Different downside risk approaches in portfolio optimisation

Variance is commonly used as risk measure in portfolio optimisation to find the trade-off between the risk and return. Investors wish to minimise the risk at the given level of return.However, the mean-variance model has been criticised because of its limitations. The meanvariance model strictly relies on the assumptions that the assets returns are normally distributed and investor has quadratic utility function. This model will become inadequate when these assumptions are violated. Besides, variance not only penalises the downside deviation but also the upside deviation. Variance does not match investor’s perception towards risk because upside deviation is desirable for investors. Therefore, downside risk measures such as semi-variance, below target risk and conditional value at risk have been proposed to overcome the deficiencies of variance as risk measure. These downside risk measures have better theoretical properties than variance because they are not restricted to normal distribution and quadratic utility function. The downside risk measures focus on return below a specified target return which better match investor’s perception towards risk. The objective of this paper is to compare the optimal portfolio composition and performance using variance, semivariance,below target risk and conditional value at risk as risk measure

Determinants, Efficiency and Wealth Effects of Malaysian Corporate Mergers and Acquisitions

This research analyses four important issues pertaining to merger and acquisition (M&A) announcements in Malaysia to the bidder and target firms, namely the effects on shareholder's wealth, firm's financial performance pre and post M&A, effects of firm's characteristics on shareholder's wealth and the determinants of shareholder's wealth. Taking into account the two distinguished economic backdrops, this study segregates M&A announcements into two sub-periods; the booming period of 1985 to 1994 and the period surrounding the financial crisis, from 1995 to 2001. Bidder and target firms are classified into the finance and non-finance sectors and the bidder firms are further categorised to bidders announcing to acquire listed target firms and bidder firms that announce to acquire non-listed targets. Employing the event study methodology, findings on the wealth effects provide evidence that the target firms do not benefit from M&A activities, regardless of classifications and sub-periods. M&A activities in the period 1985 - 1994 benefit bidder firms from the non-finance sector that acquire listed target firms while M&A activities in the period 1995 - 2001 significantly profit bidder's shareholders in the non-finance sector that acquire non-listed target firms. The regression analyses results are consistent with results reported in the literature that show profitability measures to be the main factors in determining abnormal returns. Generally, findings of this study show that during the booming economy of 1985 to 1994, M&A is not the growth strategy chosen by firms. The enhanced values of bidders that acquire listed target firms indicate that M&A is mainly done to capture larger market shares or to create monopolies. In Malaysia, M&A is considered more as the strategy to survive as depicted by the high merger announcements in the period of 1995 to 2001 when the economy is in turmoil. Though the forced merger scheme is a necessary measure taken at the time, findings of this study prove that enforced merger scheme destroys value of both bidder and target firms listed in the finance sector

Enhanced index tracking in portfolio optimization with two-stage mixed integer programming model

Enhanced index tracking is a portfolio management which aims to construct the optimal portfolio to generate higher return than the benchmark index return at minimum tracking error without purchasing all the stocks that make up the index. The objective of this paper is to propose a two-stage mixed integer programming model to improve the existing single-stage mixed integer programming model for tracking FBMKLCI Index in Malaysia. The optimal portfolio performance of both models are determined and compared in terms of portfolio mean return, tracking error, excess return and information ratio. The results of this study indicate that the optimal portfolio of the proposed model generates weekly excess return over the benchmark FBMKLCI index return at minimum tracking error. Besides that, the proposed model is able to outperform the existing model in tracking the benchmark index.Keywords: mean return; tracking error; optimal portfolio; portfolio performanc

Portfolio optimization of the construction sector companies in Malaysia with mean-semi absolute deviation model

Portfolio optimization is an important investment strategy to find the trade-off between the risk and return. In mean-semi absolute deviation model,semi absolute deviation is employed as risk measure while the expected return of the investors is represented by the mean return. The objective of this paper is to construct the optimal portfolio that will minimize the portfolio risk and can achieve the investors target rate of return by using the mean-semi absolute deviation model. The data of this study comprises 20 construction sector companies that listed in Malaysia stock market from July 2011 until June 2016. The results of this paper show that the constructed optimal portfolio can minimize the portfolio risk at the expected rate of return. In addition, the composition of the companies invested in the optimal portfolio is different.Keywords: portfolio risk; return; investment; investor

Apuntes sobre una pedagogía mediada en la pregunta desde ambientes colaborativos digitales

Este estudio pretende valorar y destacar la b?squeda de un aprendizaje significativo y el desarrollo de pensamiento cr?tico y creativo en aulas virtuales desde una metodolog?a de la indagaci?n basados en el uso de la Webquest como herramienta innovadora y alternativa pedag?gica- did?ctica en los cursos del programa de filosof?a. El prop?sito de esta metodolog?a es incentivar la indagaci?n y la investigaci?n dirigida a trav?s de la web, en donde se propone al estudiante, quien de antemano conoce la r?brica mediante la cual ser? evaluado, realizar tareas siguiendo un proceso preestablecido por el docente-tutor, que implica la realizaci?n y organizaci?n de actividades con la ayuda de unos recursos provistos. De esta forma, se espera el aprovechamiento de la expansi?n de la Internet y la mayor acogida de los ambientes virtuales de aprendizaje

Hukum Benford dan penyimpangan pasaran saham semasa pandemik Covid-19

Taburan umum digit pelopor signifikan Hukum Benford secara amnya adalah sangat pencong ke arah digit yang lebih kecil dan mengikuti taburan logaritma khusus. Digit pelopor signifikan Hukum Benford telah digunakan secara meluas sebagai alat analisis forensik untuk mengesan penyelewengan data, penipuan dan manipulasi berdasarkan kepada sesuatu ujian keakuran. Kajian ini memanfaatkan ujian keakuran Hukum Benford sebagai kaedah statistik yang digunakan untuk menilai sama ada set data mengikut atau menyimpang daripada corak teori taburan Hukum Benford. Objektif kajian adalah untuk menyiasat keakuran taburan digit pelopor signifikan pertama dan kedua indeks FBMKLCI dan SSEC sama ada mengikut taburan jangkaan Hukum Benford atau tidak semasa berlakunya pandemik COVID-19 berdasarkan ujian keakuran min sisihan mutlak. Hipotesis nol taburan kedua-dua indeks akur kepada Hukum Benford ditolak jika nilai kiraan adalah lebih besar daripada nilai kritikal. Keputusan kajian mendapati nilai ujian keakuran min sisihan mutlak bagi taburan pasaran saham menyimpang daripada corak taburan logaritma Hukum Benford. Kajian menyimpulkan kedua-dua indeks pasaran FBMKLCI dan SSEC taburan digit pelopor signifikan pertama dan kedua bagi kedua-dua pasaran FBMKLCI dan SSEC tidak akur kepada Hukum Benford semasa berlakunya wabak pandemik COVID-19 di Malaysia dan China. Ketidakakuran kepada Hukum Benford memberi implikasi mengenai integriti dan keblehpercayaan data pasaran saham selain menekankan anomali pasaran dan kelakuan pelabur

Pemeringkatan saham patuh syariah menggunakan pembuatan keputusan berbilang-kriterium: TOPSIS dan GRA

Peningkatan bilangan saham patuh Syariah yang didagangkan di Bursa Malaysia menjadikan proses pemilihan pelaburan semakin penting dan mencabar. Oleh itu, pelabur memerlukan suatu mekanisme bersifat kuantitatif dan bukan hanya berdasarkan penilaian kualitatif atau intuitif semata-mata. Dalam kajian ini, prestasi saham 10 syarikat patuh Syariah yang disenaraikan oleh Suruhanjaya Sekuriti Malaysia (SCM) dianalisis menggunakan nisbah kewangan utama syarikat iaitu pelaburan, keberuntungan, kecairan, keumpilan dan kecekapan. Fokus kajian adalah untuk memeringkatkan prestasi saham dari 2017 sehingga 2021 menggunakan dua kaedah iaitu Teknik Keutamaan Tertib Mengikut Persamaan dengan Penyelesaian Ideal (TOPSIS) dan Analisis Hubungan Kelabu (GRA). Keputusan kajian menunjukkan konsistensi pemeringkatan saham antara kedua-dua kaedah, terutama dalam memilih dua kedudukan teratas iaitu NESTLE dan PETGAS serta tiga saham kedudukan terbawah iaitu MISC, PMETAL dan AXIATA. Keputusan ini menunjukkan bahawa GRA merupakan kaedah alternatif yang setanding dengan TOPSIS dan mampu membantu para pelabur untuk mengenal pasti saham patuh Syariah terbaik

Solvency Analysis by Business Classifications of General Insurance Industry in Malaysia

A healthy and well developed insurance industry will improve the stability of an economy by transferring risks to various parties through insurance and reinsurance activities. Insurance companies’ performances have direct impact on the public welfare, to the regulatory authorities and to potential investors. Insolvency within the insurance industry has become a major concern and identification of potentially troubled firms has become a major regulatory research objective. The regulatory authority of the insurance industry in Malaysia has made compulsory the employment of risk-based capital (RBC) on the insurance industry with the objective to measure the minimum amount of capital required by an insurance company to ensure the continuous solvency and smooth running of the insurer's business operations. The measurement of RBC known as the capital adequacy ratio (CAR) must be adopted by each insurance company. However, different methods are practiced by other countries in allocating RBC. The objective of this study is to evaluate the performance of different insurance policies offered by the general insurance companies using the RBC system enforced by BNM and the RBC based on Butsic method. Study uses annual financial reports data of nineteen general insurance companies in Malaysia from 2009 to 2015. Results of the capital adequacy ratio (CAR) and expected policy deficit (EPD) found that the personal accident policy to be the most profitable and ensure solvency for the general insurance companies

Neuro-rough trading rules for mining Kuala Lumpur composite index

Stock market plays a vital role in the economic performance. Typically, it is used to infer the economic situation of a particular nation. However, information regarding a stock market is normally incomplete, uncertain and vague, making it a challenge to predict the future economic performance. In order to represent the market, attending to granular information is required. In recent years, many researches in stock market prediction are conducted using diverse Artificial Intelligence approaches. These artificial applications have shown superior prediction results. As such, in this study, a prediction enhancement alleged as Neuro-Rough (NR) is proposed to forecast the Kuala Lumpur Stock Exchange Composite Index (KLCI) movements. NR hybridizes high generality of artificial neural network (ANN) and rules extraction ability of rough sets theory (RST) by demonstrating the capability of simplifying the time series data and dealing with uncertain information. Features of stock market data are extracted and presented in a set of decision attribute to the NR systems. The length of the stock market trend is used to assist the process of identifying the trading signals. A pilot experiment is conducted to discover the best discretization algorithm and ANN structure. NR is implemented in a trading simulation and its effectiveness is verified by analyzing the classifier output against the information provided in Bursa Malaysia's annual reports. The experiments using 10 years training and testing data reveal that NR achieves an accuracy of 70% with generated annual profit in trading simulation of 74.33%