Download presentation

Presentation is loading. Please wait.

Published byAkira Furse Modified over 2 years ago

1
Study on the Romanian Capital Market Efficiency A Filter Rule Technique Application Student Robu Anca-Maria Academy of Economic Studies Bucharest Doctoral School of Finance and Banking July 2008

2
What I ll talk about … 1.Introduction 2. The EMH and The Random Walk Model 3.Methodology and Model Analysis - Autocorrelation coefficients - Unit Root Tests : ADF test PP test - Testing the linear and non-linear dependence of the returns and the Normal Distribution - Variance Ratio Test (Lo&MacKinlay ) 4.Testing the capital market efficiency using a technical strategie : the filter rule technique - The Filter Rule Algorithm - Testing The Filter Rule 5. Conclusion

3
Main issues : the issue of market efficiency for Romania, an emerging equity market increased globalization and integration of the world economies and that of the financial markets =>opportunities for investors to diversify their portfolios the Romanian capital market evolution - an upward trend in recent years.

4
… main concepts Fama (1965) : … in a market with many intelligent and well- educated investors, securities will reflect all available information and therefore be appropriately priced. The more such participants, the faster the dissemination of information, and the more efficient a market should be. three different forms of the efficient market hypothesis : weak efficient- if all price information are fully reflected in asset prices semi-strong efficient - if price changes fully reflect all publicly available information strong efficient if prices fully reflect informations the statement that the current price of a security fully reflects available information was assumed to imply that successive price changes are independent and identically distributed=> the two hypotheses of the random walk model

5
Random walk … the more efficient the market, the more random is the sequence of price changes generated by the market and the most efficient market of all is one in which price changes are completely random and unpredictable.. A stochastic process P t is a (pure) random walk if:

6
… and its tests : - autocorrelation coefficients

7
- Unit root tests : ADF test

8
… and PP test

9
-Testing the non-linear dependence of the returns For testing the non-linear independence of the returns, we use the GARCH model : return - AR(1) BET GARCH(1,1) BET-C GARCH(1,1)

10
-Variance Ratio Test (Lo&MacKinlay) Developed by Cochrane (1988), Lo-MacKinlay (1988, 1989) - Since the variance of a random walk series increases linear with time, the variance of a q-period change must be q times the variance of the 1-period change. Variance ratio test under RW1:

11
Results of Variance Ratio Test

12
Filter Rule Technique Alexander(1961): If the daily closing price of a particular security moves up at least x per cent, buy and hold the security until its price moves down at least x per cent from a subsequent high, at which time simultaneously sell and go short. The short position is maintained until the daily closing price rises at least x per cent above a subsequent low at which time one covers and buys. Moves less than x per cent in either direction are ignored.

13
Fama and Blume(1966), Perreira (1999)

14
Notation

15
The Filter Rule Algorithm … for (j in 1:N) {if ((100*(data[1,j]-ref))/ref >= filter) {position<-1 counter<-j exit} else if ((100*(ref-data[1,j]))/ref > filter) {position<-0 counter<-j exit} } for (i in (counter+1):N) { if (position==0) { if (data[1,i]
{
"@context": "http://schema.org",
"@type": "ImageObject",
"contentUrl": "http://images.slideplayer.com/5/1525805/slides/slide_15.jpg",
"name": "The Filter Rule Algorithm … for (j in 1:N) {if ((100*(data[1,j]-ref))/ref >= filter) {position<-1 counter<-j exit} else if ((100*(ref-data[1,j]))/ref > filter) {position<-0 counter<-j exit} } for (i in (counter+1):N) { if (position==0) { if (data[1,i]= filter) {position<-1 counter<-j exit} else if ((100*(ref-data[1,j]))/ref > filter) {position<-0 counter<-j exit} } for (i in (counter+1):N) { if (position==0) { if (data[1,i]

16
… The Filter Rule Algorithm { if ((100*(data[1,i]-ref))/ref >= filter) { tDays<-tDays+1 openDays<-openDays+tDays position<-1 returnD<-P/data[1,i] returnT<-returnT*returnD*((1-0.02)/(1+0.02)) returnDaily<-returnD^(1/tDays)-1 returnBH<-returnBH * ((1-returnDaily)^tDays) ref<-data[1,i] tDays<-0 P<-data[1,i] TradingD<-TradingD+1 } else { tDays<-tDays+1 }

17
RESULTS : Indexes NOT Adj./ADJ for Commissions - Twenty one different filters ranging from 1 per cent to 45 per cent have been simulated. - sample vary from September 1997 to October 2000 and the end dates are in June 2008

18
Results on average – Filter Rule vs Buy and Hold Policy ADJ.WITH COMMISSIONS in the first period the filter rule returns are significantly higher than their counterpart for the following sub-periods the filter rules applied to all indexes are over-performed by the buy and hold strategy NO COMMISSIONS The filter rule returns are significantly higher that the profitability of the buy and hold strategy in terms of average by filter

19
BET Return – Filter Rule/B&H ( entire period of observation … )

20
Evolutionary trend across subsample for BET-C index - In the first case there is a strong indication of stock market inefficiency -the evolution of the difference average returns indicates that seems to be less stock market inefficiency in the recent years.

21
BET-C Return – Filter Rule/B&H ( entire period of observation … )

22
Evolutionary trend across subsample for BET-C index -From the evolution of the returns from the filter technique it can be observed that they grow with the value of the filter. This trend can be by the decreasing number of transactions with filter size and thus, the trading costs decreasing, the overall returns increase. - Filter technique indicates that there seems to be less stock market inefficiency in the recent years.

23
Results on average for BET securities NOT adj with commissions ADJUSTED with Commissions

24
Securities ADJ with commissions

25
Conclusions … The present study has tried to reach a conclusion about the capital market efficiency in Romania during last years and about the level of efficiency in Romania as a market in developing. These results generated by the filter rule technique are consistent with those obtained using statistical tests and led us to conclude that the Romanian capital market does not follow the random walk model and thus is not an efficient one.

26
… One clear conclusion : evidence of significant change in the Romanian stock over the last 10 years in terms of its efficiency, our statistical tests and mainly, filter rules technique indicating a movement from inefficiency towards efficiency. However, the last statement must be interpreted with caution, as it is well known that market efficiency cannot be proven based on few tests only but requires significant higher amount of analysis and method sophistication.

Similar presentations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google