Reallocation, or adjust weights of portfolio is an indispensable part in portfolio management. In the practice, calendar rebalancing is a basic rebalancing strategy that either retail or institutional investors can utilize to create an optimal investment process. In calendar rebalancing, portfolio managers reallocate their portfolio at predefined intervals and use the historical data over the pass fixed time to calculate the suitable weights. It′s known that each time you rebalance the portfolio, paying for the tax and transaction fee is inevitable.However, reallocating the portfolio does not always get the relevant return.
In this study, we focus on examining the necessity of rebalancing before the regular reallocation by using changepoint detection under a product partition model. We propose a dynamic rebalancing with optimal training period (DRO) to improve the calendar rebalancing. We examine the efficiency of our rebalancing strategy by using backtesting procedure and compare with the calendar rebalancing. As a result, we discover that the DRO strategy has greater reward in terms of compound annual growth rate when the rolling window is longer. Besides, the representation of the DRO strategy is better than the calendar rebalancing in general when the economic situation is steady.
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