A Comparative Analysis of Stock Return Behavior Using a Markov Switching Model (Case Study: Zimbabwe Stock Exchange)

2017 ◽  
Author(s):  
Tawanda Dakwa ◽  
Isabel Moyo
Keyword(s):  
Comparative Analysis ◽  
Stock Return ◽  
Stock Exchange ◽  
Markov Switching ◽  
Markov Switching Model ◽  
Model Case ◽  
Switching Model ◽  
Return Behavior

scholarly journals The Adaptive Market Hypothesis and the Day‑of‑the‑Week Effect in African Stock Markets: the Markov Switching Model

2019 ◽  
Vol 22 (3) ◽  
pp. 145-162 ◽  
Author(s):  
Adefemi A. Obalade ◽  
Paul Francois Muzindutsi
Keyword(s):  
Stock Markets ◽  
Stock Exchange ◽  
Markov Switching ◽  
Markov Switching Model ◽  
Market Conditions ◽  
Analytical Technique ◽  
Switching Model ◽  
Bear Market ◽  
Johannesburg Stock Exchange ◽  
Day Of The Week

In line with the Adaptive Market Hypothesis (AMH), the objective of this study is to investigate how the day‑of‑the‑week (DOW) effect behaves under different bull and bear market conditions in African stock markets, and to examine the likelihood of being in a bull or bear regime for each market. A Markov Switching Model (MSM) was employed as the analytical technique. The results show that the DOW effect appears in one regime and disappears in another, in all markets, as rooted in the AMH. Lastly, all markets, except the Johannesburg Stock Exchange have a higher tendency to be in a bearish state than a bullish one. Our findings show that active investment management may yield profits for investors investing in most African markets during bearish conditions.

scholarly journals Stock Return and Trading Volume - An Application of the Markov Switching Model

2016 ◽  
Vol 1 (1) ◽  
pp. 55
Author(s):  
Jing-Tung Wu
Keyword(s):  
Linear Relation ◽  
Trading Volume ◽  
Stock Return ◽  
Markov Switching ◽  
Markov Switching Model ◽  
Financial Decisions ◽  
Switching Model ◽  
Non Linear ◽  
Stock Effect ◽  
The Relationship

<p>The relationship between stock return and trading volume has been extensively documented by earlier studies. Financial academics unveiled some potential reasons of this phenomenon, but it still left for several inconclusive evidences. This paper employs the Markov switching model (MSM) to investigate the relationship. The results identify that second regime exist and persist, which is consistent with earlier study indicating that there are complex influences in stock return and trading volume. One possible explanation would be that people are not always rational; their financial decisions are influenced by behavioral biases. If people change their beliefs or preferences, then the regime switches. This leads to the conclusion that, the results are strongly in favor of a non-linear relation between stock return and trading volume. Also, it is interesting to find that industry play an important role. The deviations of the regime parameters are different across industries, which stands for risk-varying and is fit to the conventional wisdoms. This paper further tests the glamour (value) stock effect by the MSM; the result shows that the factor does affect the probabilities of the expected regime duration periods.</p>

scholarly journals Modeling South African Banks closing stock prices: a Markov-Switching Approach

2016 ◽  
Vol 8 (1(J)) ◽  
pp. 36-40
Author(s):  
Diteboho Xaba ◽  
Ntebogang Dinah Moroke ◽  
Johnson Arkaah ◽  
Charlemagne Pooe
Keyword(s):  
Stock Prices ◽  
Autoregressive Model ◽  
Stock Price ◽  
Stock Exchange ◽  
Markov Switching ◽  
Markov Switching Model ◽  
Switching Model ◽  
Johannesburg Stock Exchange ◽  
Augmented Dickey Fuller ◽  
National Bank

In this paper, we provide evidence that the five variables used in the study were nonlinear in nature, while finding a better Markov-switching model. The study used dailydata obtained from the Johannesburg Stock Exchange over the period from January 2010 to December 2012. An extension of Markov Switching with autoregressive model was used for empirical analysis. Prior to using this model, the series were tested for nonlinear unit root with modified Kapetanois-Shin-Snell nonlinear Augmented Dickey-Fuller (KSS-NADF) test which successfully provided positive results.Other preliminary tests selected the first lag as optimal and confirmed that stock prices may switch between two regimes. Further empirical findings proved that stock prices can be successfully modelled with Markov Switching Autoregressive model of order one. First National bank was found to have 99.64% longer stock price stability if adjustments regards tofinancialpolicies are made. Capitec Bank was the least favoured among the banks.

Regimes in CDS Spreads: A Markov Switching Model of iTraxx Europe Indices

2006 ◽  
Author(s):  
Carol Alexander ◽  
Andreas Kaeck
Keyword(s):  
Markov Switching ◽  
Markov Switching Model ◽  
Switching Model

A Markov-Switching model for building occupant activity estimation

2019 ◽  
Vol 183 ◽  
pp. 672-683 ◽  
Author(s):  
Sebastian Wolf ◽  
Jan Kloppenborg Møller ◽  
Magnus Alexander Bitsch ◽  
John Krogstie ◽  
Henrik Madsen
Keyword(s):  
Markov Switching ◽  
Markov Switching Model ◽  
Switching Model ◽  
Activity Estimation

scholarly journals Risk-return analysis of the investment portfolio of two stocks, under the Markowitz model. Case study: Two multinational companies listed on the Mexican Stock Exchange

2020 ◽  
pp. 21-27
Author(s):  
María Trinidad ALVAREZ-MEDINA
Keyword(s):  
Stock Exchange ◽  
Industrial Sector ◽  
Financial Assets ◽  
Investment Portfolio ◽  
Model Case ◽  
Risk Return ◽  
Economic Entity ◽  
Food And Beverage ◽  
And Performance

Investment in productive and financial assets are a decision made as an alternative to direct resources to bring greater value and higher performance to an economic entity. The objective of this article is to analyze the return risk of the stocks of two companies listed on the Mexican Stock Exchange (BMV), presenting the case of the companies Grupo Bimbo, SAB de CV, and GRUMA, SAB de CV, both companies listed on the Mexican Stock Exchange, belonging to the industrial sector specifically the food and beverage sub-sector, being the most representative companies of this sector. The return on the portfolio is 0.27256% and the risk is 0.0121862, with an investment of 50% in each of them. The period analyzed was from 2015 to 2018. It is important to base decision-making by considering the risk analysis and performance of financial assets in where you wish to invest, in addition to relying on other analyzes such as fundamental and technical analysis, among others.

scholarly journals Estimasi Bullish dan Bearish dengan Model Perpindahan Markov dan Risiko Sistematis (beta) dengan Model Penilaian Modal Sharpe dalam Investasi Saham di Bursa Efek Indonesia, Tahun 2011 - 2016

2018 ◽  
Vol 8 (3) ◽  
pp. 221
Author(s):  
Prima Respati ◽  
Budi Purwanto ◽  
Abdul Kohar Irwanto
Keyword(s):  
Interest Rates ◽  
Capital Market ◽  
Stock Price ◽  
Markov Switching ◽  
Systematic Risk ◽  
Risk Identification ◽  
Pricing Model ◽  
Markov Switching Model ◽  
Switching Model ◽  
Capital Assets

<p><em>ABSTRACT</em></p><p><em>Various research including Panggabean (2010) and Usman (2016) show that the long-term trend of Indonesia's capital market is on an uptrend, marked by more bullish periods and longer duration than bearish; and the development determined by rising rates of return rather than interest rates and exchange rates (Defrizal et al, 2015). However, the research has not determined yet whether there are any difference risks in bullish and bearish conditions, especially for systematic or market risk. This study aims to 1) identify the bullish and bearish segmentation period using the Markov Switching Model, and 2) measure systematic risk using the capital assets pricing model (CAPM) with the Sharpe beta indicator. Using the composite stock price index (JCI) and trading data from TICMI (The Indonesia Capital Market Institute) period 2011-2016, consists of 560 issuers, it was found that there were 10 segments that could be identified as 5 bullish periods for 30 weeks , and 5 bearish periods for 8 weeks. Other finding indicates that the probability of switching from bullish to bearish is 3.33% and from bearish to bullish is 12.14%. That means there are positive sentiments that the market tends to be bullish rather than vice versa. The result of beta or systematic risk identification indicates that during bullish and bearish period the market proved to be different risk. Other interesting findings, in both these two different conditions there are negative betas exist that still gives a positive yield.</em></p><p> </p><p>ABSTRAK</p><p>Berbagai riset termasuk Panggabean (2010) dan Usman (2016) menunjukkan bahwa kecenderungan jangka panjang pasar modal Indonesia berada pada kecenderungan naik (uptrend), ditandai dengan periode bullish lebih banyak, dan durasi lebih panjang, daripada bearish.  Perkembangan perkembangan itu dipicu oleh kenaikan tingkat imbalan, alih-alih suku bunga dan nilai tukar (Defrizal et al 2015). Namun riset-riset tersebut tidak mengidentifikasi eksistensi kondisi bullish dan bearish dan berdampak perbedaan risiko, terutama risiko sistematis atau risiko pasar, kecuali mengasumsikan saja keberadaannya.  Penelitian ini bertujuan 1) mengidentifikasi segmentasi periode bullish dan bearish dengan menggunakan model perpindahan Markov (Markov Switching), dan mengukur risiko sistematis menggunakan model penilaian modal (capital assets pricing model, CAPM) dengan indikator beta Sharpe.  Menggunakan data indeks harga saham gabungan (IHSG) serta data perdagangan bersumber dari TICMI (The Indonesia Capital Market Institute) periode 2011-2016 yang mencakup 560 emiten, diperoleh hasil bahwa dalam periode tersebut terdapat 10 segmen yang dapat diidentifikasi sebagai 5 periode bullish selama 30 pekan, dan 5 periode bearish selama 8 pekan.  Temuan lain menunjukkan bahwa peluang perpindahan dari kondisi bullish ke bearish sebesar 3,33% dan dari kondisi bearish ke bullish sebesar 12,14%. Artinya terdapat sentimen positif bahwa pasar cenderung menjadi bullish daripada sebaliknya.  Hasil identifikasi risiko sistematis menunjukkan, berbeda dengan konsep dasar CAPM, bahwa beta pada periode bullish dan bearish tidak sama.  Temuan menarik lainnya, pada kedua kondisi tersebut terdapat beta negatif yang dapat memberikan tingat imbalan positif.</p>

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