*2.1. Data*

China's two main stock exchanges are the Shanghai and Shenzhen Stock Exchanges. On these two exchanges, two types of shares are listed: A-shares, which are priced and traded in RMB, and B-shares, which are priced in foreign currencies. We obtain stock-level data for all the A-shares from the China Stock Market and Accounting Research (CSMAR) database. We exclude financial companies from our sample because their BM ratios could have very di fferent interpretations from those of companies in other industries. The China stock market was established in 1991, but until 1995, the number of stocks listed was very small. Hence, we examine the trading strategy for the period from 1 July 1995 to 30 June 2015.

Before executing the proposed strategy, we conduct a data-clearing procedure to delete stocks with negative BM ratios and remove stocks with no trading for three consecutive months. We remove 535,685 observations, about 9% of the sample, in this clearing step. We use the daily returns calculated from the stock prices adjusted for capital changes such as dividend payout, share repurchases, and stock splits. We use the daily interest rate for the one-year fixed time deposit as the proxy for the risk-free rate.

#### *2.2. Zero-Cost Trading Strategy*

The zero-cost trading strategy is constructed as follows. For the period from 1 July 1995 to 30 June 1996, we compute the end of the 1994 book-to-market (BM) ratios of stocks. We sort the stocks in ascending order by their BM ratios and assign them into decile portfolios. Portfolio 1 consists of stocks with the lowest BM ratios while Portfolio 10 consists of stocks with the highest BM ratios. We repeat this procedure for the next 20 years with annual rebalancing.

*Economies* **2019**, *7*, 92

We then impose technical analysis on each BM portfolio with moving average timing signals rather than the passive buy-and-hold approach. First, we calculate the moving average (MA) indicator with L days of lag length as

$$A\_{j,t,l} = \frac{P\_{j,t-(L-1)} + P\_{j,t-(L-2)} + \dots + P\_{j,t-1} + P\_{j,t}}{L},\tag{1}$$

where *Pj,t* is the average price for portfolio *j* on day *t*, *L* is the length in days of the moving average window, and *Aj,t.L* is the *L*-day MA indicator for portfolio *j* on day *t*.

The trading strategy is as follows. For each BM portfolio, we either buy or continue to hold the portfolio for today if the past price index *Pj,t*−<sup>1</sup> is higher than the past MA indicator *Aj,t*−1,*<sup>L</sup>* on *t*−1. Otherwise, we invest in the risk-free asset to protect our capital. We then compute the daily average return, - *Rj*,*t*,*<sup>L</sup>* or *Rf,t,L*, for the decile portfolios as below. For comparison, we also compute the return of the traditional buy-and-hold strategy for the deciles.

$$
\widetilde{R}\_{j,t,L} = \begin{cases}
 R\_{j,t\_{\prime}} & \text{if } P\_{j,t-i} > A\_{j,t-1,L}, \\
 R\_{f,t\_{\prime}} & \text{otherwise},
\end{cases}
\tag{2}
$$

Through comparisons between the returns of the moving average strategy and the traditional buy-and-hold strategy, we can study the differing effects of these two strategies on the BM premium. We expect that the portfolio returns will be higher after the application of technical analysis. Therefore, we compute the difference between the two strategies, *MAPj*,*t*,*L*, by subtracting the return of the buy-and-hold strategy, *Rj*,*t*, from the return of the MA timing strategy, - *Rj*,*t*,*L*. If *MAPj*,*t*,*<sup>L</sup>* is significantly greater than zero, this indicates that the MA timing strategy outperforms the traditional buy-and-hold strategy.

$$MAP\_{j,t,L} = \overline{\mathcal{R}}\_{j,t,L} - R\_{j,t}, \quad j = 1, 2, \dots, 10,\tag{3}$$

After studying the difference between the two strategies, we investigate whether the return difference, if significant, is driven by exposure to alternative risk factors. We perform regressions on *MAPj*,*t*,*<sup>L</sup>* with three well-known asset pricing models in the literature: the CAPM, Fama, and French three-factor (FF3F), and liquidity augmented four-factor (LIQ4F) models, which are

$$MAP\_{j,t,L} = \alpha\_{j,L} + \beta\_{j,L,\text{MKT}} R\_{\text{MKT},t} + \varepsilon\_{j,t,L}, \quad j = 1,2,\ldots,10,\tag{4}$$

$$MAP\_{\text{j},\text{l.}} = a\_{\text{j},\text{l.}} + \beta\_{\text{j},\text{l.},\text{MKT}}R\_{\text{MKT},\text{l}} + \beta\_{\text{j},\text{l.},\text{SMB}}R\_{\text{SMB},\text{l}} + \beta\_{\text{j},\text{l.},\text{HML}}R\_{\text{HML},\text{l}} + \varepsilon\_{\text{j},\text{l.},\text{l.}} \quad \text{j} = 1,2,\ldots,10,\tag{5}$$

$$\begin{aligned} \text{MAP}\_{\text{j},\text{l.}} &= a\_{\text{j},\text{l.}} + \beta\_{\text{j},\text{l.},\text{MKT}} \text{R}\_{\text{MKT},\text{t}} + \beta\_{\text{j},\text{L,SMB}} \text{R}\_{\text{SMB},\text{t}} + \beta\_{\text{j},\text{L,HML}} \text{R}\_{\text{HML},\text{t}} + \beta\_{\text{j},\text{L,LQ}} \text{R}\_{\text{LIQ},\text{t}} + \varepsilon\_{\text{j},\text{t},\text{L}} \\ &\quad \text{j} = 1, 2, \dots, 10, \end{aligned} \tag{6}$$

where *RMKT*,*<sup>t</sup>* is market excess returns; *RSMB*,*<sup>t</sup>* is the size factor; *RHML*,*<sup>t</sup>* is the book-to-market factor; *RLIQ*,*<sup>t</sup>* is the liquidity risk factor proxied by turnover ratio; <sup>ε</sup>*j*,*t*,*<sup>L</sup>* is the error term assumed to have a zero mean and to be uncorrelated with all other explanatory variables; and the factor sensitivities or loadings β*j*,*L*,*MKT*, β*j*,*L*,*SMB*, β*j*,*L*,*HML*, and β*j*,*L*,*LIQ* are the slope coefficients for the factors, respectively. <sup>α</sup>*j*,*<sup>L</sup>* is the intercept of the regression.

Our new technical analysis enhanced BM strategy is implemented as follows. We start with the traditional BM strategy, in which we long the highest BM portfolio and short the lowest BM portfolio. Next, we apply the moving average indicator trading strategy to the two extreme BM portfolios. If the previous index price of portfolio 10 is higher (lower) than the previous moving average indicator, it indicates that the portfolio value is about to rise (fall). Therefore, we will long portfolio 10 (risk-free asset). In the same month, if the previous index price of portfolio 1 falls below (rises above) its previous MA indicator, the portfolio value is expected to drop (increase), and we will short portfolio one

(risk-free asset). The return of such a trend-following strategy is denoted by *TLSMA*,*t*,*<sup>L</sup>* and is shown as follows.

$$TLS\_{MAP,t,L} = \begin{cases} R\_{10,t} - R\_{1,t}, & \text{if } P\_{10,t-1} > A\_{10,t-1,L} \text{ and } P\_{1,t-1} < A\_{1,t-1,L}; \\ \quad R\_{10,t} - R\_{f,t}, & \text{if } P\_{10,t-1} > A\_{10,t-1,L} \text{ and } P\_{1,t-1} > A\_{1,t-1,L}; \\ \quad R\_{f,t} - R\_{1,t}, & \text{if } P\_{10,t-1} < A\_{10,t-1,L} \text{ and } P\_{1,t-1} < A\_{1,t-1,L}; \\ \quad & \text{otherwise}, \end{cases} \tag{7}$$
 
$$TL\_{MAP,t,L} = \begin{cases} 0, & \text{if } P\_{10,t-1} > A\_{10,t-1,L} \text{ and } P\_{1,t-1} < A\_{1,t-1,L}; \\ \quad R\_{1,t} - R\_{f,t}, & \text{if } P\_{10,t-1} > A\_{10,t-1,L} \text{ and } P\_{1,t-1} > A\_{1,t-1,L}; \\ \quad R\_{f,t} - R\_{10,t}, & \text{if } P\_{10,t-1} < A\_{10,t-1,L} \text{ and } P\_{1,t-1} < A\_{1,t-1,L}; \\ \quad R\_{1,t} - R\_{10,t}, & \text{otherwise}, \end{cases} \tag{8}$$

*TLSMAP*,*t*,*<sup>L</sup>* is the result of subtracting the return of the proposed trading strategy from the return of a traditional BM strategy. It measures the difference between the proposed trading strategy, combining the value premium effect and technical analysis with the simple buy-and-hold strategy.
