Alpha Architect 의 DIY financial investor 에서 지표 관련 흥미로운 논문들에 관한 chapter 가 있는데 이 논문들을 전부 다 읽진 못했지만 상당수가 우리팀의 연구에 도움을 줬기에, 연말기념으로 공유해봄. 레퍼런스 정리는 따로 올리겠습니다.
We consider a variety of research articles that examine timing indicators related to the macroeconomic environment. This line of research typically involves data from the bureau of labor statistics or data related to assets in the marketplace, which are thought to drive economic success or failure (e.g., oil prices). Here we describe some interesting ideas in this line of research.
Consumption, Aggregate Wealth, and Expected Stock Returns
* Lettau and Ludvigson (2002): “This paper studies the role of fluctuations in the aggregate consumption– wealth ratio for predicting stock returns. Using U.S. quarterly stock market data, we find that these fluctuations in the consumption– wealth ratio are strong predictors of both real stock returns and excess returns over a Treasury bill rate.” 4
Adaptive Macro Indexes and Short-term Variation in Stock Returns
* Bai (2010) Adaptive Macro Indexes: “Fundamental economic conditions are crucial determinants of equity premia… I find that adaptive macro indexes explain a substantial fraction of the short-term variation in future stock returns and have more forecasting power than both the historical average of stock returns and commonly used predictors.” 5
Industrial Metal Returns
* Jacobsen, Marshall, and Visaltanachoti (2013): “Price movements in industrial metals, such as copper and aluminum, predict stock returns worldwide. Increasing metal prices are good news for equity markets in recessions and bad news in expansions. Industrial metals returns forecast changes in the economy and information gradually diffuses from metals to stocks through both the discount rate and cash flow channels. Out-of-sample R2′ s are as high as 9%.” 6
Oil Shocks and Market Returns
* Kilian and Park (2007): “It is shown that the reaction of U.S. real stock returns to an oil price shock differs greatly depending on whether the change in the price of oil is driven by demand or supply shocks in the oil market. The demand and supply shocks driving the global crude oil market jointly account for 22% of the long-run variation in U.S. real stock returns.” 7
Market-Wide Earning-Return Relation
* Sadka (2009): “This paper studies the effects of predictability on the earnings– returns relation for individual firms and for the aggregate. We demonstrate that prices better anticipate earnings growth at the aggregate level than at the firm level, which implies that random-walk models are inappropriate for gauging aggregate earnings expectations.” 8
Cyclically Adjusted PE Ratio (CAPE)
* Campbell and Shiller (1998): “The price-smoothed earnings ratio… is a good forecaster of ten-year growth in stock prices, with an R2statistics of 37%.”
* Siegel (2013): “The CAPE ratio was calculated by taking a broad-based index of stock market prices, such as the S& P 500, and dividing by the average of the last ten years of aggregate earnings, all measured in real terms. The CAPE ratio was then regressed against the future ten-year real returns on stocks, establishing that the CAPE ratio was a significant variable predicting long-run stock returns. 9
Implied Cost of Capital (ICC)
* Li, Ng, and Swaminathan (2013): “Theoretically, the implied cost of capital (ICC) is a good proxy for time-varying expected returns. We find that aggregate ICC strongly predicts future excess market returns at horizons ranging from one month to four years… We also find that ICC s of size and B/ M portfolios predict corresponding portfolio returns.” 10
Sum of the Parts Forecasting
* Ferreira and Santa-Clara (2011): “We propose forecasting separately the three components of stock market returns— the dividend– price ratio, earnings growth, and price– earnings ratio growth— the sum-of-the-parts (SOP) method. Our method exploits the different time-series persistence of the components and obtains out-of-sample R-squares (compared with the historical mean) of more than 1.3% with monthly data and 13.4% with yearly data.” 11
Technical indicators have been written off as heresy by many academics. The biggest complaint is that technical trading rules rely on past prices to predict futures returns. This concept flies in the face of the efficient market hypothesis, and therefore requires researchers to jump over a high bar to get their research published in a peer-reviewed journal. As academics, we understand this skepticism with technical trading rules, but as empiricists, we are willing to give any idea a fair shot. Below are some of the more interesting research papers on the subject.
Time Series Momentum
* Moskowitz, Ooi, and Pedersen (2010): “We document significant ‘time series momentum’ in equity index, currency, commodity, and bond futures for each of the 58 liquid instruments we consider… A diversified portfolio of time series momentum strategies across all asset classes delivers substantial abnormal returns with little exposure to standard asset pricing factors and performs best during extreme markets.” 13
Time-Varying Sharpe Ratios
* Tang and Whitelaw (2011): “This paper documents predictable time-variation in stock market Sharpe ratios. … In sample, estimated conditional Sharpe ratios show substantial time-variation that coincides with the phases of the business cycle. Generally, Sharpe ratios are low at the peak of the cycle and high at the trough. In an out-of-sample analysis, using 10-year rolling regressions, relatively naive market-timing strategies that exploit this predictability can identify periods with Sharpe ratios more than 45% larger than the full sample value.” 14
Simple Moving Average (MA) Rules
* Faber (2007): “This article presents a simple quantitative method that improves risk-adjusted returns across various asset classes. A moving-average timing model is tested in-sample on the United States equity market and out-of-sample on more than twenty additional domestic and foreign markets.” 15
Challenging MA Rule #1
* Scholz and Walther (2011): “The often reported empirical success of trend-following technical timing strategies remains to be puzzling… We claim that empirical timing success is possible even in perfectly efficient markets but does not indicate prediction power. We prove this by systematically tracing back timing success to the statistical characteristics of the underlying asset price time series, which is modeled by standard stochastic processes.” 16
Challenging MA Rule #2
* Zakamulin (2014): “These active timing strategies (including Faber’s MA rule) are very appealing to investors because of their extraordinary simplicity and because they promise substantial advantages over their passive counterparts. However, the ‘too good to be true’ reported performance of these market timing rules raises a legitimate concern as to whether this performance is realistic and whether investors can expect that future performance will be the same as the documented historical performance. We argue that the reported performance of market timing strategies usually contains a considerable data-mining bias and ignores important market frictions.” 17
Challenging MA Rule #3
* Marmi, Pacati, Risso, Reno (2012): “…“ A quantitative approach to tactical asset allocation” by the fund manager M. Faber, a real hit in the SSRN online library. Is this paper a counterexample to market efficiency? We reject this conclusion, showing that a lot of caution should be used in this field, and we indicate a series of bootstrapping experiments which can be easily implemented to evaluate the performance of trading strategies.” 18
Overview of Technical Analysis
* Park and Irwin (2007): “The purpose of this report is to review the evidence on the profitability of technical analysis. To achieve this purpose, the report comprehensively reviews survey, theoretical and empirical studies regarding technical trading strategies.” 19
Dual Momentum Investing
* Antonacci (2014): “By combining relative-strength momentum and absolute momentum, this unique methodology lets you take advantage of intra-market trends while avoiding large drawdowns.” 20
Malcolm Baker and Jeff Wurgler in their 2006 paper “Investor Sentiment and the Cross-section of Stock Returns” make a clear statement: “Classical finance theory leaves no role for investor sentiment.” 21 They go on to highlight that investor sentiment might play a significant role in explaining return differences among stocks. If sentiment appears to work in stock selection, one might reasonably suppose that sentiment plays a role in market timing. One idea might be that high investor sentiment represents euphoria, which predicts low future returns, and low investor sentiment represents excess pessimism, which predicts high future returns. These ideas have been explored in the academic literature and we highlight a few papers below.
Investor Sentiment Indicators
* Baker, Wurgler, and Yuan (2010): “We construct investor sentiment indices for six major stock markets and decompose them into one global and six local indices… Global sentiment is a contrarian predictor of country-level returns. Both global and local sentiment are contrarian predictors of the time series of cross-sectional returns within markets: When sentiment is high, future returns are low on relatively difficult to arbitrage and difficult to value stocks.” 22
* Zouaoui, Nouyrigat, and Beer (2011): “We test the impact of investor sentiment on a panel of international stock markets. Specifically, we examine the influence of investor sentiment on the probability of stock market crises. We find that investor sentiment increases the probability of occurrence of stock market crises within a one-year horizon. The impact of investor sentiment on stock markets is more pronounced in countries that are culturally more prone to herd-like behavior, overreaction and low institutional involvement.” 23
* Huang, Tu, Jiang, and Zhou (2014): “We propose a new investor sentiment index that is aligned with the purpose of predicting the aggregate stock market. By eliminating a common noise component in sentiment proxies, the new index has much greater predictive power than existing sentiment indices both in- and out-of-sample, and the predictability becomes both statistically and economically significant.” 24
The Equity Share in New Issues and Aggregate Stock Returns
* Baker and Wurgler (2000): “The share of equity issues in total new equity and debt issues is a strong predictor of U.S. stock market returns between 1928 and 1997. In particular, firms issue relatively more equity than debt just before periods of low market returns. The equity share in new issues has stable predictive power in both halves of the sample period and after controlling for other known predictors.” 25
Aggregate Net Exchanges of Equity Funds/ Mutual Fund Flows
* Ben-Rephael, Kandel, and Wohl (2011): “We investigate a proxy for monthly shifts between bond funds and equity funds in the USA: aggregate net exchanges of equity funds. This measure (which is negatively related to changes in VIX) is positively contemporaneously correlated with aggregate stock market excess returns: One standard deviation of net exchanges is related to 1.95% of market excess return. These findings support the notion of “noise” in aggregate market prices induced by investor sentiment.” 26
Are Discounts on Closed-End Funds a Sentiment Index?
* Chen, Kan, and Miller (1993): “In sum, we reject as unfounded the central claim of Lee et al. that ‘The evidence suggests that discounts on closed-end funds are indeed a proxy for changes in individual investor sentiment and that same sentiment affects returns of smaller capitalization stocks and other stocks held and traded by individual investors.’” 27
Yes, Discounts on Closed-End Funds Are a Sentiment Index
* Chopra, Lee, Shleifer, Thaler (1993): “In summary, none of the stones Chen, Kan and Miller (CKM) throw seem to have hit. There is nothing embarrassing for Lee et al. in the fact that utility stocks rise when fund discounts narrow.” 28
Short Interest and Aggregate Market Returns
* Rapach, Ringgenberg, and Zhou (2014): “We show that aggregate short interest is one of the strongest known predictors of the equity risk premium. High aggregate short interest predicts lower future equity returns at monthly, quarterly, semi-annual, and annual horizons.” 29
Risk and return are linked in financial markets. If you take on more risk, you generally can expect to earn a higher return, and vice versa. Risk is often measured in terms or standard deviation, which is deemed “volatility.” And while more expected volatility usually means higher returns in the context of investing, researchers have asked a related question: Can we use market volatility to predict future market returns? The following papers explore a few ways in which researchers have tried to use volatility metrics to “predict the market.”
Expected Stock Returns and Volatility
* French, Schwert, and Stambaugh (1987): “This paper examines the relation between stock returns and stock market volatility. We find evidence that the expected market risk premium (the expected return on a stock portfolio minus the Treasury bill yield) is positively related to the predictable volatility of stock returns.” 30
Implied Volatility Spread
* Atilgan, Bali, and Demirtas (2014): “This paper investigates the intertemporal relation between volatility spreads and expected returns on the aggregate stock market. We provide evidence for a significantly negative link between volatility spreads and expected returns at the daily and weekly frequencies. We argue that this link is driven by the information flow from option markets to stock markets.” 31
* Bali and Hovakimian (2009): “We examine the relation between expected future volatility (options’ implied volatility) and the cross-section of expected returns. A trading strategy buying stocks in the highest implied volatility quintile and shorting stocks in the lowest implied volatility quintile generates insignificant returns.” 32
Variance Risk Premia
* Bollerslev, Tauchen, and Zhou (2009): “… the difference between implied and realized variation, or the variance risk premium, is able to explain a nontrivial fraction of the time-series variation in post-1990 aggregate stock market returns, with high (low) premia predicting high (low) future returns.” 33
Market Volatility Index (VIX)
* Copeland and Copeland (1999): “Changes in the Market Volatility Index (VIX) of the Chicago Board Options Exchange are statistically significant leading indicator of daily market returns. On days that follow increases in the VIX, portfolios of large-capitalization stocks outperform portfolios of small-capitalization stocks and value-based portfolios outperform growth-based portfolios. On days following a decrease in the VIX, the opposite occur.” 34
Tail Risk and Asset Prices
* Kelly and Jiang (2014): “We propose a new measure of time-varying tail risk that is directly estimable from the cross-section of returns. We exploit firm-level price crashes every month to identify common fluctuations in tail risk among individual stocks. Our tail measure is significantly correlated with tail risk measures extracted from S& P 500 index options and negatively predicts real economic activity. We show that tail risk has strong predictive power for aggregate market returns.” 35
Comprehensive Summary of Different Predictors
Because there is such a glut of research trying to predict the stock market, some researchers have tried to combine various methodologies in an attempt to identify if there is a way to predict the market with a combination of factors.
* Neely, Rapach, Tu, and Zhou (2014): “Our paper fills this gap by comparing the forecasting ability of technical indicators with that of macroeconomic variables. Technical indicators display statistically and economically significant in-sample and out-of-sample forecasting power, matching or exceeding that of macroeconomic variables… we show that combining information from both technical indicators and macroeconomic variables significantly improves equity risk premium forecasts versus using either type of information alone.” 36
* Rapach and Zhou (2013): “We survey the literature on stock return forecasting, highlighting the challenges faced by forecasters as well as strategies for improving return forecasts. We focus on U.S. equity premium forecastability and illustrate key issues via an empirical application based on updated data. Some studies argue that, despite extensive in-sample evidence of equity premium predictability, popular predictors from the literature fail to outperform the simple historical average benchmark forecast in out-of-sample tests. Recent studies, however, provide improved forecasting strategies that deliver statistically and economically significant out-of-sample gains relative to the historical average benchmark. These strategies— including economically motivated model restrictions, forecast combination, diffusion indices, and regime shifts— improve forecasting performance by addressing the substantial model uncertainty and parameter instability surrounding the data-generating process for stock returns. In addition to the U.S. equity premium, we succinctly survey out-of-sample evidence supporting U.S. cross-sectional and international stock return forecastability. The significant evidence of stock return forecastability worldwide has important implications for the development of both asset pricing models and investment management strategies.” 37
그리고 논문 레퍼런스 정리입니다.
1 Ivo Welch and Amit Goyal, “A Comprehensive Look at The Empirical Performance of Equity Premium Prediction,” Review of Financial Studies 21, no. 4 (2008): 1455– 1508.
4 Martin Lettau and Sydney Ludvigson, “Consumption, Aggregate Wealth, and Expected Stock Returns,” Journal of Finance 56, no. 3 (2001): 815– 849.
5 Jennie Bai, “Equity Premium Predictions with Adaptive Macro Indexes,” Federal Reserve Bank of New York Staff Reports, no. 475 (2010).
6 Ben Jacobsen, Ben R. Marshall, and Nuttawat Visaltanachoti, “Stock Market Predictability and Industrial Metal Returns,” 23rd Australasian Finance and Banking Conference 2010 Paper.
7 Lutz Kilian and Cheolbeom Park, “The Impact of Oil Price Shocks on the US Stock Market,” International Economic Review, 50, no. 4 (2007): 1267– 1287.
8 Gil Sadka and Ronnie Sadka, “Predictability and the Earnings– Returns Relation,” Journal of Financial Economics 94, no. 1 (2009): 87– 106.
9 John Y. Campbell and Robert J. Shiller, “Valuation Ratios and the Long-Run Stock Market Outlook,” Journal of Portfolio Management 24, no. 2 (1998): 11– 26.
10 Yan Li, David T. Ng, and Bhaskaran Swaminathan, “Predicting Market Returns Using Aggregate Implied Cost of Capital,” Journal of Financial Economics (2013).
11 Miguel A. Ferreira and Pedro Santa-Clara, “Forecasting Stock Market Returns: The Sum of the Parts Is More than the Whole,” Journal of Financial Economics 100 (2011): 87– 106.
12 Lutz Kilian and Cheolbeom Park, “The Impact of Oil Price Shocks on the US Stock Market,” Journal of Financial Economics 104 (2007): 228– 250.
13 Toby Moskowitz, Yao Hua Ooi, and Lasse Pedersen, “Time Series Momentum,” Journal of Financial Economics 104 (2012): 228– 250.
14 Yi Tang and Robert F. Whitelaw, “Time-Varying Sharpe Ratios and Market Timing,” Journal of Finance 1, no. 3 (2011): 465– 493.
15 Mebane Faber Mebane, “A Quantitative Approach to Tactical Asset Allocation,” Journal of Wealth Management 9, no. 4 (2006): 69– 79.
16 Scholz Peter and Walther Ursula, “The Trend Is Not Your Friend! Why Empirical Timing Success Is Determined by the Underlying’s Price Characteristics and Market Efficiency Is Irrelevant,” CPQF Working Paper No. 29 (2011).
17 Valeriy Zakamulin, “The Real-Life Performance of Market Timing with Moving Average and Time-Series Momentum Rules,” Journal of Asset Management 15 (2014): 261– 278.
18 Stefano Marmi, Claudio Pacati, Wiston Adrian Risso, and Roberto Reno, “A Quantitative Approach to Faber’s Tactical Asset Allocation,” Working paper series (2012).
19 Cheol-Ho Park and Scott H. Irwin, “What Do We Know about the Profitability of Technical Analysis,” Journal of Economic Surveys 21, no. 4 (2007): 786– 826.
20 Gary Antonacci, Dual-Momentum Investing, 1st ed. (New York: McGraw-Hill, 2014).
21 Malcolm P. Baker and Jeffrey Wurgler, “Investor Sentiment and the Cross-Section of Stock Returns,” Journal of Finance 61 (2006): 1645– 1680.
22 Malcolm P. Baker, Jeffrey Wurgler, and Yu Yuan, “Global, Local, and Contagious Investor Sentiment,” Journal of Financial Economics 104 (2010): 272– 287.
23 Mohamed Zouaoui, Geneviève Nouyrigat, and Francisca Beer, “How Does Investor Sentiment Affect Stock Market Crises? Evidence from Panel Data,” Financial Review 46, no. 4 (2011): 723– 747.
24 Dashan Huang, Fuwei Jiang, Jun Tu, and Guofu Zhou, “Investor Sentiment Aligned: A Powerful Predictor of Stock Returns,” Review of Financial Studies (2014).
25 Malcolm Baker and Jeffery Wurgler, “The Equity Share in New Issues and Aggregate Stock Returns,” Journal of Finance 55, no. 5 (2000): 2219– 2257.
26 Azi Ben-Rephael, Shmuel Kandel, and Avi Wohl, “Measuring Investor Sentiment with Mutual Fund Flows,” Journal of Finance Economics 104, no. 2 (2011): 363– 382.
27 Nai-Fu Chen, Raymond Kan, and Merton H. Miller, “Are the Discounts on Closed-End Funds a Sentiment Index,” Journal of Finance 48, no. 2 (1993): 795– 800.
28 Navin Chopra, Charles M. C. Lee, Andrei Shleifer, and Richard H. Thaler, “Yes, Discounts on Closed-End Funds Are a Sentiment Index,” Journal of Finance 48, no. 2 (1993): 801– 808.
29 David Rapach, Matthew Ringgenberg, and Guofu Zhou, “Short Interest and Aggregate Market Returns,” WFA— Center for Finance and Accounting Research Working Paper, No. 14/ 002, 2014.
30 Kenneth R. French, G. William Schwert, and Robert F. Stambaugh, “Expected Stock Returns and Volatility,” Journal of Financial Economics 19 (1987): 3– 29.
31 Yigit Atilgan, Turan G. Bali, and K. Ozgur Demirtas, “Implied Volatility Spreads and Expected Market Returns,” Journal of Business & Economic Statistics (2014).
32 Turan G. Bali and Armen Hovakimian, “Volatility Spreads and Expected Stock Returns,” Management Science 55 (2009): 1797– 1812.
33 Tim Bollerslev, George Tauchen, and Hao Zhou, “Expected Stock Return and Variance Risk Premia,” Review of Financial Studies 22, no. 11 (2009): 4463– 4492.
34 Maggie M. Copeland and Thomas E. Copeland, “Market Timing: Style and Size Rotation Using the VIX,” Financial Analysts Journal 55, no. 2 (1999): 73– 81.
35 Bryan Kelly and Hao Jiang, “Tail Risk and Asset Prices,” Review of Financial Studies (2014).
36 Christopher J. Neely, David E. Rapach, Jun Tu, and Zhou, “Forecasting the Equity Risk Premium: The Role of Technical Indicators,” Management Science 60, no. 7 (2014): 1772– 1791.
37 David Rapach and Guofu Zhou, “Forecasting Stock Returns.” In Handbook of Economic Forecasting,
Gray, Wesley R.; Vogel, Jack R.; Foulke, David P.. DIY Financial Advisor: A Simple Solution to Build and Protect Your Wealth (Wiley Finance) (Kindle Locations 3615-3650). Wiley. Kindle Edition.
Value and momentum everywhere- CLIFFORD S. ASNESS, TOBIAS J. MOSKOWITZ, and LASSE HEJE PEDERSEN