TY  - JOUR
T1  - Extracting Similar Patterns to Markovian Distance: A Case Study on Predicting Stock Trends
Behavior
AU - Sadrnezhad, Zohreh AU - Vafaei Jahan, Majid AU - Zarrabi Darban, Sina 
JO  - International Journal of Soft Computing
VL  - 15
IS  - 2
SP  - 41
EP  - 43
PY  - 2020
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2020.41.43
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2020.41.43
KW  - Relative strength index
KW  -stock prediction
KW  -markov-distance
KW  -markov chain
KW  -approach
AB  - In this study, a new concept named
Markovian-distance is presented. Markovian-distance is
defined based on Markov-Chain concepts but in contrast
with Markov-Chain previous states have an effective
impact not the prior state alone but also depends on all
former states. This implication has various usages such as
extracting similar patterns. In this paper case study of
predicting stock trends behavior using Markov-distance
is described. This approach is able of extracting similar
patterns based on resembling transfers between different
states of stock price (increase, decrease and price
stability) and similar patterns with differentiated lengths.
To predict stock trends behavior in the proposed method,
we extracted similar patterns on Relative Strength Index
(RSI) indicator because in this indicator speed and rate of
price changes are measured over a time period and it also
has different levels in which every one of them has a
distinct conduct. In addition, every similar pattern in
every level will probably follow a specific trend in future.
So by using this indicator among similar patterns, patterns
which are at a resembling level to the RSI current
volatility level could be extracted to have a more accurate
prediction. Finally, the evaluation of the proposed method
is done by using Isfahan Steel Stock on Tehran stock
market and Apple Corporation Stock dataset. Results
show that the proposed method predicts the price trend in
Isfahan Steel Stock with 88.5% and Apple Stock with
84.87% accuracy.
ER  -