An integrated new threshold FCMs Markov chain based forecasting model for analyzing the power of stock trading trend

Ganesan, Kavitha; Annamalai, Udhayakumar; Nagarajan, D.

doi:10.1186/s40854-019-0150-4

Cited by 7 publications

(5 citation statements)

References 38 publications

(31 reference statements)

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“…In addition, the true direction of the stock cycles appeared to be misleading when the changing variance is not accounted for. The findings of this study are in agreement with previous studies of [7][8][9][10][11][12][13][14][15], and that Markov chain approach is sufficient for modeling stock/investment trends but proud of its novelty by application of logarithmic transformation of the transition matrix and provided the needed improvement to the study of [9]. The implication is that, a quicker convergence with higher precision estimates of the steady state probabilities is achieved when the variance is stabilized.…”

Section: Percentage Difference Between the Steady State Transition Probability Matrix Of Stock Prices And Natural Logarithm Of Stock Pricsupporting

confidence: 90%

“…Prior studies such as [7][8][9][10] applied Markov chain model approach to analyze and forecast stock trends. On the other hand, given the fact that Markov chain models are appropriate in tracking long-run behavior of trends but failed in tracking short-term behavior prompted the studies of [11][12][13][14][15] to provide the missing link. However, no previous studies have attempted to take into consideration the effect of changing variance while analyzing investment trends through Markov chains.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Investment Trends: A Logarithmic-Modified Markov Chain Approach

Moffat¹,

Ukpabio²,

Akpan³

2020

JSTA

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The study aimed at stabilizing the changing variance using the logarithmic transformation to achieve a significant proportion of stability and a faster rate of convergence of the steady state transition probability in Markov chains. The traditional Markov chain and logarithmic-modified Markov chain were considered. On exploring the yearly data on the stock prices from 2015 to 2018 as obtained from the Nigerian Stock Exchange, it was found that the steady state of logarithmic-modified Markov chain converged faster than the tradition Markov chain with efficiency in tracking the correct cycles where the stock movements are trending irrespective of which cycle it starts at time zero with differences in probability values by 1.1%, 0.7%, −0.41% and −1.37% for accumulation, markup, distribution and mark-down cycles, respectively. Thus, it could be deduced that the logarithmic modification enhances the ability of the Markov chain to tract the variation of the steady state probabilities faster than the traditional counterpart.

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Section: Percentage Difference Between the Steady State Transition Probability Matrix Of Stock Prices And Natural Logarithm Of Stock Pricsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Modeling Investment Trends: A Logarithmic-Modified Markov Chain Approach

Moffat¹,

Ukpabio²,

Akpan³

2020

JSTA

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show abstract

“…They fully concentrated on biblometric analysis and use special software to classify this paper. Ganesan et al (2019) discussed a Markov chain process to access the stock for four months by predicting the close value for the stock price. They are using root mean square error to predict the tolerance error.…”

Section: An Inventory Model For Fish Marketingmentioning

confidence: 99%

An inventory model for fish marketing under uncertain lockdown situation and normal backordering situation

Kuppulakshmi¹,

Sugapriya²,

Nagarajan

2021

JAMR

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PurposeThis research formulated to obtain the optimum ordered quantity and optimum inventory range of fish products under the conditions: (1) fully back ordered (lockdown) and (2) partial back ordered (normal geographical market). In both the cases, due to the deterioration nature and in quarantine situation some vendors are not able to satisfy the customers (retailers). So in this model, the cost of penalty is introduced in quarantine time to obtain the optimal total cost.Design/methodology/approachTo find the total cost, holding cost, shortage cost and deterioration cost have to be considered. There are so many disadvantages in holding the deteriorating food products. Due to the demand and deterioration, the holding cost of the fish products is determined. The supply chain of fish marketing process to find the optimum total cost and optimum back ordered quantity in the two situations, namely, (1) normal backordering and (2) Quarantine period is explained.FindingsThe conclusion of this research is exhibited for the uncertain lockdown situation and the normal geographical markets. But in both the cases, the demand function is dependent on the backorder quantity. The expected total cost of the retailers of fish products increased at the least possible range with the increase in the shortage parameter, cost of penalty and variance. But the change in mean value leads to decreasing in the back ordered quantity, inventory level and the annual total cost of the retailers. This analysis contributes to the service of supply chain from wholesaler to retailer in high level.Research limitations/implicationsFish products are very essential for nourishment and economic spread in India. This study has spotlight the efficient method for reducing the total cost of the retailers of fish marketing. The cost of deterioration of fish is high because of its perishable nature. Due to lockdown situation, the holding cost of the fish products depends upon the backordered quantity of geographical market of fish.Practical implicationsThis research formulated to obtain the optimum ordered quantity and optimum inventory range of fish products under the conditions: (1) fully back ordered (lockdown) and (2) partial back ordered (normal geographical market).Social implicationsDue to lockdown situation, the holding cost of the fish products depends upon the backordered quantity of geographical market of fish. This research formulated to obtain the optimum ordered quantity and optimum inventory range of fish products.Originality/valueThis research formulated to obtain the optimum ordered quantity and optimum inventory range of fish products under the conditions: (1) fully back ordered (lockdown) and (2) partial back ordered (normal geographical market). In both the cases, due to the deterioration nature and in quarantine situation some vendors are not able to satisfy the customers (retailers). So in this model, the cost of penalty is introduced in quarantine time to obtain the optimal total cost. A few number of sensitivity analysis are carried out for deterioration rate, shortage parameter and cost of penalty to indicate the existence of total cost in the least possible range.

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“…The transition process of performance states for each ME possesses a Markovian property. 39 Besides, we also assume that the performance of each ME varies with time t . Thus, the transition process of performance states for each ME follows a CTMC process.…”

Section: A Reliability Evaluation Algorithm Of An Sws With Phased Mismentioning

confidence: 99%

Optimal allocation of multi-state elements in a sliding window system with phased missions

Kou

Gao

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part O:

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Many real-world engineering systems such as aerospace systems, intelligent transportation systems and high-performance computing systems are designed to complete missions in multiple phases. These types of systems are known as phased-mission systems. Inspired by an industrial heating system, this research proposes a generalized linear sliding window system with phased missions. The proposed system consists of N nodes with M multi-state elements that are subject to degradation. The linear sliding window system fails if the cumulative performance of any r consecutive nodes is less than the pre-determined demand in any phase. The degradation process of each element is modeled by a continuous-time Markov chain. A novel reliability evaluation algorithm is proposed for the linear sliding window system with phased missions by extending the universal generating function technique. Furthermore, the optimal element allocation strategy is determined using the particle swarm optimization. The effectiveness of the proposed algorithm is confirmed by a set of numerical experiments.

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An integrated new threshold FCMs Markov chain based forecasting model for analyzing the power of stock trading trend

Cited by 7 publications

References 38 publications

Modeling Investment Trends: A Logarithmic-Modified Markov Chain Approach

Modeling Investment Trends: A Logarithmic-Modified Markov Chain Approach

An inventory model for fish marketing under uncertain lockdown situation and normal backordering situation

Optimal allocation of multi-state elements in a sliding window system with phased missions

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