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Stocks

  • Zura Kakushadze
  • Juan Andrés Serur
Chapter

Abstract

This chapter provides detailed descriptions for a host of equities trading strategies, including pertinent mathematical formulas for portfolio construction, estimation, and holding periods, etc. Momentum-based strategies include price-momentum, earnings-momentum, residual momentum, and, more loosely, strategies based on moving averages. Other factor-based strategies include value, which is based on the book-to-price ratio, low-volatility anomaly, strategies based on using implied volatility from options to forecast future stock returns, as well as multifactor portfolios combining two or more factors, such as momentum and value. Mean-reversion strategies include pairs trading and its generalizations to multi-stock portfolios, including using weighted regression, and, more loosely, channel and support-and-resistance based strategies. The chapter further discusses statistical arbitrage using mean-variance portfolio optimization with constraints such as dollar-neutrality, event-driven strategies such as cash and stock mergers, strategies using machine learning techniques such as k-nearest neighbor algorithms, and market making. The chapter also discusses in detail trading strategies based on combining a large number of faint trading signals (alphas) to enhance the resultant portfolio performance.

Keywords

Price-momentum Earnings-momentum Residual momentum Moving average Value Book-to-price ratio Low-volatility anomaly Implied volatility Multifactor portfolio Mean-reversion Pairs trading Weighted regression Channel trading Support Resistance Statistical arbitrage Mean-variance portfolio optimization Dollar-neutrality Event-driven strategies Cash merger Stock merger Machine learning K-nearest neighbor algorithm Market making Alpha combos 

References

  1. Adam, F., & Lin, L. H. (2001). An Analysis of the Applications of Neural Networks in Finance. Interfaces, 31(4), 112–122.CrossRefGoogle Scholar
  2. Aldridge, I. (2013). High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems (2nd ed.). Hoboken, NJ: Wiley.Google Scholar
  3. Altman, N. S. (1992). An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression. American Statistician, 46(3), 175–185.Google Scholar
  4. Amenc, N., Ducoulombier, F., Goltz, F., & Ulahel, J. (2016). Ten Misconceptions about Smart Beta (Working Paper). Available online: https://www.edhec.edu/sites/www.edhec-portail.pprod.net/files/publications/pdf/edhec-position-paper-ten-misconceptions-about-smart-beta%5F1468395239135-pdfjpg.
  5. Amenc, N., Goltz, F., Sivasubramanian, S., & Lodh, A. (2015). Robustness of Smart Beta Strategies. Journal of Index Investing, 6(1), 17–38.CrossRefGoogle Scholar
  6. Amihud, Y. (2002). Illiquidity and Stock Returns: Cross-Section and Time-Series Effects. Journal of Financial Markets, 5(1), 31–56.CrossRefGoogle Scholar
  7. Amiri, M., Zandieh, M., Vahdani, B., Soltani, R., & Roshanaei, V. (2010). An Integrated Eigenvector-DEA-TOPSIS Methodology for Portfolio Risk Evaluation in the FOREX Spot Market. Expert Systems with Applications, 37(1), 509–516.CrossRefGoogle Scholar
  8. Anand, A., & Venkataraman, K. (2016). Market Conditions, Fragility, and the Economics of Market Making. Journal of Financial Economics, 121(2), 327–349.CrossRefGoogle Scholar
  9. An, B.-J., Ang, A., Bali, T. G., & Cakici, N. (2014). The Joint Cross Section of Stocks and Options. Journal of Finance, 69(5), 2279–2337.CrossRefGoogle Scholar
  10. Andrade, G., Mitchell, M., & Stafford, E. (2001). New Evidence and Perspectives on Mergers. Journal of Economic Perspectives, 15(2), 103–120.CrossRefGoogle Scholar
  11. Andrieş, A. M., & Vîrlan, C. A. (2017). Risk Arbitrage in Emerging Europe: Are Cross-Border Mergers and Acquisition Deals More Risky? Economic Research—Ekonomska Istraživanja, 30(1), 1367–1389.CrossRefGoogle Scholar
  12. Ang, A., Hodrick, R., Xing, Y., & Zhang, X. (2006). The Cross-Section of Volatility and Expected Returns. Journal of Finance, 61(1), 259–299.CrossRefGoogle Scholar
  13. Ang, A., Hodrick, R., Xing, Y., & Zhang, X. (2009). High Idiosyncratic Volatility and Low Returns: International and Further U.S. Evidence. Journal of Financial Economics, 91(1), 1–23.CrossRefGoogle Scholar
  14. Ang, K. K., & Quek, C. (2006). Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach. IEEE Transactions on Neural Networks, 17(5), 1301–1315.CrossRefGoogle Scholar
  15. Anson, M. (2013). Performance Measurement in Private Equity: The Impact of FAS 157 on the Lagged Beta Effect. Journal of Private Equity, 17(1), 29–44.CrossRefGoogle Scholar
  16. Antonacci, G. (2017). Risk Premia Harvesting Through Dual Momentum. Journal of Management & Entrepreneurship, 11(1), 27–55.Google Scholar
  17. Arnott, R. D., Hsu, J., Kalesnik, V., & Tindall, P. (2013). The Surprising Alpha from Malkiel’s Monkey and Upside-Down Strategies. Journal of Portfolio Management, 39(4), 91–105.CrossRefGoogle Scholar
  18. Asem, E., & Tian, G. (2010). Market Dynamics and Momentum Profits. Journal of Financial and Quantitative Analysis, 45(6), 1549–1562.CrossRefGoogle Scholar
  19. Asness, C. S. (1994). Variables that Explain Stock Returns. Ph.D. thesis, University of Chicago, Chicago, IL.Google Scholar
  20. Asness, C. S. (1995). The Power of Past Stock Returns to Explain Future Stock Returns (Working Paper, Unpublished). New York, NY: Goldman Sachs Asset Management.Google Scholar
  21. Asness, C. S., Porter, R. B., & Stevens, R. L. (2000). Predicting Stock Returns Using Industry-Relative Firm Characteristics (Working Paper). Available online: https://ssrn.com/abstract=213872.
  22. Asness, C. S. (1997). The Interaction of Value and Momentum Strategies. Financial Analysts Journal, 53(2), 29–36.CrossRefGoogle Scholar
  23. Asness, C. S., Frazzini, A., Israel, R., & Moskowitz, T. (2014). Fact, Fiction, and Momentum Investing. Journal of Portfolio Management, 40(5), 75–92.CrossRefGoogle Scholar
  24. Asness, C. S., Krail, R. J., & Liew, J. M. (2001). Do Hedge Funds Hedge? Journal of Portfolio Management, 28(1), 6–19.CrossRefGoogle Scholar
  25. Asness, C. S., Moskowitz, T., & Pedersen, L. H. (2013). Value and Momentum Everywhere. Journal of Finance, 68(3), 929–985.CrossRefGoogle Scholar
  26. Avellaneda, M., & Lee, J. H. (2010). Statistical Arbitrage in the U.S. Equity Market. Quantitative Finance, 10(7), 761–782.CrossRefGoogle Scholar
  27. Avellaneda, M., & Stoikov, S. (2008). High Frequency Trading in a Limit Order Book. Quantitative Finance, 8(3), 217–224.CrossRefGoogle Scholar
  28. Baker, M., Bradley, B., & Wurgler, J. (2011). Benchmarks as Limits to Arbitrage: Understanding the Low-Volatility Anomaly. Financial Analysts Journal, 67(1), 40–54.CrossRefGoogle Scholar
  29. Baker, M., Pan, A., & Wurgler, J. (2012). The Effect of Reference Point Prices on Mergers and Acquisitions. Journal of Financial Economics, 106(1), 49–71.CrossRefGoogle Scholar
  30. Baker, M., & Savaşoglu, S. (2002). Limited Arbitrage in Mergers and Acquisitions. Journal of Financial Economics, 64(1), 91–115.CrossRefGoogle Scholar
  31. Bali, T. G., & Hovakimian, A. (2009). Volatility Spreads and Expected Stock Returns. Management Science, 55(11), 1797–1812.CrossRefGoogle Scholar
  32. Banz, R. (1981). The Relationship Between Return and Market Value of Common Stocks. Journal of Financial Economics, 9(1), 3–18.CrossRefGoogle Scholar
  33. Barber, J., Bennett, S., & Gvozdeva, E. (2015). How to Choose a Strategic Multifactor Equity Portfolio? Journal of Index Investing, 6(2), 34–45.CrossRefGoogle Scholar
  34. Baron, M., Brogaard, J., Hagströmer, B., & Kirilenko, A. (2014). Risk and Return in High-Frequency Trading. Journal of Financial and Quantitative Analysis (forthcoming). Available online: https://ssrn.com/abstract=2433118.
  35. Barroso, P., & Santa-Clara, P. (2014). Momentum Has Its Moments. Journal of Financial Economics, 116(1), 111–120.CrossRefGoogle Scholar
  36. Bartov, E., Radhakrishnan, S., & Krinsky, I. (2005). Investor Sophistication and Patterns in Stock Returns after Earnings Announcements. Accounting Review, 75(1), 289–319.Google Scholar
  37. Basu, S. (1977). The Investment Performance of Common Stocks in Relation to Their Price to Earnings Ratios: A Test of the Efficient Market Hypothesis. Journal of Finance, 32(3), 663–682.CrossRefGoogle Scholar
  38. Battalio, R., & Mendenhall, R. (2007). Post-Earnings Announcement Drift: Intra-Day Timing and Liquidity Costs (Working Paper). Available online: https://ssrn.com/abstract=937257.
  39. Batten, J., & Ellis, C. (1996). Technical Trading System Performance in the Australian Share Market: Some Empirical Evidence. Asia Pacific Journal of Management, 13(1), 87–99.CrossRefGoogle Scholar
  40. Benos, E., Brugler, J., Hjalmarsson, E., & Zikes, F. (2017). Interactions Among High-Frequency Traders. Journal of Financial and Quantitative Analysis, 52(4), 1375–1402.CrossRefGoogle Scholar
  41. Benos, E., & Sagade, S. (2016). Price Discovery and the Cross-Section of High-Frequency Trading. Journal of Financial Markets, 30, 54–77.CrossRefGoogle Scholar
  42. BenZion, U., Klein, P., Shachmurove, Y., & Yagil, J. (2003). Efficiency Differences Between the S&P 500 and the Tel-Aviv 25 Indices: A Moving Average Comparison. International Journal of Business, 8(3), 267–284.Google Scholar
  43. Bernard, V. L., & Thomas, J. K. (1989). Post-Earnings-Announcement Drift: Delayed Price Response or Risk Premium? Journal of Accounting Research, 27, 1–36.CrossRefGoogle Scholar
  44. Bernard, V. L., & Thomas, J. K. (1990). Evidence That Stock Prices Do Not Fully Reflect the Implications of Current Earnings for Future Earnings. Journal of Accounting and Economics, 13(4), 305–340.CrossRefGoogle Scholar
  45. Bester, A., Martinez, V. H., & Rosu, I. (2017). Cash Mergers and the Volatility Smile (Working Paper). Available online: https://ssrn.com/abstract=1364491.
  46. Bhojraj, S., & Swaminathan, B. (2006). Macromomentum: Returns Predictability in International Equity Indices. Journal of Business, 79(1), 429–451.CrossRefGoogle Scholar
  47. Bhushan, R. (1994). An Informational Efficiency Perspective on the Post-Earnings Announcement Drift. Journal of Accounting and Economics, 18(1), 45–65.CrossRefGoogle Scholar
  48. Biais, B., & Foucault, T. (2014). HFT and Market Quality. Bankers, Markets & Investors, 128, 5–19.Google Scholar
  49. Biais, B., Foucault, T., & Moinas, S. (2014). Equilibrium Fast Trading (Working Paper). Available online: https://ssrn.com/abstract=2024360.
  50. Birari, A., & Rode, M. (2014). Edge Ratio of Nifty for Last 15 Years on Donchian Channel. SIJ Transactions on Industrial, Financial & Business Management (IFBM), 2(5), 247–254.Google Scholar
  51. Black, F. (1972). Capital Market Equilibrium with Restricted Borrowing. Journal of Business, 45(3), 444–455.CrossRefGoogle Scholar
  52. Black, F., & Litterman, R. (1991). Asset Allocation: Combining Investors’ Views with Market Equilibrium. Journal of Fixed Income, 1(2), 7–18.CrossRefGoogle Scholar
  53. Black, F., & Litterman, R. (1992). Global Portfolio Optimization. Financial Analysts Journal, 48(5), 28–43.CrossRefGoogle Scholar
  54. Blitz, D. C., Huij, J., Lansdorp, S., & Verbeek, M. (2013). Short-Term Residual Reversal. Journal of Financial Markets, 16(3), 477–504.CrossRefGoogle Scholar
  55. Blitz, D. C., Huij, J., & Martens, M. (2011). Residual Momentum. Journal of Empirical Finance, 18(3), 506–521.CrossRefGoogle Scholar
  56. Blitz, D. C., & van Vliet, P. (2007). The Volatility Effect: Lower Risk without Lower Return. Journal of Portfolio Management, 34(1), 102–113.CrossRefGoogle Scholar
  57. Bogomolov, T. (2013). Pairs Trading Based on Statistical Variability of the Spread Process. Quantitative Finance, 13(9), 1411–1430.CrossRefGoogle Scholar
  58. Bollen, N. P. B., & Whaley, R. (2004). Does Net Buying Pressure Affect the Shape of Implied Volatility Functions? Journal of Finance, 59(2), 711–754.CrossRefGoogle Scholar
  59. Boudoukh, J., Richardson, M., & Whitelaw, R. F. (1994). Industry Returns and the Fisher Effect. Journal of Finance, 49(5), 1595–1615.CrossRefGoogle Scholar
  60. Bowen, D. A., & Hutchinson, M. C. (2016). Pairs Trading in the UK Equity Market: Risk and Return. European Journal of Finance, 22(14), 1363–1387.CrossRefGoogle Scholar
  61. Bowen, D. A., Hutchinson, M. C., & O’Sullivan, N. (2010). High Frequency Equity Pairs Trading: Transaction Costs, Speed of Execution and Patterns in Returns. Journal of Trading, 5(3), 31–38.CrossRefGoogle Scholar
  62. Bozdog, D., Florescu, I., Khashanah, K., & Wang, J. (2011). Rare Events Analysis of High-Frequency Equity Data. Wilmott Magazine, 54, 74–81.CrossRefGoogle Scholar
  63. Brock, W., Lakonishock, J., & LeBaron, B. (1992). Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. Journal of Finance, 47(5), 1731–1764.CrossRefGoogle Scholar
  64. Brogaard, J., & Garriott, C. (2018). High-Frequency Trading Competition (Working Paper). Available online: https://ssrn.com/abstract=2435999.
  65. Brogaard, J., Hagströmer, B., Nordén, L., & Riordan, R. (2015). Trading Fast and Slow: Colocation and Liquidity. Review of Financial Studies, 28(12), 3407–3443.CrossRefGoogle Scholar
  66. Brogaard, J., Hendershott, T., & Riordan, R. (2014). High-Frequency Trading and Price Discovery. Review of Financial Studies, 27(8), 2267–2306.CrossRefGoogle Scholar
  67. Brown, K. C., & Raymond, M. V. (1986). Risk Arbitrage and the Prediction of Successful Corporate Takeovers. Financial Management, 15(3), 54–63.CrossRefGoogle Scholar
  68. Budish, E., Cramton, P., & Shim, J. (2015). The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response. Quarterly Journal of Economics, 130(4), 1547–1621.CrossRefGoogle Scholar
  69. Busch, T., Christensen, B. J., & Nielsen, M. Ø. (2011). The Role of Implied Volatility in Forecasting Future Realized Volatility and Jumps in Foreign Exchange, Stock, and Bond Markets. Journal of Econometrics, 160(1), 48–57.CrossRefGoogle Scholar
  70. Caldeira, J., & Moura, G. V. (2013). Selection of a Portfolio of Pairs Based on Cointegration: A Statistical Arbitrage Strategy (Working Paper). Available online: https://ssrn.com/abstract=2196391.
  71. Cao, C., Goldie, B., Liang, B., & Petrasek, L. (2016). What Is the Nature of Hedge Fund Manager Skills? Evidence from the Risk-Arbitrage Strategy. Journal of Financial and Quantitative Analysis, 51(3), 929–957.CrossRefGoogle Scholar
  72. Carhart, M. M. (1997). Persistence in Mutual Fund Performance. Journal of Finance, 52(1), 57–82.CrossRefGoogle Scholar
  73. Carrion, A. (2013). Very Fast Money: High-Frequency Trading on the NASDAQ. Journal of Financial Markets, 16(4), 680–711.CrossRefGoogle Scholar
  74. Carrion, A., & Kolay, M. (2017). Trade Signing in Fast Markets (Working Paper). Available online: https://ssrn.com/abstract=2489868.
  75. Chakravarty, S., Gulen, H., & Mayhew, S. (2004). Informed Trading in Stock and Option Markets. Journal of Finance, 59(3), 1235–1257.CrossRefGoogle Scholar
  76. Chang, R. P., Ko, K.-C., Nakano, S., & Rhee, S. G. (2016). Residual Momentum and Investor Underreaction in Japan (Working Paper). Available online: http://sfm.finance.nsysu.edu.tw/php/Papers/CompletePaper/134-1136665035.pdf.
  77. Chan, K. C., Jegadeesh, N., & Lakonishok, J. (1996). Momentum Strategies. Journal of Finance, 51(5), 1681–1713.CrossRefGoogle Scholar
  78. Chaves, D. B. (2012). Eureka! A Momentum Strategy That also Works in Japan (Working Paper). Available online: https://ssrn.com/abstract=1982100.
  79. Chen, H. J., Chen, S. J., Chen, Z., & Li, F. (2017). Empirical Investigation of an Equity Pairs Trading Strategy. Management Science (forthcoming). https://doi.org/10.1287/mnsc.2017.2825.
  80. Chen, M. Y. (2014). A High-Order Fuzzy Time Series Forecasting Model for Internet Stock Trading. Future Generation Computer Systems, 37, 461–467.CrossRefGoogle Scholar
  81. Chen, T. F., Chung, S. L., & Tsai, W. C. (2016). Option-Implied Equity Risk and the Cross-Section of Stock Returns. Financial Analysts Journal, 72(6), 42–55.CrossRefGoogle Scholar
  82. Chen, A. S., Leung, M. T., & Daouk, H. (2003). Application of Neural Networks to an Emerging Financial Market: Forecasting and Trading the Taiwan Stock Index. Computers & Operations Research, 30(6), 901–923.CrossRefGoogle Scholar
  83. Cheung, W. (2010). The Black-Litterman Model Explained. Journal of Asset Management, 11(4), 229–243.CrossRefGoogle Scholar
  84. Chin, J. Y. F., Prevost, A. K., & Gottesman, A. A. (2002). Contrarian Investing in a Small Capitalization Market: Evidence from New Zealand. Financial Review, 37(3), 421–446.CrossRefGoogle Scholar
  85. Chordia, T., Goyal, A., Sadka, G., Sadka, R., & Shivakumar, L. (2009). Liquidity and the Post-Earnings-Announcement Drift. Financial Analysts Journal, 65(4), 18–32.CrossRefGoogle Scholar
  86. Chordia, T., & Shivakumar, L. (2002). Momentum, Business Cycle, and Time-Varying Expected Returns. Journal of Finance, 57(2), 985–1019.CrossRefGoogle Scholar
  87. Chordia, T., & Shivakumar, L. (2006). Earnings and Price Momentum. Journal of Financial Economics, 80(3), 627–656.CrossRefGoogle Scholar
  88. Chuang, H. (2015). Time Series Residual Momentum (Working Paper). Available online: http://www.econ.tohoku.ac.jp/econ/datascience/DDSR-DP/no38.pdf.
  89. Chuang, H., & Ho, H.-C. (2014). Implied Price Risk and Momentum Strategy. Review of Finance, 18(2), 591–622.CrossRefGoogle Scholar
  90. Clarke, R. G., de Silva, H., & Thorley, S. (2006). Minimum-Variance Portfolios in the U.S. Equity Market. Journal of Portfolio Management, 33(1), 10–24.CrossRefGoogle Scholar
  91. Clarke, R. G., de Silva, H., & Thorley, S. (2010). Know Your VMS Exposure. Journal of Portfolio Management, 36(2), 52–59.CrossRefGoogle Scholar
  92. Cochrane, J. H. (1999). Portfolio Advice for a Multifactor World. Federal Reserve Bank of Chicago, Economic Perspectives, 23(3), 59–78.Google Scholar
  93. Conrad, J., Dittmar, R. F., & Ghysels, E. (2013). Ex Ante Skewness and Expected Stock Returns. Journal of Finance, 68(1), 85–124.CrossRefGoogle Scholar
  94. Conrad, J., & Kaul, G. (1998). An Anatomy of Trading Strategies. Review of Financial Studies, 11(3), 489–519.CrossRefGoogle Scholar
  95. Cooper, M. J., Gutierrez, R. C., Jr., & Hameed, A. (2004). Market States and Momentum. Journal of Finance, 59(3), 1345–1365.CrossRefGoogle Scholar
  96. Cornelli, F., & Li, D. D. (2002). Risk Arbitrage in Takeovers. Review of Financial Studies, 15(3), 837–868.CrossRefGoogle Scholar
  97. Creamer, G. G., & Freund, Y. (2007). A Boosting Approach for Automated Trading. Journal of Trading, 2(3), 84–96.CrossRefGoogle Scholar
  98. Creamer, G. G., & Freund, Y. (2010). Automated Trading with Boosting and Expert Weighting. Quantitative Finance, 10(4), 401–420.CrossRefGoogle Scholar
  99. Cremers, M., & Weinbaum, D. (2010). Deviations from Put-Call Parity and Stock Return Predictability. Journal of Financial and Quantitative Analysis, 45(2), 335–367.CrossRefGoogle Scholar
  100. Czaja, M.-G., Kaufmann, P., & Scholz, H. (2013). Enhancing the Profitability of Earnings Momentum Strategies: The Role of Price Momentum, Information Diffusion and Earnings Uncertainty. Journal of Investment Strategies, 2(4), 3–57.CrossRefGoogle Scholar
  101. Da Silva, A. S., Lee, W., & Pornrojnangkool, B. (2009). The Black-Litterman Model for Active Portfolio Management. Journal of Portfolio Management, 35(2), 61–70.CrossRefGoogle Scholar
  102. Daniel, K. (2001). The Power and Size of Mean Reversion Tests. Journal of Empirical Finance, 8(5), 493–535.CrossRefGoogle Scholar
  103. Daniel, K., & Moskowitz, T. J. (2016). Momentum Crashes. Journal of Financial Economics, 122(2), 221–247.CrossRefGoogle Scholar
  104. De Zwart, G., Markwat, T., Swinkels, L., & van Dijk, D. (2009). The Economic Value of Fundamental and Technical Information in Emerging Currency Markets. Journal of International Money and Finance, 28(4), 581–604.CrossRefGoogle Scholar
  105. Dempster, M. A. H., & Jones, C. M. (2002). Can Channel Pattern Trading be Profitably Automated? European Journal of Finance, 8(3), 275–301.CrossRefGoogle Scholar
  106. Doan, M. P., Alexeev, V., & Brooks, R. (2014). Concurrent Momentum and Contrarian Strategies in the Australian Stock Market. Australian Journal of Management, 41(1), 77–106.CrossRefGoogle Scholar
  107. Do, B., & Faff, R. (2010). Does Simple Pairs Trading Still Work? Financial Analysts Journal, 66(4), 83–95.CrossRefGoogle Scholar
  108. Do, B., & Faff, R. (2012). Are Pairs Trading Profits Robust to Trading Costs? Journal of Financial Research, 35(2), 261–287.CrossRefGoogle Scholar
  109. Donchian, R. D. (1960). High Finance in Copper. Financial Analysts Journal, 16(6), 133–142.CrossRefGoogle Scholar
  110. Doyle, J. T., Lundholm, R. J., & Soliman, M. T. (2006). The Extreme Future Stock Returns Following I/B/E/S Earnings Surprises. Journal of Accounting Research, 44(5), 849–887.CrossRefGoogle Scholar
  111. Drobetz, W. (2001). How to Avoid the Pitfalls in Portfolio Optimization? Putting the Black-Litterman Approach at Work. Financial Markets and Portfolio Management, 15(1), 59–75.CrossRefGoogle Scholar
  112. Dukes, W. P., Frolich, C. J., & Ma, C. K. (1992). Risk Arbitrage in Tender Offers. Journal of Portfolio Management, 18(4), 47–55.CrossRefGoogle Scholar
  113. Dzikevičius, A., & Šanranda, S. (2010). EMA Versus SMA: Usage to Forecast Stock Markets: The Case of S&P 500 and OMX Baltic Benchmark. Verslas: teorija ir praktika—Business: Theory and Practice, 11(3), 248–255.Google Scholar
  114. Easley, D., López de Prado, M. M., & O’Hara, M. (2011). The Microstructure of the ‘Flash Crash’: Flow Toxicity, Liquidity Crashes and the Probability of Informed Trading. Journal of Portfolio Management, 37(2), 118–128.CrossRefGoogle Scholar
  115. Easley, D., López de Prado, M. M., & O’Hara, M. (2012). The Volume Clock: Insights into the High Frequency Paradigm. Journal of Portfolio Management, 39(1), 19–29.CrossRefGoogle Scholar
  116. Edwards, R., & Magee, J. (1992). Technical Analysis of Stock Trends. New York, NY: New York Institute of Finance.Google Scholar
  117. Egginton, J. F., Van Ness, B. F., & Van Ness, R. A. (2016). Quote Stuffing. Financial Management, 45(3), 583–608.CrossRefGoogle Scholar
  118. Elder, A. (2014). The New Trading for a Living. Hoboken, NJ: Wiley.Google Scholar
  119. Elliott, R. J., van der Hoek, J., & Malcolm, W. P. (2005). Pairs Trading. Quantitative Finance, 5(3), 271–276.CrossRefGoogle Scholar
  120. Engle, R. F., & Granger, C. W. J. (1987). Co-integration and Error Correction: Representation, Estimation and Testing. Econometrica, 55(2), 251–276.CrossRefGoogle Scholar
  121. Erb, C., & Harvey, C. (2006). The Strategic and Tactical Value of Commodity Futures. Financial Analysts Journal, 62(2), 69–97.CrossRefGoogle Scholar
  122. Faber, M. (2007). A Quantitative Approach to Tactical Asset Allocation. Journal of Wealth Management, 9(4), 69–79.CrossRefGoogle Scholar
  123. Fama, E. F. (1996). Multifactor Portfolio Efficiency and Multifactor Asset Pricing. Journal of Financial and Quantitative Analysis, 31(4), 441–465.CrossRefGoogle Scholar
  124. Fama, E. F., & French, K. R. (1992). The Cross-Section of Expected Stock Returns. Journal of Finance, 47(2), 427–465.CrossRefGoogle Scholar
  125. Fama, E. F., & French, K. R. (1993). Common Risk Factors in the Returns on Stocks and Bonds. Journal of Financial Economics, 33(1), 3–56.CrossRefGoogle Scholar
  126. Fama, E. F., & French, K. R. (1996). Multifactor Explanations of Asset Pricing Anomalies. Journal of Finance, 51(1), 55–84.CrossRefGoogle Scholar
  127. Fama, E. F., & French, K. R. (1998). Value Versus Growth: The International Evidence. Journal of Finance, 53(6), 1975–1999.CrossRefGoogle Scholar
  128. Fama, E. F., & French, K. R. (2012). Size, Value and Momentum in International Stock Returns. Journal of Financial Economics, 105(3), 457–472.CrossRefGoogle Scholar
  129. Félix, J. A., & Rodríguez, F. F. (2008). Improving Moving Average Trading Rules with Boosting and Statistical Learning Methods. Journal of Forecasting, 27(5), 433–449.CrossRefGoogle Scholar
  130. Fifield, S. G. M., Power, D. M., & Knipe, D. G. S. (2008). The Performance of Moving Average Rules in Emerging Stock Markets. Applied Financial Economics, 18(19), 1515–1532.CrossRefGoogle Scholar
  131. Fisher, G., Shah, R., & Titman, S. (2016). Combining Value and Momentum. Journal of Investment Management, 14(2), 33–48.Google Scholar
  132. Fong, W. M., & Yong, L. H. M. (2005). Chasing Trends: Recursive Moving Average Trading Rules and Internet Stocks. Journal of Empirical Finance, 12(1), 43–76.CrossRefGoogle Scholar
  133. Foster, G., Olsen, C., & Shevlin, T. (1984). Earnings Releases, Anomalies, and the Behavior of Security Returns. Accounting Review, 59(4), 574–603.Google Scholar
  134. Frazzini, A., & Pedersen, L. H. (2014). Betting Against Beta. Journal of Financial Economics, 111(1), 1–25.CrossRefGoogle Scholar
  135. Fu, F. (2009). Idiosyncratic Risk and the Cross-Section of Expected Stock Returns. Journal of Financial Economics, 91(1), 24–37.CrossRefGoogle Scholar
  136. Garcia-Feijóo, L., Kochard, L., Sullivan, R. N., & Wang, P. (2015). Low-Volatility Cycles: The Influence of Valuation and Momentum on Low-Volatility Portfolios. Financial Analysts Journal, 71(3), 47–60.CrossRefGoogle Scholar
  137. Garzarelli, F., Cristelli, M., Pompa, G., Zaccaria, A., & Pietronero, L. (2014). Memory Effects in Stock Price Dynamics: Evidences of Technical Trading. Scientific Reports, 4, 4487.CrossRefGoogle Scholar
  138. Gatev, E., Goetzmann, W. N., & Rouwenhorst, K. G. (2006). Pairs Trading: Performance of a Relative-Value Arbitrage Rule. Review of Financial Studies, 19(3), 797–827.CrossRefGoogle Scholar
  139. Géczy, C. C., & Samonov, M. (2016). Two Centuries of Price-Return Momentum. Financial Analysts Journal, 72(5), 32–56.CrossRefGoogle Scholar
  140. Gençay, R. (1996). Nonlinear Prediction of Security Returns with Moving Average Rules. Journal of Forecasting, 15(3), 165–174.CrossRefGoogle Scholar
  141. Gençay, R. (1998). The Predictability of Securities Returns with Simple Technical Rules. Journal of Empirical Finance, 5(4), 347–359.CrossRefGoogle Scholar
  142. Gençay, R., & Stengos, T. (1998). Moving Average Rules, Volume and the Predictability of Security Returns with Feedforward Networks. Journal of Forecasting, 17(5–6), 401–414.CrossRefGoogle Scholar
  143. Gerakos, J., & Linnainmaa, J. (2012). Decomposing Value (Working Paper). Available online: https://ssrn.com/abstract=2083166.
  144. Gestel, T., Suykens, J. A. K., Baestaend, D. E., Lambrechts, A., Lanckriet, G., Vandaele, B., et al. (2001). Financial Time Series Prediction Using Least Squares Support Vector Machines Within the Evidence Framework. IEEE Transactions on Neural Networks, 12(4), 809–821.CrossRefGoogle Scholar
  145. Glabadanidis, P. (2015). Market Timing with Moving Averages. International Review of Finance, 15(3), 387–425.CrossRefGoogle Scholar
  146. Griffin, J. M., Ji, X., & Martin, J. S. (2003). Momentum Investing and Business Cycle Risks: Evidence from Pole to Pole. Journal of Finance, 58(6), 2515–2547.CrossRefGoogle Scholar
  147. Grinblatt, M., & Moskowitz, T. J. (2004). Predicting Stock Price Movements from Past Returns: The Role of Consistency and Tax-Loss Selling. Journal of Financial Economics, 71(3), 541–579.CrossRefGoogle Scholar
  148. Grinold, R. C., & Kahn, R. N. (2000). Active Portfolio Management. New York, NY: McGraw-Hill.Google Scholar
  149. Grudnitski, G., & Osborn, L. (1993). Forecasting S&P and Gold Futures Prices: An Application of Neural Networks. Journal of Futures Markets, 13(6), 631–643.CrossRefGoogle Scholar
  150. Grundy, B. D., & Martin, J. S. (2001). Understanding the Nature of the Risks and the Source of the Rewards to Momentum Investing. Review of Financial Studies, 14(1), 29–78.CrossRefGoogle Scholar
  151. Gunasekarage, A., & Power, D. M. (2001). The Profitability of Moving Average Trading Rules in South Asian Stock Markets. Emerging Markets Review, 2(1), 17–33.CrossRefGoogle Scholar
  152. Gutierrez, R. C., & Prinsky, C. A. (2007). Momentum, Reversal, and the Trading Behaviors of Institutions. Journal of Financial Markets, 10(1), 48–75.CrossRefGoogle Scholar
  153. Hagströmer, B., & Nordén, L. (2013). The Diversity of High-Frequency Traders. Journal of Financial Markets, 16(4), 741–770.CrossRefGoogle Scholar
  154. Hagströmer, B., Nordén, L., & Zhang, D. (2014). The Aggressiveness of High-Frequency Traders. Financial Review, 49(2), 395–419.CrossRefGoogle Scholar
  155. Hall, P., Park, B. U., & Samworth, R. J. (2008). Choice of Neighbor Order in Nearest-Neighbor Classification. Annals of Statistics, 36(5), 2135–2152.CrossRefGoogle Scholar
  156. Hall, J., Pinnuck, M., & Thorne, M. (2013). Market Risk Exposure of Merger Arbitrage in Australia. Accounting & Finance, 53(1), 185–215.CrossRefGoogle Scholar
  157. Hardy, C. C. (1978). The Investor’s Guide to Technical Analysis. New York, NY: McGraw-Hill.Google Scholar
  158. Harford, J. (2005). What Drives Merger Waves? Journal of Financial Economics, 77(3), 529–560.CrossRefGoogle Scholar
  159. Harris, L. E., & Namvar, E. (2016). The Economics of Flash Orders and Trading. Journal of Investment Management, 14(4), 74–86.Google Scholar
  160. Hasbrouck, J., & Saar, G. (2013). Low-Latency Trading. Journal of Financial Markets, 16(4), 646–679.CrossRefGoogle Scholar
  161. Haugen, R. A. (1995). The New Finance: The Case Against Efficient Markets. Upper Saddle River, NJ: Prentice Hall.Google Scholar
  162. Hendershott, T., Jones, C., & Menkveld, A. (2011). Does Algorithmic Trading Improve Liquidity? Journal of Finance, 66(1), 1–33.CrossRefGoogle Scholar
  163. Hendershott, T., Jones, C., & Menkveld, A. (2013). Implementation Shortfall with Transitory Price Effects. In D. Easley, M. López de Prado, & M. O’Hara (Eds.), High Frequency Trading: New Realities for Traders, Markets and Regulators (Chapter 9). London, UK: Risk Books.Google Scholar
  164. Hendershott, T., & Riordan, R. (2013). Algorithmic Trading and the Market for Liquidity. Journal of Financial and Quantitative Analysis, 48(4), 1001–1024.CrossRefGoogle Scholar
  165. Hew, D., Skerratt, L., Strong, N., & Walker, M. (1996). Post-Earnings-Announcement Drift: Some Preliminary Evidence for the UK. Accounting & Business Research, 26(4), 283–293.CrossRefGoogle Scholar
  166. Hirschey, N. (2018). Do High-Frequency Traders Anticipate Buying and Selling Pressure? (Working Paper). Available online: https://ssrn.com/abstract=2238516.
  167. Hirshleifer, D., Lim, S. S., & Teoh, S. H. (2009). Driven to Distraction: Extraneous Events and Underreaction to Earnings News. Journal of Finance, 64(5), 2289–2325.CrossRefGoogle Scholar
  168. Hodges, S., & Carverhill, A. (1993). Quasi Mean Reversion in an Efficient Stock Market: The Characterization of Economic Equilibria which Support Black-Scholes Option Pricing. Economic Journal, 103(417), 395–405.CrossRefGoogle Scholar
  169. Holden, C. W., & Jacobsen, S. (2014). Liquidity Measurement Problems in Fast Competitive Markets: Expensive and Cheap Solutions. Journal of Finance, 69(4), 1747–1885.CrossRefGoogle Scholar
  170. Hsieh, J., & Walkling, R. A. (2005). Determinants and Implications of Arbitrage Holdings in Acquisitions. Journal of Financial Economics, 77(3), 605–648.CrossRefGoogle Scholar
  171. Hsu, Y.-C., Lin, H.-W. and Vincent, K. (2018). Analyzing the Performance of Multi-factor Investment Strategies Under Multiple Testing Framework (Working Paper). Available online: http://www.econ.sinica.edu.tw/UpFiles/2013092817175327692/Seminar_PDF2013093010102890633/17-A0001(all).pdf.
  172. Huang, W., Nakamori, Y., & Wang, S.-Y. (2005). Forecasting Stock Market Movement Direction with Support Vector Machine. Computers & Operation Research, 32(10), 2513–2522.CrossRefGoogle Scholar
  173. Huang, C. L., & Tsai, C. Y. (2009). A Hybrid SOFM-SVR with a Filter-Based Feature Selection for Stock Market Forecasting. Expert Systems with Applications, 36(2), 1529–1539.CrossRefGoogle Scholar
  174. Huck, N. (2009). Pairs Selection and Outranking: An Application to the S&P 100 Index. European Journal of Operational Research, 196(2), 819–825.CrossRefGoogle Scholar
  175. Huck, N. (2015). Pairs Trading: Does Volatility Timing Matter? Applied Economics, 47(57), 6239–6256.CrossRefGoogle Scholar
  176. Huck, N., & Afawubo, K. (2014). Pairs Trading and Selection Methods: Is Cointegration Superior? Applied Economics, 47(6), 599–613.CrossRefGoogle Scholar
  177. Huerta, R., Elkan, C., & Corbacho, F. (2013). Nonlinear Support Vector Machines Can Systematically Identify Stocks with High and Low Future Returns. Algorithmic Finance, 2(1), 45–58.Google Scholar
  178. Hühn, H., & Scholz, H. (2017). Alpha Momentum and Price Momentum (Working Paper). Available online: https://ssrn.com/abstract=2287848.
  179. Huij, J., & Lansdorp, S. (2017). Residual Momentum and Reversal Strategies Revisited (Working Paper). Available online: https://ssrn.com/abstract=2929306.
  180. Hung, N. H. (2016). Various Moving Average Convergence Divergence Trading Strategies: A Comparison. Investment Management and Financial Innovations, 13(2), 363–369.CrossRefGoogle Scholar
  181. Hutson, E. (2000). Takeover Targets and the Probability of Bid Success: Evidence from the Australian Market. International Review of Financial Analysis, 9(1), 45–65.CrossRefGoogle Scholar
  182. Hwang, C.-Y., & George, T. J. (2004). The 52-Week High and Momentum Investing. Journal of Finance, 59(5), 2145–2176.CrossRefGoogle Scholar
  183. Idzorek, T. (2007). A Step-by-Step Guide to the Black-Litterman Model. In S. Satchell (Ed.), Forecasting Expected Returns in the Financial Markets. Waltham, MA: Academic Press.Google Scholar
  184. Jacobs, H., & Weber, M. (2015). On the Determinants of Pairs Trading Profitability. Journal of Financial Markets, 23, 75–97.CrossRefGoogle Scholar
  185. James, F. E., Jr. (1968). Monthly Moving Averages—An Effective Investment Tool? Journal of Financial and Quantitative Analysis, 3(3), 315–326.CrossRefGoogle Scholar
  186. Jansen, I. P., & Nikiforov, A. L. (2016). Fear and Greed: A Returns-Based Trading Strategy Around Earnings Announcements. Journal of Portfolio Management, 42(4), 88–95.CrossRefGoogle Scholar
  187. Jarrow, R. A., & Protter, P. (2012). A Dysfunctional Role of High Frequency Trading in Electronic Markets. International Journal of Theoretical and Applied Finance, 15(3), 1250022.CrossRefGoogle Scholar
  188. Jasemi, M., & Kimiagari, A. M. (2012). An Investigation of Model Selection Criteria for Technical Analysis of Moving Average. Journal of Industrial Engineering International, 8, 5.CrossRefGoogle Scholar
  189. Jegadeesh, N. (1990). Evidence of Predictable Behavior of Security Returns. Journal of Finance, 45(3), 881–898.CrossRefGoogle Scholar
  190. Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65–91.CrossRefGoogle Scholar
  191. Jegadeesh, N., & Titman, S. (1995). Overreaction, Delayed Reaction, and Contrarian Profits. Review of Financial Studies, 8(4), 973–993.CrossRefGoogle Scholar
  192. Jegadeesh, N., & Titman, S. (2001). Profitability of Momentum Strategies: An Evaluation of Alternative Explanations. Journal of Finance, 56(2), 699–720.CrossRefGoogle Scholar
  193. Jensen, M. C. (1968). The Performance of Mutual Funds in the Period 1945–1964. Journal of Finance, 23(2), 389–416.CrossRefGoogle Scholar
  194. Jetley, G., & Ji, X. (2010). The Shrinking Merger Arbitrage Spread: Reasons and Implications. Financial Analysts Journal, 66(2), 54–68.CrossRefGoogle Scholar
  195. Kablan, A. (2009). Adaptive Neuro-Fuzzy Inference System for Financial Trading Using Intraday Seasonality Observation Model. International Journal of Economics and Management Engineering, 3(10), 1909–1918.Google Scholar
  196. Kahn, R. N., & Lemmon, M. (2015). Smart Beta: The Owner’s Manual. Journal of Portfolio Management, 41(2), 76–83.CrossRefGoogle Scholar
  197. Kahn, R. N., & Lemmon, M. (2016). The Asset Manager’s Dilemma: How Smart Beta Is Disrupting the Investment Management Industry. Financial Analysts Journal, 72(1), 15–20.CrossRefGoogle Scholar
  198. Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision Under Risk. Econometrica, 47(2), 263–292.CrossRefGoogle Scholar
  199. Kakushadze, Z. (2015a). Mean-Reversion and Optimization. Journal of Asset Management, 16(1), 14–40. Available online: https://ssrn.com/abstract=2478345.CrossRefGoogle Scholar
  200. Kakushadze, Z. (2015b). 4-Factor Model for Overnight Returns. Wilmott Magazine, 2015(79), 56–62. Available online: https://ssrn.com/abstract=2511874.CrossRefGoogle Scholar
  201. Kakushadze, Z. (2015c). On Origins of Alpha. Hedge Fund Journal, 108, 47–50. Available online: https://ssrn.com/abstract=2575007.
  202. Kakushadze, Z. (2015d). Heterotic Risk Models. Wilmott Magazine, 2015(80), 40–55. Available online: https://ssrn.com/abstract=2600798.CrossRefGoogle Scholar
  203. Kakushadze, Z. (2016). 101 Formulaic Alphas. Wilmott Magazine, 2016(84), 72–80. Available online: https://ssrn.com/abstract=2701346.CrossRefGoogle Scholar
  204. Kakushadze, Z., & Tulchinsky, I. (2016). Performance v. Turnover: A Story by 4,000 Alphas. Journal of Investment Strategies, 5(2), 75–89. Available online: http://ssrn.com/abstract=2657603.
  205. Kakushadze, Z., & Yu, W. (2016a). Multifactor Risk Models and Heterotic CAPM. Journal of Investment Strategies, 5(4), 1–49. Available online: https://ssrn.com/abstract=2722093.CrossRefGoogle Scholar
  206. Kakushadze, Z., & Yu, W. (2016b). Statistical Industry Classification. Journal of Risk & Control, 3(1), 17–65. Available online: https://ssrn.com/abstract=2802753.
  207. Kakushadze, Z., & Yu, W. (2017a). Statistical Risk Models. Journal of Investment Strategies, 6(2), 1–40. Available online: https://ssrn.com/abstract=2732453.
  208. Kakushadze, Z., & Yu, W. (2017b). How to Combine a Billion Alphas. Journal of Asset Management, 18(1), 64–80. Available online: https://ssrn.com/abstract=2739219.CrossRefGoogle Scholar
  209. Kakushadze, Z., & Yu, W. (2017c). *K-Means and Cluster Models for Cancer Signatures. Biomolecular Detection and Quantification, 13, 7–31. Available online: https://ssrn.com/abstract=2908286.CrossRefGoogle Scholar
  210. Kakushadze, Z., & Yu, W. (2018). Decoding Stock Market with Quant Alphas. Journal of Asset Management, 19(1), 38–48. Available online: https://ssrn.com/abstract=2965224.CrossRefGoogle Scholar
  211. Kang, J., Liu, M. H., & Ni, S. X. (2002). Contrarian and Momentum Strategies in the China Stock Market: 1993–2000. Pacific-Basin Finance Journal, 10(3), 243–265.CrossRefGoogle Scholar
  212. Kara, Y., Boyacioglu, M. A., & Baykan, O. K. (2011). Predicting Direction of Stock Price Index Movement Using Artificial Neural Networks and Support Vector Machines: The Sample of the Istanbul Stock Exchange. Expert Systems with Applications, 38(5), 5311–5319.CrossRefGoogle Scholar
  213. Karolyi, G. A., & Kho, B. C. (2004). Momentum Strategies: Some Bootstrap Tests. Journal of Empirical Finance, 11(4), 509–536.CrossRefGoogle Scholar
  214. Karolyi, G. A., & Shannon, J. (1999). Where’s the Risk in Risk Arbitrage? Canadian Investment Review, 12(2), 12–18.Google Scholar
  215. Khan, S. A. (2002). Merger Arbitrage: A Long-Term Investment Strategy. Journal of Wealth Management, 4(4), 76–81.CrossRefGoogle Scholar
  216. Khandani, A., & Lo, A. W. (2011). What Happened to the Quants in August 2007? Evidence from Factors and Transactions Data. Journal of Financial Markets, 14(1), 1–46.CrossRefGoogle Scholar
  217. Kilgallen, T. (2012). Testing the Simple Moving Average Across Commodities, Global Stock Indices, and Currencies. Journal of Wealth Management, 15(1), 82–100.CrossRefGoogle Scholar
  218. Kim, K. (2011). Performance Analysis of Pairs Trading Strategy Utilizing High Frequency Data with an Application to KOSPI 100 Equities (Working Paper). Available online: https://ssrn.com/abstract=1913707.
  219. Kim, K. J. (2003). Financial Time Series Forecasting Using Support Vector Machines. Neurocomputing, 55(1–2), 307–319.CrossRefGoogle Scholar
  220. Kim, K. J. (2006). Artificial Neural Networks with Evolutionary Instance Selection for Financial Forecasting. Expert Systems with Applications, 30(3), 519–526.CrossRefGoogle Scholar
  221. Kim, K. J., & Han, I. (2000). Genetic Algorithms Approach to Feature Discretization in Artificial Neural Networks for the Prediction of Stock Price Index. Expert Systems with Applications, 19(2), 125–132.CrossRefGoogle Scholar
  222. Kirilenko, A., Kyle, A., Samadi, M., & Tuzun, T. (2017). The Flash Crash: High-Frequency Trading in an Electronic Market. Journal of Finance, 72(3), 967–998.CrossRefGoogle Scholar
  223. Kishore, V. (2012). Optimizing Pairs Trading of US Equities in a High Frequency Setting (Working Paper). Available online: https://repository.upenn.edu/cgi/viewcontent.cgi?article=1095&context=wharton_research_scholars.
  224. Korajczyk, R. A., & Murphy, D. (2017). High Frequency Market Making to Large Institutional Trades (Working Paper). Available online: https://ssrn.com/abstract=2567016.
  225. Korajczyk, R. A., & Sadka, R. (2004). Are Momentum Profits Robust to Trading Costs? Journal of Finance, 59(3), 1039–1082.CrossRefGoogle Scholar
  226. Kordos, M., & Cwiok, A. (2011). A New Approach to Neural Network Based Stock Trading Strategy. In H. Yin, W. Wang, & V. Rayward-Smith (Eds.), Intelligent Data Engineering and Automated Learning-IDEAL (pp. 429–436). Berlin, Germany: Springer.Google Scholar
  227. Kozhan, R., & Tham, W. W. (2012). Execution Risk in High-Frequency Arbitrage. Management Science, 58(11), 2131–2149.CrossRefGoogle Scholar
  228. Kozlov, M., & Petajisto, A. (2013). Global Return Premiums on Earnings Quality, Value, and Size (Working Paper). Available online: https://ssrn.com/abstract=2179247.
  229. Krauss, C. (2017). Statistical Arbitrage Pairs Trading Strategies: Review and Outlook. Journal of Economic Surveys, 31(2), 513–545.CrossRefGoogle Scholar
  230. Krauss, C., & Stübinger, J. (2017). Non-linear Dependence Modelling with Bivariate Copulas: Statistical Arbitrage Pairs Trading on the S&P 100. Applied Economics, 23(1), 1–18.Google Scholar
  231. Kryzanowski, L., Galler, M., & Wright, D. (1993). Using Artificial Neural Networks to Pick Stocks. Financial Analysts Journal, 49(4), 21–27.CrossRefGoogle Scholar
  232. Kudryavtsev, A. (2012). Overnight Stock Price Reversals. Journal of Advanced Studies in Finance, 3(2), 162–170.Google Scholar
  233. Kumar, M., & Thenmozhi, M. (2001). Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest (Working Paper). Available online: https://ssrn.com/abstract=876544.
  234. Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian Investment, Extrapolation, and Risk. Journal of Finance, 49(5), 1541–1578.CrossRefGoogle Scholar
  235. Larker, D., & Lys, T. (1987). An Empirical Analysis of the Incentives to Engage in Costly Information Acquisition: The Case of Risk Arbitrage. Journal of Financial Economics, 18(1), 111–126.CrossRefGoogle Scholar
  236. Lehmann, B. N. (1990). Fads, Martingales, and Market Efficiency. Quarterly Journal of Economics, 105(1), 1–28.CrossRefGoogle Scholar
  237. Li, X., Deng, X., Zhu, S., Wang, F., & Xie, H. (2014). An Intelligent Market Making Strategy in Algorithmic Trading. Frontiers of Computer Science, 8(4), 596–608.CrossRefGoogle Scholar
  238. Li, B., Hoi, S. C. H., Sahoo, D., & Liu, Z.-Y. (2015). Moving Average Reversion Strategy for On-line Portfolio Selection. Artificial Intelligence, 222, 104–123.CrossRefGoogle Scholar
  239. Li, X., Sullivan, R. N., & Garcia-Feijóo, L. (2014). The Limits to Arbitrage and the Low-Volatility Anomaly. Financial Analysts Journal, 70(1), 52–63.CrossRefGoogle Scholar
  240. Li, X., Sullivan, R. N., & Garcia-Feijóo, L. (2016). The Low-Volatility Anomaly: Market Evidence on Systematic Risk vs. Mispricing. Financial Analysts Journal, 72(1), 36–47.CrossRefGoogle Scholar
  241. Li, B., Zhao, P., Hoi, S. C. H., & Gopalkrishnan, V. (2012). PAMR: Passive Aggressive Mean Reversion Strategy for Portfolio Selection. Machine Learning, 87(2), 221–258.CrossRefGoogle Scholar
  242. Liew, J. K.-S., & Mayster, B. (2018). Forecasting ETFs with Machine Learning Algorithms. Journal of Alternative Investments, 20(3), 58–78.CrossRefGoogle Scholar
  243. Liew, J., & Roberts, R. (2013). U.S. Equity Mean-Reversion Examined. Risks, 1(3), 162–175.CrossRefGoogle Scholar
  244. Liew, J., & Vassalou, M. (2000). Can Book-to-Market, Size and Momentum be Risk Factors that Predict Economic Growth? Journal of Financial Economics, 57(2), 221–245.CrossRefGoogle Scholar
  245. Liew, R., & Wu, Y. (2013). Pairs Trading: A Copula Approach. Journal of Derivatives & Hedge Funds, 19(1), 12–30.CrossRefGoogle Scholar
  246. Lin, L., Lan, L.-H., & Chuang, S.-S. (2013). An Option-Based Approach to Risk Arbitrage in Emerging Markets: Evidence from Taiwan Takeover Attempts. Journal of Forecasting, 32(6), 512–521.CrossRefGoogle Scholar
  247. Lin, Y.-X., McCrae, M., & Gulati, C. (2006). Loss Protection in Pairs Trading Through Minimum Profit Bounds: A Cointegration Approach. Journal of Applied Mathematics and Decision Sciences, 4, 1–14.CrossRefGoogle Scholar
  248. Liu, B., Chang, L. B., & Geman, H. (2017). Intraday Pairs Trading Strategies on High Frequency Data: The Case of Oil Companies. Quantitative Finance, 17(1), 87–100.CrossRefGoogle Scholar
  249. Liu, L. X., & Zhang, L. (2008). Momentum Profits, Factor Pricing, and Macroeconomic Risk. Review of Financial Studies, 21(6), 2417–2448.CrossRefGoogle Scholar
  250. Livnat, J., & Mendenhall, R. R. (2006). Comparing the Post-Earnings Announcement Drift for Surprises Calculated from Analyst and Time Series Forecasts. Journal of Accounting Research, 44(1), 177–205.CrossRefGoogle Scholar
  251. Lo, A. W. (2008). Where Do Alphas Come From? A New Measure of the Value of Active Investment Management. Journal of Investment Management, 6(2), 1–29.Google Scholar
  252. Lo, A. W., & MacKinlay, A. C. (1990). When Are Contrarian Profits Due to Stock Market Overreaction? Review of Financial Studies, 3(3), 175–205.CrossRefGoogle Scholar
  253. Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation. Journal of Finance, 55(4), 1705–1765.CrossRefGoogle Scholar
  254. Loh, R. K., & Warachka, M. (2012). Streaks in Earnings Surprises and the Cross-Section of Stock Returns. Management Science, 58(7), 1305–1321.CrossRefGoogle Scholar
  255. Lu, C. J., Lee, T. S., & Chiu, C. (2009). Financial Time Series Forecasting Using Independent Component Analysis and Support Vector Regression. Decision Support Systems, 47(2), 115–125.CrossRefGoogle Scholar
  256. Madhavan, A. (2012). Exchange-Traded Funds, Market Structure, and the Flash Crash. Financial Analysts Journal, 68(4), 20–35.CrossRefGoogle Scholar
  257. Maheswaran, K., & Yeoh, S. C. (2005). The Profitability of Merger Arbitrage: Some Australian Evidence. Australian Journal of Management, 30(1), 111–126.CrossRefGoogle Scholar
  258. Malkiel, B. G. (2014). Is Smart Beta Really Smart? Journal of Portfolio Management, 40(5), 127–134.CrossRefGoogle Scholar
  259. Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77–91.Google Scholar
  260. Mendenhall, R. (2004). Arbitrage Risk and the Post-Earnings-Announcement Drift. Journal of Business, 77(6), 875–894.CrossRefGoogle Scholar
  261. Menkveld, A. J. (2013). High Frequency Trading and the New Market Makers. Journal of Financial Markets, 16(4), 712–740.CrossRefGoogle Scholar
  262. Menkveld, A. J. (2016). The Economics of High-Frequency Trading: Taking Stock. Annual Review of Financial Economics, 8, 1–24.CrossRefGoogle Scholar
  263. Merton, R. C. (1987). A Simple Model of Capital Market Equilibrium with Incomplete Information. Journal of Finance, 42(3), 483–510.CrossRefGoogle Scholar
  264. Metghalchi, M., Marcucci, J., & Chang, Y.-H. (2012). Are Moving Average Trading Rules Profitable? Evidence from the European Stock Markets. Applied Economics, 44(12), 1539–1559.CrossRefGoogle Scholar
  265. Miao, G. J. (2014). High Frequency and Dynamic Pairs Trading Based on Statistical Arbitrage Using a Two-Stage Correlation and Cointegration Approach. International Journal of Economics and Finance, 6(3), 96–110.CrossRefGoogle Scholar
  266. Milosevic, N. (2016). Equity Forecast: Predicting Long Term Stock Price Movement Using Machine Learning. Journal of Economics Library, 3(2), 288–294.Google Scholar
  267. Mitchell, M., & Pulvino, T. (2001). Characteristics of Risk and Return in Risk Arbitrage. Journal of Finance, 56(6), 2135–2175.CrossRefGoogle Scholar
  268. Moskowitz, T. J., & Grinblatt, M. (1999). Do Industries Explain Momentum? Journal of Finance, 54(4), 1249–1290.CrossRefGoogle Scholar
  269. Mun, J. C., Vasconcellos, G. M., & Kish, R. (2000). The Contrarian Overreaction Hypothesis: An Analysis of the US and Canadian Stock Markets. Global Finance Journal, 11(1–2), 53–72.CrossRefGoogle Scholar
  270. Murphy, J. J. (1986). Technical Analysis of the Futures Markets: A Comprehensive Guide to Trading Methods and Applications. New York, NY: New York Institute of Finance.Google Scholar
  271. Muthuswamy, J., Palmer, J., Richie, N., & Webb, R. (2011). High-Frequency Trading: Implications for Markets, Regulators, and Efficiency. Journal of Trading, 6(1), 87–97.CrossRefGoogle Scholar
  272. Ng, J., Rusticus, T., & Verdi, R. (2008). Implications of Transaction Costs for the Post-Earnings Announcement Drift. Journal of Accounting Research, 46(3), 661–696.CrossRefGoogle Scholar
  273. Novak, M. G., & Velušçek, D. (2016). Prediction of Stock Price Movement Based on Daily High Prices. Quantitative Finance, 16(5), 793–826.CrossRefGoogle Scholar
  274. Novy-Marx, R. (2013). The Other Side of Value: The Gross Profitability Premium. Journal of Financial Economics, 108(1), 1–28.CrossRefGoogle Scholar
  275. Officer, M. S. (2004). Collars and Renegotiation in Mergers and Acquisitions. Journal of Finance, 59(6), 2719–2743.CrossRefGoogle Scholar
  276. Officer, M. S. (2006). The Market Pricing of Implicit Options in Merger Collars. Journal of Business, 79(1), 115–136.CrossRefGoogle Scholar
  277. O’Hara, M. (2015). High Frequency Market Microstructure. Journal of Financial Economics, 116(2), 257–270.CrossRefGoogle Scholar
  278. Osler, C. L. (2000). Support for Resistance: Technical Analysis and Intraday Exchange Rates. Federal Reserve Bank of New York, Economic Policy Review, 6(2), 53–68.Google Scholar
  279. Osler, C. L. (2003). Currency Orders and Exchange Rate Dynamics: An Explanation for the Predictive Success of Technical Analysis. Journal of Finance, 58(5), 1791–1819.CrossRefGoogle Scholar
  280. O’Tool, R. (2013). The Black-Litterman Model: A Risk Budgeting Perspective. Journal of Asset Management, 14(1), 2–13.CrossRefGoogle Scholar
  281. Ou, P., & Wang, H. (2009). Prediction of Stock Market Index Movement by Ten Data Mining Techniques. Modern Applied Science, 3(12), 28–42.CrossRefGoogle Scholar
  282. Pagnotta, E., & Philippon, T. (2012). Competing on Speed (Working Paper). Available online: https://ssrn.com/abstract=1972807.
  283. Pan, J., & Poteshman, A. M. (2006). The Information in Option Volume for Future Stock Prices. Review of Financial Studies, 19(3), 871–908.CrossRefGoogle Scholar
  284. Pástor, L’., & Stambaugh, R. F. (2003). Liquidity Risk and Expected Stock Returns. Journal of Political Economy, 111(3), 642–685.CrossRefGoogle Scholar
  285. Pätäri, E., & Vilska, M. (2014). Performance of Moving Average Trading Strategies Over Varying Stock Market Conditions: The Finnish Evidence. Applied Economics, 46(24), 2851–2872.CrossRefGoogle Scholar
  286. Perlin, M. S. (2009). Evaluation of Pairs-Trading Strategy at the Brazilian Financial Market. Journal of Derivatives & Hedge Funds, 15(2), 122–136.CrossRefGoogle Scholar
  287. Person, J. L. (2007). Candlestick and Pivot Point Trading Triggers. Hoboken, NJ: Wiley.Google Scholar
  288. Piotroski, J. D. (2000). Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers. Journal of Accounting Research, 38, 1–41.CrossRefGoogle Scholar
  289. Piotroski, J. D., & So, E. C. (2012). Identifying Expectation Errors in Value/Glamour Strategies: A Fundamental Analysis Approach. Review of Financial Studies, 25(9), 2841–2875.CrossRefGoogle Scholar
  290. Pizzutilo, F. (2013). A Note on the Effectiveness of Pairs Trading for Individual Investors. International Journal of Economics and Financial Issues, 3(3), 763–771.Google Scholar
  291. Pole, A. (2007). Statistical Arbitrage: Algorithmic Trading Insights and Techniques. Hoboken, NJ: Wiley.Google Scholar
  292. Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27–59.CrossRefGoogle Scholar
  293. Pring, M. J. (1985). Technical Analysis Explained: The Successful Investor’s Guide to Spotting Investment Trends and Turning Points (3rd ed.). New York, NY: McGraw-Hill Inc.Google Scholar
  294. Rad, H., Low, R. K. Y., & Faff, R. (2016). The Profitability of Pairs Trading Strategies: Distance, Cointegration and Copula Methods. Quantitative Finance, 16(10), 1541–1558.CrossRefGoogle Scholar
  295. Refenes, A. N., Zapranis, A. S., & Francis, G. (1994). Stock Performance Modeling Using Neural Networks: Comparative Study with Regressive Models. Neural Networks, 7(2), 375–388.CrossRefGoogle Scholar
  296. Rendleman, R. J., Jones, C. P., & Latané, H. A. (1982). Empirical Anomalies Based on Unexpected Earnings and the Importance of Risk Adjustments. Journal of Financial Economics, 10(3), 269–287.CrossRefGoogle Scholar
  297. Riordan, R., & Storkenmaier, A. (2012). Latency, Liquidity and Price Discovery. Journal of Financial Markets, 15(4), 416–437.CrossRefGoogle Scholar
  298. Rodríguez-González, A., García-Crespo, Á., Colomo-Palacios, R., Iglesias, F. G., & Gómez-Berbís, J. M. (2011). CAST: Using Neural Networks to Improve Trading Systems Based on Technical Analysis by Means of the RSI Financial Indicator. Expert Systems with Applications, 38(9), 11489–11500.CrossRefGoogle Scholar
  299. Rosenberg, B., Reid, K., & Lanstein, R. (1985). Persuasive Evidence of Market Inefficiency. Journal of Portfolio Management, 11(3), 9–16.CrossRefGoogle Scholar
  300. Rouwenhorst, K. G. (1998). International Momentum Strategies. Journal of Finance, 53(1), 267–284.CrossRefGoogle Scholar
  301. Saad, E. W., Prokhorov, D. V., & Wunsch, D. C. (1998). Comparative Study of Stock Trend Prediction Using Time Delay, Recurrent and Probabilistic Neural Networks. IEEE Transactions on Neural Networks, 9(6), 1456–1470.CrossRefGoogle Scholar
  302. Sadka, R. (2002). The Seasonality of Momentum: Analysis of Tradability (Working Paper). Available online: https://ssrn.com/abstract=306371.
  303. Samuelson, W., & Rosenthal, L. (1986). Price Movements as Indicators of Tender Offer Success. Journal of Finance, 41(2), 481–499.CrossRefGoogle Scholar
  304. Samworth, R. J. (2012). Optimal Weighted Nearest Neighbour Classifiers. Annals of Statistics, 40(5), 2733–2763.CrossRefGoogle Scholar
  305. Satchell, S., & Scowcroft, A. (2000). A Demystification of the Black-Litterman Model: Managing Quantitative and Traditional Portfolio Construction. Journal of Asset Management, 1(2), 138–150.CrossRefGoogle Scholar
  306. Schiereck, D., Bondt, W. D., & Weber, M. (1999). Contrarian and Momentum Strategies in Germany. Financial Analysts Journal, 55(6), 104–116.CrossRefGoogle Scholar
  307. Scholes, M., & Williams, J. (1977). Estimating Betas from Nonsynchronous Data. Journal of Financial Economics, 5(3), 309–327.CrossRefGoogle Scholar
  308. Schumaker, R. P., & Chen, H. (2010). A Discrete Stock Price Prediction Engine Based on Financial News. Computer, 43(1), 51–56.CrossRefGoogle Scholar
  309. Sharpe, W. F. (1966). Mutual Fund Performance. Journal of Business, 39(1), 119–138.CrossRefGoogle Scholar
  310. Sharpe, W. F. (1994). The Sharpe Ratio. Journal of Portfolio Management, 21(1), 49–58.CrossRefGoogle Scholar
  311. Shi, H.-L., Jiang, Z.-Q., & Zhou, W.-X. (2015). Profitability of Contrarian Strategies in the Chinese Stock Market. PLoS ONE, 10(9), e0137892.CrossRefGoogle Scholar
  312. Shiu, Y.-M., & Lu, T.-H. (2011). Pinpoint and Synergistic Trading Strategies of Candlesticks. International Journal of Economics and Finance, 3(1), 234–244.Google Scholar
  313. Siganos, A., & Chelley-Steeley, P. (2006). Momentum Profits Following Bull and Bear Markets. Journal of Asset Management, 6(5), 381–388.CrossRefGoogle Scholar
  314. Stattman, D. (1980). Book Values and Stock Returns. Chicago MBA: A Journal of Selected Papers, 4, 25–45.Google Scholar
  315. Stickel, S. E. (1991). Common Stock Returns Surrounding Earnings Forecast Revisions: More Puzzling Evidence. Accounting Review, 66(2), 402–416.Google Scholar
  316. Stivers, C., & Sun, L. (2010). Cross-Sectional Return Dispersion and Time Variation in Value and Momentum Premiums. Journal of Financial and Quantitative Analysis, 45(4), 987–1014.CrossRefGoogle Scholar
  317. Stübinger, J., & Bredthauer, J. (2017). Statistical Arbitrage Pairs Trading with High-Frequency Data. International Journal of Economics and Financial Issues, 7(4), 650–662.Google Scholar
  318. Stübinger, J., & Endres, S. (2017). Pairs Trading with a Mean-Reverting Jump-Diffusion Model on High-Frequency Data. Quantitative Finance (forthcoming). https://doi.org/10.1080/14697688.2017.1417624.CrossRefGoogle Scholar
  319. Subha, M., & Nambi, S. (2012). Classification of Stock Index Movement Using k-Nearest Neighbours (k-NN) Algorithm. WSEAS Transactions on Information Science and Applications, 9(9), 261–270.Google Scholar
  320. Subramanian, A. (2004). Option Pricing on Stocks in Mergers and Acquisitions. Journal of Finance, 59(2), 795–829.CrossRefGoogle Scholar
  321. Suhonen, A., Lennkh, M., & Perez, F. (2017). Quantifying Backtest Overfitting in Alternative Beta Strategies. Journal of Portfolio Management, 43(2), 90–104.CrossRefGoogle Scholar
  322. Sullivan, R., Timmermann, A., & White, H. (1999). Data-Snooping, Technical Trading Rule Performance, and the Bootstrap. Journal of Finance, 54(5), 1647–1691.CrossRefGoogle Scholar
  323. Tay, F. E. H., & Cao, L. (2001). Application of Support Vector Machines in Financial Time Series Forecasting. Omega, 29(4), 309–317.CrossRefGoogle Scholar
  324. Taylor, M. P., & Allen, H. (1992). The Use of Technical Analysis in the Foreign Exchange Market. Journal of International Money and Finance, 11(3), 304–314.CrossRefGoogle Scholar
  325. Teixeira, L. A., & de Oliveira, A. L. I. (2010). A Method for Automatic Stock Trading Combining Technical Analysis and Nearest Neighbor Classification. Expert Systems with Applications, 37(10), 6885–6890.CrossRefGoogle Scholar
  326. Thomsett, M. C. (2003). Support and Resistance Simplified. Columbia, MD: Marketplace Books.Google Scholar
  327. Tsai, C. F., & Hsiao, Y. C. (2010). Combining Multiple Feature Selection Methods for Stock Prediction: Union, Intersection, and Multi-intersection Approaches. Decision Support Systems, 50(1), 258–269.CrossRefGoogle Scholar
  328. Tulchinsky, I., et al. (2015). Finding Alphas: A Quantitative Approach to Building Trading Strategies. New York, NY: Wiley.CrossRefGoogle Scholar
  329. Vaitonis, M., & Masteika, S. (2016). Research in High Frequency Trading and Pairs Selection Algorithm with Baltic Region Stocks. In G. Dregvaite & R. Damasevicius (Eds.), Proceedings of the 22nd International Conference on Information and Software Technologies (ICIST 2016) (pp. 208–217). Cham, Switzerland: Springer.Google Scholar
  330. Van Kervel, V., & Menkveld, A. J. (2017). High-Frequency Trading Around Large Institutional Orders. Journal of Finance (forthcoming). Available online: https://ssrn.com/abstract=2619686.
  331. Van Oord, J. A. (2016). Essays on Momentum Strategies in Finance. Ph.D. thesis, Erasmus University, Rotterdam, The Netherlands. Available online: https://repub.eur.nl/pub/80036/EPS2016380F-A9789058924445.pdf.
  332. Van Tassel, P. (2016). Merger Options and Risk Arbitrage (Federal Reserve Bank of New York Staff Reports, No. 761). Available online: https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr761.pdf?la=en.
  333. Vanstone, B., & Finnie, G. (2009). An Empirical Methodology for Developing Stockmarket Trading Systems Using Artificial Neural Networks. Expert Systems with Applications, 36(3), 6668–6680.CrossRefGoogle Scholar
  334. Vidyamurthy, G. (2004). Pairs Trading: Quantitative Methods and Analysis. Hoboken, NJ: Wiley.Google Scholar
  335. Walkling, R. A. (1985). Predicting Tender Offer Success: A Logistic Analysis. Journal of Financial and Quantitative Analysis, 20(4), 461–478.CrossRefGoogle Scholar
  336. Wang, K. Q. (2005). Multifactor Evaluation of Style Rotation. Journal of Financial and Quantitative Analysis, 40(2), 349–372.CrossRefGoogle Scholar
  337. Watts, R. L. (1978). Systematic ‘Abnormal’ Returns After Quarterly Earnings Announcements. Journal of Financial Economics, 6(2–3), 127–150.CrossRefGoogle Scholar
  338. Weller, P. A., Friesen, G. C., & Dunham, L. M. (2009). Price Trends and Patterns in Technical Analysis: A Theoretical and Empirical Examination. Journal of Banking & Finance, 6(33), 1089–1100.Google Scholar
  339. Xie, W., Liew, Q. R., Wu, Y., & Zou, X. (2014). Pairs Trading with Copulas (Working Paper). Available online: https://ssrn.com/abstract=2383185.
  340. Xing, Y., Zhang, X., & Zhao, R. (2010). What Does Individual Option Volatility Smirk Tell Us About Future Equity Returns? Journal of Financial and Quantitative Analysis, 45(3), 641–662.CrossRefGoogle Scholar
  341. Yao, Y. (2012). Momentum, Contrarian, and the January Seasonality. Journal of Banking & Finance, 36(10), 2757–2769.CrossRefGoogle Scholar
  342. Yao, J., & Tan, C. L. (2000). A Case Study on Using Neural Networks to Perform Technical Forecasting of Forex. Neurocomputing, 34(1–4), 79–98.CrossRefGoogle Scholar
  343. Yao, J., Tan, C. L., & Poh, H. L. (1999). Neural Networks for Technical Analysis: A Study on KLCI. International Journal of Theoretical and Applied Finance, 2(2), 221–241.CrossRefGoogle Scholar
  344. Yoshikawa, D. (2017). An Entropic Approach for Pair Trading. Entropy, 19(7), 320.CrossRefGoogle Scholar
  345. Yu, L., Wang, S., & Lai, K. K. (2005). Mining Stock Market Tendency Using GA-Based Support Vector Machines. In X. Deng & Y. Ye (Eds.), Internet and Network Economics. WINE 2005. Lecture Notes in Computer Science (Vol. 3828, pp. 336–345). Berlin, Germany: Springer.Google Scholar
  346. Zakamulin, V. (2014). The Real-Life Performance of Market Timing with Moving Average and Time-Series Momentum Rules. Journal of Asset Management, 15(4), 261–278.CrossRefGoogle Scholar
  347. Zakamulin, V. (2015). A Comprehensive Look at the Empirical Performance of Moving Average Trading Strategies (Working Paper). Available online: https://ssrn.com/abstract=2677212.
  348. Zapranis, A., & Tsinaslanidis, P. E. (2012). Identifying and Evaluating Horizontal Support and Resistance Levels: An Empirical Study on US Stock Markets. Applied Financial Economics, 22(19), 1571–1585.CrossRefGoogle Scholar
  349. Zeng, Z., & Lee, C. G. (2014). Pairs Trading: Optimal Thresholds and Profitability. Quantitative Finance, 14(11), 1881–1893.CrossRefGoogle Scholar
  350. Zhang, L. (2005). The Value Premium. Journal of Finance, 60(1), 67–103.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2018

Authors and Affiliations

  • Zura Kakushadze
    • 1
  • Juan Andrés Serur
    • 2
  1. 1.Quantigic Solutions LLCStamfordUSA
  2. 2.Universidad del CEMABuenos AiresArgentina

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