**1. Introduction**

In this paper, we investigate whether technical analysis can add additional value to investments in the Chinese stock market. In particular, we try to answer the research question of whether technical strategies can enhance conventional value investment (high BM-minus-low BM) in the Chinese market. For this purpose, we propose and examine the performance of a zero-cost strategy that combines technical analysis and value premium investing. The results show that the combined strategy can generate superior performance compared to simple buy-and-hold value investment. The excess profits made from the proposed strategy remain prominent after transaction costs are taken into consideration.

The value premium refers to the excess average return on stocks with high book-to-market (BM) ratios over those with low BM ratios. The positive relationship between BM ratio and average return are first documented in the 1980s (e.g., Rosenberg et al. 1985). Numerous studies have since confirmed the value premium effect using U.S. (Lakonishok et al. 1994; Fama and French 1992, 1996) or global data (Fama and French 1998; Bauman et al. 1998). Two main theories have been proposed to explain why the value premium exists. The first theory links the value premium with the financial distress risk of high BM firms (Fama and French 1992, 1996). The second theory claims that overreacting investors underprice the distressed firms, which leads to higher returns on high BM stocks (Daniel and Titman 1997).

The value premium has also been found to be prevalent in China's financial market. Wang (2004) shows that there is a significant value premium in China using different investment portfolios. Su and Xu (2006) argue that the value premium exists generally in China and has a greater influence on small-size stocks. Xie and Qu (2016) study the period after the non-tradable share reform and find that the significant value premium exists from 2005 to 2012. However, these papers focus on the

value premium generated from the conventional buy-and-hold returns on BM portfolios; none of them extend the research scope to the influence of technical analysis on value investing.

Technical analysis, which encompasses trading strategies using past price or volume information to predict future asset prices, has been shown to be a useful tool in the stock market. For example, Brock et al. (1992) and Lo et al. (2000) sugges<sup>t</sup> that technical analysis strategy can add value to investment and is helpful for making better decisions. Zhu and Zhou (2009) explore the usefulness of moving average analysis and propose that technical analysis strategy becomes more remarkable in an incomplete information environment. Han et al. (2013) find that moving average timing strategy outperforms simple buy-and-hold strategy and has a greater effect on portfolios with higher information uncertainty. Wong et al. (2003) show that technical analysis tools such as moving average and relative strength index generate considerable profits in Singapore's market, and Du and Wong (2018) find the simple moving average trading rule significantly outperform the buy-and-hold strategy on the Singapore Straits Times Index.

Menkhoff (2010) uses an international survey with fund managers and finds that technical analysis is prevalently used in the industry, which has a grea<sup>t</sup> relation with the belief of fund managers that psychological influences on stock prices are substantial. Han et al. (2016) use the moving average indicator to construct a new equity pricing factor, the trend factor, which can capture three types of trend related anomalies (short-, intermediate-, and long-term trends) and outperform other well-known reversal and momentum related factors. Technical analysis tools can also be used in forecasting market level equity risk premium (Neely et al. 2014).

According to research conducted by Lim and Luo (2012), who examine 14 Asian stock markets, including China's, none of the stock returns follow a martingale difference sequence. This indicates that in the Chinese market, the stock price does not reflect information immediately; therefore, the application of technical analysis should be meaningful and useful in this information-asymmetric environment. A number of studies support this idea. For instance, Wong et al. (2005) find a group of moving average indicators can generate positive excess returns in Chinese stock markets. Wang et al. (2011) find that there is significant predictability of technical analysis in price changes in China.

However, the usefulness of technical analysis is debatable. Shynkevich (2012) find that, using four families of technical indicators, the performance of technical indicators deteriorated substantially in the second half of 2010s due to the improvement of efficiency in the US market. Chen and Li (2006) study the application of technical analysis in China and do not find it to be superior to passive trading strategies, as they conclude that the past price or volume cannot provide extra information in China. Zhu et al. (2015) investigate the profitability of technical trading strategies, including the moving average and trading range break, on the Shanghai Composite Index, and they show that these strategies cannot beat the buy-and-hold strategy if transaction fees are taken into consideration.

Our study is motivated by at least three causes. First, although the value premium generated from buy-and-hold strategy has been proven to be existent in China's stock market, no study has explored whether technical analysis can further enhance the value investment in China. Our paper attempts to fill this gap. Second, a study by Ko et al. (2014) finds that in the Taiwan stock market, a market with no value premium, a combined strategy with value investment and moving average based trading can generate a better average return than the simple buy-and-hold value investment strategy. Because our sample is from a market with a positive value premium, our findings help determine whether the profitability of the combined strategy is independent of the existence of the value premium. Third, the mixed results on the usefulness of technical analysis in the Chinese market give us an incentive to provide additional evidence on this issue that can contribute to the literature on technical analysis-based trading in China.

Zhu and Zhou (2009) provide theoretical support for the rationale of our proposed approach. They utilize an asset allocation perspective to demonstrate that rational risk-averse investors would purposely adopt the moving average strategy combined with fixed wealth allocation rules, which can improve their expected utility substantially. This explains why the moving average strategy is widely used in practice among both institutional and individual investors.

We first construct decile portfolios from the sample according to the BM ratio of shares each year, with portfolio one consisting of stocks with the lowest BM ratios and portfolio ten consisting of the highest BM ratios. We then construct a zero-cost arbitrage portfolio by longing the highest BM portfolio and shorting the smallest BM portfolio at the same time. The return from such a portfolio is called the value premium. We then impose technical analysis on each BM portfolio with 20-day moving average timing signals. Based on the signals, we choose to hold either the stock portfolio or the risk-free asset.

We then form a new trading strategy by integrating the value premium e ffect and the moving average timing indicator into one. Under this combined strategy, we use the technical analysis based on the conventional buy-and-hold trading portfolio sorted by BM ratio. We further investigate whether the positive excess returns generated by the strategy are risk-driven. We compute the risk-adjusted excess returns by considering the risk from the capital asset pricing model (CAPM), Fama and French's (1993) three-factor and liquidity-augmented four-factor models (Datar et al. 1998; Lam and Tam 2011). Moreover, we try di fferent lag lengths of the moving average, such as 5, 10, 20, 50, 100, and 200 days of lag on the technical analysis portfolios. We also examine whether transactional costs eliminate the risk-adjusted excess returns.

We also perform robustness tests to ensure that the excess returns generated from the new strategy are reliable. First, we investigate the sub-period performance of the strategy by testing the risk-adjusted excess returns of these two sub-periods. Second, we use regression tests to check whether business cycles can a ffect the excess return. Third, we examine the timing ability of the strategy. Lastly, we perform tests on a subsample containing stocks that can be short sold.

The remainder of this paper is organized as follows. Section 2 describes the sample data and the details of the combined zero-cost trading strategy. Section 3 presents the empirical results. Section 4 discusses the robustness tests. Section 5 concludes the paper.

#### **2. Data and Methodology**
