Forecasting, valuation and portfolio returns of stock market evolution: problems, paradoxes and efficient information. Worldwide implications and Romanian evidence
Abstract
The purpose of this paper is to make a quantitative and qualitative critical analyse regarding the three important aspects of stock market evolution. First, the forecasting problems are presented and analyse in order to establish the main problems and the potential solutions. Second, the valuation problems are tackled in order to observe different trends and directions of solving these issues. Third, the portfolio return forecasts are mandatory in order to establish the results of the titles/market evolutions. The methods used in our research reveal the importance of adopting some important econometric tools in order to test the robustness of different main theories of the stock market and some important practices used among investors. The scope of the research was to give a quid pro quo in order to confer potential solutions regarding problems, paradoxes and efficient information of the stock market. The empirical results reveal that besides the critical side of the theories this paper sets a basis for a new eclectic approach regarding the probabilities that a title achieves certain values within a reasonable time frame. The main conclusion of this article suggests’ that the current theories register some gaps regarding the adherence into stock markets’ realities.
First published online 16 December 2019
Keyword : stock markets, market forecast, business valuation, modern portfolio theory, technical analysis, betting
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Alțăr, M. (2002). Teoria portofoliului. București: Academia de Studii Economice.
Anderson, P., Blackshaw, I., Siekmann, R., & Janwillem S. (2012). Sports betting: Law and policy. Berlin Heidelberg: Springer-Verlag Asser Press. https://doi.org/10.1007/978-90-6704-799-9
ANEVAR. (2018). Standardele de evaluare a bunurilor.
Anghel, A., Dumitrescu, D., & Tudor, C. (2015). Modeling portfolio returns on Bucharest Stock Exchange using the Fama-Frech multifactor model. Romanian Journal of Economic Forecasting, XVIII(1), 22-46. Retrieved from http://www.ipe.ro/rjef/rjef1_15/rjef1_2015p22-46.pdf
Anghel, D. G. (2017). Intraday market efficiency for a typical Central and Eastern European stock market. Romanian Journal of Economic Forecasting, XX(3), 88-109. Retrieved from http://www.ipe.ro/rjef/rjef3_17/rjef3_2017p88-109.pdf
Arneric, J., & Scrabic-Peric, B. (2018). Panel Garch model with cross-sectional dependence between CEE emerging markets in trading day effects analysis. Romanian Journal of Economic Forecasting, XXI(4), 71-84. Retrieved from http://www.ipe.ro/rjef/rjef4_18/rjef4_2018p71-84.pdf
Azevedo, J. M., Almeida, R. M. P., & Almeida, D. P. (2012). Using data mining with time series in short – term stock prediction: A literature review. International Journal of Intelligence Sciences, 2(4A), 176-180. https://doi.org/10.4236/ijis.2012.224023
Baker, H. K., & Filbeck, G. (2013). Portfolio theory and management. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199829699.001.0001
Bako, E. D., & Sechel, I. C. (2013). Technical and fundamental anomalies. Paradoxes of stock exchange markets. Annals of Faculty of Economic, University of Oradea, 1(1), 37-43. Retrieved from https://ideas.repec.org/a/ora/journl/v1y2013i1p37-43.html
Brynjolfsson, E., Rock, D., & Syverson, C. (2017). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. NBER Working Paper No. 24.001, 1-44. Retrieved from http://faculty.chicagobooth.edu/chad.syverson/research/aiparadox.pdf
Betfair. (2019). Retrieved from https://www.betfair.ro/exchange/plus/financial-bets
Bucharest Stock Exchange. (2019). Retrieved from https://www.bvb.ro/FinancialInstruments/Details/FinancialInstrumentsDetails.aspx?s=SIF1
Cartwright, E. (2018). Behavioral economics. London: Routledge. https://doi.org/10.4324/9781315105079
Chen, J. (2010). Essentials of technical analysis for financial markets. John Wiley & Sons, Inc.
Damian, V., & Cepoi, C. O. (2016). Volatility estimators with high-frequency data from Bucharest Stock Exchange. Economic Computation and Cybernetics Studies and Research, 50(3), 247-264. Retrieved from http://www.ecocyb.ase.ro/nr20163/14%20-%20DAMIAN%20Virgil,%20Cosmin%20%20Cepoi%20(T).pdf
Degutis, A., & Novickyte, L. (2014). The efficient market hypothesis: A critical review of literature and methodology. Economika, 93(2), 7- 23. https://doi.org/10.15388/Ekon.2014.2.3549
Demirer, R., Pierdzioch, C., & Zhang, H. (2017). On short – term predictability on stock returns: A quantile boosting approach. Finance Research Letters, 22, 35-41. https://doi.org/10.1016/j.frl.2016.12.032
De Prado, M. L. (2018). Advances in financial machine learning. New York: John Wiley & Sons, Inc.
Elton, E., Gruber, M., Brown, S., & Goetzmann, W. (2014). Modern portfolio theory and investment analysis (9th ed.). New York: John Wiley & Sons, Inc.
Fahling, E., Steurer, E., Schadler, T., & Voltz, A. (2018). Next level in risk management? Hedging and trading strategies of volatility derivatives using VIX futures. Journal of Financial Risk Management, 7, 442-459. https://doi.org/10.4236/jfrm.2018.74024
Francis, J. C., & Kim, D. (2013). Modern portfolio theory. Foundations, analysis, and new developments. John Wiley & Sons, Inc., Hoboken, New Jersey.
Franco, C., & Zakoian, J. M. (2010). GARCH models: structure, statistical inference, and financial applications. New York: John Wiley & Sons Ltd. https://doi.org/10.1002/9780470670057
Fong, W. M. (2014). The lottery mindset: Investors, gambling and the stock market. New York: Palgrave Macmillan. https://doi.org/10.1057/9781137381736
Fulga, C. (2017). Integrated decision support system for portfolio selection with enhanced behavioral content. Economic Computation and Cybernetics Studies and Research, 51(3), 127-142. Retrieved from http://www.ecocyb.ase.ro/nr2017_3/08%20-%20Fulga%20Cristinca%20(T)(N).pdf
Gao, L., Han, Y., Zhengzi Li, S., & Zhou, G. (2018). Market intraday momentum. Journal of Financial Economics, 129(2), 394-414. https://doi.org/10.1016/j.jfineco.2018.05.009
Georgescu, V. (2016). Using nature-inspired metaheuristics to train predictive machines. Economic Computation and Cybernetics Studies and Research, 50(2), 5-24. Retrieved from http://www.ecocyb.ase.ro/nr20162/01%20-%20Georgescu%20Vasile%20(T).pdf
Hull, J. (2018). Options, futures, and other derivatives (10th ed.). New York: Pearson Education.
ifbf Finwest. (2019). Retrieved from https://www.ifbfinwest.ro
investing.com. (2019). Retrieved from https://www.investing.com
International Valuation Standards Council. (2017). International Valuation Standards.
Jeffrey, R. (2004). Subjective probability. The real thing. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511816161
Javadi, F., Ftiti, Z., & Hdia, M. (2017). Assessing efficiency and investment opportunities in commodities: A time series and portfolio simulations approach. Economic Modeling, 64, 567-588. https://doi.org/10.1016/j.econmod.2017.04.021
Joldes, C. C. (2019). Modeling the volatility of the Bucharest Stock Exchange using the GARCH models. Economic Computation and Cybernetics Studies and Research, 53(1), 281-298. Retrieved from http://www.ecocyb.ase.ro/nr2019_1/18%20-%20Joldes%20Camelia%20Catalina%20(18).pdf
Jerald, E. P., Robinson, T. R., Pinto J. E., & McLeavey, D. W. (2010). Equity asset valuation (2nd ed.). New Jersey: CFA Institute.
Karlis, D., & Ntzoufras, I. (2003). Analysis of sports data by using bivariate Poisson models. The Statistician, 53(3), 381-393. https://doi.org/10.1111/1467-9884.00366
Lim, M. A. (2016). The handbook of technical analysis. Singapore: John Wiley & Sons Singapore Pte. Ltd.
Litov, L., Moreton, P., & Zenger, T. R. (2012). Corporate strategy, analist coverage and the uniqueness paradox. Management Sciences, 58(10), 1797-1815. https://doi.org/10.1287/mnsc.1120.1530
Longarela, I. R., & Mayoral, S. (2015). Quote inefficiency in options markets. Journal of Banking and Finance, 55(C), 23-36. https://doi.org/10.1016/j.jbankfin.2014.11.003
Lungu, I., Bâra, A., Cărutașu, G., Pîrjan, A., & Oprea, S. V. (2016). Prediction intelligent system in the field of renewable energies through neural networks. Economic Computation and Cybernetics Studies and Research, 50(1), 85-102. Retrieved from http://www.ecocyb.ase.ro/nr20161/05%20-%20Lungu%20Ion,%20final%20(T).pdf
Lupu, I. (2015). European stock markets correlations in a Markov switching framework. Romanian Journal of Economic Forecasting, XVIII(3), 103-119. Retrieved from http://www.ipe.ro/rjef/rjef3_15/rjef3_2015p103-119.pdf
Lupu, I., Hurduzu, G., & Nicolae, M. (2016). Connections between sentiment indices and reduced volatilities of sustainability stock market indices. Economic Computation and Cybernetics Studies and Research, 50(1), 157-174. Retrieved from http://www.ecocyb.ase.ro/nr20161/09%20-%20Lupu%20Hurduzeu%20Nicolae%20(T).pdf
Mago, T., Wang, X., & Modave, F. (2010). Application of fuzzy measures and interval computation to financial portfolio selection. International Journal of Intelligent Systems, 25, 621-635. Wiley Periodicals, Inc. https://doi.org/10.1002/int.20415
Markowitz, H. M. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91. https://doi.org/10.2307/2975974
Marcu, N., Dobrotă, C. E., & Antoneac (Calin), R. (2017). An investigation of the day-of-the-week effect in conditional variance at the Bucharest Stock Exchange. Romanian Journal of Economic Forecasting, XX(2), 124-134. Retrieved from http://www.ipe.ro/rjef/rjef2_17/rjef2_2017p124-134.pdf
Murphy, J. (1999). Technical analysis of the financial markets. New York: New York Institute of Finance. Oddsportal.com. (2017–2018). Retrieved from http://www.oddsportal.com/soccer/europe/champions-league/real-madrid-liverpool-hMiM2xC0/
Pati, P. C., Barai, P., & Rajib, P. (2018). Forecasting stock market volatility and information content on implied volatility index. Applied Economics, 50(23), 2552-2568. https://doi.org/10.1080/00036846.2017.1403557
Pesavento, L., & Smoleny, S. (2015). A trader’s guide to financial astrology. Forecasting market cycles using planetary and lunar movements. New Jersey: John Wiley & Sons, Inc., Hoboken. https://doi.org/10.1002/9781118646953
Pratt, S., & Grabowski, R. (2010). Cost of capital: application and examples (4th ed.). New York: John Wiley & Sons, Inc.
Pyun, S. (2019). Variance risk in aggregate stock returns and time-varying return predictability. Journal of Financial Economics, 132(1), 150-174. https://doi.org/10.1016/j.jfineco.2018.10.002
Richard, J. F. (2005). Bourse: Ce qu’anticipent les astre jusqu’en 2010. Astrologie. Paris: Éditions du rocher.
Ruxanda, G., & Opincaru, S. (2018). Bayesian neural networks with dependent Dirichlet process priors. Application to pairs trading, Economic Computation and Cybernetics Studies and Research, 52(4), 5-18. Retrieved from http://www.ecocyb.ase.ro/nr2018_4/01%20-%20Ruxanda%20Gh.,%20Sorin%20Opincariu.pdf
Sappideen, R. (2009). The paradox of securities markets efficiency: Where to next? Singapore Journal of Legal Studies, Special Issue, 1(1), 80-108. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1491370
Saman, C. (2015). Asymmetric interaction between stock price index and exchange rates: Empirical evidence for Romania. Romanian Journal of Economic Forecasting, XVIII(4), 90-109. Retrieved from http://www.ipe.ro/rjef/rjef4_15/rjef4_2015p90-109.pdf
Schmidlin, N. (2014). The art of company valuation and financial statement analysis. New York: Verlag Franz Vahlen GmbH (John Wiley & Sons Ltd.).
Schulmerich, M., Leporcher, Y. M., & Eu, C. H. (2015). Applied asset and risk management. A guide to modern portfolio management and behavior-driven markets. Berlin Heidelberg: Springer-Verlag.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk*. The Journal of Finance, XIX(3), 425-442. https://doi.org/10.1111/j.1540-6261.1964.tb02865.x
Shim, H., Kim, H., Kim, J., & Ryu, D. (2015). Weather and stock market volatility: the case of a leading emerging market. Applied Economics Letters, 22(12), 987-992. https://doi.org/10.1080/13504851.2014.993129
Singht, V., Roca, E., & Li, B. (2018). Cointegration networks in stock markets. Applied Economics Letters, 25(10), 663-667. https://doi.org/10.1080/13504851.2017.1355534
Smales, L. A. (2017). The importance of fear: investor sentiment and stock market returns. Applied Economics, 49(34), 3395-3421. https://doi.org/10.1080/00036846.2016.1259754
Stadnik, B., Raudeliuniene, J., & Davidaviciene, V. (2016). Fourier analyzing for stock price forecasting: Assumption and evidence. Journal of Business Economics & Management, 17(3), 365-380. https://doi.org/10.3846/16111699.2016.1184180
Stooq. (2019). Retrieved from https://stooq.com/q/d/?s=^dji
Symitsi, E., Symeonidis, L., Kourtis, A., & Markellos, R. (2018). Covariance forecasting in equity markets. Journal of Banking & Finance, 96, 153-168. https://doi.org/10.1016/j.jbankfin.2018.08.013
Sortino, A., & Price, L. (1994). Performance measurement in a downside risk framework. The Journal of Investing, III(3), 59-64. https://doi.org/10.3905/joi.3.3.59
Sortino, F. A., & Satchell, S. E. (2001). Managing downside risk in financial markets: Theory, practice and implementation. Reed Educational and Professional Publishing Ltd.
Taleb, N. N. (2017). The black swan: The impact of the highly improbable. New York: Random House.
Tsinaslanidis, P. E., & Zapranis, A. D. (2016). Technical analysis for algorithmic pattern recognition. Bern: Springer International Publishing Switzerland. https://doi.org/10.1007/978-3-319-23636-0
Thomsett, M. (2017). The mathematics of options. New York: Palgrave Macmillan. https://doi.org/10.1007/978-3-319-56635-1
Turcaș, F., Dumiter, F., Braica, A., Brezeanu, P., & Opreț, A. (2016). Using technical analysis for portfolio selection and post-investment analysis. Economic Computation and Cybernetics Studies and Research, 50(1), 197-214. Retrieved from http://www.ecocyb.ase.ro/nr20161/12%20-%20Turcas%20Florin,%20Demeter%20(T).pdf
Turcaș, F., Dumiter, F., Brezeanu, P., Fărcaș, P., & Coroiu, S. (2017). Practical aspects of portfolio selection and optimization on capital market. Economic Research-Ekonomska Istraživanja, 30(1), 14-30. https://doi.org/10.1080/1331677X.2016.1265893
Turcaș, F., Dumiter, F., Braica, A., Brezeanu, P., & Neagu, O. (2018). Arbitrage on Romanian stock market. Economic Computation and Cybernetics Studies and Research, 52(1), 43-58. Retrieved from http://www.ecocyb.ase.ro/nr2018_1/03%20-%20Turcas%20Florin,%20P.%20%20Brezeanu%20(T).pdf
Turcaș, F. (2008). Strategia X și 0. AATROM.
UNIBET. (2019). Retrieved from https://www.unibet.ro
Zhang, H. (2018). The forecasting model of stock price based on PCA and BP neural network. Journal of Financial Risk Management, 7, 369-385. https://doi.org/10.4236/jfrm.2018.74021
Zhang, Y., Zeng, Q., MA., F., & Shi, B. (2019). Forecasting stock returns: Do less powerful predictors help? Economic Modeling, 78, 32-39. https://doi.org/10.1016/j.econmod.2018.09.014
Zivkov, D., Njegic, J., & Milenkovic, I. (2018). Interrelationship between DAX Index and Four Largest Eastern European Stock Markets. Journal for Economic Forecasting, Institute for Economic Forecasting, 0(3), 88-103.