آزمون تغييرپذيري عوامل موثر در پيش بيني بازده سهام با استفاده از مدلهاي ميانگين گيري پويا (DMA)

Factor Variability Test in Stock Return Forecasting Using Dynamic Model Averaging (DMA

در اﯾﻦ ﺗﺤﻘﯿﻖ ﺗﻼش ﺷﺪه اﺳﺖ ﺑﺎ اﺳﺘﻔﺎده از ﻣﺪلﻫﺎي ﻣﯿﺎﻧﮕﯿﻦﮔﯿﺮي ﭘﻮﯾﺎ و دادهﻫﺎي ﻣﺎﻫﺎﻧﻪ در ﺑﺎزه زﻣﺎﻧﯽ 1388:1 ﺗﺎ 1396:12 ﺑﺎزدﻫﯽ ﺳﻬﺎم در ﺑﻮرس اوراق ﺑﻬﺎدار ﺗﻬﺮان ﺑﺮرﺳﯽ ﺷﻮد. در اﯾﻦ راﺳﺘﺎ ﻣﺘﻐﯿﺮﻫﺎي ﮐﻼن و ﺷﺎﺧﺺﻫﺎي ﺑﺎزارﻫﺎي ﻣﻮازي ﺑﻪ ﻣﻨﻈﻮر ﭘﯿﺶﺑﯿﻨﯽ ﺑﺎزده ﺳﻬﺎم ﻣﻮرد اﺳﺘﻔﺎده ﻗﺮار ﮔﺮﻓﺘﻪاﺳﺖ. ﻧﺨﺴﺖ ﺑﺎ ﺑﺮآورد ﻣﺪلﻫﺎي رﮔﺮﺳﯿﻮن ﺑﺎزﮔﺸﺘﯽ، ﻣﺪلﻫﺎي ﭘﺎراﻣﺘﺮ ﻣﺘﻐﯿﺮ-زﻣﺎن )TVP(، ﻣﺪل اﻧﺘﺨﺎﺑﯽ ﭘﻮﯾﺎ )DMS( و ﻣﺪل ﻣﯿﺎﻧﮕﯿﻦﮔﯿﺮي ﭘﻮﯾﺎ )DMA( در ﻧﺮم اﻓﺰار ﻣﺘﻠﺐ ﻣﺸﺎﻫﺪه ﮔﺮدﯾﺪ ﻣﺪل DMA ﺑﺎ 0/95 =α = β ﺑﺮ اﺳﺎس ﻣﻌﯿﺎرﻫﺎي ﺳﻨﺠﺶ ﻋﻤﻠﮑﺮد ﭘﯿﺶﺑﯿﻨﯽ) MSFE ،MAFE و )Log(PL ( از دﻗﺖ ﭘﯿﺶﺑﯿﻨﯽ ﺑﺎﻻﺗﺮي در ﻣﻘﺎﯾﺴﻪ ﺑﺎ ﺳﺎﯾﺮ روشﻫﺎ ﺑﺮﺧﻮردار اﺳﺖ. ﻫﻤﭽﻨﯿﻦ ﺑﺮ اﺳﺎس ﻧﺘﺎﯾﺞ ﺑﺮآورد ﻣﺘﻐﯿﺮ ﻗﯿﻤﺖ ﻃﻼ )48 دوره(، ﻧﺮخ ارز )36 دوره( و ﻣﺘﻐﯿﺮ ﺗﻮرم )30 دوره( ﺑﻪ ﺗﺮﺗﯿﺐ ﺑﺎﻻﺗﺮﯾﻦ و ﻣﺘﻐﯿﺮﻫﺎي ﻗﯿﻤﺖ ﺟﻬﺎﻧﯽ ﻧﻔﺖ و ﺗﻮﻟﯿﺪ ﻧﺎﺧﺎﻟﺺ داﺧﻠﯽ ﻧﯿﺰ ﺑﻪ ﺗﺮﺗﯿﺐ ﺑﺎ 28 و 2 ﺗﮑﺮار ﮐﻤﺘﺮﯾﻦ ﺗﺎﺛﯿﺮ را ﺑﺮ ﺑﺎزدﻫﯽ ﺳﻬﺎم داﺷﺘﻪاﻧﺪ. ﻧﺘﺎﯾﺞ ﻣﺒﯿﻦ آن اﺳﺖ ﮐﻪ اﺳﺘﻔﺎده از ﻣﺪلﻫﺎي ﭘﻮﯾﺎ ﺑﺎ در ﻧﻈﺮ ﮔﺮﻓﺘﻦ ﺗﻐﯿﯿﺮات زﻣﺎﻧﯽ ﭘﺎراﻣﺘﺮﻫﺎ و ﺗﻐﯿﯿﺮ در ﻣﺪل، ﮐﺎراﯾﯽ ﭘﯿﺶﺑﯿﻨﯽ ﺑﺎزدﻫﯽ ﺳﻬﺎم را اﻓﺰاﯾﺶ ﻣﯽدﻫد

In this study, using dynamic averaging models and monthly data in the period 2001:4 until 2018:3, Tehran Stock Exchange returns be investigated. In this regard, macroeconomics variables and parallel markets indices have been used to forecast the stock returns. Initially, estimating various models such as Recursive models, time-varying parameter models (TVP), dynamic model selection (DMS) and dynamic model averaging (DMA) in Matlab software, It was observed that DMS model with α = β = 0.95 had higher forecast accuracy (based on MAFE, MSFE and Log (PL) metrics). Gold price (48-period), exchange rate (36-period) and inflation rate (30-period) had the highest effect on stock returns, respectively, and global oil prices and GDP had the lowest effect by 28 and 2, respectively. Finally, the results indicate that utilizing dynamic models by considering time variations in parameters and the variation of the model increases the efficiency of forecasting stock returns. Keywords: Forecasting, Stock Returns, time-varying Parameter (TVP), Dynamic Model Averaging (DMA).