 5, and 10-year historical perspectives, respectively[9] - **Multi-period interest rate timing signals**: - **Long-term signal**: Upward breakthrough[10] - **Medium-term signal**: No signal[10] - **Short-term signal**: Downward breakthrough[10] - **Overall signal**: Neutral-to-bearish[10]