MATRIZ DE CONSISTENCIA

Chapter 3 fills a theoretical gap by showing that investor sentiment disproportionally affects the returns of different assets. I extend De Long et al. (1990a) model into a noise trader risk model with multiple risky assets. In my model, I allow the risky assets to have different exposure to overall market investor sentiment and provide theoretical predictions consistent with the empirical evidence on the effect of investor sentiment on the cross-sectional stock returns.

Motivated by the model, I also decompose investor sentiment into long- and short-run components. Consistent with the theory, I find that the long-run sentiment component is a contrarian predictor of future long-short portfolio returns and the short-run sentiment is positively correlated with contemporaneous long-short portfolio returns, where the long-short portfolios long the sentiment-prone stocks and short the sentiment-immune stocks.

Furthermore, I check whether the effect of the sentiment components can be attributed to the time-varying beta loading of the market premium (or other risk factors). My empirical findings show that the impact of sentiment components on the cross-sectional return is not related to systematic risk. Accordingly, the behavioural story holds. Further analysis suggests that my results are robust to alternative sentiment measures, different sample periods, additional control variables, and the use of value-weighted returns.

Chapter 4

Technical Analysis as a Sentiment

Barometer and the Cross-Section of

Stock Returns

4.1

Introduction

Technical analysis (hereafter TA) is one of the most debated issues between financial aca- demics and investment practitioners. The traditional academic wisdom posits that publicly available information, such as past prices and trading volume, which serve as the basis of TA, is already incorporated into asset prices. Accordingly, any attempt to predict future returns using TA must "share a pedestal with alchemy" (Malkiel, 1999). However, TA continues to be popular among experienced traders and top fund managers.1 For example, Sushil Wadhwani has stated that overcoming the prejudice against TA was the most important lesson he had to

1_{Taylor and Allen (1992) document that at least 90% of experienced traders place some weight on technical}

analysis. Schwager (2012) and Lo and Hasanhodzic (2010) report that many of the top traders and fund managers they interviewed believe that technical analysis works.

72 Technical Analysis Sentiment and Stock Returns

learn when moving from academia to the fund management industry.2 This disparity between theory and practice raises important questions, such as the underlying reasons for traders’ adoption of TA, and investors’ reasons for paying them to do so.

This study aims to reconcile these conflicting views by proposing a new channel through which TA can add value. Explicitly, I argue that TA has the potential to serve as a barometer for investor sentiment. My view is based on the theoretical model of Brown and Jennings (1989), which considers TA as a vital tool for extracting information from current and past prices. Several models also acknowledge that asset prices can carry information about investor sentiment (e.g., De Long et al., 1990a). If investor sentiment is not directly observable (as is usually the case in reality) and if prices are noisy predictors of sentiment,3 investors may find combining current and past prices helpful in drawing inferences about sentiment signals.

Despite the absence of empirical literature in this direction, the role of TA as a barometer of investor sentiment has been prevailingly recognised among practitioners and in the media. For example, some analysts argue that the term "technical analysis" is a misnomer and should be replaced by "investor sentiment analysis".4 In one of the most popular books on TA, Pring (1991, pp. 2-3) states that:

". . . Since the technical approach is based on the theory that the price is a reflection of mass psychology (’the crowd’) in action, it attempts to forecast future price movements on the assumption that crowd psychology moves between panic, fear, and pessimism on one hand and confidence, excessive optimism, and greed on the other."

In the same vein, an article in Forbes magazine5states that:

2_{See "Technical analysis pulled out of the bin", October 17, 2010, Financial Times.}

3_{In De Long et al. (1990a), the spot price reveals contemporaneous sentiment fully and leaves no room for}

past prices (hence TA) to improve inference of sentiment. Such an implication does not hold when there is a random supply shock and current prices are a noisy predictor of sentiment.

4_{See http://www.centimetrics.com/explanation/}

4.1 Introduction 73

"Technical analysis also attempts to measure the collective investor psyche, calling heavily on the psychology of crowds and the cycle of greed and fear."

This chapter provides the first empirical evidence on the link between TA and investor sentiment. To this end, I build a daily market sentiment indicator (hereafter TA sentiment) based on the average of trading signals generated from applying 2,127 technical trading strategies to benchmark stock market indices such as the S&P 500 index and DJIA. By averaging the trading signals across different trading rules helping remove the idiosyncratic noise contained in signals from individual trading rules, a buying (selling) signal indicates a sentiment increase (decline). I use the same universe of trading strategies as Qi and Wu (2006), which nests nearly all the technical trading rules studied in the top three finance journals. I show that the level and the change of TA sentiment are significantly correlated with the level and the change of both market- and survey-based sentiment indicators, such as the CBOE Volatility Indicator (VIX), the CBOE Options Total Put-Call ratio, the detrended trading volume of S&P 500 (VOL), and the Bull-Bear spread from surveys of retail investors and institutional investors. These correlations are not driven by persistence in TA and other sentiment indicators because I also observe strong contemporaneous positive associations between innovations in TA sentiment and innovations in other sentiment indicators. This evidence is consistent with some practitioners’ view that TA reflects investor sentiment.

After demonstrating that TA reflects investor sentiment, I examine whether TA sentiment affects the cross-section of stock returns. I build my analysis on the delayed arbitrage models in Abreu and Brunnermeier (2002, 2003). In these models, a single arbitrageur cannot move the market and mispricing correction happens only when a sufficient mass of arbitrageurs arbitrage in coordination. If an arbitrageur attacks mispricing too early, she may find herself sailing against the wind and suffering substantial losses. Due to this synchronisation risk, rational arbitrageurs may be unwilling to correct mispricing when they are uncertain whether others are aware of the mispricing or hold similar opinions. Instead,

74 Technical Analysis Sentiment and Stock Returns

rational arbitrageurs, who detect investor sentiment through TA, may delay arbitrage and ride mispricing. Thus, based on the delayed arbitrage models, I expect high TA sentiment to be contemporaneously accompanied with higher returns for sentiment-prone stocks than for sentiment-immune stocks. I also expect sentiment-prone stocks to continue to earn higher returns than sentiment-immune stocks following an increase in TA sentiment,6 as rational arbitrageurs ride mispricing.7 Such a cross-sectional predictive pattern will reverse when sentiment decays away and rational arbitrageurs manage to coordinate their attack on mispricing. A coordinated attack of rational arbitrageurs on overpricing also implies a sharper decline in the returns of more overpriced stocks. Therefore, a high TA sentiment predicts a higher subsequent crash risk among the sentiment-prone and difficult-to-arbitrage stocks.

To test the empirical implications of the delayed arbitrage models of Abreu and Brunner- meier (2002, 2003), I follow Baker and Wurgler (2006) to compute the returns of long-short portfolios that long the sentiment-prone and difficult-to-arbitrage stocks (e.g., small, young, and high volatility stocks) and short the sentiment-immune and easy-to-arbitrage stocks (e.g., big, old, and low volatility stocks). Consistent with the delayed arbitrage models, I find that the change in TA sentiment positively correlates with the contemporaneous returns of the long-short portfolios. I also find that a rise in TA sentiment predicts an increase in the next- day returns but a decline in the subsequent returns of the long-short portfolios. Controlling for the commonly used risk factors, such as the Fama-French five factors and liquidity risk, and time-varying factor loadings does not alter my results. Further tests also show that when

6_{While most previous studies consider that investor sentiment predicts future return reversal, several recent}

studies demonstrate that investor sentiment also predicts short-term momentum (see, e.g., Chou et al., 2016; Han and Li, 2017; Tu et al., 2016). Another strand of studies demonstrate the profitability of trading strategies that benefit from the return momentum induced by the news-based sentiment (Huynh and Smith, 2017; Sun et al., 2016; Uhl, 2017).

7_{Several studies also show that sophisticated investors ride mispricing (see, e.g., Bhojraj et al., 2009;}

Brunnermeier and Nagel, 2004; Griffin et al., 2011; Temin and Voth, 2004). Others also show that trend following is a predominant strategy of hedge funds (e.g., Hurst et al., 2013; Schauten et al., 2015) and commodity traders (Billingsley and Chance, 1996).

4.1 Introduction 75

beginning-of- period TA sentiment is low, sentiment-prone stocks have higher crash risk than sentiment-immune stocks. Finally, consistent with the view that sophisticated investors earn higher returns by riding mispricing, I show that a simple ’trend chasing’ trading strategy that buys portfolios of the sentiment-prone stocks after a TA sentiment increase and sells them following a TA sentiment decrease generates an average abnormal return of 12% per annum. The returns of out TA trading strategies remain sizable and saliently positive after controlling for the traditional risk factors and transactions costs.

Our study contributes to the literature in a number of ways. First, I provide a new explanation for how TA can create value. Several studies suggest that past prices (or volumes) are useful for inferring information that is not fully impounded into the current price (Brown and Jennings, 1989; Hellwig, 1982; Treynor and Ferguson, 1985). TA also arises naturally when traders learn about the quality of signals they receive from analyzing the sequences of price and volume (Blume et al., 1994), or inferring information about the market depth from limit order book (Kavajecz and Odders-White, 2004). Furthermore, TA improves asset allocation when returns are predictable and there exists uncertainty about their predictability (Zhu and Zhou, 2009). I contribute to this strand of research by demonstrating that TA reflects investor sentiment and assists sophisticated investors to time the market.8

Second, I test the profitability of TA strategies from a sentiment perspective. Most of the existing studies apply TA to a single asset or a market index to show that TA strategies generate prominent returns (e.g., Allen and Taylor, 1990; Brock et al., 1992; Lui and Mole, 1998; Neely and Weller, 2003; Osler, 2003; Taylor and Allen, 1992). To the best of my knowledge, this is the first attempt to apply TA to market indexes in order to construct a market-wide sentiment indicator, which is then used to devise trading strategies using portfolios of stocks with different exposures to investor sentiment. My new trading strategies are motivated by the cross-sectional effect of market sentiment, and generates substantial

8_{In line with my results, Smith et al. (2016) find that hedge funds that use TA perform better than non-users,}

76 Technical Analysis Sentiment and Stock Returns

profitability in the cross-section. While Han et al. (2013) also report notable cross-sectional profitability from applying the Moving Average rules to individual portfolios constructed with proxies of information uncertainty, I consider a much broader spectrum of trading rules and apply TA to market indexes rather than individual stocks.

Finally, I introduce a novel, easy-to-construct, and real-time sentiment measure that is available at daily frequency. Since the only data required for constructing a TA sentiment indicator is historical prices, my approach can be useful when alternative sentiment indicators are difficult to convey due to data availability. Furthermore, although I restrict the analysis in this chapter to the stock market, my approach can be used to build sentiment indicators for other asset classes.

The rest of Chapter 4 is organized as follows. Section 4.2 explains my data and the construction of a TA sentiment index and various portfolios. Section 4.3 provides empirical evidence of the predictive power of TA sentiment on cross-sectional return premium and crash risk. Section 4.4 examines the profitability of timing strategies using TA sentiment. Section 4.5 concludes.