BWFF2043 Advanced Financial Management

Introduction

There are different strategies and ways from which one can trade on stocks. It is the different Investment strategies, which brings together fundamental and technical analysis in the common ground. Some of the common strategy used while constituting the portfolio are Day Trading where the trading strategy gives emphasis on the technical factors of a stock and the quick responses to the stock at a given point of time. For successful day trading of equity stocks, one must buy and sell stocks in the same day. This type of trading is common amongst the professional trader who usually trade in the stock whenever there is a news, report or any technical pattern involved with the stock. Momentum Trading where the equity trading strategy involves buying or short selling stock for a certain period. The trading strategy combines both fundamental and technical factors (Hoffmann & Shefrin 2014).

Volume Breakout strategy shows the dispersion of volumes in the stock, as soon there is a larger number of volumes abnormality observed a trade can be executed depending on the movement of the stock. Four*Four Strategy is the strategy which shows that after four continuous falling trend in share price of the company after there is a upside in the script. After every such pattern, a trade gets identified and executed (Wei, Cheng & Wu, 2014).

There were other strategies even which were used to identify the trade and execute the trade like the price momentum, volume breakout strategy, fundamentals, growth and outlook of the company were some of the several factors taken into consideration. Portfolio 1 was able to beat and outperform the market, which was on the technical support and analysis of the stock (Kevin, 2015).

Literature Review

Theoretical and Empirical evidence of Efficient Market Hypothesis.

The term refers to the incorporation of all information about investment and financial securities, which includes stocks and bonds. The current price of the gets incorporated with all the latest information and news. If the theory stays true then no analysis or information can help an investor earn more with respect to others. The theory shows rational behavior of the market as a whole and not the investors where the market acts proactively and incorporates each and every relevant news and information. There are different forms of efficient market hypothesis, which includes weak form of market hypothesis, semi-string and strong form of hypothesis (Suliman, 2017).

• Weak Form Hypothesis:The hypothesis states that all the past reports, informations and news are reflected in the securities. In this type of market, fundamental analysis can help an investor earn above market rate of return. Technical Analysis plays no or little role in this type of market (Erdem, 2017).
• Semi-Strong Market Hypothesis:In this form of market, neither fundamental nor the technical analysis can provide above market return to an investors. Every information, report and details are already reflected in the security. All publically available information is reflected in the securities (Suliman, 2017).
• Strong Form Market Hypothesis:Securities are priced with all the public and private information and none of the investors can influence the price of the securities. Abnormal returns are not possible in this type of markets.

Empirical Evidence: Since 1970s, evidence of efficient market hypothesis has been the key portion of finance and is studied in all prospects. There has been a major development and improvements in the field of data analytics and advances in the statistical analysis in the models but these all factors gives less consensus in the validation and existence of the hypothesis. An efficient market hypothesis always reflect information in the security and it depend on the risk preference of the investor. Therefore, empirical evidence suggests that the test for the same should be on both the investors risk profile and preferences and the information reflected in the security. Information comes with a cost and if the market fully reflects the correct price of the securities then the information may be not viable. If an investor buys an asset with the expectation that, the price of the assets will rise tomorrow. Empirical evidence shows that the price and distribution of the stock price is sub martingale in nature (Hamid, et.al 2017).

Theoretical and Empirical evidence of Behavioral Finance.

Behavioral finance is all about the combination of money and economics. The concept behind the term is about the preferences one make about the money, which is irrational in nature. It combines psychology theories with those of personal finance and economics. It is the study of understanding preferences regarding money in the human psychology, which can influence the economy directly and indirectly. It also shows the movements in the price of the securities irrespective of the corporate announcement and actions. It shows how people can influence the price of the securities in respect to anticipation on an information and knowledge (Grauwe & Grimaldi, 2018).

The study is crucial as it tells about the human behavior in certain situations. In certain instances of the tobacco, company where the company share price showed abnormality because of the certain laws and taxes. Increase of tax rate and rules imposing the ban of such products may harm the production of the company and this had implied a massive fall in the share price of the company. Since, the investors are aware of the past events, which had led to drop in the share price of the company and if such taxes and regulations are imposed on, the company it will again make the investors sell their shares. This perception of past happenings in the current scenario affects not only a particular company but also to a group of other companies who are in the same industry (Bondt, et.al 2015).

Rational and Methodology

There were several steps and strategies used for selection of the stocks these strategies were used and applied and trades were executed on that behalf. Portfolio. The SGD 100,000$ was invested on the ratio of 50:50 where stocks in Portfolio 1 were selected based on the technical charts and analysis done.  The period for the Investment selected were between 4 June 2018 and 24 August 2018. Portfolio was based on the strategies of Fundamental Analysis and the growth and outlook of the companies (Ahmad & Aljifri, 2018).

Stocks in Portfolio 1 mainly used technical analysis and strategies such as four*four strategy where a script were selected on the basis of the rising share price followed by four consecutive down trend a trend was identified in the Capital Land Ltd which gave a return of 13.06% (Edwards, Magee & Bassetti, 2018). The Price volatility strategy was used where the stocks showed price abnormality and the trade was identified and executed. Singtel Ltd in the telecommunication services were the one where the price volatility strategy were used it delivered 8.28% (Patel et.al 2015). Several stocks were selected based on the technical strategies identified and executed. Stocks where such trades were identified are Capital Land Ltd, OCBC Bank Ltd, Capital Land commercial trust and Genting Singapore were some of the stocks in the Portfolio 1 where technical analysis strategies were used (Nazário, et.al 2017).

Stocks in Portfolio 2 was selected based on fundamental analysis of the stock. The stocks were chosen for the positive outlook and turnaround in the company (Deshpande, 2017). Sats Ltd was one of them where the positive alignment of growth and turnaround in the operations of the company. Capital Land Mall Trust got selected on the scale of their great results, divestments and the positive outlook of the company (Mellen & Evans, 2018). Whereas companies like Ascendas Real Estate Investment Trust was selected because of their stable operations and the growth of the real estate sector. Sectorial investment strategy were used and diversification was done by investing in sectors like consumer discretionary and capital goods. Portfolio 2 had around 25% of the investment were done on scripts like Sembercorp Industries Ltd, Jardine Cycle and Carriage Ltd and Yangzijiang Shipbuilding Holdings ltd. Stocks like Comfort Delgro Corporation Ltd was chosen because of the organic and inorganic growth strategies the company were deploying (Kaizoji & Miyano, 2017).

Results and Analysis

Portfolio 1

The stocks in the portfolio are on the technical analysis and technical charts, patterns and strategies were used. For trading and execute the same in the portfolio, the performance of the stocks is given below:

Portfolio 2

The scripts in the portfolio are on the fundamental analysis of the stock certain factors such as background of the company, positive outlook, growth, stable operations of the company, valuation and turnaround in the company.

Summary of the trades done and the return generated

Performance of Portfolio

The performance of the FTSE ST-ALL Index share from the period 4 June 2018 to 24 August 2018 was:

                                        Performance Analysis of the Trades done and return generated:

The trade results shows that efficient market hypothesis was not given much importance because of the varying trade results in accordance to the market index. Portfolio 1 has outperformed the Index and this can be because of the factors and perception of behavioral finance.

Conclusion

The study of the report shows the various strategies and techniques used in the portfolio. The main assumptions followed while executing the trade was defining the target price and the holding period of the stock. Portfolio 1 had certainly outperformed the benchmark index. Several theoretical and empirical evidence of efficient market hypothesis and behavioral finance gives us the perception of market behavior and investors psychology. Short-term strategies are very useful when the period if for a shorter period as in our case and which stand out the reason that technical analysis may provide better short-term strategies and profitable opportunities.

Reference

Ahmad, H. I., & Aljifri, K. (2018). The Role Of Company Specific Information In Valuation Models Used In The Uae. Accounting & Taxation, 10(1), 77-86.

De Bondt, W. F., Muradoglu, Y. G., Shefrin, H., & Staikouras, S. K. (2015). Behavioral finance: Quo vadis?.

De Grauwe, P., & Grimaldi, M. (2018). The exchange rate in a behavioral finance framework. Princeton University Press.

Deshpande, R. (2017). Semi-Strong Form of Market Efficiency: Does all Critical Information Affect Stock Price Valuations?. Indian Journal of Research in Capital Markets, 4(2), 15-24.

Edwards, R.D., Magee, J. & Bassetti, W.H.C., 2018. Technical analysis of stock trends. CRC Press.

Erdem, O. (2017). Efficient market hypothesis.

Hamid, K., Suleman, M. T., Ali Shah, S. Z., Akash, I., & Shahid, R. (2017). Testing the weak form of efficient market hypothesis: Empirical evidence from Asia-Pacific markets.

Hoffmann, A. O., & Shefrin, H. (2014). Technical analysis and individual investors. Journal of Economic Behavior & Organization, 107, 487-511.

Kaizoji, T., & Miyano, M. (2017). Zipf's law for share price and company fundamentals. arXiv preprint arXiv:1702.00144.

Kevin, S. (2015). Security analysis and portfolio management. PHI Learning Pvt. Ltd..

Mellen, C. M., & Evans, F. C. (2018). Valuation for M&A: Building and Measuring Private Company Value. John Wiley & Sons.

Nazário, R. T. F., e Silva, J. L., Sobreiro, V. A., & Kimura, H. (2017). A literature review of technical analysis on stock markets. The Quarterly Review of Economics and Finance.

Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259-268.

Suliman, O. (2017). EFFICIENT MARKET HYPOTHESIS. The American Middle Class: An Economic Encyclopedia of Progress and Poverty [2 volumes], 70, 126.

Suliman, O. (2017). EFFICIENT MARKET HYPOTHESIS. The American Middle Class: An Economic Encyclopedia of Progress and Poverty [2 volumes], 70, 126.

Wei, L. Y., Cheng, C. H., & Wu, H. H. (2014). A hybrid ANFIS based on n-period moving average model to forecast TAIEX stock. Applied Soft Computing, 19, 86-92.