In this section we offer brief comments about the results presented in this thesis by reference to real-world trading practice, as well as investable indices in the Malaysian market. The overall results of this study (Chapters 4 and 5) can be validated by the real- world dimension. Looking from this standpoint, we argue that if fundamental, corporate governance, technical and/or fusion trading strategies are not deemed or proven beneficial, one would not expect practitioners and traders to employ them extensively for making trading decisions. This is especially the case among finance professionals, such as advisors, analysts and brokers, since they put millions (or even billions) of dollars at stake and their reputations (and possible legal repercussions) on the line. The same argument can be made for position sizing and risk management strategies. Conversely, if these strategies are indeed useful (as demonstrated by our results), we can reasonably expect their use, at least among professionals, to be prevalent.
Keeping the above in mind, the fact that a large number of firms, professionals and/or traders (see Arnold & Moizer 1984; Maditinos, Šević & Theriou 2007; Menkhoff 2010; Renneboog, Ter Horst & Zhang 2008; Taylor & Ellen 1992) employ the above strategies for investment appraisals lends further credibility to our findings. The achievements of well-known traders too, such as Buffett, Darvas, Keynes and O’Neil, as discussed earlier, add gravity to our results. The findings can be further reinforced by extant surveys within the local context. For example, Mohamad and Nassir (1997) and Saadouni and Simon (2004) observe that both fundamental and technical strategies are
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those used most predominantly by Malaysian analysts. In terms of anti-Martingale and/or stop loss rules, their practicality and benefits can be supported by Darvas (1960), Rotella (1992) and Tharp (1998).
Finally, it is interesting to explore how the full-fledged trading systems engineered in this thesis perform when compared to the performance of different aspects of the Malaysian market. More specifically, these comparisons can offer further insights into the efficacies of our combination (and constituent) trading systems against the large, mid, small cap and Shariah-compliant capital segments of the Bursa Malaysia.85 If the full-fledged trading systems can indeed outperform the performance of these market segments, this will strengthen our results and validate the benefits of using the strategies built in this thesis.
85 For example, Vanstone and Hahn (2010) contest the performance of their ANN-based fundamental and technical trading strategies against one benchmark index, the ASX 200 (in addition to their non-ANN strategies). Instead of only one index, however, and in addition to the benchmark B&H policy presented earlier, we also briefly compare the results of our trading systems against seven popular investable indices in the Bursa Malaysia.
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Summary Performance of the FTSE Bursa Malaysia and Dow Jones Indices versus the Best and the Least Performing Trading Systems
Key Trading Metrics Sharpe Ratio Maximum Drawdown % Recovery Factor Ulcer Index Sortino Ratio 1 FUSION-NNTSa 1.95 -5.68% 4.59 1.77 4.81 2 TA-NNTSb 1.12 -24.82% 2.85 7.75 2.10 3 FBM 70 0.85 -34.30% 1.59 12.01 1.13 4 FBM 100 0.75 -30.45% 1.26 11.58 1.11 5 FBM EMAS 0.73 -29.46% 1.21 11.24 1.08 6 FBM KLCI 0.69 -29.85% 1.11 11.46 1.03 7 FBM SC 0.53 -35.62% 0.88 13.50 0.97 8 FBM S 0.49 -32.88% 0.64 13.07 0.84 9 DJMY 25 0.25 -38.87% 0.23 16.92 0.58
The table provides several key performance metrics for FTSE Bursa Malaysia and Dow Jones investable indices in Malaysia against those produced by the best and the least performing trading systems, ranked in descending order of the Sharpe ratios. The out-of-sample period ranges from 1 July 2008 to 30 June 2011. a (b) indicates the best (least) performing trading system. FBM XX denotes the specific index of the FTSE Bursa Malaysia, where XX refers to the following (in descending order of the rank): 70 = Mid 70 Index; 100 = Top 100 Index; EMAS = EMAS Index; KLCI = KLCI Index (which is the benchmark index for the Malaysian market); SC = Small Cap Index; S = EMAS Shariah Index. DJMY 25 refers to the Dow Jones Islamic Market Malaysia Titans 25 Index. Historical price data for the indices is sourced from Bloomberg. Consistent with our neurally enhanced trading systems, the results take into account brokerage fees, stamp duty and clearing fees, computed as 0.83% one way (or 1.66% round-trip). Sharpe ratio conveys the risk- adjusted return for the trading systems, computed by dividing the annualised average return with its annualised standard deviation. Maximum drawdown % is the percentage decline of the largest peak to valley in the equity curve. Recovery factor is computed by dividing the absolute value of net profit by the maximum drawdown. Ulcer index is measured by square rooting the quotient of sum squared drawdowns divided by the period. Sortino ratio is similar to the Sharpe ratio, but utilises downside deviation instead of standard deviation in the denominator. Both Sharpe and Sortino ratios assume a zero risk-free rate of return. Note that the table does not report profit factor and payoff ratio. By definition, since there is only one trade for buying and holding an index, the numerator or denominator for computing the metrics will be zero. In this case, these two metrics will not be useful in providing any meaningful analysis.
Table 5.9 gives the results from seven investable indices in Malaysia (from FTSE and Dow Jones) during the identical out-of-sample period, 1 July 2008 to 30 June 2011, as compared to the top (FUSION-NNTS) and bottom (TA-NNTS) performing trading systems. In a nutshell, we can see from the table that the best index, FBM 70, yields a Sharpe (Sortino) measure of only 0.85 (1.13), while the market barometer, FBM KLCI, yields only 0.69 (1.03).86 The results confirm that none of the FTSE and Dow Jones
86 Notice that the results from the FBM KLCI are somewhat close to the metrics yielded by the B&H rule, in particular the Sharpe ratio. This suggests that the performance of the B&H portfolio reasonably mimics
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indices dominate our full-fledged trading systems. For example, even the best performing index, FBM 70, underperforms the least performing trading system, which is TA-NNTS (with a Sharpe ratio of 1.12). Therefore, we can conclude that the ANN-based trading systems guided by fundamental, corporate governance and technical information (in isolation), and more importantly the classical and novel fusion approaches, produce superior results (greater Sharpe, Sortino and recovery measures, and lower maximum percentage drawdown and ulcer index) over the market. In other words, our trading systems not only dominate the Malaysian stock market in terms of the main barometer, but also each of the market segments.