Fo recasting Stock M arket Q uo tation s via Fuzzy N eu ral N etw o rk Based on T 2S M odel

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1 : (2001) T 2S,, (, ) : T 2S,,, ( ),,. : ; ; α Fo recasting Stock M arket Q uo tation s via Fuzzy N eu ral N etw o rk Based on T 2S M odel CH EN X ing, M EN G W ei2dong, YAN T ai2hua (Schoo l of Bu siness & A dm in istration, Chongqing U n iversity, Chongqing ) Abstract T h is paper p resents a m ethod fo r stock m arket modeling and fo recasting via fuzzy neural netw o rk based on T 2S model, in w h ich the imp roved genetic algo rithm is used to train the connection w eigh ts of the fuzzy neural netw o rk, the algo rithm s of mom entum and self- adap tive learning rate are used to learn m em bersh ip param eterṡ It has been show n by the modeling and fo recasting results about Shanghai stock m arket p rice index and X iax in electron p rice ( a com pany stock p rice) that the m ethod has reinfo rcem en t learn ing p roperties, m app ing capab ilities, reflecting ab ility. W ith respect to modeling and fo recasting o r relative decision of stock m arket o r som e o ther sim ilar nonlinear econom ic system, the m ethod is available. Keywords stock m arket; fuzzy neural netw o rk; fo recasting 1,,,,,,,,,,,,,,,, (A rtificialn euraln etw o rk s, ANN ) [1 5, ] ANN,, ANN, α :

2 2 T 2S 67,,,,,,,,, (Fuzzy N eural N etw o rk s, FNN ), [3, 6 ], FNN,,,,,,,, (Genetic A lgo rithm s, GA ),, [7 9 ] T 2S,,, ( ), 2 T-S, :, NB, PB ;,. T akagi Sugeno, T 2S. X x i T (x i) = {A 1 i,a 2 i,, A m i },, 2,, n. A s i i (s, 2,,m i) x i si, x i u s i A i (x i) (, 2,, n; s, 2,,m i) Y, T 2S R j: IF x 1 is A s 1j 1, x 2 is A s 2j 2,, x n is A s nj n TH EN y j = W j0 + W j1 x 1 + W j2 x W jn x n j = 1, 2,,m, m Φ 7 n m i, X, a j = u s 1j A 1 (x 1) u s 2j A 2 (x 2) A n (x n), u s nj Y = a jy j a j = a λ jy y, a λ j = a j, a j 1,,, f (q) (q- 1) (q- 1) (q- 1) j (x 1, x 2,, x n q- 1 ; w j1 (q), w q j2,, w q jn q- 1 ) x (q) j = g (q) j, j g (q) j f (q) (f (q) j ) q j, x i, f (1) i = x (0) = x i, x (1) i = g (1) i = f (1) i, i= 0, 1,, n; 0 x 0= 1, W j0, j = 1, 2,,m N 1= n i, i = u s i A i,, 2,, n; s, 2,,m in,m i si u s i u s i (1) (x, f (2) i - c ) 2 =, x (2) = u s i i = g (2) = e f (2) e - (x i - c isi ) 2 Ρ 2,, 2,, n; s, 2,, m i C Ρ, Ρ (2) =

3 T - S N 2 = 6 n m i 2,, f (3) j = m in{x (2) 1s 1j, x (2) 2s 2j,, x (2) ns nj }= m in u s 1j 1, u s 2j 2,, u s nj n }, s1j {1, 2,,m 1}, s2j {1, 2,,m 2},, snj {1, 2,, m n} x (3) j = a j= g (3) j = f (3) j, j = 1, 2,,m, m = 7 n m i N 3 = m,, 3 f (4) j = x (3) j x (3) i = a j a i, x (4) j = a λ j = g (4) f (4) j, a λ j= a j a, 2,,m. N 4 = m m,,, y j= W j0+ W j1 x 1+ W j2 x 2+ + W jn x n= 6 n W j lx l, j = 1, 2,,m. l= 0, Y = x (5) = g (5) = f (5) = a jy j, Y. j = a j= j = 1a λ jy j, a λ j = a j a j,, 3 3, W j i (j = 1, 2,, m ; i= 0, 1,, n) 1 c Ρ (, 2,, n; si = 1, 2,,m i).

4 2 T 2S 69 E = 1 2 (Yϖ - Y ) 2, ϖ Y Y 3. 1 c Ρ c Ρ, (1) D (k) = c (k + 1) = c (k) + Α(k) [ (1 - Γ)D (k) + ΓD (k - 1) ] (1) Ρ (k + 1) = Ρ (k) + Α(k) [ (1 - Γ)D (k) + ΓD (k - 1) ] (2) - 5E 5c (k), (2) D (k) = - 5E 5Ρ (k), Α (k) = 2 Κ Α(k+ 1), Κ= sign [D (k)d (k - 1) ],, 2,, n, s, 2,,m iα,,, ;,, Γ, 0Φ Γ< 1.,, 3. 2 W j i W j i M ich igan John H. Ho lland, A dapation in N ature and [8 A tificial System ] (, ),,,, Go ldberg SGA (Simp le GA, SGA ), :, ;, ;, ;, SGA, 2 1),,,, (W 10, W 11,,W 1n, W 20, W 21,, W 2n,, W m 0, W m 1,, W m n), 2) f,, f = 1 6 j E 2 j 3) :, e - gχg 4) :, P s : P s = f i 6 f i, f i i. 5) : P c ( ), P c,, P c, 2

5 P c = k c (f m ax - f c) 2 g(f m ax - f θ ) 2, f c Ε f θ k c f c < f θ k c 1, f c, f m ax f θ, f m ax- f θ,, 6) :, P m P m,,,, P m, km P m = km (f m ax - f m ) 2 g(f m ax - f θ ) 2, f m Ε f θ km f m < f θ 1, km 4 ( ),,,, : ( g ), :,, t,,,,,, M A CD WM S KDJ R S I B IA S PSY A R OBV ADR,,,,, 5 ( 5, (), 5, B IA S R S I A R K OBV : 1) x 1, 10 (B IA S (10) ) x 1= B IA S (10) = 2) x 2, 5 (M A (5) ) MA (5), B IA S30 x 2= B IA S = 6 n+ 13 MA (5) (30) = 3) x 3, 14 R S I(14) x 3= R S I(14) = i= n C 10- M A (10), C M A (10) (M A (30) ), M A (5) B IA S (30), M A (5) - M A (30) M A (30) (C i - C i+ 1), C i+ 1- C i< 0; C i i 4) x 4, 26 (A R (26) ) x 4 = A R (26) = A A + B =, A = 6 n+ 13 (C i+ 1 - C i), C i+ 1- C i> 0; B 6 n+ 25 (H i - O i) 6 n+ 25 (O i - L i) i= n H i,o i,l i i ; 6 n+ 25 (H i - O i) 26, 6 n+ 25 (O i - L i) 26 5) x 5, K (K (9) ) x 5= K (today) = 2 3 K (yestaday) R SV (today) R SV (row

6 2 T 2S 71 stochastic value,,, WM S% ) 9 R SV, R SV : 9 R SV = C 9- L 9 H 9- L C 9, H 9, L 9 9 6) x 6, 6 OBV (on balance vo lum e, ),OBV (6) x 6 = 6 n+ 5 2 ci - h i - li i= n h i - li li i,, v i : Y = M A (5) : ( (x 1, x 2, x 3, x 4, x 5, x 6) gy ), ( (B IA S (10), B IA S MA (5) (30), R S I(14), A R (26), K (9), OBV (6) ) gm A (5) ) v i ci, h i,, 5 c Ρ Γ= W j i, k c= 0. 15, km = 0. 08, N 80, , ,, 2. 02, 78, 3. 85, 70 3 (, ), , ,, 3. 15%, 75%, 5. 87%, 68% 4 (, ) 3 4,,,,, (,, ), ( ),,,,

7 ,,, 1998,,, ,,,,,, 9000,, 5,,,,,,,,,,,,,, : [1 ] R efenes A N. et al. Stock perfo rm ance modeling using neural netw o rk s: a comparative w ith R egression M odels[j ]. N eural N etw o rk, 1994, 7 (2): [2 ] L ee K C et al. Integration of hum an know ledge and m ach ine know ledge by using fuzzy po st adjustm ent: its perfo rm ance in stock m arket tim ing p rediction[j ]. Expert System, 1995, 12 (4): [3 ],. [M ]. :, [4 ]. [M ]. :, [ 5 ]. [J ]. ( ), 1998 (8): [6 ]. T 2S [J ]. ( ), 1997 (3): [7 ] Ho lland J. A dap tation in N atural and A rtificial System [M ]. A nn A rbo r : U niveṙ of M ich igan P ress, [8 ] Go ldberg D. Genetic A lgo rithm s in Search, Op tim ization and M ach ine L earning [ M ]. M assacheusettes: A ddision- W esley Pub., 1989 [9 ] W h itley D et al. Genetic algo rithm s and neural netw o rk s: op tim ization connections and connectivity [J ]. Parallel Computing, 1990, 14: [10 ] T akagi T, Sugeno M. Fuzzy Identification of System s and its A pp lication to M odeling and Contro l [J ]. IEEE T ransaction on System s, M an, and Cybernetics, 1985, 15 (1): [11 ]. [J ]., 1998 (9): [12 ]. [J ]., 1999, 19 (2): [13 ]. [D ]., 1999.

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