Predicting the Evolution of Romania s Stock Market Using the Technical Analysis. Adriana Georgescu, Academy of Economic Studies Bucharest, Romania
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1 Conference of the International Journal of Arts and Sciences 1(6): (2009) CD-ROM. ISSN: InternationalJournal.org Predicting the Evolution of Romania s Stock Market Using the Technical Analysis Adriana Georgescu, Academy of Economic Studies Bucharest, Romania Abstract: The goal of this study is to predict the evolution of Romania s Stock Market using the technical analysis and to analyze if the predicted evolution fits to the real evolution of the market. The technical indicators were applied on the main Romanian Stock Exchange Indexes (BET, BET- C, BET-FI) as well as on multiple individual stocks for a time period spanning 8 years ( ). This time frame is significant for the Romanian Stock Market: since the beginning of 2004, the Stock Market registered a sustained positive evolution, the main cause being the acceleration of the liberalisation process of the whole Romanian market. The peak was reached by the Romanian Stock Exchange Indexes during the year 2007, after Romania joined the European Union. Becoming a member of the EU, Romania adopted the European Legislation, which introduced new rules in order to protect investors, safeguard market integrity and promote fair, transparent, efficient and integrated financial markets, factors which had a positive impact on the stock market. The results of my research provide sufficient evidence to conclude that by using a model containing key technical indicators and by appropriately selecting the parameters that are to be used for these indicators, it is possible to predict the evolution of a Stock Market. 1. Introduction The Technical Analysis is a method for forecasting the future financial price movements through examining past market data, primarily price and volume. Technical Analysis takes a completely different approach in comparison to the Fundamental Analysis. Fundamental Analysis is based on analyzing the financial characteristics of a company in order to estimate its value. Technical Analysis doesn t take into account the value of the company, being based on the assumptions that factors which influence the price movements are incorporated into the price, the price moves in trends and the history repeats itself. The objective of this study is to identify a model containing key technical indicators in order to predict the evolution of Romania s Stock Market and
2 to explore if the by this model fit to the real evolution of the market. 2. Research Methodology Short presentation of the research: 1. The model developed in this study contains the main technical indicators: DMI, Envelopes, MACD, Moving Average, A/D Line, Momentum, RSI, Stochastik, CCI, R%, OBV, Chaikin Oscillator. 2. Data Collection: Romanian stock market data comprising of daily stock prices of all companies listed at the Bucharest Stock Exchange (60 companies) and of the main Romanian Stock Exchange Indexes (BET, BET-C, BET-FI) were used. 3. Time frame: January 2001 December This time frame is significant for the Romanian stock market, because the Bucharest Stock Exchange was reestablished on the 21st of April 1995 after an absence of over 40 years due to the communist regime. The first important companies started being listed at the BSE in Since the beginning of 2004, the Stock Market registered a sustained positive evolution, the main cause being the acceleration of the liberalisation process of the whole Romanian market. The Romanian government published the stages of the liberalization process and the characteristics of the market open to competition as follows: Table 1: The stages of the Romanian liberalisation process 1st Stage (before2003) Exclusive rights - partial liberalisation - few diversified offers - high costs 2nd Stage ( ) Beginning of the liberalisation - liberalisation of each segment of the market - new competitors on the market - big competition - lower rates 3rd Stage ( ) Acceleration of the liberalisation process - strong competition - diversification of offers - lower rates - changes of the whole market - accent on high quality and innovation as competitive advantages
3 4. The technical indicators were calculated individually for all companies listed at the Bucharest Stock Exchange and the by them (Buy the the price will rise, Sell the price will decrease) were compared with the real evolution of the stock price. The signal is classified as true if it fits to the real evolution of the price, or as false if it does not. The parameters used for calculating the technical indicators are the same for all stocks. 5. The next step of the research is to determine for every company listed at the Stock Exchange a resulting signal (Buy or Sell) of the analized indicators for every day. The daily of all indicators integrated in the model are compared and the signal which occurs mostly is considered as result of the analysis. The resulted signal is classified as true if it fits to the real evolution of the price, or as false if it does not. 3. Research Results The results of my research demonstrate that by interpreting separately the technical indicators for each company, we can have the case where the signal by an indicator can be different from the others for the same day. Another aspect is that an indicator can obtain a high percentage of true for a company and a low one for the others. Table 2: Example of by some of the technical indicators for two companies Company listed at the Bucharest Stock Exchange Signals by the technical indicators by DMI by Envelopes by MACD by A/D Line BRD BCC TRUE 59,97% 60,31% 61,60% 77,16% FALSE 40,03% 39,69% 38,40% 22,84% Total 100,00% 100,00% 100,00% 100,00% TRUE 53,12% 34,52% 62,60% 75,63% FALSE 46,88% 65,48% 37,40% 24,37% Total 100,00% 100,00% 100,00% 100,00%
4 In this situation it is very difficult to rely on a specific indicator. This was the reason, why I tried to elaborate a model containing key technical indicators and to find the parameters for these indicators in order to be applied for the entire market. Through establishing a majority signal for the elaborated model, the final results obtained during the research are stable for all companies listed at the Bucharest Stock Exchange having a percentage of true of over 74%. Table 3: Output example - % of true by the model Company listed at the Bucharest Stock Exchange % of true by the model (which fit to the real evolution) BRD 76,48% ALR 75,74% BCC 76,66% TLV 76,33% BIO 76,21% BRK 79,76% IMP 78,60% SNP 76,69% PCL 76,55% References DeFusco A. Richard, Dennis W. McLeavey, Jerald E. Pinto, David E. Runkle. (2004). Quantitative Methods for Investment Analysis, ISBN Edwards Roberts D., Magee John (1992). Technical Analysis of Stock Trends, Sixth Edition, Boston, MA: John Magee, Inc., 1992 (Distributed by New York Institute of Finance.) Appel, Gerald, Hitschler. (1980). Stock Market Trading Systems, Homewood, IL: Dow Jones-Irwin.
5 Arms, Richard W. (1994). Volume Cycles in the Stock Market, UT: EQUIS International, Inc. Cesar, Gerald. (1996). Aktienanalyse heute: Gewinnmaximierung an der Börse, Germany. Edwards Roberts D., Magee John. (1994). Technische Analyse von Aktien Trends, Germany. Fojtl, Christian. (1989). Technical analysis of the stock markets in New York, Vienna and Frankfurt, Vienna. Fosback, Norman G. (1992). Stock Market Logic, Chicago, IL: Dearborn Financial Publishing, Inc. Gifford, Elli. (1995). Investor's guide to technical analysis, London. Kahn, Michael N. (2001). Financial Times Börsenpraxis: technische Analyse, Germany. Murphy J. John. (1999). Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications (New York Institute of Finance), ISBN Müller Thomas, Nietzer Harald. (1999). Das grosse Buch der technischen Indikatoren, Germany. O'Neil, William J. (1995). How to Make Money in Stocks, Second Edition. New York, NY: McGraw-Hill, Inc. O Shaughnessy, James. (1999). Die besten Anlagestrategien aller Zeiten, Germany. Reilly K. Frank, Brown C. Keith. (2003). Investment Analysis, Portfolio Management, ISBN Buckle, M. J., A D Clare and S H Thomas. (1999). Developing a Trading Rule from the FTSE-100 Stock Index Futures Contract; Evidence in Support of EMH, Journal of Business, Finance and Accounting, 26(1), Jan.-Mar.:
6 Caginalp, Gunduz and D Balenovich. (2003). A Theoretical Foundation for Technical Analysis, Journal of Technical Analysis, Winter-Spring: Wing-Keung Wong, Meher Manzur, Boon-Kiat Chew. (2003). How Rewarding Is Technical Analysis? Evidence From Singapore Stock Market. Wing-Keung Wong, Jun Du, Terence Tai Leung Chong. (2005). Do the technical indicators reward chartists? A study on the stock markets of China, Hong Kong and Taiwan. Mizrach B., Weerts S. (2006). Highs and Lows: A Behavioral and Technical Analysis. Fernando Fernández-Rodríguez, Simon Sosvilla-Rivero, Julian Andrada-Felix. (1999). Technical analysis in the Madrid stock exchange. Sullivan R., Timmermann A., White H. (1997). Data-Snooping, Technical Trading Rule Performance, and the Bootstrap. Blume L., Easley D., O'Hara M. (1994). Market Statistics and Technical Analysis: The Role of Volume.
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