人工知能学会研究会資料 SIG-FPAI-B Predicting stock returns based on the time lag in information diffusion through supply chain networks 1 1 Yukinobu HA

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1 人工知能学会研究会資料 SIG-FPAI-B Predicting stock returns based on the time lag in information diffusion through supply chain networks 1 1 Yukinobu HAMURO 1 Katsuhiko OKADA Kwansei Gakuin University Abstract: As Cohen and Frazzini demonstrates, stock prices do not promptly incorporate news about economically related firms, generating return predictability across assets. This paper demonstrates a unique methodology to exploit such phenomenon for alpha generation. Using a Fact Set Revere data, we construct a portfolio of stocks that has economic links with a suddenly rising firm, that is presumed to have met with good fundamental news. Calendar-time value-weighted portfolio generates large abnormal return after controlling for size and book-to-market ratio. We also found that the performance further improves when we create edge-betweenness weighted portfolio. 1 Cohen and Frazzini (supplier)- (customer) [1] (investor inattention) Cohen and Frazzini t N t G t = (N t, E t ) E t 2 (supplier) (customer) G SC t = (N t, Et SC ) ( SC ) G CS t ( CS ) SC 2 a, b, ( (a, b) Et SC ) a b b a hamuro@kwansei.ac.jp

2 2.2 a t d r a t,d = (ra t d+1, ra t d+2,..., ra t ) rt a a t ra t = c a t /c a t 1 n t /n t 1 c a t t a n t t US S&P500 d 1 0 d u = (0, 0,..., 1) 2 c a t,d u sim(, ) ρ a t t t d + 1 t 1 t U t (1) U t = {a sim(r a t,d, u) ρ, δ L rt a 1 d 1 rt i a δ U } a Nt (1) d 1 i=1 d = 9, ρ = 0.8, δ L = 0.05, δ U = 0.1 d = 9 10 ( 2 ) δ U 2.3 t U t G t = (N t, E t ) t Q t (2) Q t = {b (a, b) E t, b U t } a Ut (2) a b Q t (SNS) (Node-Betweenness Centrality) (Edge-Betweenness Centrality) a C NB (a) (3) g ij i j g ij (a) i j a C NB (a) = i V j V ;j i g ij (a) g ij (3) a (a, b) C EB (a, b) (4) g ij (a, b) i j (a, b) C EB (a, b) = i V j V ;j i g ij (a, b) g ij (4) Apple Toyota

3 t SC ( CS ) a Q t s(a) U t a wt NB (a) (5) ( ) wt EB (a) (6) &'()*+,%-./!"#$%% 01%- 0,$- w NB t (a) = w EB t (a) = C NB (s(a)) i Q t C NB (s(i)) C EB (s(a), a) i Q t C EB (s(i), i) (5) (6) 1: 20% S&P500 3 FactSet Research Systems (Revere ) ( 15 ) 20% () S&P500 20% %( 10%) S&P %( 7.5%) Revere Factset (Form 10-K annual fillingsa) (investor presentations) Revere 2 2: ( ) ( ) ( ) 4 FactSet Revere t a Q t t h ( t + h) h = 1 G SC t G SC t 2 1) 2) wt NB 3) wt EB Lyon Fama- French Three Factor Model[2] Three Factor Model

4 9:;<=!,-&./&'01%*+!,-&456&'307*+,!-&456&'3%8*+!,-&2/&'3%(*+,!-&./&')7$*+,!-&2/&')%$*+!"#$%%&'()$*+ α (0.03%) 22.6% 31.4% max drawdown SC EB 3: 2008 SC: CS: org: NB: EB: : R p t R f t = α + β(r m t R f t ) + s(s t ) + h(h t ) + ϵ t (7) R p t t R f t 10 Rt m S&P500 S t H t α P P Three Factor Model Sharpe max drawdown Sharpe max drawdown 3 (CS SC ) α (α P SC:1.75%,CS:0.29%) CS NB, EB α SC NB, EB NB SC EB h = : 1 5 α P (%) P <5% EB CS NB org EB SC NB org CS SC BtoC iphone

5 5.2 ρ ( δ L δ U ) ρ = 0.8 δ L = 0.05 δ U = ρ = 0.7 u ρ = 0.8, () u (-0.8 ) δ L = 0.1, δ U = idiosyncratic (U t ) (Q t ) 2: Frama-French Three Factor α P (bp) (%) EB CS NB org EB SC NB org / JST CREST (B) [1] Cohen, L and Frazzini, A, Economic Links and predictable returns,journal of Finance, 63, 4, pp (2008) [2] Lyon, J., D., Brad M. Barber, Chih-Ling Tsai, Improved Methods for Tests of Long-Run Abnormal Stock Returns, The Journal of Finance, 54, 1, pp (1999)

6 3: 6 Frama-French Three Factor 5% α bp 1/10000 α P SD Sharpe max draw-down (bp) (%) ( %) ( %) org /04/ /11/20 CS NB /04/ /11/20 EB /06/ /11/20 org /04/ /03/17 SC NB /03/ /03/17 EB /04/ /03/19 4: ρ, (δ L, δ U ) α P (%), P <5% ρ = 0.7 ρ = 0.8 ρ = % 2.5-5% 5-10% 0-2.5% 2.5-5% 5-10% 0-2.5% 2.5-5% 5-10% org CS NB EB org SC NB EB

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