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Financial Markets and Portfolio Management

Erschienen in:

15.04.2020

Portfolio creation using artificial neural networks and classification probabilities: a Canadian study

verfasst von: Tania Morris, Jules Comeau

Erschienen in: Financial Markets and Portfolio Management | Ausgabe 2/2020

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Abstract

This study aims to verify whether using artificial neural networks (ANNs) to establish classification probabilities generates portfolios with higher excess returns than using ANNs in their traditional role of predicting portfolio returns. Our sample includes all companies listed on the Toronto Stock Exchange from 1994 to 2014 with a monthly average of 16,324 company-month observations. Results indicate that portfolios based on the classification probabilities yield mean returns ranging from 7.81 to 14.40% annually over a 16-year period and that portfolios based on both predicted returns and classification probabilities generate returns that are superior to the market index. In addition, there is evidence that ranking securities based on their probability of beating the market has some benefit.

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Abdullah, D.A., Hayworth, S.C.: Macroeconometrics of stock price fluctuations. Q. J. Bus. Econ. 32(1), 50–67 (1993)

Afolabi, M.O., Olude, O.: Predicting stock prices using a hybrid Kohonen self organizing map (SOM). In: 40th Annual Hawaii International Conference on System Sciences, 2007. HICSS 2007, pp. 48–48 (2007). https://doi.org/10.1109/HICSS.2007.441

Aggarwal, R., Demaskey, A.L.: Using derivatives in major currencies for cross-hedging currency risks in Asian emerging markets. J. Futures Mark. 17(7), 781–796 (1997)CrossRef

Angas, L.L.B.: Investment for Appreciation. Somerset Publishing Company, Fulshear (1936)

Atsalakis, G.S., Valavanis, K.P.: Surveying stock market forecasting techniques—part II: soft computing methods. Expert Syst. Appl. 36(3, Part 2), 5932–5941 (2009). https://doi.org/10.1016/j.eswa.2008.07.006 CrossRef

Balvers, R.J., Cosimano, T.F., Mcdonald, B.: Predicting stock returns in an efficient market. J. Finance 45(4), 1109–1128 (1990). https://doi.org/10.1111/j.1540-6261.1990.tb02429.x CrossRef

Banz, R.W.: The relationship between return and market value of common stocks. J. Financ. Econ. 9(1), 3–18 (1981). https://doi.org/10.1016/0304-405X(81)90018-0 CrossRef

Basu, S.: Investment performance of common stocks in relation to their price-earnings ratios: a test of the efficient market hypothesis. J. Finance 32(3), 663–682 (1977)CrossRef

Basu, S.: The relationship between earnings’ yield, market value and return for NYSE common stocks: further evidence. J. Financ. Econ. 12(1), 129–156 (1983)CrossRef

Brock, W., Lakonishok, J., LeBaron, B.: Simple technical trading rules and the stochastic properties of stock returns. J. Finance 47(5), 1731–1764 (1992)CrossRef

Campbell, J.Y.: Stock returns and the term structure. J. Financ. Econ. 18(2), 373–399 (1987). https://doi.org/10.1016/0304-405X(87)90045-6 CrossRef

Chen, L., Pelger, M., Zhu, J.: Deep Learning in Asset Pricing. SSRN Scholarly Paper No ID 3350138. Consulté à l’adresse Social Science Research Network. https://papers.ssrn.com/abstract=3350138 (2019)

Chen, N.-F., Roll, R., Ross, S.A.: Economic forces and the stock market. J. Bus. 59(3), 383–403 (1986)CrossRef

Chong, E., Han, C., Park, F.C.: Deep learning networks for stock market analysis and prediction: methodology, data representations, and case studies. Expert Syst. Appl. 83, 187–205 (2017). https://doi.org/10.1016/j.eswa.2017.04.030 CrossRef

Chong, T.T.-L., Ng, W.-K.: Technical analysis and the London stock exchange: testing the MACD and RSI rules using the FT30. Appl. Econ. Lett. 15(14), 1111–1114 (2008). https://doi.org/10.1080/13504850600993598 CrossRef

Cochrane, J.H.: Presidential address: discount rates. J. Finance 66(4), 1047–1108 (2011). https://doi.org/10.1111/j.1540-6261.2011.01671.x CrossRef

Eakins, S.G., Stansell, S.R.: Can value-based stock selection criteria yield superior risk-adjusted returns: an application of neural networks. Int. Rev. Financ. Anal. 12(1), 83–97 (2003). https://doi.org/10.1016/S1057-5219(02)00124-2 CrossRef

Enke, D., Thawornwong, S.: The use of data mining and neural networks for forecasting stock market returns. Expert Syst. Appl. 29(4), 927–940 (2005). https://doi.org/10.1016/j.eswa.2005.06.024 CrossRef

Fama, E.F.: Stock returns, real activity, inflation, and money. Am. Econ. Rev. 71(4), 545–565 (1981)

Fama, E.F.: Inflation, output, and money. J. Bus. 55(2), 201–231 (1982)CrossRef

Fama, E.F., French, K.R.: Business conditions and expected returns on stocks and bonds. J. Financ. Econ. 25(1), 23–49 (1989). https://doi.org/10.1016/0304-405X(89)90095-0 CrossRef

Fama, E.F., French, K.R.: The cross-section of expected stock returns. J. Finance 47(2), 427–465 (1992)CrossRef

Fama, E.F., French, K.R.: Multifactor explanations of asset pricing anomalies. J. Finance 51(1), 55–84 (1996)CrossRef

Fama, E.F., Schwert, G.W.: Human capital and capital market equilibrium. J. Financ. Econ. 4(1), 95–125 (1977). https://doi.org/10.1016/0304-405X(77)90038-1 CrossRef

Ferson, W.E., Harvey, C.R.: The risk and predictability of international equity returns. Rev. Financ. Stud. 6(3), 527–566 (1993). https://doi.org/10.1093/rfs/6.3.527 CrossRef

Fu, F.: Idiosyncratic risk and the cross-section of expected stock returns. J. Financ. Econ. 91(1), 24–37 (2009)CrossRef

Geske, R., Roll, R.: The fiscal and monetary linkage between stock returns and inflation. J. Finance 38(1), 1–33 (1983)CrossRef

Glosten, L.R., Jagannathan, R., Runkle, D.E.: On the relation between the expected value and the volatility of the nominal excess return on stocks. J. Finance 48(5), 1779–1801 (1993)CrossRef

Guidolin, M., Hyde, S., McMillan, D., Ono, S.: Non-linear predictability in stock and bond returns: when and where is it exploitable? Int. J. Forecast. 25(2), 373–399 (2009). https://doi.org/10.1016/j.ijforecast.2009.01.002 CrossRef

Hudson, R., Dempsey, M., Keasey, K.: A note on the weak form efficiency of capital markets: the application of simple technical trading rules to UK stock prices—1935 to 1994. J. Bank. Finance 20(6), 1121–1132 (1996). https://doi.org/10.1016/0378-4266(95)00043-7 CrossRef

Israel, R., Moskowitz, T.J.: The role of shorting, firm size, and time on market anomalies. J. Financ. Econ. 108(2), 275–301 (2013)CrossRef

Jasic, T., Wood, D.: The profitability of daily stock market indices trades based on neural network predictions: case study for the S&P 500, the DAX, the TOPIX and the FTSE in the period 1965–1999. Appl. Financ. Econ. 14(4), 285–297 (2004). https://doi.org/10.1080/0960310042000201228 CrossRef

Jegadeesh, N., Titman, S.: Returns to buying winners and selling losers: implications for stock market efficiency. J. Finance 48(1), 65–91 (1993). https://doi.org/10.2307/2328882 CrossRef

Jegadeesh, N., Titman, S.: Profitability of momentum strategies: an evaluation of alternative explanations. J. Finance 56(2), 699–720 (2001)CrossRef

Kanas, A.: Neural network linear forecasts for stock returns. Int. J. Finance Econ. 6(3), 245–254 (2001)CrossRef

Kara, Y., Acar Boyacioglu, M., Baykan, Ö.K.: Predicting direction of stock price index movement using artificial neural networks and support vector machines: the sample of the Istanbul Stock Exchange. Expert Syst. Appl. 38(5), 5311–5319 (2011). https://doi.org/10.1016/j.eswa.2010.10.027 CrossRef

Kim, K., Han, I.: Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Syst. Appl. 19(2), 125–132 (2000). https://doi.org/10.1016/S0957-4174(00)00027-0 CrossRef

Kothari, S.P., Shanken, J.: Book-to-market, dividend yield, and expected market returns: a time-series analysis. J. Financ. Econ. 44(2), 169–203 (1997). https://doi.org/10.1016/S0304-405X(97)00002-0 CrossRef

Kozak, S., Nagel, S., Santosh, S.: Shrinking the cross-section. J. Financ. Econ. (2019). https://doi.org/10.1016/j.jfineco.2019.06.008 CrossRef

Kuo, R.J., Chen, C.H., Hwang, Y.C.: An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network. Fuzzy Sets Syst. 118(1), 21–45 (2001)CrossRef

Kwon, K.-Y., Kish, R.J.: Technical trading strategies and return predictability: NYSE. Appl. Financ. Econ. 12(9), 639–653 (2002). https://doi.org/10.1080/09603100010016139 CrossRef

Leung, M.T., Daouk, H., Chen, A.-S.: Forecasting stock indices: a comparison of classification and level estimation models. Int. J. Forecast. 16(2), 173–190 (2000). https://doi.org/10.1016/S0169-2070(99)00048-5 CrossRef

Lintner, J.: The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. In: Ziemba, W.T., Vickson, R.G. (eds.) Stochastic Optimization Models in Finance, pp. 131–155. Elsevier, Amsterdam (1975)CrossRef

Long, W., Lu, Z., Cui, L.: Deep learning-based feature engineering for stock price movement prediction. Knowl. Based Syst. 164, 163–173 (2019). https://doi.org/10.1016/j.knosys.2018.10.034 CrossRef

McMillan, D.G.: Nonlinear predictability of stock market returns: evidence from nonparametric and threshold models. Int. Rev. Econ. Finance 10(4), 353–368 (2001). https://doi.org/10.1016/S1059-0560(01)00093-4 CrossRef

Messmer, M.: Deep Learning and the Cross-Section of Expected Returns. SSRN Scholarly Paper No ID 3081555. Consulté à l’adresse Social Science Research Network. https://papers.ssrn.com/abstract=3081555 (2017)

Mills, T.C.: Technical analysis and the London Stock Exchange: testing trading rules using the FT30. Int. J. Finance Econ. 2(4), 319–331 (1997). https://doi.org/10.1002/(SICI)1099-1158(199710)2:4%3c319:AID-JFE53%3e3.0.CO;2-6 CrossRef

Mukherjee, T.K., Naka, A.: Dynamic relations between macroeconomic variables and the Japanese stock market: an application of a vector error correction model. J. Financ. Res. 18(2), 223–237 (1995)CrossRef

Nelson, C.R.: Inflation and rates of return on common stocks. J. Finance 31(2), 471–483 (1976)CrossRef

Olson, D., Mossman, C.: Neural network forecasts of Canadian stock returns using accounting ratios. Int. J. Forecast. 19(3), 453–465 (2003). https://doi.org/10.1016/S0169-2070(02)00058-4 CrossRef

Otuteye, E.: How economic forces explain Canadian stock returns. Can. Invest. Rev. 4, 93–99 (1991)

Pesaran, M.H., Timmermann, A.: Forecasting stock returns an examination of stock market trading in the presence of transaction costs. J. Forecast. 13(4), 335–367 (1994)CrossRef

Pontiff, J., Schall, L.D.: Book-to-market ratios as predictors of market returns. J. Financ. Econ. 49(2), 141–160 (1998)CrossRef

Qi, M.: Nonlinear predictability of stock returns using financial and economic variables. J. Bus. Econ. Stat. 17(4), 419–429 (1999). https://doi.org/10.1080/07350015.1999.10524830 CrossRef

Quah, T.-S., Srinivasan, B.: Improving returns on stock investment through neural network selection. Expert Syst. Appl. 17(4), 295–301 (1999). https://doi.org/10.1016/S0957-4174(99)00041-X CrossRef

Ratanapakorn, O., Sharma, S.C.: Dynamic analysis between the US stock returns and the macroeconomic variables. Appl. Financ. Econ. 17(5), 369–377 (2007). https://doi.org/10.1080/09603100600638944 CrossRef

Rather, A.M., Agarwal, A., Sastry, V.N.: Recurrent neural network and a hybrid model for prediction of stock returns. Expert Syst. Appl. 42(6), 3234–3241 (2015)CrossRef

Rosenberg, B., Reid, K., Lanstein, R.: Persuasive evidence of market inefficiency. J. Portf. Manag. 11(3), 9–16 (1985). https://doi.org/10.3905/jpm.1985.409007 CrossRef

Shadbolt, J., Taylor, J.G.: Neural Networks and the Financial Markets: Bpredicting, Combining, and Portfolio Optimisation. Springer, Berlin (2002)CrossRef

Sharpe, W.F.: A simplified model for portfolio analysis. Manag. Sci. 9(2), 277–293 (1963). https://doi.org/10.1287/mnsc.9.2.277 CrossRef

Sharpe, W.F.: Capital asset prices: a theory of market equilibrium under conditions of risk. J. Finance 19(3), 425–442 (1964)

Spiegel, M.I. Wang, X.: Cross-sectional variation in stock returns: liquidity and idiosyncratic risk. Yale ICF Working Paper No. 05–13; EFA 2005 Moscow Meetings Paper (September 8, 2005). Available at SSRN: https://ssrn.com/abstract=709781

Thawornwong, S., Enke, D.: The adaptive selection of financial and economic variables for use with artificial neural networks. Neurocomputing 56, 205–232 (2004). https://doi.org/10.1016/j.neucom.2003.05.001 CrossRef

Ticknor, J.L.: A Bayesian regularized artificial neural network for stock market forecasting. Expert Syst. Appl. 40(14), 5501–5506 (2013). https://doi.org/10.1016/j.eswa.2013.04.013 CrossRef

Zhong, X., Enke, D.: A comprehensive cluster and classification mining procedure for daily stock market return forecasting. Neurocomputing 267, 152–168 (2017). https://doi.org/10.1016/j.neucom.2017.06.010 CrossRef

Titel: Portfolio creation using artificial neural networks and classification probabilities: a Canadian study
verfasst von: Tania Morris
Jules Comeau
Publikationsdatum: 15.04.2020
Verlag: Springer US
Erschienen in: Financial Markets and Portfolio Management / Ausgabe 2/2020
Print ISSN: 1934-4554
Elektronische ISSN: 2373-8529
DOI: https://doi.org/10.1007/s11408-020-00350-8

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