本研究以美林时钟为基础，提出了一种基于宏观经济周期和因子模型的大类资产配置模型，从择时和选股两个角度对传统的大类资产配置模型进行了改善。

      本研究先从经济周期与货币政策的维度对大类资产的表现进行了分时期的研究，使用HP滤波模型验证了中国市场上存在美林时钟现象，研究发现，中国市场上存在资产轮动，表现为复苏期适合投资商品，过热期适合投资股票，衰退期适合投资国债，滞胀期适合投资商品或股票；同时，股票作为高收益、高风险的一类资产，在大类资产配置组合中需要进一步研究和优化。

       在大类资产配置模型的研究中，为不引入未来函数，本研究基于隐马尔科夫模型实现了对四类经济时期的动态预测。基于此结果，对股票类资产的因子投资模型进行了深入的研究：一是基于Barra USE3模型中的10类风格因子构建了纯因子轮动模型，二是基于股票因子值排序构建了多头因子轮动模型，对照组则为目前市场常见的行业因子轮动模型。研究发现，Barra模型提出的10类风格因子可以通过择时实现稳定取得超额收益，其夏普比率最高为3.51，单期最佳投资标的数量N=5；多头因子轮动模型也相较行业轮动模型取得了明显的夏普比率提升，多头因子轮动模型夏普比率最高为0.47，行业轮动模型夏普比率最高为0.20。

      本研究随后提出了基于宏观经济周期和因子模型的大类资产配置模型，研究发现，基于隐马尔可夫模型对经济周期的识别，可以将大类资产组合的夏普比率由0.2044提升至0.5453，当用多头因子轮动组合替代沪深300指数后，夏普比率可进一步提升至0.7651。

       本研究在现有文献研究的基础上，关注到大类资产模型中股票类资产高收益、高风险的投资特点，以Barra模型为基础，验证了纯因子模型具有夏普比率高、波动小的稳健特质的同时，创新性地构建了多头因子组合及其轮动模型，将因子投资的思想引入到权益类资产投资中来，提出了基于宏观经济周期和因子模型的大类资产配置新模型，为当下市场的权益类资产投资提供了行之有效的新思路。

Based on the Investment Clock, this research proposes a new asset allocation model, improving the performance of portfolio on the stock picking and timing. It has two advantages: one is using Hidden Markov Model distinguishing four kinds of stages of Chinese macroeconomic cycle, the other one is constructing a Long-only factor rotation model, updating the equity investing strategy.

Firstly, this research focuses on the asset performance from the aspects of macroeconomic cycle and monetary policy, which found that Chinese market almost followed the Investment Clock, and it suggests invest commodities in recovery period, stocks in overheat period, bonds in recession period and commodities or stocks in stagflation period. In order to get higher return in asset allocation, it’s important to improve stock investment strategy due to its high return and high volatility.

Therefore, this research contracts three kinds of stock rotation model based on the macroeconomic cycle result of Hidden Markov Model(HMM), including industry rotation, Barra factor rotation and long-only factor rotation. It found that Barra factor rotation portfolio had steady return and low max drawdown. When invests 5 factors in a month, Barra factor rotation model has the highest Sharpe Ratio 3.51. Long-only factor rotation model mostly wins the industry rotation model, which proves factor investment strategy can improve traditional equity portfolio performance.

Finally, this research establishes two types of asset allocation models, one is traditional models without timing, the other one is models based on macroeconomic cycles. After comparing models without timing and the one with timing on macroeconomic cycles, it proves that timing based on HMM can improve Sharpe Ratio from 0.2044 to 0.5453. When instead HS300 Index with long-only factor rotation model(N=3), the Sharpe Ratio reaches 0.7651.

In summary, this research answers what we should invest in Chinese macroeconomic cycles and how the asset allocation portfolio gets better performance. It presents a new asset allocation model, with pioneering use of long-only factor model and Hidden Markov Model.