1. Neural network forecasting in prediction Sharpe ratio: Evidence from EU debt market. (2020). Maiti, Moinak ; Matsiuk, Natalia ; Vyklyuk, Yaroslav ; Vukovic, Darko.
   In: Physica A: Statistical Mechanics and its Applications.
   RePEc:eee:phsmap:v:542:y:2020:i:c:s0378437119318655.

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1. / is the average excess return for the k-th trading rule out of K trading rules and N=T-200 is the sample size, and is a consistent estimator for the standard deviation of ∗ • The joint distribution of all trading rules is empirically drawn by applying stationary bootstrap method of Politis and Romano (1994) to the observed values of , • In each bootstrapping simulation, we compute the sample average of the bootstrapped returns denoted by , ∗ The process is repeated B times and we construct the following bootstrap test statistics to form the distribution for ; , = ∑ ∗ ( , ∗ − ∗ ( ∗ / !)) / (2) where i = 1,2,….B and I is an indicator function which equals one when the condition is satisfied and zero otherwise, and A = 2 ln ln N • The test’s p-value is subsequently obtained by comparing V with the quantiles of V,.
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2. 2. All bond indices are rebased with value at Jan 3 2012 equals 100. 3. Greece is excluded from the calculation for other emerging market economies due to a much more volatile index series when compared to its peers. Source: Bloomberg US monetary cycle Figure 7 Note: Areas not shaded denote US monetary easing phase.
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10. Emerging Asia Australia Hong Kong Norway China Brazil Nigeria Austria Iceland Portugal India Chile Peru Belgium Ireland Singapore Indonesia Czech Republic Poland Canada Italy Spain Korea Egypt Russia Denmark Japan Sweden Malaysia Greece Slovakia Finland Luxembourg Switzerland Philippines Hungary Slovenia France Netherlands UK Taiwan Mexico South Africa Germany New Zealand US Thailand Morocco Turkey Advanced economies Other emerging market economies 20 Appendix: US monetary and business cycles 0 1 2 3 4 5 6 7 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 %

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25. Predictability in sovereign bond returns using technical trading rules: Do developed and emerging markets differ? 19 Hsu, P.H., Taylor, M.P., and Wang, Z. (2016). “Technical Trading: Is it Still Beating the Foreign Exchange Market?” Journal of International Economics, 102, 188-208.

26. Predictability in sovereign bond returns using technical trading rules: do developed and emerging markets differ? Presented by Tom Fong Hong Kong Monetary Authority (collaborated with Gabriel Wu) Bank Indonesia / IFC “International Workshop on Big Data for Central Bank Policies” Bali, 25 July 2 Agenda • Objective of the study • Major findings • Analytical framework • Empirical results • Conclusion 3 Sovereign bonds appear to be more responsive since major AEs begin their monetary policy normalization 0 20 40 60 80 100 120 140 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Jan 3 2012 = 100 Advanced economies Emerging Asia Other emerging market economies Notes: 1. The time series plots refer to the average bond index values for sovereign bond markets under each economic group.
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27. Puy, D. (2016). “Mutual fund flows and the geography of contagion,” Journal of International Money and Finance, 60, 73-93.
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32. Sources: Federal Reserve Bank of St. Louis and author estimates. 0 20 40 60 80 100 120 140 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Jan 3 2012 = 100 Advanced economies Emerging Asia Other emerging market economies 0 1 2 3 4 5 6 7 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 % US Monetary tightening Fed Fund Target Rate Predictability in sovereign bond returns using technical trading rules: Do developed and emerging markets differ? 31 US business cycle based on OECD definition Figure 8 Note: Areas not shaded denote US economic expansion phase. Sources: Federal Reserve Bank of St. Louis and OECD.
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33. Sullivan, R., Timmermann, A., and White, H. (1999). “Data snooping, technical trading rule performance, and the bootstrap,” Journal of Finance, 1647-1691.
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36. US Monetary tightening Fed Fund Target Rate 80 85 90 95 100 105 110 115 120 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2010=100, Seasonally Adjusted US Economic Recession (OECD) US Real GDP Note: Areas not shaded denote US monetary easing phase. Sources: Federal Reserve Bank of St. Louis and author estimates. Note: Areas not shaded denote US economic expansion phase. Sources: Federal Reserve Bank of St. Louis and OECD. Appendix: Technical details of the SPA test 21 • The SPA test in this study is based on the following test statistics = ∑ ∗ / (1) where = ∑ ,
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37. White, H. (2000) “A Reality Check for Data Snooping,” Econometrica, 68, 1097-1126.

1. Day-of-the-week returns and mood: an exterior template approach. (2017). Zilca, Shlomo.
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2. The predictive power of Japanese candlestick charting in Chinese stock market. (2016). Chen, Shi ; Bao, SI ; Zhou, YU.
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3. Conţinutul analizei seriilor de timp financiare. (2015). Stefanescu, Razvan ; Dumitriu, Ramona.
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4. Trend definition or holding strategy: What determines the profitability of candlestick charting?. (2015). Hsu, Yu-Chin ; Lu, Tsung-Hsun ; Chen, Yi-Chi.
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5. How exactly do markets adapt? Evidence from the moving average rule in three developed markets. (2015). Hudson, Robert ; Gebka, Bartosz ; Urquhart, Andrew.
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6. The benefits of combining seasonal anomalies and technical trading rules. (2015). Hudson, Robert ; Gebka, Bartosz ; Atanasova, Christina V.
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7. History of share prices and market efficiency of the Madrid general stock index. (2015). Hayes, Linda A. ; Chen, Chien-Ping ; Metghalchi, Massoud .
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10. Spread trading strategies in the crude oil futures market. (2014). Lubnau, Thorben .
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11. The profitability, costs and systematic risk of the post-earnings-announcement-drift trading strategy. (2014). Zhang, QI ; Keasey, Kevin ; Cai, Charlie .
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12. The profitability of candlestick charting in the Taiwan stock market. (2014). Lu, Tsung-Hsun .
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13. The rise and fall of technical trading rule success. (2014). Taylor, Nick.
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14. Profiteering from the Dot-com Bubble, Sub-Prime Crisis and Asian Financial Crisis. (2013). Wong, Wing-Keung ; McAleer, Michael ; Suen, John .
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15. Profiteering from the Dot-com Bubble, Sub-Prime Crisis and Asian Financial Crisis. (2013). Wong, Wing-Keung ; McAleer, Michael ; Suen, John .
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16. Time-series momentum as an intra- and inter-industry effect: Implications for market efficiency. (2013). Shynkevich, Andrei.
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17. Drivers of technical trend-following rules profitability in world stock markets. (2013). Ülkü, Numan ; Prodan, Eugeniu ; Ulku, Numan.
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18. On the efficiency of the European carbon market: New evidence from Phase II. (2013). Daskalakis, George.
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19. Profiteering from the Dot-com Bubble, Sub-Prime Crisis and Asian Financial Crisis. (2013). Wong, Wing-Keung ; McAleer, Michael ; Suen, John .
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20. Investment strategies beating the market. What can we squeeze from the market?. (2012). Sakowski, Pawel ; Åšlepaczuk, Robert ; Zakrzewski, Grzegorz .
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21. Technical trading revisited: False discoveries, persistence tests, and transaction costs. (2012). Scaillet, Olivier ; Bajgrowicz, Pierre .
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22. Performance of technical analysis in growth and small cap segments of the US equity market. (2012). Shynkevich, Andrei.
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23. An improved test for statistical arbitrage. (2012). Jarrow, Robert ; Teo, Melvyn ; Tse, Yiu Kuen ; Warachka, Mitch .
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24. Technical Analysis with a Long-Term Perspective: Trading Strategies and Market Timing Ability. (2011). Isakov, Dusan ; Marti, Didier .
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25. Testing the Profitability of Technical Analysis as a Portfolio Selection Strategy. (2009). Pavlov, Vlad ; Hurn, Stan.
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26. Stars, crows, and doji: The use of candlesticks in stock selection. (2009). Horton, Marshall J..
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27. Price trends and patterns in technical analysis: A theoretical and empirical examination. (2009). Friesen, Geoffrey C. ; Weller, Paul A. ; Dunham, Lee M..
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28. Predictability of Technical Trading Rules: Evidence from the Taiwan Stock Market. (2009). Kung, James J..
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29. Can commodity futures be profitably traded with quantitative market timing strategies?. (2008). Marshall, Ben ; Cahan, Rochester H..
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30. Is the Swedish stock market efficient? Evidence from some simple trading rules. (2008). Marcucci, Juri ; Metghalchi, Massoud ; Chang, Yung-Ho.
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31. Does intraday technical analysis in the U.S. equity market have value?. (2008). Marshall, Ben ; Cahan, Rochester H..
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32. Technical Trading Rules in Emerging Markets and the 1997 Asian Currency Crises. (2007). McKenzie, Michael D..
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33. On the returns generating process and the profitability of trading rules in emerging capital markets. (2007). Hatgioannides, John ; Mesomeris, Spyros.
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34. WHAT DO WE KNOW ABOUT THE PROFITABILITY OF TECHNICAL ANALYSIS?. (2007). Irwin, Scott ; Park, Cheol-Ho.
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35. Technical trading strategies and cross-national information linkage: the case of Taiwan stock market. (2006). Chan, Chia-Chung ; Chang, Yung-Ho ; Metghalchi, Massoud .
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36. Testing for predictability in equity returns for European transition markets. (2006). Tabak, Benjamin ; Cajueiro, Daniel.
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37. The profitability of the simple moving averages and trading range breakout in the Asian stock markets. (2006). Ming-Ming, Lai ; Siok-Hwa, Lau.
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38. An analysis of trading strategies in eleven European stock markets. (2005). Power, David ; Fifield, Suzanne ; Sinclair, Donald C..
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39. Technical analysis profitability when exchange rates are pegged: A note. (2005). Maillet, Bertrand ; Michel, Thierry .
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40. Risk adjusted returns from technical trading: a genetic programming approach. (2005). Fyfe, Colin ; Marney, John ; Tarbert, Heather .
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41. Is technical analysis profitable on a stock market which has characteristics that suggest it may be inefficient?. (2005). Marshall, Ben ; Cahan, Rochester H..
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42. Risk-adjusted, ex ante, optimal technical trading rules in equity markets. (2003). Neely, Christopher.
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43. The predictability of asset returns: an approach combining technical analysis and time series forecasts. (2003). Fang, Yue.
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44. Risk-adjusted, ex ante, optimal technical trading rules in equity markets. (2001). Neely, Christopher.
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45. Trading rule profits in Latin American currency spot rates. (2001). Lee, Chun I ; Gleason, Kimberly C. ; Mathur, Ike.
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46. Financial returns and efficiency as seen by an artificial technical analyst. (2001). Skouras, Spyros.
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47. Stock index and price dynamics in the UK and the US: new evidence from a trading rule and statistical analysis. (2000). Taylor, Stephen J..
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48. Forecasting Ability But No Profitability: An Empirical Evaluation of Genetic Algorithm-optimised Technical Trading Rules. (1999). Pereira, Robert.
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49. Profits on technical trading rules and time-varying expected returns: evidence from Pacific-Basin equity markets. (1999). Ito, Akitoshi.
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50. Tests of technical trading strategies in the emerging equity markets of Latin America and Asia. (1999). Leal, Ricardo P. C., ; Ratner, Mitchell .
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