4  CONCLUSIONS 
The  study  evaluates  the  performance  of  4  feature 
combinations in the simultaneous prediction of stock 
closing prices and volatility.A decade-long dataset of 
stock  market  records  from  3  companies  (Amazon, 
Google,  and  Microsoft)  was  analyzed.  To  address 
redundancy  issues  inherent  in  multi-objective 
forecasting frameworks, two distinct target variables 
were established: 1) the closing price after 5 days, and 
2)  the  difference  between  the  highest  and  lowest 
prices after 5 days. Given the relative importance of 
closing  price  prediction  compared  to  volatility 
forecasting  and  to  reduce  parameter  bias  in  multi-
target  prediction  models,  conventional  multi-output 
approaches were abandoned in favor of a sequential 
methodology. Instead, in the study, 3 feature selection 
methods helped identify key features for closing price 
prediction. Then these selected features were used as 
inputs to predict price volatility. 
Empirical  results  revealed  company-specific 
variations in optimal feature combinations for multi-
objective  prediction.  However,  the  feature 
combination  selected  through  Lasso  Regression 
consistently  demonstrated  superior  predictive 
performance  across  all  companies  compared  to 
alternative selection methods. 
There are still some limitations of this paper. First, 
the analytical scope was restricted to three established 
feature  selection  techniques,  potentially  limiting 
comprehensive  exploration  of  the  feature  space. 
Second, the Mutual Information and Random Forest 
methods exhibited similar tendencies toward feature 
selection,  leading  to  repeated  results  in  feature 
combinations. 
Future research can build upon this work in 
several  directions.  First,  more  diverse  feature 
selection methods could be incorporated, particularly 
those  leveraging  automatic  feature  extraction 
techniques  integrated  with  deep  learning.  Second, 
alternative  evaluation  metrics,  such  as  return-based 
assessments,  could  be  adopted  to  improve  the 
practical  applicability  and  robustness  of  the  model. 
These  avenues  of  research  have  the  potential  to 
further  enhance  the  precision  of  multi-target 
prediction  models,  providing  valuable  support  for 
financial decision-making 
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