Developing a new stock scoring model for Shariah-compliant investment

Simon, Shahril (2019) Developing a new stock scoring model for Shariah-compliant investment. PhD thesis, University of Bolton.

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This study aims to develop a new stock scoring model, !_#$%&' model, that based on the musharakah parameters by using a momentum technique that separate the out-performing Shariahcompliant stocks from the under-performing. Motivation for this study is centred towards the performance dragged from Shariah-compliant stocks in relative to the conventional stocks during the stock market recovery and the out-performance of Shariah-compliant portfolio attributed by a few stocks only. Hence, separating the out-performing from the under-performing Shariahcompliant stocks will enhance the portfolio returns. In doing so, a quantitative research in time series analysis is designed to measure the momentum, periodical changes, of the musharakah parameters. The essential musharakah parameters identified are industry performance, management style, profitability ratios and capital growth. These musharakah parameters are then represented by the financial indicators such as sector return, book value, cash flow, equity return, asset return, total assets and enterprise value to determine the momentum of stock price returns. There are several main findings based on the quantitative analysis of the research results. First, this study has evidenced that musharakah parameters explain stock price returns since they have monotonic positive relationship with the newly developed !_#$%&' model. Second, the model improves its statistical significance when the financial indicators are progressively added into the equation. Importantly, the !_#$%&' model requires all musharakah parameters to be included in generating robust results. Third, there has been no concern on the temporal issue in the !_#$%&' model since it responds well to every stock market cycle and to diverse investment horizons. Fourth, the !_#$%&' model also has monotonic positive relationship with company size, stock orientations, trading volume, stock price or leverage position. Fifth, the predictive power has improved substantially when the !_#$%&' model employs active investment strategies i.e. long-only and long-short and has further improved when the restriction is relaxed by allowing short selling. On another note, this study has contributed in several ways. On theoretical side; in contrast to efficient market hypothesis theory, the !_#$%&' model shows that stock market is inefficient and therefore, stock price returns are predictable. Although past performance is no guarantee of future returns, historical data remains the ideal tool to forecast the stock prices. As on the empirical side; the !_#$%&' model captures most of the financial information and helps process recent information better. When applied to various portfolio strategies, the !_#$%&' model has shown that active investing produces higher excess returns than passive investing. Moreover, the !_#$%&' model does not discriminate stock specific characteristics like the company size, value or growth orientation, liquidity, stock price and leverage position. On methodological side; unlike many other models, using momentum of multiple financial indicators on !_#$%&' model has addressed the concern of single variable biasness. Furthermore, the !_#$%&' model does not require long historical data to produce robust results. In addition, the model is flexible to handle missing values and can withstand the outliers. Accordingly, this study discovers that the !_#$%&' model can assist those investing in Shariah-compliant stocks to make informed investment decisions by using the model as an alternative investment analysis tool to forecast stock price returns, to determine market timing and to construct profitable stock portfolio returns.

Item Type: Thesis (PhD)
Additional Information: This is an electronic version of the thesis submitted in fulfilment of the requirements of the University of Bolton for the degree of Doctor of Philosophy in Islamic Finance
Divisions: University of Bolton Research Centres > Centre for Islamic Finance
University of Bolton Theses > Centre for Islamic Finance
Depositing User: Tracey Gill
Date Deposited: 24 Apr 2019 13:39
Last Modified: 24 Apr 2019 13:39

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