Forecasting for the generation of trading signals in financial markets

✍ Scribed by Kin Lam; King Chung Lam

• Publisher: John Wiley and Sons
• Year: 2000
• Tongue: English
• Weight: 154 KB
• Volume: 19
• Category: Article
• ISSN: 0277-6693
• DOI: 10.1002/(sici)1099-131x(200001)19:1<39::aid-for743>3.0.co;2-b

✦ Synopsis

In this paper we show that optimal trading results can be achieved if we can forecast a key summary statistic of future prices. Consider the following optimization problem. Let the return r i (over time i 1, 2, . . . , n) for the ith day be given and the investor has to make investment decision d i on the ith day with d i 1 representing a long' position and d i 0 a neutral' position. The investment return is given by r n i1 r i d i À c n1 i1 jd i À d iÀ1 j, where c is the transaction cost. The mathematical programming problem of choosing d 1 , . . . , d n to maximize r under a given transaction cost c is shown to have an analytic solution, which is a function of a key summary statistic called the largest change before reversal. The largest change before reversal is recommended to be used as an output in a neural network for the generation of trading signals. When neural network forecasting is applied to a dataset of Hang Seng Index Futures Contract traded in Hong Kong, it is shown that forecasting the largest change before reversal outperforms the k-step-ahead forecast in achieving higher trading pro®ts.