Technical analysis as a sentiment barometer and the cross-section of stock returns

References

• Abreu, D. and Brunnermeier, M.K., Synchronization risk and delayed arbitrage. J. Financ. Econ., 2002, 66, 341–360.
   Web of Science ®Google Scholar
• Abreu, D. and Brunnermeier, M.K., Bubbles and crashes. Econometrica, 2003, 71, 173–204.
   Web of Science ®Google Scholar
• Aboody, D., Even-Tov, O., Lehavy, R. and Trueman, B., Overnight returns and firm-specific investor sentiment. J. Finan. Quant. Anal., 2018, 53, 485–505.
   Web of Science ®Google Scholar
• Allen, F. and Karjalainen, R., Using genetic algorithms to find technical trading rules. J. Financ. Econ., 1999, 51, 245–271.
   Web of Science ®Google Scholar
• Antoniou, C., Doukas, J.A. and Subrahmanyam, A., Cognitive dissonance, sentiment, and momentum. J. Financ. Quant. Anal., 2013, 48, 245–275.
   Web of Science ®Google Scholar
• Aruoba, S.B., Diebold, F.X. and Scotti, C., Real-time measurement of business conditions. J. Bus. Econ. Statist., 2009, 27, 417–427.
   Web of Science ®Google Scholar
• Avramov, D., Kaplanski, G. and Levy, H., Talking numbers: Technical versus fundamental investment recommendations. J. Bank. Financ., 2018, 92, 100–114.
   Web of Science ®Google Scholar
• Baker, M. and Wurgler, J., Investor sentiment and the cross-section of stock returns. J. Finance, 2006, 61, 1645–1680.
   Web of Science ®Google Scholar
• Baker, M. and Wurgler, J., Investor sentiment in the stock market. J. Econ. Perspect., 2007, 21, 129–151.
   Web of Science ®Google Scholar
• Baker, S.R., Bloom, N. and Davis, S.J., Measuring economic policy uncertainty. Q. J. Econ., 2016, 131, 1593–1636.
   Web of Science ®Google Scholar
• Bessembinder, H. and Chan, K., Market efficiency and the returns to technical analysis. Financ. Manage., 1998, 27, 5–17.
   Web of Science ®Google Scholar
• Blume, L., Easley, D. and Ohara, M., Market statistics and technical analysis: The role of volume. J. Finance, 1994, 49, 153–181.
   Web of Science ®Google Scholar
• Brock, W., Lakonishok, J. and Lebaron, B., Simple technical trading rules and the stochastic properties of stock returns. J. Finance, 1992, 47, 1731–1764.
   Web of Science ®Google Scholar
• Brown, G.W. and Cliff, M.T., Investor sentiment and the near-term stock market. J. Empirical Finance, 2004, 11, 1–27.
   Google Scholar
• Brown, G.W. and Cliff, M.T., Investor sentiment and asset valuation. J. Bus., 2005, 78, 405–440.
   Web of Science ®Google Scholar
• Brown, D.P. and Jennings, R.H., On technical analysis. Rev. Financ. Stud., 1989, 2, 527–551.
   Web of Science ®Google Scholar
• Carhart, M.M., On persistence in mutual fund performance. J. Finance, 1997, 52, 57–82.
   Web of Science ®Google Scholar
• Cespa, G. and Vives, X., Dynamic trading and asset prices: Keynes vs. Hayek. Rev. Econ. Stud., 2012, 79, 539–580.
   Web of Science ®Google Scholar
• Chan, L.K., Jegadeesh, N. and Lakonishok, J., Momentum strategies. J. Finance., 1996, 51, 1681–1713.
   Web of Science ®Google Scholar
• Chau, F., Deesomsak, R. and Lau, M.C., Investor sentiment and feedback trading: Evidence from the exchange-traded fund markets. Int. Rev. Financ. Anal., 2011, 20, 292–305.
   Web of Science ®Google Scholar
• Chiarella, C. and Ladley, D., Chasing trends at the micro-level: The effect of technical trading on order book dynamics. J. Bank. Financ., 2016, 72, S119–S131.
   Web of Science ®Google Scholar
• Coqueret, G., Stock-specific sentiment and return predictability. Quant. Finance, 2020, 20, 1531–1551.
   Web of Science ®Google Scholar
• Cowles, A., Can stock market forecasters forecast? Econometrica, 1933, 1, 309–324.
   Google Scholar
• Da, Z., Engelberg, J. and Gao, P., The sum of all fears investor sentiment and asset prices. Rev. Finan. Stud., 2015, 28, 1–32.
   Web of Science ®Google Scholar
• Delong, J.B., Shleifer, A., Summers, L.H. and Waldmann, R.J., Positive feedback investment strategies and destabilizing rational speculation. J. Finance, 1990a, 45, 379–395.
   Web of Science ®Google Scholar
• Delong, J.B., Shleifer, A., Summers, L.H. and Waldmann, R.J., Noise trader risk in financial markets. J. Polit. Econ., 1990b, 98, 703–738.
   Web of Science ®Google Scholar
• Detzel, A., Liu, H., Strauss, J., Zhou, G. and Zhu, Y., Learning and predictability via technical analysis: Evidence from bitcoin and stocks with hard-to-value fundamentals. Finan. Manage., 2021, 50, 107–137.
   Web of Science ®Google Scholar
• Ebert, S. and Hilpert, C., Skewness preference and the popularity of technical analysis. J. Bank. Financ., 2019, 109, 105675–24.
   Web of Science ®Google Scholar
• Fama, E.F. and Blume, M.E., Filter rules and stock-market trading. J. Bus., 1966, 39, 226–241.
   Web of Science ®Google Scholar
• Fama, E.F. and French, K.R., A five-factor asset pricing model. J. Financ. Econ., 2015, 116, 1–22.
   Web of Science ®Google Scholar
• Feng, S., Wang, N. and Zychowicz, E.J., Sentiment and the performance of technical indicators. J. Portf. Manag., 2017, 43, 112–125.
   Web of Science ®Google Scholar
• Grundy, B.D. and McNichols, M., Trade and the revelation of information through prices and direct disclosure. Rev. Financ. Stud., 1989, 2, 495–526.
   Web of Science ®Google Scholar
• Han, Y., Yang, K. and Zhou, G., A new anomaly: The cross-sectional profitability of technical analysis. J. Financ. Quant. Anal., 2013, 48, 1433–1461.
   Web of Science ®Google Scholar
• Han, Y., Zhou, G. and Zhu, Y., A trend factor: Any economic gains from using information over investment horizons? J. Financ. Econ., 2016, 122, 352–375.
   Web of Science ®Google Scholar
• Hansen, P.R., Asymptotic tests of composite hypotheses, 2003.
   Google Scholar
• Hellwig, M.F., Rational expectations equilibrium with conditioning on past prices: A mean-variance example. J. Econ. Theory, 1982, 26, 279–312.
   Web of Science ®Google Scholar
• Henriksson, R.D. and Merton, R.C., On market timing and investment performance. Ii. Statistical procedures for evaluating forecasting skills. J. Bus., 1981, 54, 513–533.
   Web of Science ®Google Scholar
• Hoffmann, A.O. and Shefrin, H., Technical analysis and individual investors. J. Econ. Behav. Organ., 2014, 107, 487–511.
   Web of Science ®Google Scholar
• Huang, D.S., Jiang, F.W., Tu, J. and Zhou, G.F., Investor sentiment aligned: A powerful predictor of stock returns. Rev. Financ. Stud., 2015, 28, 791–837.
   Web of Science ®Google Scholar
• Jegadeesh, N. and Titman, S., Profitability of momentum strategies: An evaluation of alternative explanations. J. Finance, 2001, 56, 699–720.
   Web of Science ®Google Scholar
• Kavajecz, K.A. and Odders-White, E.R., Technical analysis and liquidity provision. Rev. Financ. Stud., 2004, 17, 1043–1071.
   Web of Science ®Google Scholar
• Kurov, A., Investor sentiment, trading behavior and informational efficiency in index futures markets. Financ. Rev., 2008, 43, 107–127.
   Google Scholar
• Lee, C.I. and Mathur, I., Trading rule profits in European currency spot cross-rates. J. Bank. Financ., 1996, 20, 949–962.
   Web of Science ®Google Scholar
• Lee, W.Y., Jiang, C.X. and Indro, D.C., Stock market volatility, excess returns, and the role of investor sentiment. J. Bank. Financ, 2002, 26, 2277–2299.
   Web of Science ®Google Scholar
• Lemmon, M. and Portniaguina, E., Consumer confidence and asset prices: Some empirical evidence. Rev. Financ. Stud., 2006, 19, 1499–1529.
   Web of Science ®Google Scholar
• Lin, Q., Technical analysis and stock return predictability: An aligned approach. J. Financial Mark., 2018, 38, 103–123.
   Web of Science ®Google Scholar
• Lo, A.W., Mamaysky, H. and Wang, J., Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. J. Finance, 2000, 55, 1705–1765.
   Web of Science ®Google Scholar
• Lynch, A.W. and Balduzzi, P., Predictability and transaction costs: The impact on rebalancing rules and behavior. J. Finance, 2000, 55, 2285–2309.
   Web of Science ®Google Scholar
• Marshall, B.R., Nguyen, N. and Visaltanachoti, N., Time-series momentum versus technical analysis. Quant. Finance, 2017, 17, 405–421.
   Web of Science ®Google Scholar
• Mazouz, K., Joseph, N.L. and Joulmer, J., Stock price reaction following large one-day price changes: UK evidence. J. Bank. Financ., 2009, 33, 1481–1493.
   Web of Science ®Google Scholar
• Menkhoff, L., The use of technical analysis by fund managers: International evidence. J. Bank. Financ., 2010, 34, 2573–2586.
   Web of Science ®Google Scholar
• Neely, C., Weller, P. and Dittmar, R., Is technical analysis in the foreign exchange market profitable? A genetic programming approach. J. Finan. Quant. Anal., 1997, 32, 405–426.
   Web of Science ®Google Scholar
• Neely, C.J., Rapach, D.E., Tu, J. and Zhou, G., Forecasting the equity risk premium: The role of technical indicators. Manage. Sci., 2014, 60, 1772–1791.
   Web of Science ®Google Scholar
• Newey, W.K. and West, K.D., A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 1987, 55, 703–708.
   Web of Science ®Google Scholar
• Osler, C.L., Currency orders and exchange rate dynamics: An explanation for the predictive success of technical analysis. J. Finance, 2003, 58, 1791–1819.
   Web of Science ®Google Scholar
• Park, C.H. and Irwin, S.H., What do we know about the profitability of technical analysis? J. Econ. Surveys, 2007, 21, 786–826.
   Web of Science ®Google Scholar
• Peng, L. and Xiong, W., Investor attention, overconfidence and category learning. J. Financ. Econ., 2006, 80, 563–602.
   Web of Science ®Google Scholar
• Pring, M.J., Technical Analysis Explained: ‘The successful Investors’ Guide to Spotting Investment Trends and Turning Points, 1991 (McGraw Hill: New York).
   Google Scholar
• Qi, M. and Wu, Y., Technical trading-rule profitability, data snooping, and reality check: Evidence from the foreign exchange market. J. Money, Credit, Banking, 2006, 38, 2135–2158.
   Web of Science ®Google Scholar
• Qiu, L. and Welch, I., Investor sentiment measures. Working Paper, National Bureau of Economic Research, 2006.
   Google Scholar
• Schwager, J.D., Market Wizards: Interviews with Top Traders, 2012 (John Wiley & Sons: New York).
   Google Scholar
• Shleifer, A. and Summers, L.H., The noise trader approach to finance. J. Econ. Perspect., 1990, 4, 19–33.
   Web of Science ®Google Scholar
• Smith, D.M., Wang, N., Wang, Y. and Zychowicz, E.J., Sentiment and the effectiveness of technical analysis: Evidence from the hedge fund industry. J. Financ. Quant. Anal., 2016, 51, 1991–2013.
   Web of Science ®Google Scholar
• Stambaugh, R.F., Predictive regressions. J. Financ. Econ., 1999, 54, 375–421.
   Web of Science ®Google Scholar
• Sullivan, R., Timmermann, A. and White, H., Data-snooping, technical trading rule performance, and the bootstrap. J. Finance, 1999, 54, 1647–1691.
   Web of Science ®Google Scholar
• Taylor, M.P. and Allen, H., The use of technical analysis in the foreign exchange market. J. Int. Money Finance, 1992, 11, 304–314.
   Web of Science ®Google Scholar
• Treynor, J.L. and Ferguson, R., In defense of technical analysis. J. Finance, 1985, 40, 757–773.
   Web of Science ®Google Scholar
• Treynor, J. and Mazuy, K., Can mutual funds outguess the market? Harvard Bus. Rev., 1966, 44, 131–136.
   Web of Science ®Google Scholar
• Whaley, R.E., The investor fear gauge. J. Portfol. Manage., 2000, 26, 12–17.
   Web of Science ®Google Scholar
• Yang, S.Y., Liu, A., Chen, J. and Hawkes, A., Applications of a multivariate hawkes process to joint modeling of sentiment and market return events. Quant. Finance, 2018, 18, 295–310.
   Web of Science ®Google Scholar
• Zakamulin, V. and Giner, J., Trend following with momentum versus moving averages: A tale of differences. Quant. Finance, 2020, 20, 985–1007.
   Web of Science ®Google Scholar
• Zhou, G., Measuring investor sentiment. Annu. Rev. Financ. Econ., 2018, 10, 239–259.
   Web of Science ®Google Scholar
• Zhou, G. and Zhu, Y., A theory of technical trading using moving averages. Available at SSRN 2326650, 2014.
   Google Scholar
• Zhu, Y. and Zhou, G., Technical analysis: An asset allocation perspective on the use of moving averages. J. Financ. Econ., 2009, 92, 519–544.
   Web of Science ®Google Scholar