Abstract

     In order to solve the problem of portfolio optimization, this paper proposes a method that combines multi-objective optimization and multi-attribute decision-making to solve the dual-objective portfolio optimization model with conditional value-at-risk (CVaR) measuring risk and including transaction costs. First, in the multi-objective optimization stage, a multi-population parallel NSGA-II based on sparsity strategy (SMP-NSGA-II) is proposed to obtain multiple Pareto optimal solutions of the model. Second, in the multi-attribute decision-making stage, in order to reflect different investment preferences, the Pareto optimal set obtained is clustered through the fuzzy C-means, and then the grey relational projection method is used to evaluate the solutions belonging to the same cluster to select the optimal compromise solution. Finally, a case study of 9 semiconductor stocks in China’s Shanghai and Shenzhen stock markets is carried out, and the optimal compromise portfolio under different investment preferences is given. At the same time, the proposed algorithm is compared with the other six multi-objective evolutionary algorithms (MOEAs), which verifies that the algorithm in this paper has certain competitiveness.

Introduction

     Nowadays, in the field of securities investment, the indicators used to quantify risk mainly include value-atrisk (VaR) and conditional value-at-risk (CVaR). Among them, VaR is represented by nonlinear, non-convex and nondifferentiable function with multiple local optima, making it difficult to calculate. To solve these problems, Rockafellar et al. [1] introduced the CVaR, which is a coherent risk measure that considers risk as the most serious loss in a given scenario, taking into account a certain degree of confidence. Since CVaR is a convex function, it can effectively solve the optimization problem that uses CVaR as a minimization goal or constraint [2, 3]. At the same time, Yu et al. [4] compared five different risk models and verified through experiments that using CVaR to measure risk is a good choice.

     As the complexity of practical applications continues to increase, scholars have developed various heuristic algorithms to solve portfolio optimization problems. The application of heuristic algorithms in portfolio optimization problems is divided into two categories. The first category simplifies portfolio objectives through the setting of weight coefficients [5–7], and obtains a risk-return curve by continuously changing the risk avoidance parameters of representative investors. This method has a certain degree of subjectivity. The second type uses multi-objective evolutionary algorithm (MOEA) to directly optimize risks and benefits simultaneously [8–11], and can obtain a complete effective frontier in one operation. Obviously, it is more convenient to use MOEAs to solve portfolio optimization problems.

Conclusion

     In order to solve the dual-objective portfolio optimization model with conditional value-at-risk (CVaR) as a measure of risk and including transaction costs, this paper proposes a method combining multi-objective optimization and multi-attribute decision-making. In the multi-objective optimization stage, this paper proposes a multi-population parallel NSGA-II based on sparsity strategy (SMP-NSGAII). In the case studies of 9 stocks in the semiconductor industry, we compared SMP-NSGA-II with the other six MOEAs through two performance evaluation indicators (HV and SP) and running time, then verified the feasibility of the SMP-NSGA-II algorithm. In the multi-attribute decision-making stage, this paper adopts the FCM-GRP hybrid method to give the optimal compromise investment portfolio under different DM preferences.

     There are a couple of drawbacks in the present study. Firstly, the model studied in this paper is relatively simple and does not take into account the many unstable factors of the real securities market; Secondly, the space complexity of the proposed SMP-NSGA-II algorithm is also high.

Abstract

     Based on the startup investment database, we empirically examine the positive effects of a portfolio financing strategy for startups. It is known that some investment banks or venture companies have supplied risky and patient capital to U.S. startups of all financial corporations. They provide differentiated debt financing to startups who already received equity financing and invest more in equity when the startup is younger. Empirical analysis shows that the core risk management is to have a dynamic financing ratio between equity and debt as well as varied equity cost of capital, lending rate and investment horizon based on the age, technology, and financial soundness of a startup. We also find that optimal portfolio financing strategy can help startups raise funds with ease, but also it provides investors with higher expected return on investment, which can facilitate financing for startups and furthermore small and medium enterprises.

Introduction

     Startups play a significant role in economic growth. First, they create many jobs in various industries. If a startup drives technological innovation, new industries are created, resulting in various new jobs. After David Packard and William R. Hewlett set up their audio oscillator business in Silicon Valley in 1939, radio and telegraph technology grew rapidly, providing jobs to many young people. Over the years, innovative startups have emerged in the semiconductor and information technology industries, such as Apple, Facebook, Google, IBM, Microsoft, and Uber. Furthermore, as a startup grows into a large company, it also inspires workers to establish their own startups by taking inspiration from entrepreneurs who have already experienced success. Simply put, just as startups create jobs, an entrepreneur with a created job establishes a new startup, causing a positive feedback loop in job creation. As shown in Fig. 1, the job growth in Silicon Valley and San Francisco rose by 30%–35% from 2010 to 2018, which is much higher than the average job growth rates of the USA, Germany, Japan, South Korea, and China.

Conclusion and implications

     Startups drive economic growth by creating jobs, increasing TFP, and allocating resources efficiently. A portfolio financing strategy can help activate startups. Some investment banks, such as the SVB, have adopted a differentiated portfolio financing strategy for both debt and equity financing. They invest more in equity when the firm is young and invest more in debt as the firm ages. They also manage the credit risk of loan portfolios.

Abstract

     This paper explores the determinants of credit risk for the Colombian economy, a small emerging economy in Latin American. Using a sample of 28 large banks over the 2009–2019 period and the dynamic data panel approach, we find that the macroeconomic environment’s deterioration affects the credit risk perception held by banks as measured through non-performing loans and loan loss provisions. On the other hand, a better political environment brought about by peace accords smoothed such an impact. Estimates indicate different reactions when distinguishing by loan type. Business credit depends heavily on unemployment, while consumer credit risk is more sensitive to the interest rate. In the case of mortgage loans, economic growth and the unemployment rate are the most critical variables to mitigate risk. These results shed light on the impact of the economic environment on credit lines with different features.

Introduction

     The current economic recession caused by the COVID-19 pandemic has drawn attention to the consequences it may have on the banking system. As a result, there is great interest in knowing which indicators can provide efficient signals about financial instability (Chau, Lin, and Lin 2020). Loan performance is an essential channel to analyze the effect that macroeconomic factors have on banking crises. Unfavorable economic conditions such as lower economic growth and high unemployment can increase loan defaults and bankruptcies, so the issue of the procyclicality of the financial system should attract the attention of both academics and policymakers because it can exacerbate the economic downturn (Castro 2013: Cecchetti and Kohler 2014; De Moraes and De Mendonça 2019).

     Credit risk is the main channel for relating economic shocks to bank balance sheets. It is defined as the risk that a loan will not be paid (partially or totally) to the lender and it is a problem that has received extensive theoretical research. The seminal paper of Altman and Saunders (1998) initially surveyed the approaches that existed on the subject. Credit risk analyses have changed from so-called banking expert systems analyses to more objective schemes. Until the late 1980s, bankers used microeconomic information on the borrower such as reputation, capital, ability to pay, and collaterals to make subjective judgments about credit risk. By the late 1990s, these models began to fail because they did not consider the economic environment in which banks operate. New approaches began to explore alternatives to explain credit risk based on the possible states of the economy. In particular, the study by Salas and Saurina (2002) pioneered this issue by combining micro and macroeconomic data to assess credit risk.

Conclusions

     The global economic crisis is paying attention to the problems it can bring to the financial system. In this sense, this study analyzed the effect of macroeconomic perspectives on credit risk for the Colombian case. Through a dynamic data panel approach for the period 2009–2019, it was found that the macroeconomic environment is the primary determinant of Colombia’s credit risk. Credit risk increases with declines in GDP growth, rising unemployment, and tightening monetary policy rates.

     In 2020 the Colombian economy entered a severe recession and, according to the results of this study, credit risk is likely to increase. In terms of economic policy, this means a threat to financial stability and the possibility of a credit crunch (Louzis, Vouldis, and Metaxas 2012), so different incentives for each type of loan are needed to recover the financial system. Government endorsements and mortgage subsidy policies can help mitigate financial market failures.

1- Introduction

2- Methods

3- Results

4- Discussion

5- Conclusions and ways forward

References

Abstract

Designing optimal financial products for rural households in poor countries remains a challenge, especially in a risk management perspective in disaster-prone areas. In addition to financial intermediaries’ difficulties with reaching potential customers efficiently, information asymmetries prevent fully understanding customers and developing suitable products for them. The clientele’s weak information systems, coupled with cultural aspects often unfamiliar to the supplier of financial services, make the analysis difficult and costly. This article builds on a financial statement approach to empirically explore the combination of farmers’ financial and non-financial strategies, in a portfolio risk management perspective, with a focus on shock-type risk exposure. It emerges that farmers’ decisions follow a sequential process with different combinations of savings, credit, and insurance that interact with non-financial strategies to help managing risks. The study relies on panel data collected in Ethiopia. The empirical analysis confirms the vulnerability of sample households to natural systemic shocks and entails that the conclusions of this paper can be considered part of the broad literature on the disaster risk management of low-income households.

CONCLUSIONS AND WAYS FORWARD

This article was inspired by the use of a conceptual framework based on a portfolio approach to study rural households’ risk management strategies and had a particular interest in the potential demand for insurance. The structure of the sample shows that, while several risks are affecting the households, a large percentage of the interviewees declared to suffer of disastertype events: droughts and floods. The outcome of the current analysis confirms the results of previous, simplified, attempts to prove that although insurance is often considered an alternative measure to farmers’ own riskcoping strategies, it may be a complementary solution. The information obtained by using a system analysis (in this case, the SURE methodology) is that the main dimensions of a portfolio approach to risk management can be analysed in their reciprocal relationships. Based on the findings, insurance and savings decisions are affected by the overall composition of risk management strategies (savings, insurance, loans, livestock), and borrowing decisions depend on all factors but savings. Another finding is that the direction of the influence in some cases is constant (always positive, suggesting complementarity), and in other cases, the direction changes according to the sequence of the portfolio choice. This phenomenon is evident in the case of borrowing.

1- Introduction

2- Literature Review and Hypothesis Development

3- Data and variable construction

4- Model and Empirical Analysis

5- Conclusions

References

Abstract

Low tax morale is associated with domestic tax evasion. We find evidence of cross-border equity flows designed to evade taxes in low tax morale countries. Using Foreign Portfolio Equity Investment (FPI) flows into 21 OECD countries from 138 source countries and an index of tax morale from the World Value Survey (WVS), we show that individuals in countries with low tax morale engage in tax evasion via roundtripping through tax havens. This allows them to benefit from differential taxes applied to foreign investors vis-a-vis domestic investors. Our results remain robust to various measures of tax morale and distinct subsamples.

Introduction

Tax morale, the intrinsic motivation to pay taxes or feel guilt from failure to comply (Luttmer & Singhal, 2014), has been linked to domestic tax evasion by underreporting taxable income (Alm, Sanchez, & de Juan, 1995; Alm & McClellan, 2012; Halla, 2012; inter alia). Are these tax morale effects so pervasive that they motivate individuals also to engage in international evasion schemes, even to the extent that international portfolio investment flows are influenced? This study finds robust evidence that this, in fact, is the case. The determinants of tax morale have been widely explored (for example, Hofmann, Hoelzl, & Kirchler, 2008; DeBacker, Heim, & Tran, 2015; OECD, 2013; Luttmer & Singhal, 2014). Tax morale is negatively correlated with shadow economy activities and positively correlated with direct democracy. This may be explained by differences in the fairness of tax administration, perceived equity of fiscal exchange, attitude toward respective governments (Cummings, Martinez-Vazquez, McKee, & Torgler, 2004), differences in culture, which also interact with demographics (Botelho, Harrison, Hirsch, & Elisabet, 2001), and trustworthiness (Ashraf, Bohnet, & Piankov, 2006). However, proxies for tax morale are challenging to construct. Slemrod and Weber (2012) argue that a single tax morale indicator, rather than an index of indicators, provides a more straightforward gauge of its relative importance. Recent survey efforts on culture and social values include questions on attitudes toward tax paying and corruption, inter alia (World Values Survey; Inglehart et al., 2014). Herein, we use an indicator based on country-level survey response data to questions of perceived tax fairness.1 Since measuring tax evasion is challenging (Alm & Torgler, 2011), we use roundtripping as an indirect approach to capture it. We focus on OECD foreign portfolio investment (FPI) inflows through tax havens that are attributable to changes in tax savings. The concept of roundtripping is straightforward and illustrated in Fig. 1. Domestic investors create and capitalize shell companies overseas, in tax havens, before they invest those funds back into their home capital markets, where incoming funds are now recorded as foreign capital and taxed at a more favorable rate. Changes in the tax differential between a domestic investor and a foreign investor is an important determinant of foreign equity flows (Hanlon, Maydew, & Thornock, 2015; Kemme, Parikh, & Steigner, 2017) and an indicator of tax evasion, but not the only one. We find that the attitude of individuals toward the payment of taxes in general, or tax morale, is another noteworthy determinant of tax evasion, which has not been studied extensively in this context.

1. Introduction

2. The model

3. Equilibrium

4. Example

5. Conclusion

Acknowledgments

Appendix A. Supplementary materials

Research Data

References

Abstract

In Das et al. (2010), an agent divides his or her wealth among mental accounts that have different goals and optimal portfolios. While the moments of the distribution of asset returns are exogenous in their normative model, they are endogenous in our corresponding positive model. We obtain the following results. First, there are multiple equilibria that we parameterize by the implied risk aversion coefficient of the agent’s aggregate portfolio. Second, equilibrium asset prices and the composition of optimal portfolios within accounts depend on this coefficient. Third, altering the goal of any given account affects the composition of each portfolio.

Introduction

Das et al. (2010, DMSS) combine certain aspects of behavioral and mean-variance (MV) portfolio selection models. Like Shefrin and Statman (2000), DMSS consider an agent who divides his or her wealth among mental accounts (hereafter ‘accounts’) with different goals such as retirement or bequests.1 For each account, short sales are allowed and the agent maximizes its expected return subject to: (1) fully investing the wealth assigned to it; and (2) the probability of its return being less than or equal to some threshold return (e.g., −20%) not exceeding some threshold probability (e.g., 5%).2 The threshold return and threshold probability (hereafter ‘thresholds’) can vary across accounts to reflect different goals. Assuming that a risk-free asset is absent and risky asset returns have a multivariate normal distribution, DMSS show that optimal portfolios within accounts and the resulting aggregate portfolio are all on the MV frontier of Markowitz (1952). In their normative model, the moments of this distribution are exogenous.

Our paper develops a corresponding positive model where these moments are endogenous in four types of economies. The first is a single-agent economy where the agent has an objective function defined over the expected value and variance of his or her future wealth as well as a single account (hereafter ‘MV agent’). The second is also a single-agent economy but the agent has an objective function as in DMSS (hereafter ‘DMSS agent’) and a single account. The third is a single-agent economy with a DMSS agent but with multiple accounts. The fourth is a two-agent economy with an MV agent (who has a single account) and a DMSS agent who has multiple accounts

1- Introduction

2- SVM method

3- Portfolio selection model: MV method

4- The experiment

5- Results

6- Conclusions

References

Abstract

Forecasting stock returns is an exacting prospect in the context of financial time series. This study proposes a unique decision-making model for day trading investments on the stock market. In this regard, the model was developed using a fusion approach of a classifier based on machine learning, with the support vector machine (SVM) method, and the mean-variance (MV) method for portfolio selection. The model’s experimental evaluation was based on assets from the São Paulo Stock Exchange Index (Ibovespa). Monthly rolling windows were used to choose the best-performing parameter sets (the in-sample phase) and testing (the out-of-sample phase). The monthly windows were composed of daily rolling windows, with new training of the classifying algorithm and portfolio optimization. A total of 81 parameter arrangements were formulated. To compare the proposed model’s performance, two other models were suggested: (i) SVM + 1/N, which maintained the process of classifying the trends of the assets that reached a certain target of gain and which invested equally in all assets that had positive signals in their classifications, and (ii) Random + MV, which also maintained the selection of those assets with a tendency to reach a certain target of gain, but where the selection was randomly defined. Then, the portfolio’s composition was determined using the MV method. Together, the alternative models registered 36 parameter variations. In addition to these two models, the results were also compared with the Ibovespa’s performance. The experiments were formulated using historical data for 3716 trading days for the out-of-sample analysis. Simulations were conducted without including transaction costs and also with the inclusion of a proportion of such costs. We specifically analyzed the effect of brokerage costs on buying and selling stocks on the Brazilian market. This study also evaluated the classifier’s performance, portfolios’ cardinality, and models’ returns and risks. The proposed main model showed significant results, although demand for trading value can be a limiting factor for its implementation. Nonetheless, this study extends the theoretical application of machine learning and offers a potentially practical approach to anticipating stock prices.

Introduction

Predicting stock returns is considered to be one of the most challenging tasks when dealing with financial time series because the stock market is dynamic, complex, evolutionary, nonlinear, nebulous, nonparametric, and chaotic by nature. Additionally, the stock market is extremely sensitive to political factors, microeconomic and macroeconomic conditions, and investors’ expectations and insecurities (Ballings, Van den Poel, Hespeels, & Gryp, 2015; Kara, Boyacioglu, & Baykan, 2011; Tan, Quek, & Ng, 2007). According to mainstream financial theory, predicting financial asset prices is impossible. The efficient market hypothesis (EMH), which is the literature’s main theoretical pillar, suggests that the task of predicting future prices based on financial assets’ past behavior cannot achieve abnormal returns. The reason is that the distribution function of a financial time series denotes a Brownian motion, which has random, independent, and Gaussian distribution characteristics. However, some studies reject the EMH, arguing that the stock market is not actually established at random and that financial time series have long-term memory. For example, recent studies dispute the EMH in the context of different markets and periods. Cervelló-Royo, Guijarro, and Michniuk (2015) showed empirical evidence that compromises the premise of the EMH. The authors researched the intraday markets of the American Dow Jones Industrial Average (DJIA) index, the German Deutscher Aktien 30 Index (DAX), and the British Financial Times Stock Exchange (FTSE) index from 2000 to 2013. By using a methodology based on flag patterns, they tested 96 different configurations. The results were very profitable for all the markets. Chourmouziadis and Chatzoglou (2016) researched daily data from the Athens Stock Exchange from 1996 to 2012. They combined technical analysis and fuzzy logic. The profitability results of the proposed methodology were surprisingly higher than the baselines. Kim and Enke (2016) studied the intraday data of the Korea Composite Stock Price Index (KOSPI) 200 from 2007 to 2014. They formulated a set of rules based on technical analysis combined with genetic algorithms and reported abnormal profits. Chen and Chen (2016) proposed a model based on flag patterns for recognizing bullish reversal patterns. They tested the strategy with the National Association of Securities Dealers Automated Quotation System (NASDAQ) from 1989 to 2004 and with the Taiwan Capitalization Weighted Stock Index (TAIEX) from 1990 to 2004. The results showed high levels of profitability. Kampouridis and Otero (2017) studied the intraday foreign exchange market, analyzing a 10-month period between 2013 and 2014. They proposed a model that combined technical indicators, physical time scales, and genetic algorithms. The results guaranteed higher returns than those of the baselines.

2- Approaches to resource allocation for biosecurity in Australia

3- The portfolio model

4- Application to hawkweed, FMD, RIFA, and PFF in Australia

5- Portfolio allocation under different budget constraints

6- Policy implications for budget allocation in biosecurity

7- Concluding remarks

References

Abstract

This paper presents a practical model for optimally allocating a budget across different biosecurity threats and measures (e.g. prevention or border quarantine, active surveillance for early detection, and containment and eradication measures) to ensure the highest rate of return. Our portfolio model differs from the common principle, which ranks alternative projects by their benefit cost ratios and picks the one that generates the highest average benefit cost ratio. The model we propose, instead, aims to allocate shares of the budget to the species where it is most cost‐effective, and consequently determine the optimal scale of the control program for each threat under varying budget constraints. The cost‐effectiveness of each block of budget spent on a threat is determined by minimising its expected total cost, including the damages it inflicts, and the control expenditures incurred in preventing or mitigating damages. As an illustration, the model is applied to the optimal allocation of a budget across four of Australia’s most dangerous pests and diseases: red imported fire ants; foot‐and‐mouth disease; papaya fruit fly; and orange hawkweed. The model can readily be extended to consider more species and activities, and more complex settings including cases where detailed spatial and temporal information needs to be considered.

Introduction

Biological invasions and pathogens pose a major threat to industry, the environment and human health, causing billions of dollars in damages every year (Pimentel et al. 2005; Sinden et al. 2005). The cost inflicted by biosecurity threats includes not only economic and environmental damages, but also the control costs incurred in preventing or mitigating their effects. Since resources to address these ever-increasing threats are limited, it is essential that these resources are used efficiently. As a result, public biosecurity agencies face challenging decisions of how best to allocate scarce resources when they attempt to prevent, suppress, and eradicate exotic and established pests and diseases (Perrings et al. 2010; Yemshanov et al. 2014; Akter et al. 2015).

Many new methods and concepts have emerged in financial portfolio construction, risk management and performance evaluation since Markowitz’s (1952) pioneering work. The use of the variance, or standard deviation, of returns as a proxy for investment risk has been questioned, and alternative risk measures have been proposed, according to Rom and Ferguson (1994); Roman and Mitra (2009) and Araújo and Montini (2015). The use of the original sample covariance matrix to estimate the risk of an asset portfolio has also been questioned, according to Chan et al. (1999) and Santos and Tessari (2012). Not even the use of quantitative methods to optimize portfolios or to allocate resources among different asset classes has been spared from criticism (DeMiguel et al. (2009). Given the vast academic literature that proposes, explains and details numerous methods for portfolio construction and risk management and performance evaluation, the following central research question is addressed in this paper: among the major quantitative techniques for portfolio construction, risk management and performance evaluation suggested by the international and Brazilian academic literature, which ones are actually adopted by financial market practitioners? The main objective is to identify possible differences between what is taught in classrooms or discussed in academic conferences and what is involved in the day-to-day practice of asset managers and to determine whether a mismatch exists. In addition to the central research question, the present work aims to compare Brazilian data with European data to inform us about the degree of globalization of the Brazilian asset management industry. Another specific goal is to check the existence of different practices among subgroups in the industry, such as companies of foreign and Brazilian origin, and to investigate the determinants of optimization and risk budgeting. To achieve these objectives, we conduct a literature review and obtain 78 responses from a field survey with 274 asset management companies. The number of respondents is considerable, especially given the limited universe of professional asset management companies and the difficulty of extracting information from participants in a highly competitive and results-oriented segment. The field survey used an online questionnaire similar to that applied by Amenc et al. (2011) in Europe. To analyze the data, the present paper uses Pearson’s chi-square independence test, multiple regressions using ordinary least squares (OLS) and a probit model.

The quick transformations in the world economy have required firms to open to new ideas and acknowledge the importance of Intellectual Property (IP) to achieve a competitive advantage and profit from its commercial use (Hall & Ziedonis 2001; Gans & Stern, 2010). The acquisition of advantage from knowledge and its transformation into IP rights (Yoon et al. 2008) has resulted in several economic benefits for firms: getting a powerful market positioning, increasing their market, and conveying an image of high-level innovation. These targets cannot be pursued unless a strategic use of IP is adopted (Pitkethly 2007; Somaya 2012). Patents, similarly to the other forms of IP, represent value assets and, as such, their proper strategic management can affect firm value creation (Klaila & Hall 2000; Teece, 2000). As is usual with all value assets, firms should understand the actual contribution of the patents to reach the objectives and, consequently, take on to manage patents in a strategic way by assessing their technological and monetary value (Jeong & Yoon 2015; Ponomarev et al. 2014). It is well known that highly technological firms, not only large ones, but also SMEs, benefit from patenting and own a multitude of patents in the result of the acceleration in the growth of their trend of patenting. This is because often firms’ patenting answers the need for maintaining the strategic positioning in a given technology, or for expanding their activity in a different technological area from the core one, or for protecting or blocking their technological inventions from other competitors. For these reasons, we can say that several factors lead firm managers to analyze the actual technology condition of patent portfolios. First of all, the frequently changeable technological context that requires a continual refresh of patent congruence with the firm goals. Second, the obsolescence, over time, of patents that end up to lose their core quality to the company for many of which it could be devised a better purpose. Third, firm budgetary adjustments that could inspire patent exploitation actions.

Almost 40 years since Mintzberg introduced the concept of emergent strategy (Mintzberg, 1978; Mintzberg and Waters, 1985), the impetus to understand the complexity of strategy processes, both deliberate and emergent, is strong and growing (Vaara and Whittington, 2012). Acting alone, formal and rigid strategy processes have been shown to be insufficient in shaping strategy in response to change (McKiernan and Morris, 1994; Parnell et al., 2012). In today’s volatile and unforgiving competitive environment, it might be the emergent strategic processes that are particularly relevant for organizational adaptability and survival (Chari et al., 2014; Hamel, 2009; Thomas and D’Aveni, 2009; Whittington et al., 2016). Emergent strategies are realized in absence of or despite formal strategic intention and by nature fall outside traditional strategy processes; this makes it difficult to control their influence and has prompted exploration of alternate methods to manage ‘planned emergence’ (Grant, 2003; Levina and Su, 2008). This study explores how strategic control mechanisms implemented at the portfolio level can influence organizational performance by not only measuring performance, but also providing impetus and direction for change and emergence in strategic processes (Bititci et al., 2012; Thomas and Ambrosini, 2015). The bulk of strategic management research focuses on deliberate strategies and strategy content and formulation, while strategy implementation is sometimes portrayed as an autonomous activity and is not subjected to much scrutiny (Bower and Gilbert, 2005; Hrebiniak, 2006; Hutzschenreuter and Kleindienst, 2006; Raes et al., 2011). However, according to Mankins and Steele (2005), firms realize only 63% of their strategies’ potential value, and Johnson (2004) reports that 66% of corporate strategy is never implemented. Despite warnings that it is more difficult to make strategy work than to develop it (Hrebiniak, 2006), approaches to implementing strategy tend to take a narrow view and look for the best way to implement the deliberate strategy, rather than to consider other possibilities such as the role of emergence (Meskendahl, 2010; Morgan et al., 2008; Unger et al., 2012).

The current technologies are diverse, they are combined easily, transferred quickly from one industry to another. Even if, usually, the technologies are expensive and their creation is gradually becoming a business itself, can not overlook the fact that technology is the most important instrument for sustaining the competitive advantage of the companies with the potential to change the structure of the existing industries and to create new industries. All this dynamic challenges for the companies’ management born on the need the replacement of the old technologies, the assessment of the existing ones into the market, the integration of the new technologies, taking into account the strategic objectives of the business. The paper aims to identify a number of aspects relevant to the analysis of a portfolio of technologies of a company and to do an analysis of how these issues differ at the level of the industrial branch. Within the paper, two representative industries were chosen, namely the machine building industry and the IT & C industry, they are representative for Romania through dynamics and the economic impact. The paper work is part of a more complex approach that aims to extend the analysis to several industries in Romania.

Construction is a project-based industry. The unit of analysis and focus of decision-making (bidding, risk assessment, etc.) are the “projects”. During decision-making, construction projects are generally handled as they are independent from each other and the decision support tools are usually designed to be used at the “project level”. However, most of the companies are executing projects simultaneously and there exist dependencies between these projects due to shared resources, similar technical requirements, physical locations, contractual agreements and similar external environment. When a project success is dependent on other projects, it can be stated that relationship exists between these projects1 . Projects may share many resources and may have common objectives to be achieved. Therefore, there can be a resource, outcome, market/benefit, financial, or learning dependency between projects2 . Thus, projects need to be handled from a “portfolio” perspective and managed at the “portfolio level” as it has been widely discussed in the literature3,4,5. Although, research on portfolio management has been widely carried out in the industries such as finance and regarding projects such as technology, innovation, and research and development projects; construction industry specific studies have been very limited in this area6,7,8. Portfolio success is considerably dependent on identification of relations between projects and generation of strategies accordingly2,4,9 . In his study, Rungi (2010a)5 states the importance of evaluation of dependencies between projects to achieve portfolio success and argues that interdependency management is a critical success factor. Portfolio management is a complicated process since it requires comprehensive analysis of strategic objectives, financial profit, project performance, demand conditions, resources, capabilities, risks and other similar parameters10,11.