Entry and Exit Rules 79

Following Vanstone and Hahn (2010), we derive the optimal threshold for signalling a trade based on the ANN in-sample performance evaluation, where the line between negative and positive percentage of average output can be identified. Neuro-Lab® 1.0 produces an output that ranges between zero and 100. This is measured by multiplying the true output from the logistic sigmoid function (scaled between zero and one) by 100. When the network produces a low (high) value, it is forecasting that the output will be near the low (high) spectrum of the output range. In other words, the ANN signals a higher (lower) output value when it expects higher (lower) stock returns (Vanstone 2006). The optimal threshold is the point where the ANN signals exceeding it indicate the actual output being higher than the average output for all observations, and vice versa. Section 3.6.1.1 outlines the trading rules for the individual systems. This is followed by the fusion systems in Section 3.6.1.2.

80 3.6.1.1 Individual Trading Systems

Figure 3.4 shows how the pseudo code for the entry and exit rules is programmed. For illustration purposes, assume x is the optimal network threshold.

Figure 3.4

Blueprint for the Individual Trading Rules

1 IF XX-NNt > x 2 AND XX-NNt > XX-NNt-1 THEN 3 BUY at OPENt+1, 4 IF XX-NN t≤x 5 AND XX-NN t < XX-NN t-1 THEN 6 SELL at OPENt+1

The figure demonstrates how the buy (sell) signal for the neurally enhanced trading rule is emitted. The actual code is based on the C# programming language. XX-NN is the individual neural network, where XX refers to FA (fundamental), CG (corporate governance) or TA (technical). The signals are emitted after the market closes at day t, while buy (sell) trades are only executed on the next day (t+1) based on the prevailing market open price (OPEN). This produces a valid trading rule, simulates a realistic trading environment and mitigates any possibility of look-ahead bias.

In short, the figure above shows that XX-NN emits a buy (tomorrow) signal when XX- NN (today) > x and XX-NN (today) > XX-NN (yesterday), and generates a sell

(tomorrow) signal when XX-NN (today) ≤x and XX-NN (today) < XX-NN (yesterday).

This approach is in line with Vanstone (2006). Notice that the trades are executed at the next day’s opening market price (t+1) using the information available to traders at time t. This way, any possibility of look-ahead bias will be mitigated.

3.6.1.2 Fusion Trading Systems

For the fusion trading systems, we employ the general principle of combining stock selection with market timing, which is consistent with prior studies. Following the approach described by Bollinger (2002), the fusion trading systems first screen stocks according to their related buy and sell ‘lists’. More specifically, for the classical fusion rule, this is based on the neurally enhanced fundamental signals. For the novel fusion rule, the list is based on both fundamental and corporate governance signals. For both cases, the buy (sell) trade is only executed on the corresponding buy (sell) list using the

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signals emitted by the ANN-based technical rules. To simplify, Figures 3.5 and 3.6 present the CFUS-NN and FUSION-NN trading rules. Assume the optimal thresholds for FA-NN, CG-NN and TA-NN are x, y and z, respectively.

Figure 3.5

Blueprint for the CFUS-NN Trading Rule

1 IF FA-NNt > x

2 AND TA-NNt > z

3 AND TA-NNt > TA-NNt-1 THEN

4 BUY at OPENt+1,

5 IF FA-NNt≤x

6 AND TA-NNt≤z

7 AND TA-NNt < TA-NNt-1 THEN

8 SELL at OPENt+1

The figure demonstrates the pseudo code for signalling buy (sell) signals for the neurally enhanced classical fusion trading rule. The actual code is based on the C# programming language. The signals are emitted after the market closes at day t, while buy (sell) trades are only executed on the next day (t+1) based on the prevailing market open price (OPEN). This produces a valid trading rule, simulates a realistic trading environment and mitigates any possibility of look-ahead bias.

As can be seen from the figure above, the classical fusion rule generates a buy (tomorrow) signal when FA-NN (today) > x, TA-NN (today) > z and TA-NN (today) >

TA-NN (yesterday), while the sell (tomorrow) signal is generated when FA-NN (today) ≤ x, TA-NN (today) ≤ z and TA-NN (today) < CG-NN (yesterday). The FUSION-NN

extends the classical approach by taking it a step further, via incorporating a corporate governance neural network.

82 Figure 3.6

Blueprint for the FUSION-NN Trading Rule

1 IF FA-NNt > x

2 AND CG-NNt > y

3 AND TA-NNt > z

4 AND TA-NNt > TA-NNt-1 THEN

5 BUY at OPENt+1,

6 IF FA-NNt≤x

7 AND CG-NNt≤y

8 AND TA-NNt≤z

9 AND TA-NNt < TA-NNt-1 THEN

10 SELL at OPENt+1

The figure demonstrates the pseudo code for signalling buy (sell) signals for the neurally enhanced novel fusion trading rule. The actual code is based on the C# programming language. The signals are emitted after the market closes at day t, while buy (sell) trades are only executed on the next day (t+1) based on the prevailing market open price (OPEN). This produces a valid trading rule, simulates a realistic trading environment and mitigates any possibility of look-ahead bias.

As shown above, the novel fusion rule generates a buy (tomorrow) signal when FA-NN (today) > x, CG-NN (today) > y, TA-NN (today) > z and TA-NN (today) > TA-NN

(yesterday), while the sell (tomorrow) signal is generated when FA-NN (today) ≤ x,

CG-NN (today) ≤y, TA-NN (today) ≤z and TA-NN (today) < CG-NN (yesterday).

In a nutshell, CFUS-NN first screens stocks with a ‘good’ fundamental indicator, while buy trades are only executed when the technical indicator is also ‘good’ (market entry). In contrast, if the fundamental indicator is ‘bad’, the sell trade is executed only when the technical indicator is also ‘bad’ (exit timing). FUSION-NN adopts the same methodology, but also considers corporate governance factors to screen for the best possible investments. By design, it is obvious that for both cases, the entry (exit) trade somewhat approaches the long term (since the execution of trades must depend on the fundamental screens, rather than solely on short-term market factors). Therefore, these hybrid approaches are somewhat consistent with what is practiced in the financial industry, where professionals tend to use fusion analysis for longer-term trading (see Maditinos, Šević & Theriou 2007).

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