Profitable Portfolio Research Articles

Islamic banking in the United Arab Emirates: Is embeddedness a contextual antecedent of performance and corporate governance outcomes?

Research issue and theoretical foundation<br/> Embeddedness refers to the financial, operational and structural mooring of an organisation in a particular social system or community. It presents itself as a central tenet of Williamson's (1975) classic theory of Transaction Cost Economics (TCE) and as such is a concept long overdue attention. In this paper, this concept is problematised as a macro- or organisation-level construct, and the paper also explores some of its consequents.<br/> Findings of the study<br/> Using a thick description approach, case study evidence is built for the Islamic Banking (IB) business model. It is shown how this indigenous business model (i.e., IB) has leveraged the power of social networks to build a profitable portfolio of investments over time, in the specific context of the UAE (United Arab Emirates) and its unique socio-political and cultural aspects. Dimensions of the embeddedness construct are proposed. Further, causal linkages are theorised with two distinct but inter-related consequents of embeddedness: corporate governance and performance outcomes of the organisation. It is envisaged that both linkages are mediated by the construct of board interlocks.<br/> Implications<br/> Proximally, this study has the potential to extend scholarship in the domains of business relations, trust and indigenous business models. As a distal possibility, researchers might use it to build on the organisational ecosystem paradigm.<br/> Scope and contributions<br/> The main contribution is to develop the concept of embeddedness. Additionally, this study uses the concept to build contextualised theory pertaining to the network characteristics of an indigenous business model.

Behavioral stock portfolio optimization considering holding periods of B-stocks with short-selling

This study exploits the extra information provided by behavioral stocks (B-stocks) and proposes portfolio selection models that produce profitable portfolios which can outperform traditional investment benchmarks (market index, mutual funds, and exchange-traded funds). Using the embedded holding period (T-day) information of a B-stock, in order to maximize the expected effect (cumulative abnormal return CAR ≥ 1%), this study proposes a buy and hold strategy such that B-stocks are bought when their respective causes are spotted and held until their respective T-days are reached. In addition, in performing the back-tests the actual transaction costs are considered to represent more realistic investments. Short-selling is also considered to provide a more flexible investment such that some B-stocks are shorted when causes are spotted and wait until their respective T-days are reached to exploit the expected effect (CAR≤−1%). The resulting portfolios are then compared to traditional benchmarks. Overall, this study addresses three issues that were not addressed by previous works on B-stocks. These issues are: (1) having portfolios that are profitable even after all transactions costs are considered; (2) identifying short-sell B-stocks for consideration of short-selling in the portfolio selection; and (3) generating portfolios that outperform traditional investment benchmarks.

Public Venture Capital in a Regional Economy: The Welsh Development Agency, 1976–1994

Between 1976 and 1994 the UK Government’s Welsh Development Agency made 2,304 loan and equity investments totaling £117.8 million. The agency aimed to address difficulties faced by firms in obtaining finance, and such intervention was justified by the market failure and spillover hypotheses. This article assesses the agency’s investment activities against both justifications. It finds that while some investments succeeded, the portfolio’s financial performance was poor, and the agency did not address widespread market failure. Evidence of spillover returns existed, but cannot be quantified accurately across the portfolio. The article argues that the agency’s two venture capital objectives, to assemble a profitable portfolio and to grow employment levels through boosting commercial activity, were incompatible within a poorly performing regional economy. Although spillovers can justify public venture capital in such economies, expectations as to financial performance should be realistic in the absence of an ecosystem that facilitates demand for capital.

The Impact of Merger and Acquisition on Bank Performance: A Case of Pakistani Banking Sector

Merger and acquisition is the strategy used by banks to expand its development process. In the current study operating and market performance has been assessed of the Banks exercised the M&A by taking the data from 2005-17. The main focus of the study is to evaluate the Banks performance using data collected from nine banks gone through the merger and acquisition strategy with the help of ordinary least square model. The results show significant relationship operating performance but insignificant relation with market performance. Findings provided an opportunity for the Banks to study and utilize the M&A strategy for capturing market share and further development in the competitive market. Furthermore, a glimpse for potential investors has been provided who want to create a profitable portfolio according to market concentration. The implications demands that proper improvement should be considered for the mechanism and regulatory policies to ensure the security of Banks.

AIRMS: A risk management tool using machine learning

In this work, an innovative approach to exploit the true potentials of machine learning (ML) by the financial industry is presented; using ML technology not as a source of investment ideas but as a consultant for trading decisions. In particular, the artificial intelligent risk management system (AIRMS) is presented that introduces one of the first efforts in the literature to utilize supervised ML as a risk management tool. Two AIRMSs systems are developed based on two well-known ML algorithms, i.e. artificial neural networks and decision trees. These two systems are applied into the five major currency pairs (FOREX) using signals obtained from an existing technical breakout trading strategy introduced in previous study by the authors, covering a seven-year period (2010–2016). Technical indicators and times series of the past entry points feed AIRMS in order to classify produced signals from the trading strategy into two classes: profitable and not. Constructing new portfolios using signals classified only as profitable resulted in an increased profit of more than 50% compared to the original ones. In this work, technical improvements are also proposed on the application of ML algorithms to financial data related to evaluation metrics and smoothing inputs. The obtained results revealed that the two AIRMSs can achieve impressive improvements to the performance of already profitable portfolios and proved that using ML to build risk management tools is very promising.

Predicting Stock Returns Using Neural Networks

A single hidden layer neural network can be trained to predict whether a stock will be in the top, middle, or bottom third of sample stocks based on its return over the next month based on return, trading volume, and volatility measures available at the end of this month. In my preliminary work using S&P 500 stocks, the network has limited success in predicting which stocks are likely to go up but the prediction strength is not strong enough to help build profitable portfolios. While neural networks have pushed artificial intelligence forward in many fields, and while the investment industry has been shifting more towards quantitative prediction using neural networks and other machine learning models, their place in empirical finance research has been limited. My work aims to contribute to this growing literature.

Investment recommendation by discovering high-quality opinions in investor based social networks

Investor based social networks, such as StockTwist, are gaining increasing popularity. These sites allow users to post their investment opinions in the form of microblogs. Given the growth of the posted data, a significant and challenging research problem is how to utilize the personal wisdom and different viewpoints in these opinions to help investment. A typical way is to aggregate sentiments related to stocks and generates buy or hold recommendations for stocks obtaining favorable votes while suggesting sell or short actions for stocks with negative votes. However, considering the fact that there always exist unreasonable or misleading posts, sentiment aggregation should be improved to be robust to noise. In our work, we study how to estimate qualities of investment opinions in investor based social networks. To predict the quality of an investment opinion, we use multiple categories of factors generated from the author information, opinion content and the characteristics of stocks to which the opinion refers. With predicted qualities of investment opinions, we perform two types of investment recommendation. The first is recommending high-quality opinions to users and the second is recommending portfolios generated by sentiment aggregation in a quality-sensitive manner. Experimental results on real datasets demonstrate the effectiveness of our work in recommending high-quality investment opinions and profitable portfolios.

Construction of currency portfolios by means of an optimized investment strategy

This work focuses on the development of a technical breakout trading strategy based on the Donchian Channel approach, aiming to the construction of profitable portfolios. In this direction, the Modified Renko Bars (MRBs) were developed first; that proved to be a useful trading tool that responses more accurately than the normal candle sticks to the nature and characteristics of the FOREX market. Subsequently, the parameters of the trading strategy (or system) are calibrated for eight currency pairs, over a period of four years (2006–2009), by comparing the performance of three global search derivative-free optimization algorithms. Then, the returns of the developed system are tested for the next seven years (2010–2016) for each pair and two types of portfolios are constructed; an equal weighted one and a portfolio based on the Kelly criterion. The ultimate objective of this paper is to create currency portfolios based on a novel optimized trading strategy, which could beat constantly the main investors’ benchmarks (i.e. S&P500, Barclay CTA Index).

A goal programming capital budgeting model under uncertainty in Construction industry

Due to the increase in investments in construction projects and the lack of practical models in this area developing new practical models is essential. In this paper, researchers suggest a new model in which (1) its assumptions are adopted based on the real world, (2) goal programming is used because of the soft nature of the budget constraints; and (3) risk of variations in cash flows is considered. The presented model chooses the most profitable portfolio of projects and determines their respective financing resources, area under construction, and pre-sale and sale amounts for each period such that the cumulative cash flow at the end of the time horizon is maximized. The fuzzy analytic hierarchy process (FAHP) is used to determine the weight of the objectives. The exact solution to the model is obtained using the ILOG CPLEX software. The presented solution seems efficient; since it yields very small elapsed times to exactly solve the real-world-sized problems. Also, the sensitivity analysis is performed and the results are deliberately studied and analyzed. Parameters such as pre-sale prices, mean and variance of the sale price and construction costs are among the highly sensitive parameters.

Detection of non-technical losses in smart meter data based on load curve profiling and time series analysis

The advent and progressive deployment of the so-called Smart Grid has unleashed a profitable portfolio of new possibilities for an efficient management of the low-voltage distribution network supported by the introduction of information and communication technologies to exploit its digitalization. Among all such possibilities this work focuses on the detection of anomalous energy consumption traces: disregarding whether they are due to malfunctioning metering equipment or fraudulent purposes, strong efforts are invested by utilities to detect such outlying events and address them to optimize the power distribution and avoid significant income costs. In this context this manuscript introduce a novel algorithmic approach for the identification of consumption outliers in Smart Grids that relies on concepts from probabilistic data mining and time series analysis. A key ingredient of the proposed technique is its ability to accommodate time irregularities – shifts and warps – in the consumption habits of the user by concentrating on the shape of the consumption rather than on its temporal properties. Simulation results over real data from a Spanish utility are presented and discussed, from where it is concluded that the proposed approach excels at detecting different outlier cases emulated on the aforementioned consumption traces.

Kinerja Portofolio Saham pada Perusahaan Makanan, Property dan Pertambangan di Bursa Efek Indonesia

The Stock Portfolios' Performance of Food, Property, and Mining Industry This research aimed to analyze the Portfolio's performance of food industry, property, and mining. There are three parameters, namely: Sharpe Performance, Traynor Performance, and Jensen Performance. The three performance measurements assume that there is a linear connection between the return portfolios with the return of some market index. During the period, 2009 to 2013, and the mining and gas sub-sector are more productive than the other sub-sectors. The mining sector is better than the Property Sector and the Food Sector. Then based on the method of Jensen ALPHA it showed the highest performance group of mining stocks, in proportion to the risk which is also very high. The study suggests that the existing has to be maintained and further improved so that the mission to develop sources of potential energy into a profitable investment portfolio can sustain. DOI: 10.15408/ess.v7i2.5354

An Investigation into Returns from Financial Statement Analysis among High Book-to-Market Stocks

The motivation behind this paper was to see if financial statement analysis could be employed by investors to design portfolios of high book-to-market stocks that could help them earn excess returns in Indian context. Using Piotroski (2000) framework which employs a financial statement analysis based F-Score, capable of separating healthy stocks among high book-to-market stocks, and portfolio formation on the basis of the F-Score, we find convincing evidence that financial statement analysis can help investors form profitable portfolios among high book-to-market stocks. We find that portfolios with high F-Score stocks (8 to 9) provide outstanding returns on market adjusted basis. At the same time, portfolios with low F-Score (up to 3) offer poor returns and underperform the markets. Thus a value stock investor could shift his distribution of returns rightwards by investing in portfolios of only high F-Score stocks; simultaneously shorting low F-Score stocks would further amplify the returns.

Short selling, margin buying and stock return in China market

AbstractIt is well argued that short sellers are informed traders, and short interests predict future stock returns significantly. However, most researches neglect margin buyers, as twin sisters of short sellers, and keep silent about their impact on stock returns. In this article, we demonstrate that margin buyers significantly impact predictive power of conventional short measures. We document that conventional short measures neglecting margin‐buying activities, short interest ratio (SIR) and days to cover (DTC) fail to predict stock return unless our analysis is confined to lightly margin bought stocks. We also show that short‐margin trading ratio (SMTR), revised short measure with consideration of margin buying, predict stock return more sharply. What is more, we can form profitable portfolios by the new short measure.

Teaching Business and Economic Journalism

Teachers of business and economic journalism are developing creative approaches to attracting students and educating them in a field many find daunting. To recruit, some avoid using the terms business or economic in course titles, preferring financial basics and financial literacy. Many use novel classroom techniques: to educate students about stock markets, for instance, some set up competitions where students vie to develop the most profitable portfolio. Provocative videos, cleverly developed assignments and trips to business-news sites are highlights in some courses. This article reviews approaches by a broad array of teachers.

A Portfolio Strategy Using Glassdoor’s Business Outlook Ratings

I document that a simple portfolio strategy, selling stocks with worsening business outlook, provides significant abnormal returns. I construct a portfolio of firms one day after they experience a change in business outlook for all sample trading days. Over the sample of 56 consecutive trading days, 3,502 adjustments in business outlook took place. The daily average abnormal return, produced by shorting a worsening portfolio of firms, is 0.232 %. The accumulated raw return of this portfolio over the 56 day sample period is 4.3%. The purpose of this study is two-fold. I, not only show the opportunity for a profitable portfolio strategy, but I also look at the implications that significant portfolio returns have on the usefulness and quality of employee-sourced data.

Stock Selection with a Novel Sigmoid-Based Mixed Discrete-Continuous Differential Evolution Algorithm

A stock selection model with both discrete and continuous decision variables is proposed, in which a novel sigmoid-based mixed discrete-continuous differential evolution algorithm is especially developed for model optimization. In particular, a stock scoring mechanism is first designed to evaluate candidate stocks based on their fundamental and technical features, and the top-ranked stocks are selected to formulate an equal-weighted portfolio. Generally, the proposed model makes literature contributions from two main perspectives. First, to determine the optimal solution in terms of feature selections (discrete variables) and the corresponding weights (continuous variables), the original differential evolution algorithm focusing only on continuous problems is extended to a novel mixed discrete-continuous variant based on sigmoid-based conversion for the discrete part. Second, the stock selection model also resolves the gap of the application of differential evolution algorithm to stock selection. Using the Shanghai A share market of China as the study sample, the empirical results show that the novel stock selection model can make a profitable portfolio and significantly outperform its benchmarks (with other model designs and optimization algorithms used in the existing studies) in terms of both investment return and model robustness.

Parallel Optimization of Sparse Portfolios with AR-HMMs

In this paper we optimize mean reverting portfolios subject to cardinality constraints. First, the parameters of the corresponding Ornstein---Uhlenbeck (OU) process are estimated by auto-regressive Hidden Markov Models (AR-HMM), in order to capture the underlying characteristics of the financial time series. Portfolio optimization is then performed by maximizing the return achieved with a predefined probability instead of optimizing the predictability parameter, which provides more profitable portfolios. The selection of the optimal portfolio according to the goal function is carried out by stochastic search algorithms. The presented solutions satisfy the cardinality constraint in terms of providing a sparse portfolios which minimize the transaction costs (and, as a result, maximize the interpretability of the results). In order to use the method for high frequency trading (HFT) we utilize a massively parallel GPGPU architecture. Both the portfolio optimization and the model identification algorithms are successfully tailored to be running on GPGPU to meet the challenges of efficient software implementation and fast execution time. The performance of the new method has been extensively tested both on historical daily and intraday FOREX data and on artificially generated data series. The results demonstrate that a good average return can be achieved by the proposed trading algorithm in realistic scenarios. The speed profiling has proven that GPGPU is capable of HFT, achieving high-throughput real-time performance.

The Quality-Assuring Role of Mutual Fund Advisory Fees

Active fund managers implicitly promise to research profitable portfolio selection. But active management is an experience good subject to moral hazard. Investors cannot tell high from low quality up front and therefore fear manager shirking. We show how the parties mitigate the moral hazard by paying the manager a premium fee sufficiently high that the manager's one-time gain from shirking is less than the capitalized value of the premium stream he earns from maintaining his promise to provide high quality. Premium advisory fees act as a quality-assuring bond. Our model has a number of revealing extensions and comparative statics.

The quality-assuring role of mutual fund advisory fees

Active fund managers implicitly promise to research profitable portfolio selection. But active management is an experience good subject to moral hazard. Investors cannot tell high from low quality up front and therefore fear manager shirking. We show how the parties mitigate the moral hazard by paying the manager a premium fee sufficiently high that the manager's one-time gain from shirking is less than the capitalized value of the premium stream he earns from maintaining his promise to provide high quality. Premium advisory fees act as a quality-assuring bond. Our model has a number of revealing extensions and comparative statics.

Rola underwrittingu w ubezpieczeniach komunikacyjnych floty pojazdów - aspekty praktyczne

Underwriting is one of the most important functions that characterises tic the insurance market. Underwriting policies identifies areas and types of insurance that should be developed and those products that can be developed under certain condi­ tions. The main factor that determines the undisputed position of underwriting is experi­ ence with the poor economic condition of insurance companies caused by the excessive accumulation of losses resulting from an insufficient recognition of risk. Professional underwriting results in a profitable portfolio, ie a portfolio that meets the criteria of premium rate calculation. It enables a reduction of risk, adverse devia­ tions from the expected results and the administrative work load. The primary purpose of underwriting is to reduce the „negative selection”. Negative selection undoubtedly causes the weak financial condition of insurance companies but also badly affects a cer­ tain group of customers. As a consequence the clients who maintain appropriate safety standards still have to pay a relatively high premium as the companies compensate for the losses paid out to by customers with an adverse loss history.