Summary
The main purpose of this research was to assess the acceptability of the Time Series Analysis model using six statistical indicators of the trend of the Vietnam stock index and application of these indicators to each separate stock to help investors make trading decisions (buy/sell/hold). Purposively, it aimed to analyze the effect of the reliability, efficiency and availability variables on the level of acceptability of the trend forecasting model using the Time Series Analysis of stock indices. Specifically, the researcher sought to answer the following questions:
1. What is the profile of the respondents?
2. How useful is the TSA model in terms of:
a) Reliability b) Efficiency
c) Availability of required software and input data?
3. What level of acceptability do users experience when they use the TSA model in assisting them to make investment decisions?
This study used the descriptive correlation design in analyzing the investigated variables. One hundred twenty (120) respondents were requested to answer the questionnaire. They were randomly selected using the fish bowl technique. Weighted mean was utilized to describe the independent variables: reliability, efficiency and availability. Pearson Correlation Coefficient was used to determine the significant relationship of the variables and level of acceptability. Multiple regressions were used to find out the predictability of the influence of independent variables: reliability, efficiency and dependent variable: Level of acceptability. The statistical analysis was done using the Statistical Package for Social Sciences (SPSS), now also known as predictive analysis software.
Conclusions
Profile of the Respondents
The profile of the respondents was defined in terms of Name, Company name, Sex, Age, Type of Agency, Educational attainment, amount of money investment, years of experience of trading.
Age and Gender:
+ Group of 25 years old and below: 29 male and 10 female respondents.
+ Group of 26-50 year olds: 43 male and 15 female respondents
+ Group of 51 years old and above: 12 female and 11 male respondents.
Educational Attainment: Of the total 120 respondents, there were 90 respondents who have a Bachelor's degree (accounts for 75%), 18 respondents who have a Master’s Degree (15%), 2 respondents who have a Doctorate Degree (2%), and Others (10 respondents accounting for 8%).
Amount of money investment: Of the total 120 respondents, there were 50 respondents who have money investment in the $10,001-$50,000 group (accounting for 42%), 30 respondents who have money investment in the $10,000 and below group (accounting for 25%), 25 respondents who have money investment in the $50,001-$100,000 group (21%), and the $100,001 and above group has 15 respondents (13%).
Years of experience of trading: Of the total 120 respondents, there are 50 respondents who are in the 1 year and below group (accounting for 42%), 40 respondents who have years of experience of trading in the 1-5 year range (accounts for 33%), 20 respondents who have 6-10 years of trading experience (17%), and the 10 year and above group has 10 respondents (accounting for 8%).
Type of stock broking firm: Of the total of 120 respondents: there are 54 respondents from SSI (45%), 40 respondents from HSC (accounting for 33.3%), and ACBS has 26 respondents (21.7%).
- Reliability, as an independent variable, got an average weighted mean of 3.67
The reliability of the Time Series Analysis model in assisting respondents to make investment decisions, as a variable, got an average weighted mean of 3.67, with the equivalent description of ―very accurate‖.
- Efficiency, as a variable, got an average weighted mean of 3.56
The Efficiency of Time Series Analysis model in assisting respondents to make investment decisions, as a variable, got an average weighted mean of 3.56, with the equivalent description of ―very efficient‖.
- Availability, as a variable, got an average weighted mean of 3.99
The Availability of Time Series Analysis model in assisting respondents to make investment decisions, as a variable, got an average weighted mean of 3.99, with the equivalent description of ―very available‖.
- The relationships between Level of acceptability and reliability, efficiency and availability
As analyzed and reported in Table 4.9, the reliability, efficiency and availability were found to be significantly related to Level of acceptability with Pearson’s correlation coefficients of 0.952; 0.849; and 0.944respectively at a 0.01 significance level. This finding was expected and leads to the following conclusions:
- The level of acceptability is positively related to the efficiency of the three independent variables: reliability, efficiency and availability
- The better the reliability, efficiency and availability, the higher the level of acceptability
- By improving the reliability, efficiency and availability, the TSA model can get a better level of acceptability.
- Regression Analysis for impact of Reliability, Efficiency and Availability on Level of acceptability.
REL (Reliability) is positively related to Level of acceptability at a significance level of 0.0001 and with the standardized correlation coefficient of 0.114.
EFF (Efficiency) is positively related to Level of acceptability at a significance level of 0.0001 and with the standardized correlation coefficient of 0.110.
AVA (Availability) is positively related to Level of acceptability at a significance level of 0.0001 and with the standardized correlation coefficient of 0.019.
Level of acceptability
From 120 respondents, 43 percent of the respondents gave a level of acceptability when they used the TSA model in assisting them to make investment decisions in the range from 76 percent to 100 percent, 22 percent of respondents gave level of acceptability in the range from 26 percent to 50 percent, and 35% of respondents gave level of acceptability in the range from 51 percent to 75 percent. Specially, there were no respondents gave level of acceptability in the range from 0 percent to 25 percent. On average, the level of acceptability is 72.46 percent, min of level of acceptability is 40%, max of level of acceptability is 100% with standard deviation 0.18. Overall the level of
acceptability of the Trend forecasting model using Time Series Analysis of Stock indexes is acceptable.
Recommendations
Based from the findings of the study, the following are recommended:
1. In the profile of respondents in this research, there are a minority proportion of respondents in the age group 51 years old and more, in the ―Educational Attainment‖ group with doctorate degree, in the
―Amount of money investment‖ group with $100,001 and above, and in terms of Years of experience of trading in the 10 year and above group. Therefore, increasing the number of respondents in the above minority proportions will, more comprehensively and more accurately, assess the level of acceptability of the trend forecasting model using the Time Series Analysis of stock indices.
2. Level of Acceptability and Reliability, Efficiency and Availability are significantly correlated with the correlation coefficient R = 0.976 with the Coefficient of Determination R2= 0.951 at a significant level of p = 0.0001. The Coefficient of Determination indicated that 78.1% of the variation in Level of acceptability for the sample of 120 respondents can be explained by the changes in Reliability, Efficiency and Availability while 21.9% remains unexplained. For that reason, parallel increased in population using more variables and longer time frame should be conducted to further quantify and qualify results.
3. From 120 respondents, the average level of acceptability when they used the TSA model in assisting them to make investment decisions is 72.46 percent. Additionally, by improving the reliability, efficiency and availability, the TSA model can gain a better Level of Acceptability.
Therefore, using more indicators or combining Time Series Analysis model with other methods, like quantitative and price actions, may improve the reliability, efficiency and availability of the TSA model.
4. Lastly, future research may be conducted to further explore other variables not identified in this study and yet could have significant impact on the level of acceptability of the Trend forecasting model using Time Series Analysis of Stock indices. This TSA model could also be used as a core model to combine with other methods or more time series indicators to build a better model which would help investors make superior trading decisions.
REFERENCES
Alvin, M., (2012) Definition of 'Downtrend', Retrieved July 23, 2012 from http://www.investopedia.com/terms/d/ downtrend.asp
Anderson J., (2012) Definition of 'Resistance (Resistance Level)', Retrieved July 22, 2012 from http://www.investopedia.com/ terms /r/resistance.asp#axzz23yUHCmv7
Adkins, T., (2012) Definition of 'Retracement' Retrieved July 21, 2012 from http://www.investopedia.com/terms/r/retracement.asp#axzz23yUHCmv 7
Adkins, T., (2012) Support (Support Level) Retrieved July 21, 2012 from http://www.investopedia.com/terms/ s/support.asp
Adkins, T., (2012) Definition of 'Support (Support Level)' Retrieved July 21, 2012 from http://www.investopedia.com/terms/s/support.asp#
axzz23yUHCmv7
Brown, Constance M. (1999) Technical Analysis for the Trading Professional Irwin Trader's Edge Series, McGraw-Hill, Isbn13: 9780070120624.
Brown, Kedrick F. (1975) Trend trading: timing market tides, Wiley, ISBN-13 978-0-471-98021-6.
Chan, S. and Lu, M. (2004). Understanding internet banking adoption and use behavior: A hong kong perspective. Global Information Management, 12(3), 21-43.
Elder, A. (1993) Trading for a living Psychology, trading tactics, money management, Wiley.
Edward, Gately (1997) Forecasting Profits Using Price and Time, Wiley, ISBN: 978-0-471-15539-3.
Elliott, N. (2007) Ichimoku Charts An Introduction to Ichimoku Kinko Clouds, Harriman House, ISBN 1-897-59784-3978-1-897597-84-2
Garfield, MJ. (2005) ―Acceptance of Ubiquitous Computing", Information Systems Management, 22, 4, 24-31.
General Office for Population and Family Planning, (2013) Population structure by sex. age group and sex ratio Retrieved July 16, 2013 from http://www.gopfp.gov.vn/solieu?p_p_id=62_INSTANCE_77Ys&p_p_life cycle=0&p_p_state=maximized&p_p_mode=view&p_p_col_id=column 3&p_p_col_count=1&_62_INSTANCE_77Ys_struts_action=%2Fjournal
_articles%2Fview&_62_INSTANCE_77Ys_groupId=18&_62_INSTANC E_77Ys_articleId=389123&_62_INSTANCE_77Ys_version=1.0
Knight, T.(2007) Chart your way to profits: the online trader’s guide to technical analysis, Wiley , ISBN: 978-0-470-04350-9.
Larson, Mark L. (2001) Technical Charting for Profits, Publisher: Wiley, New York, NY, USA, ISBN: 9780471437987
Monte, A. J., & Swope, R.(2008) The Market Guys’ Five Points for Trading Success, McGraw-Hill, ISBN 978-0-470-13897-7.
Murphy J. (2008) Charting made easy, Wiley, ISBN 1-883272-59-9
McCoy, S. and Everard, A. & Jones, B. (2005), An examination of the technology acceptance model in uruguay and the US: A focus on culture. Global Information Technology Management, 8(2), 27-45.
Russell, R., (2012) The Dow Theory Retrieved July 25, 2012 from http://ww1.dowtheoryletters.com/
Welles J., Wilder Jr. (1978) New concepts in technical trading systems, Trend Research, ISBN-13: 978-0894590276.
Wiley, J. and Sons, J. (1997) Trading with Oscillators: Pinpointing Market Extremes--theory and Practice, Wiley, ISBN: 978-0-471-15538-6.