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We study the inter-temporally optimal innovation strategies of incumbent manufacturing firms that compete in an established market and can extend their product line through product innovation. Firms invest in production capacity and R&D knowledge stock, where the R&D knowledge stock and the current R&D investment determine the hazard rate of innovation. Our findings show that the firms’ optimal R&D strategies are driven by a subtle interplay between the relative positions of their R&D knowledge stocks and their current relative positions on the established market. First, we find that under symmetric investment costs the knowledge leader should spend more on R&D than the knowledge laggard only if it has a substantially smaller market share on the established market. If the knowledge leader’s market share is sufficiently large, its optimal investment in R&D is so small that its innovation rate is lower than the knowledge laggard’s. Second, optimal investment in R&D knowledge is negatively affected by the opponent’s production capacity on the established market if the competitor has not innovated yet. However, we find that this effect is reversed after the competitor has successfully introduced the new product on the submarket. Third, the manufacturing firm with higher costs of adjusting production capacity for the established product has a higher incentive to engage in product innovation and might even achieve a higher total discounted profit than its more efficient competitor.

Motivated by the practices of technology investment in the new energy vehicle manufacturing industry, this article investigates an outsourcing supply chain in which an original brand manufacturer (OBM) possessing the core new energy technology R&D outsources production to a competitive manufacturer (CM). To cater to market demands and increase market competitiveness, the CM may actively invest in technology with the OBM or by himself, or not, i.e., Strategies JI, OI, and NI, respectively. In particular, owing to technology spillover effect, the CM could use the technology inferior to the OBM's to produce his own-branded products in Strategy NI. Our results reveal that the OBM consistently profits the most in Strategy JI. However, the CM's preference strategy is influenced by the competition intensity, technology spillover level, consumers’ product preference, and cost-sharing ratio. In specific, when both the competition intensity and technology spillover are weak or the competition intensity is strong for arbitrary technology spillover level, the CM may prefer Strategy JI or OI depending on consumers’ product preference and cost-sharing ratio and never prefers Strategy NI. However, when the competition intensity is weak but the technology spillover level is strong, the CM may prefer Strategy NI in certain situations. Furthermore, our study extends the model to involve variable production costs, and reveals that the main results derived in the basic model still hold.

We measure value creation by activist investors via structural estimation of a model of the choice between passive investment and activism. Our estimates imply that average returns following activist intent announcements consist of 74.8 % expected value creation, or treatment, 13.4 % stock picking, and 11.8 % sample selection effects. Higher treatment values predict improvements in firm performance and lower proxy contest probabilities, whereas abnormal announcements returns do not, suggesting that our estimate identifies more effective activism campaigns. The evidence demonstrates the importance of using the joint distribution of investment strategies and announcement returns to recover the expected returns and costs of activism.

In this paper, we study whether firms manage their pension risk exposures within an integrated corporate risk management framework or they manage their pension and firm risk exposures independently following the adoption of fair value pension reporting regulation (IAS 19). Controlling for known factors affecting the risk-taking in pension plan investments, we document a robust negative association both at the cross-section and over time between pension risk and firm systematic risk and operating asset risk during the post-IAS 19 adoption period. The findings suggest that firms manage pension risk as an integral part of firm risk, consistent with a coordinated risk management explanation. Overall, our evidence highlights that pension investment strategy is a dynamic process that is ultimately determined by sponsoring firms' strategic corporate risk management considerations and the important role that changing pension reporting regulation plays in shaping these dynamics. Our conclusions have potential wider implications for ongoing reforms in financial reporting and disclosure policy settings.

The low performance of Nigerian firms despite investment in intellectual capital is a major concern. While studies have shown that corporate governance practices strengthens the subsisting relationship between investment in intellectual capital and performance in the developed economies, this moderating effect in Nigeria is yet to be adequately explored as research focus is limited to possible effects of intellectual capital and performance. It is against this background, this study investigated the moderating role of corporate governance on the relationship between intellectual capital and performance of listed non-financial companies in Nigeria. The study adopted ex-post facto research design, and data were drawn from the audited annual reports of fifty (50) listed non-financial firms for a period of 2007 to 2017. Multiple regression techniques were employed to test the relationship among the variables. The results of the study revealed that both intellectual capital and corporate governance drive financial performance as the relationship is found significant in all components. The study concluded that corporate governance moderated the effect of investment in intellectual capital on financial performance. The study recommends that Board of directors should adopt measurable corporate governance mechanism which strengthens and helps in investment strategy that increases and improves performance. Also, there is need to entrench corporate governance as a control strategy and impetus towards attaining organization’s goals.

To stay competitive, high-technology manufacturers not only frequently source new technologies from their suppliers, but also financially support the development of these new technologies into component products or production tools. We consider a manufacturer that can either source a new but immature technology from a financially constrained supplier, or source a mature technology from an existing supplier if and only if the development of the new technology fails. To support the new technology, the manufacturer can choose to inject capital in the form of an equity or loan. The investment strategy not only affects the new supplier’s development effort and the probability of technical success (PTS), but also affects the existing supplier’s effort to improve the mature technology, which presents the manufacturer with a trade-off. Following the debt financing literature, we find that a loan contract is associated with a cost-shifting effect and often leads to a higher PTS. However, because the manufacturer not only maintains an investment but also a procurement relationship with the new supplier, we find a profit-sharing effect associated with an equity investment, which does not exist in the traditional equity issuance literature. In particular, we show that the profit-sharing effect can dominate the cost-shifting effect and lead to a higher PTS when the new supplier’s technological capability is sufficiently high. Nonetheless, we also show that the strategy that derives a higher PTS does not necessarily generate a higher payoff for the manufacturer. On the one hand, a loan can be preferred even when it leads to a lower PTS because the cost-shifting effect allows the manufacturer to offer a sufficiently low procurement payment while maintaining a sufficiently high PTS. On the other hand, when the existing supplier is very capable of reducing its costs, a loan can over-incentivize the new supplier to exert excessive effort and backfire. This paper was accepted by Charles Corbett, operations management.

Generation Expansion Planning (GEP) can inform regulation, electricity market design, and regional system planning by identifying adaptive investment strategies. Relevant uncertainties include hourly variability in load and renewable generation and decadal-scale uncertainty in technology, markets, and regulation. A multi-stage and multi-scale stochastic GEP model that represents these uncertainties at sufficient resolution becomes intractable. We present an approach for representing this multi-scale uncertainty, and compare it to existing methods, applied to a two-stage stochastic GEP model with a cumulative carbon emission target. For long-term uncertainty, we compare partitioning methods, which reduce the number of decision variables but retain all scenarios, to representative scenario methods, which retain only a subset of the original scenarios. For short-term uncertainty, we compare methods that select representative weeks based on distance metrics in the parameter space to methods that use the covariance of outcomes across feasible decisions to select weeks. We find that scenario reduction methods struggle to find the appropriate investment levels for variable renewable generation and consequently produce more costly plans than scenario partitioning methods. While simple approximating methods perform well with larger models, covariance based approximations have the best performance overall.

A volatility surface is an important tool for pricing and hedging derivatives. The surface shows the volatility that is implied by the market price of an option on an asset as a function of the option's strike price and maturity. Often, market data is incomplete and it is necessary to estimate missing points on partially observed surfaces. In this paper, we show how variational autoencoders can be used for this task. The first step is to derive latent variables that can be used to construct synthetic volatility surfaces that are indistinguishable from those observed historically. The second step is to determine the synthetic surface generated by our latent variables that fits available data as closely as possible. As a dividend of our first step, the synthetic surfaces produced can also be used in stress testing, in market simulators for developing quantitative investment strategies, and for the valuation of exotic options. We illustrate our procedure and demonstrate its power using foreign exchange market data.

Transition of Georgia to market economy is a complex and continuous process, closely related to both, economic growth and social challenges. In this regard domestic as well as foreign direct investments are one of the mechanisms of utmost importance. The country with economy in transition has many social-economic problems that make it essential for government to look for the most efficient ways of solving them implementing proper investment strategy.The paper considers the main trends and tendencies of investments from macroeconomic point of view, concerning the governmental investment policy aspects, investment climate, the dynamics of investments flows, capital revenues, investments’ share in GDP, the structure of investments in term of countries, sectors, etc. The article considers the core issues of the investment policy in Georgia from pragmatic as well as conceptual point of view, where the role and importance of investment policy are presented, legislative base of investments are discussed, inevitability of transparency, measurability and effectiveness of investment process is reasoned, proactive investment approach is prioritized, the importance of privatization policy is given, forms of privatization and their effectiveness are analyzed, complex utilization of investment marketing instruments, effective management of investment cycle, facilitation of post-investment activity, investigation of investment potential, Brown-field and Green-field investments and the PPI model and other topical questions are also characterized. Herewith, the author suggests some corresponding problem-solving recommendations which, as the author suggests, will help policy implementation persons at state, regional and local governmental levels of the country.

This article examines institutions’ investment strategies towards environmental and social (E&S) stocks in the first quarter of 2020, coinciding with the COVID-19 pandemic outbreak. Backed with both institutional- and firm-level analyses, we find that institutional investors shift towards stocks with higher E&S performance. The high E&S portfolios exhibit lower risk and return characteristics, outperforming (underperforming) their peers on market-down (-up) days. Further analysis shows this shift towards E&S is not a permanent transition, rather it reversed with the market rebound in the second quarter, thereby suggesting that the underlying driver of institutional E&S investment strategy in the pandemic is downside-risk protection. JEL Classification: G01, G12, G23, M14

We study a portfolio optimization problem involving the rational policyholder of a variable annuity with a guaranteed minimum maturity benefit. This financial guarantee is fi nanced via a fee withdrawn directly from the investment account, which impacts the net investment return. We propose a new fee structure that adjusts to the investment mix. A fair pricing constraint is de fined in terms of the risk-neutral value of the fi nal contract payout. We seek the investment strategy that maximizes the policyholder's expected utility of terminal wealth after the application of a fi nancial guarantee and subject to the fair pricing constraint. We solve the associated constrained stochastic control problem using a martingale approach and analyze the impact of the fee structure on the optimal investment strategies and payoff. Numerical results show that it is possible to nd an optimal portfolio for a wide range of fees, while keeping the contract fairly priced.

TitleResearch Funding for Male Reproductive Health and Infertility in the UK and USA [2016 – 2019]Study questionWhat is the research funding for male reproductive health and infertility in the UK and US between 2016 to 2019?Summary answerThe average funding for a research project in male reproductive health and infertility was not significantly different to that for female-based projects (£653,733 in the UK and $779,707 in the US). However, only 0.07% and 0.83% of government funds from NIHR (UK) and NICHD (USA) was granted for male reproductive health, respectively.What is known alreadyThere is a marked paucity of data on research funding for male reproductive health.Study design, size, durationExamined government databases over a total 4-year period from January 2016 to December 2019.Participants/materials, setting, methodsInformation on the funding provided to male-based and female-based research was collected using public accessed web-databases from the UKRI-GTR, the NIHR’s Open Data Summary, and the US’ NIH RePORT. Funded projects that began research activity between January 2016 to December 2019 were recorded, along with their grant and project details. Strict inclusion-exclusion criteria were followed for both UK and US data with a primary research focus of male infertility, reproductive health and disorders, and contraception development. Funding support was divided into three research groups: male-based, female-based, and not-specified research. Between the 4-year period, the UK is divided into 5 funding periods, starting from 2015/16 to 2019/20, and the US is divided into 5 fiscal years, from 2016 to 2020.Main results and the role of chanceBetween January 2016 to December 2019, UK agencies awarded a total of £11,767,190 to 18 projects for male-based research and £29,850,945 to 40 projects for female-based research. There was no statistically significant difference in funding average between the two research groups (P=0.56, W=392). The US NIH funded 76 projects totaling $59,257,746 for male-based research and 99 projects totaling $83,272,898 for female-based research. There was no statistically significant difference in funding average between the two groups (P=0.83, W=3834).Limitations, reasons for cautionThe findings of this study cannot be used to generalize and reflect global funding trends towards infertility and reproductive health as the data collected followed a narrow funding timeframe from government agencies and only two countries. Other funding sources such as charities, industry and major philanthropic organizations were not evaluated.Wider implications of the findingsThis is the first study examining funding granted by main government research agencies from the UK and US for male reproductive health. This study should stimulate further discussion of the challenges of tackling male infertility and reproductive health disorders and formulate appropriate investment strategies.Study funding/competing interest(s)CLRB is Editor for RBMO and has received lecturing fees from Merck, Pharmasure, and Ferring. His laboratory is funded by Bill and Melinda Gates Foundation, CSO, Genus. No other authors declare a conflict of interest.

The pricing strategy and decision-making investments of crowdfunding platforms are crucial for crowdfunding. This study examines how crowdfunding platforms influence platform profits by utilizing the mixed strategies of pricing strategy and investment decisions. According to the Hotelling specification and the characteristics of crowdfunding markets in which fundraisers are single homes and funders are multiple homes, this paper explores how “all or nothing” crowdfunding platforms set prices to balance the revenues and the costs of performance investment and thus maximize platform profits. We find that the investment level of crowdfunding platforms is determined by the difference in the preferences for performance investment between funders and fundraisers. In addition, the cross-network externalities of investors, the horizontal differences between platforms, the proportion of transaction fees drawn by platforms, and the range of the fundraisers’ fixed costs all play roles in shaping the pricing strategy and performance investment strategy of crowdfunding platforms.

Problem definition: Whereas firms are increasingly relying on sourcing globally as a key constituent of their supply chain strategy, there is no empirical evidence on whether investors of these firms adequately reflect firms’ global sourcing strategy (GSS) in their stock-valuation process. In this paper, we empirically test whether stock market participants are efficient in doing so. Methodology/results: Using the empirical asset-pricing framework, we find that information concerning firms’ GSS strongly predicts their future stock returns. We compile a transaction-level imports database for U.S.-listed firms and construct measures for five widely studied GSS aspects in the operations management literature: the extent of global sourcing, supplier relationship strength, supplier concentration, sourcing lead time, and sourcing countries’ logistical efficiency. For each measure, we examine returns of a zero-cost investment strategy of buying from the highest and selling from the lowest quintile of that measure. Collectively, these investment strategies yield an average annual four-factor alpha of 6%–9.6% (6%–13.9%) with value (equal)-weighted portfolios. Their return predictability is incremental over other operations- and cost arbitrage–motivated predictors, such as inventory turnover, cash conversion cycle, and gross profitability; is persistent across different supply chain positions; and is robust to alternate risk models, subsamples, and empirical specifications. Together, our results indicate that the GSS measures embody independent information about firms’ future profitability, and this information is mispriced by market participants, leading to predictable returns. In accordance with this mechanism, we find that the GSS measures strongly predict both firms’ future earnings and the surprise in market reactions around the earnings announcement days. Managerial implications: The robust return predictability of our GSS measures suggests that investors are not fully incorporating GSS-related information in their stock valuation frameworks. Therefore, our results call for greater investor education on global sourcing and better dissemination of global-sourcing information so as to mitigate valuation inefficiency. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1189 .

Socially responsible investment (SRI) is an investment strategy which complies with ethical, social, environmental or corporate governance criteria. Investments in renewable energies are commonly regarded as complying with SRI principles. This study aims to deepen the understanding of individuals' intentions to invest in wind energy. As an underlying framework the Theory of Planned Behavior's constructs attitude, subjective norm and perceived behavioral control were used and statistically tested with an online-survey of 592 participants in 2014. Further, this theory was extended by adding the constructs consumption profile and investor profile to the framework. With the inclusion of the two additional constructs the theoretical model turns to be very suitable as it doubled the share of explained variance of behavioral intention from 25% to 50% compared to the Theory of Planned Behavior. The analysis further shows that subjective norms, perceived behavioral control, consumption profile and investor experience have statistically significant influence on investment intentions in wind energy. However, attitudes towards wind energy investments are not a significant predictor of investment intentions. We conclude that, apart from the scientific implications of our results, our findings have practical applications for the conceptual design of wind energy investments.

We consider the problem of budget allocation for competitive influence maximization over social networks. In this problem, multiple competing parties (players) want to distribute their limited advertising resources over a set of social individuals to maximize their long-run cumulative payoffs. It is assumed that the individuals are connected via a social network and update their opinions based on the classical DeGroot model. The players must decide on the budget distribution among the individuals at a finite number of campaign times to maximize their overall payoff as a function of individuals’ opinions. Under some assumptions, we show that i) the optimal investment strategy for a single player can be found in polynomial time by solving a concave program, and ii) the open-loop equilibrium strategies for the multiplayer dynamic game can be computed efficiently by following natural regret-minimization dynamics. Our results extend earlier work on the static version of the problem to a dynamic multistage game.

Demographic changes increase the necessity to base the pension system more and more on the second and the third pillar, namely the occupational and private pension plans; this paper deals with Target Date Funds (TDFs), which are a typical investment opportunity for occupational pension planners. TDFs are usually identified with a decreasing fraction of wealth invested in equity (a so-called glide path) as retirement comes closer, i.e., wealth is invested more risky the younger the saver is. We investigate whether this is actually optimal in the presence of non-tradable income risk in a stochastic volatility environment. The retirement planning procedure is formulated as a stochastic optimization problem. We find it is the (random) contributions that induce the optimal path exhibiting a glide path structure, both in the constant and stochastic volatility environment. Moreover, the initial wealth and the initial contribution made to a retirement account strongly influence the fractional amount of wealth to be invested in risky assets. The risk aversion of an individual mainly determines the steepness of the glide path.

PurposeThis study aims to investigate the risk-return trade-off in the Indian equity market at both the aggregate equity market level and in the cross-sections of stock return using alternative risk measures.Design/methodology/approachThe study uses weekly and monthly data of 3,085 Bombay Stock Exchange-listed stocks spanning over 20 years from January 2000 to December 2019. The study evaluates the risk-return trade-off at the aggregate equity market level using the value-weighted and the equal-weighted broader portfolios. Eight different risk proxies belonging to the conventional, downside and extreme risk categories are considered to analyse the cross-sectional risk-return relationship.FindingsThe results show a positive equity premium on the value-weighted portfolio; however, the equal-weighted portfolio of these stocks shows an average return lower than the return on the 91-day Treasury Bills. The inverted size premium mainly causes this anomaly in the Indian equity market as the small stocks have lower returns than big stocks. The study presents a strong negative risk-return relationship across different risk proxies. However, under the subsample of more liquid stocks, the low-risk anomaly regarding other risk proxies becomes moderate except the beta-anomaly. This anomalous relationship seems to be caused by small and less liquid stocks having low institutional ownership and higher short-selling constraints.Practical implicationsThe findings have important implications for investors, managers and practitioners. Investors can incorporate the effects of different highlighted anomalies in their investment strategies to fetch higher returns. Managers can also use these findings in their capital budgeting decisions, resource allocations and other diverse range of direct and indirect decisions, particularly in emerging markets such as India. The findings provide insights to practitioners while valuing the firms.Originality/valueThe study is among the earlier attempts to examine the risk-return trade-off in an emerging equity market at both the aggregate equity market level and in the cross-sections of stock returns using alternative measures of risk and expected returns.

This article presents some of the main findings from the SDEWES conferences of 2021 within the field of renewable energy. More specifically, results are summarized and contextualised within solar energy and thermal comfort, wind power resource assessment, and biogas and biomass resources and technology. The two last sections deal more broadly with energy systems and energy systems transition issues; the systems analyses used to design complex renewable energy systems and the investment strategies and barriers associated with the transition. This article presents some of the main findings from the SDEWES conferences of 2021 within the field of renewable energy. More specifically, results are summarized and contextualised within solar energy and thermal comfort, wind power resource assessment, and biogas and biomass resources and technology. The two last sections deal more broadly with energy systems and energy systems transition issues; the systems analyses used to design complex renewable energy systems and the investment strategies and barriers associated with the transition.

As an application of machine learning techniques in financial fields, online portfolio selection has been attracting great attention from practitioners and researchers, which makes timely sequential decision making available when market information is constantly updated. For online portfolio selection, transaction costs incurred by changes of investment proportions on risky assets have a significant impact on the investment strategy and the return in long-term investment horizon. However, in many online portfolio selection studies, transaction costs are usually neglected in the decision making process. In this paper, we consider an adaptive online portfolio selection problem with transaction costs. We first propose an adaptive online moving average method (AOLMA) to predict the future returns of risky assets by incorporating an adaptive decaying factor into the moving average method, which improves the accuracy of return prediction. The net profit maximization model (NPM) is then constructed where transaction costs are considered in each decision making process. The adaptive online net profit maximization algorithm (AOLNPM) is designed to maximize the cumulative return by integrating AOLMA and NPM together. Numerical experiments show that AOLNPM dominates several state-of-the-art online portfolio selection algorithms in terms of various performance metrics, i.e., cumulative return, mean excess return, Sharpe ratio, Information ratio and Calmar ratio.

PurposeWith the rapid growth of the economy, people have increasingly higher living standards, and although people simply pursued material wealth in the past, they now pay more attention to material quality and safety and environmental protection. This paper discusses the lack of motivation for investing in fresh-keeping technology for agricultural products by individual members of an agricultural supply chain composed of a supplier and a retailer by means of mathematical models and data simulations and discuss the optimal price-invest strategies under different sales models.Design/methodology/approachFirst, based on the model of no investment by both sides (NN), this paper considers three models: supplier only (MN), retailer only (NR) and cooperative investment (MR). Then, the authors analyze the influence of consumer price sensitivity and freshness sensitivity on the investment motivation of agricultural products under four models. Subsequently, the paper makes a sensitivity analysis of the optimal strategies under several models, and makes a game analysis of the suppliers and retailers of agricultural products. Finally, we conduct an empirical analysis through specific values.FindingsThe results show that (a) when the two sides cooperate, the amount of investment is largest, the freshness of the agricultural products is highest, and the sales volume is greatest; however, when both sides do not invest, the freshness of agricultural products and sales volume are lowest. (b) The price and freshness sensitivity of the consumer have an impact on investment decisions. Greater freshness sensitivity corresponds to a higher investment, higher agricultural product price, greater sales volume, and greater supply chain member income and overall income; however, greater price sensitivity corresponds to a lower investment, lower agricultural product price, lower sales volume, fewer supply chain members and lower overall income. (c) The investment game between the supplier and retailer is not only related to the sensitivity to price and freshness but also to the coordination coefficients of interest. At the same time, the market position of agricultural products should be considered when making decisions. The market share of agricultural products will affect the final game equilibrium and then affect the final benefit of the supply chain and individual members.Practical implicationsThese results provide managerial insights for enterprises preparing to invest in agricultural products preservation technology.Originality/valueAt present, the main problem is that member enterprises of agricultural supply chains operate based on their own benefits and are resistant to investing alone to improve the freshness of agricultural products. Instead, they would prefer that other members invest so that they may reap the benefits at no cost. Therefore, the enterprises in each node of the agricultural product supply chain are not motivated enough to invest, and competition and game states are observed among them, and such behavior is definitely not conducive to improving the freshness of agricultural products. However, the current research on agricultural products is more about price, quality and greenness, etc., and there are few studies on agricultural investment. Through the establishment of the model, this paper is expected to provide theoretical suggestions for the supply chain enterprises that plan to invest in agricultural products preservation technology.

We apply dynamic programming to solve a long-horizon fund choice problem, given that the underlying market can switch between different regimes. The objective function is based on reaching a target level of wealth, following the paradigm of goal-based investing. In a world with a good regime (e.g., a bull market) and a bad regime (e.g., a bear market), we find that an investor who is cognizant of regime switching can potentially do much better over time than an investor who assumes there is only one regime. However, there is a caveat---an investor must be able to predict the regime they are in with reasonable levels of confidence, and if not, they are in fact worse off than an investor who assumes just one regime. Using data from recent history, we find that investors may be better off not switching from existing single-regime models to more complex multiple-regime models.

The topic about institutional investor being a monitoring role has been widely discussed but different results exist in previous empirical studies. Along with their progressively development, institutional investors are now playing more important role in Chinese capital market. Using samples from Chinese capital market, this paper collects fraud data and data of mutual funds’ ownership in listed firms between 2008 and 2015 to examine the monitoring function of institutional investors against accounting fraud. To go deep into the monitoring incentives of investors that fall into different categories, mutual funds are further classified as heterogeneous groups according to their investment strategy and investment durations. The monitoring role of mutual funds in different groups and their influence as the disincentive to accounting fraud of listed firms are investigated in the paper. Mutual fund ownership is found to be able to curb the incidence of accounting fraud. Active mutual funds are able to conduct more effective monitoring when compared with passive mutual funds. It is also reported that short-term mutual funds are more significant than long-term mutual funds in monitoring. Policy makers may need to normalize institutional investments by quantified indicators or in other reasonable ways. Key words: Accounting fraud, institutional investors, investment duration, monitoring role.

An agent-based model is proposed, constructing an evolutionary banking system, where interbank loans and investment strategies are, respectively, determined by liquidity shortage and utility maximization. The causes of systemic risk are then explored based on the evolutionary banking system, which is calibrated by a sample from China. The regulatory interventions indicate the positive effects of increased investment assets, while the negative but inappreciable effects of increased interbank counterparties on contagion risks decrease. This observation hints at the possibility of promoting systemic stability by incentivizing more diversifications in investment assets instead of interbank counterparties. The results also demonstrate the advantages of prudential liquidity requirements, interbank liquidity facilities, and monetary policies from the central bank in promoting banking system stability.

Sustainable and responsible finance incorporates Environmental, Social, and Governance (ESG) principles into business decisions and investment strategies. In recent years, investors have rushed to Sustainable and Responsible Investments in response to growing concerns about the risks of climate change. Asset managers look for some assessment of sustainability for guidance and benchmarking, for instance, $30 trillion of assets are invested using some ESG ratings. Several studies argue that good ESG ratings helped to prop up stock returns during the 2008 Global Financial Crisis (Lins et al. J Finance 72(4):1785–1824, 2017). The ESG score represents a benchmark of disclosures on public and private firms, it is based on different characteristics which are not directly related to the financial performance (Harvard Law School Forum on Corporate Governance, ESG reports and ratings:what they are, why they matter. https://corpgov.law.harvard.edu/2017/07/27/esg-reports-and-ratings-what-they-are-why-they-matter/ , 2017). The role of ESG ratings and their reliability have been widely discussed (Berg et al. Aggregate confusion: the divergence of ESG ratings, MIT Sloan Research Paper No. 5822-19, 2019). Sustainable investment professionals are unsatisfied with publicly traded companies’ climate-related disclosure. This negative sentiment is particularly strong in the USA, and within asset managers who do not believe that markets are consistently and correctly pricing climate risks into company and sector valuations. We believe that ESG ratings, when available, still affect business and finance strategies and may represent a crucial element in the company’s fundraising process and on shares returns. We aim to assess how structural data as balance sheet items and income statements items for traded companies affect ESG scores. Using the Bloomberg ESG scores, we investigate the role of structural variables adopting a machine learning approach, in particular, the Random Forest algorithm. We use balance sheet data for a sample of the constituents of the Euro Stoxx 600 index, referred to the last decade, and investigate how these explain the ESG Bloomberg ratings. We find that financial statements items represent a powerful tool to explain the ESG score.

Financialisation has become central to contemporary liberal welfare states. The embedding of asset-based welfare and social investment strategies has emphasised focus on maximising the number of po...

PurposeIn this study, the authors evaluate seven calendar anomalies’–the day of the week, weekend, the month of the year, January, the turn of the month (TOM), Ramadan and Eid festivals–effects in both the conventional and Islamic stock indices of Bangladesh. Also, the authors examine whether these anomalies differ between the two indices.Design/methodology/approachThe authors select the Dhaka Stock Exchange (DSE) Broad Index (DSEX) and the DSEX Shariah Index (DSES) of the DSE as representatives of the conventional and Islamic stock indices respectively. To carry out the investigation, the authors employ the generalized autoregressive conditional heteroskedasticity (GARCH) typed models from January 25, 2011, to March 25, 2020.FindingsThe study’s results indicate the presence of all these calendar anomalies in either conventional or Islamic indices or both, except for the Ramadan effect. Some significant differences in the anomalies between the two indices (excluding the Ramadan effect) are detected in both return and volatility, with the differences being somewhat more pronounced in volatility. The existence of these calendar anomalies argues against the efficient market hypothesis of the stock markets of Bangladesh.Practical implicationsThe study’s results can benefit investors and portfolio managers to comprehend different market anomalies and make investment strategies to beat the market for abnormal gains. Foreign investors can also be benefited from cross-border diversifications with DSE.Originality/valueTo the authors’ knowledge, first the calendar anomalies in the context of both conventional and Islamic stock indices for comparison purposes are evaluated, which is the novel contribution of this study. Unlike previous studies, the authors have explored seven calendar anomalies in the Bangladesh stock market's context with different indices and data sets. Importantly, no study in Bangladesh has analyzed calendar anomalies as comprehensively as the authors’.

Management companies assign some portfolio managers to run funds within a single investment objective (i.e., specialists), and others to run funds across several investment objectives (i.e., generalists). Our results show that funds achieve higher performance when they appoint superior pickers as specialists and market timers as generalists. We argue that these decisions are the result of a better match of manager mandates with the way information is collected and processed in each investment strategy. Overall our results are consistent with decision-making in fund families that add value to their investors by aiming to optimally assign or reassign portfolio managers.

A risk-averse insurance company controls its reserve, modeled as a perturbed Cramer-Lundberg process, by choice of both the premium p and the deductible K offered to potential customers. The surplus is allocated to financial investment in a riskless and a basket of risky assets potentially correlating with the insurance risks and thus serving as a partial hedge against these. Assuming customers differ in riskiness, increasing p or K reduces the number of customers n ( p , K ) and increases the arrival rate of claims per customer λ ( p , K ) through adverse selection, with a combined negative effect on the aggregate arrival rate n ( p , K ) λ ( p , K ) . We derive the optimal premium rate, deductible, investment strategy, and dividend payout rate (consumption by the owner-manager) maximizing expected discounted lifetime utility of intermediate consumption under the assumption of constant absolute risk aversion. Closed-form solutions are provided under specific assumptions on the distributions of size and frequency of claims.

Unexpected acquisitions can occur in the marketplace, which can significantly impact the market environment. Thus, predicting future pairs of the acquirer and acquiree companies is important for acquisition or investment strategy. This prediction is a challenging problem due to the following requirements: to incorporate various non-financial factors and to address the lack of negative samples. Concerning the former, we proposed including a network feature that represented the importance of an acquirer and an acquiree in the investment and category networks, as well as a company relation feature associated with their similarity and closeness. Considering the latter requirement, as negative examples, we set the pairs of acquirers and acquirees with the features that were similar to those of positive examples. This allowed learning minor differences between the companies selected for acquisition and the candidate ones. We evaluated our proposed prediction model using 2000-2018 acquisition logs collected from CrunchBase. Based on the analysis of the high SHapley additive explanation (SHAP) value features, we found that the newly considered network and company relation features had high significance (10 out of 22 top key features). We also clarified how these novel features contributed to the prediction of acquisition occurrence by interpreting the SHAP value.

In this article we consider the post-retirement phase optimization problem for a specific pension product in Germany that comes without guarantees. The continuous-time optimization problem is defined consisting of two specialties: first, we have a product-specific pension adjustment mechanism based on a certain capital coverage ratio which stipulates compulsory pension adjustments if the pension fund is underfunded or significantly overfunded. Second, due to the retiree’s fear of and aversion against pension reductions, we introduce a total wealth distribution to an investment portfolio and a buffer portfolio to lower the probability of future potential pension shortenings. The target functional in the optimization, that is to be maximized, is the client’s expected accumulated utility from the stochastic future pension cash flows. The optimization outcome is the optimal investment strategy in the proposed model. Due to the inherent complexity of the continuous-time framework, the discrete-time version of the optimization problem is considered and solved via the Bellman principle. In addition, for computational reasons, a policy function iteration algorithm is introduced to find a stationary solution to the problem in a computationally efficient and elegant fashion. A numerical case study on optimization and simulation completes the work with highlighting the benefits of the proposed model.

We considered a large number of factors from value, quality, low risk and momentum styles and show that these factors can be used to select the corporate bonds with the highest risk-adjusted returns. Our results were confirmed for the three largest corporate bond universes, namely those defined by U.S. Investment Grade, Euro Investment Grade and U.S. High Yield benchmark indices. The factors we investigated can be used to create investment strategies designed to out-perform these benchmark indices by overweighting the cheapest bonds with the strongest performance trends from the most profitable, better managed and less risky companies

The current stage of globalization involves geographically dispersed research anddevelopment (R&D) investments that are not confined to advanced economies.These cross-border R&D investments are driven by multinational enterprises’(MNEs’) strategies for exploring and/or exploiting foreign locations. In this paper,we analyse location choice and the moderating effect of project-level investmentstrategy (i.e. exploration or exploitation) and type of host economy (i.e. advancedor emerging) on the importance of the innovation framework and local innovationcapabilities. Our analysis of 588 R&D-related foreign direct investment (FDI) projectsin the pharmaceutical and biotech industries during the 2006–2016 period revealsthat whereas a host country’s innovation framework and capability overall do affectthe location decision, their ultimate effects are conditional on the combination ofproject-level investment strategy and type of economy. Our findings have policyimplications for FDI policies aiming at enhancing linkages between MNEs andlocal actors and national science, technology, innovation and educational policiesand programmes.

In this article the authors propose an appropriate commodities benchmark for pension funds. Commodity investment, like traditional investment, requires suitable benchmarks for performance measurement and attribution. Investing in commodities as an asset class has increased dramatically as a result of financialization. It is typically conducted by pension funds through the futures market. The tools to implement and benchmark the success or failure of such an investment strategy are still in their infancy. The authors argue that factor model indexes (FMIs) are a viable alternative to existing production-based indexes that make use of futures contracts. Using principal component analysis, they identify five factors related to grains, meats, industrial metals, energy, and precious metals. They suggest that FMI benchmarks constructed using such an approach would allow commodity investors to better measure their investment objectives. Key Findings ▪ Proposed commodity factor model index (FMI) derived from principal components for use by institutional investors. ▪ The FMI addresses the issue that the market capitalization concept does not make sense when applied to commodities. ▪ The FMI is a viable alternative to existing production-based indexes that make use of futures contracts.

This study developed an agent-based model of firms’ investment dynamics in education and training based on the cellular automata model. It turned the issue of the dynamics of education and training to an issue of spaces and conducted a simulation to analyze whether the strategy of firms investing in education and training will be profitable in the long term. The main results of this study can be summed up as follows: First, strategies that invest in education and training tend to be inferior to strategies that do not invest in education and training. However, even if the former strategies are inferior, a certain proportion of investing firms survive to exist in the long term. The outcome of the strategies does not largely depend on the initial proportion of education and training investment firms Second, an increase in learning speed works disadvantageously to firms that use education and training investment strategies. Third, a reduction in training cost brings a decrease in total production. Fourth, expanding the scope of information raises the productivity of a society, but acts negatively on firms that use education and training investment strategies. Fifth, an increase in the proportion of job searching due to a drop in search costs enhances the productivity of a society but works negatively on firms that use education and training investment strategies. Sixth, the government’s support for training expenses increases total production and the proportion of firms that invested in education and training as well.

This study suggests a novel investment strategy for sizing a supercapacitor in a Battery Energy Storage System (BESS) for frequency regulation. In this progress, presents hybrid operation strategy considering lifespan of the BESS. This supercapacitor-battery hybrid system can slow down the aging process of the BESS. However, the supercapacitors are relatively expensive and have a self-discharging rate of nearly 40% per day. Therefore, finding the optimal size of a supercapacitor contributes to prolonging the lifespan of the BESS at an effective cost. In the proposed operation strategy, this paper estimates the lifespan at the given supercapacitor size using an innovative variable capacity model (VCM). Because this model does not use the current and voltage characteristics to calculate the lifespan of the BESS and uses measured lifespan data sets, this method has the advantages of making quick investment decisions. It is shown through simulation that the VCM gives more realistic results than the conventional fixed capacity model estimating a battery lifespan with achievable cycle count curves. Moreover, the net benefits of the supercapacitor were measured through benefit and cost analysis and net present value analysis for the supercapacitor size in the BESS. Through this paper, frequency regulation service is considered as the application target for the proposed strategy.

This paper considers a robust optimal portfolio problem under Heston model in which the risky asset price is related to the historical performance. The finance market includes a riskless asset and a risky asset whose price is controlled by a stochastic delay equation. The objective is to choose the investment strategy to maximize the minimal expected utility of terminal wealth. By employing dynamic programming principle and Hamilton-Jacobin-Bellman (HJB) equation, we obtain the specific expression of the optimal control and the explicit solution of the corresponding HJB equation. Besides, a verification theorem is provided to ensure the value function is indeed the solution of the HJB equation. Finally, we use numerical examples to illustrate the relationship between the optimal strategy and parameters.

PurposeIn initial public offerings (IPOs), the media plays a pivotal role by disseminating the information to the investors who generally lack the expertise to understand the information through the prospectus. Thus, media coverage can impact the investment decision of the investors and the IPO performance. Media typically covers the IPO before listing, suggesting that it may play an important role in explaining the opening price rather than the closing price on the day of listing. Therefore, this study aims to disaggregate the traditional IPO underpricing into three categories: voluntary, pre-market and post-market and provides a comparative analysis of the media sentiments impact on the traditional and disaggregated IPO underpricing. The authors’ disaggregated IPO underpricing analysis will facilitate the investors in making an effective investment strategy based on media sentiments.Design/methodology/approachThe study deploys sentiment analysis using bags of n (2) grams approach to gauge the sentiments on 2,891 media articles and uses “robust-regression” technique to analyze them on a sample of 222 Indian IPOs during 2009–2018.FindingsThe study reports that the sentiment score is positively related to the traditional underpricing; the sentiment score is positively associated with the pre-market underpricing and does not have any significant relationship with the post-market underpricing; the number of media articles does not play a significant role in explaining the IPO underpricing. The findings highlight the presence of a semi-strong form of efficiency in the Indian IPO market.Originality/valueExisting literature focuses that the role of media on IPO performance is based on the developed countries. IPO laws differ based on the countries. For instance, in India, investors can check the demand by the other categories of investors on a real-time basis. Thus, it is interesting to study whether, with such a high level of transparency, media can explain IPO performance in the Indian market. Media generally covers IPO before listing; therefore, the present study disaggregates the IPO underpricing to evaluate the role of media on the primary and secondary market separately. It will help the investors to decide when to enter and exit the market.

There is an emergent field of writings on financialized landlords’ undertaking of apartment renovations as an investment strategy and its effect on housing inequalities. Seldom do these studies con...

Investment fund investment strategies and asset allocations typically are based on a multiplicity of complex (possibly competing) goals that often are incorporated in a simple return target. The challenge is to develop a portfolio whose expected return at least equals the target but has a volatility less than a disaster-avoidance risk limit. However, such a goal-matching return only provides a 50/50 chance of success. Truly important goals may require a higher probability of success, such as 60%. This article focuses on efficient portfolios and the return they can provide with a specified probability of assurance. The key step is to map the efficient frontier into an assurance frontier that plots risk-adjusted returns with a 60% success probability. This frontier typically has a peak return that is the highest available risk-adjusted return. In some cases, this peak portfolio is at the risk limit. In other cases, even with a monotonically rising efficient frontier, the peak may fall well below the maximum volatility limit. Key Findings ▪ Portfolios with expected returns that just match a goal-based target may have only a 50/50 assurance of success (even after those targets have been properly revised to account for, e.g., inflation, fund expenses, and geometric effects). ▪ Portfolios that have the best risk-adjusted return can sometimes be found at a volatility that is well short of a fund’s maximum risk limit. ▪ Longer horizons generally lead to higher peak returns that can enhance prospects of success.

The authors show that the slope of the volatility decile portfolio’s return profile contains valuable information that can be used to time volatility under different market conditions in the United States. During good (bad) market conditions, the high- (low-) volatility portfolio produces the highest return. The authors proceed to devise a volatility timing strategy based on statistical tests on the slope of the volatility decile portfolio’s return profile. Volatility timing is achieved by being aggressive during strong growth periods and conservative during market downturns. Superior performance is obtained, with an additional return of 4.1% observed in the volatility timing strategy, resulting in a fivefold improvement on accumulated wealth, along with statistically significant improvement in the Sortini ratio and the information ratio. The authors also demonstrate that stocks in the high-volatility portfolio are more strongly correlated compared to stocks in the low-volatility portfolio. Hence, the profitability of the volatility timing strategy can be attributed to successfully holding a diversified portfolio during bear markets and holding a concentrated growth portfolio during bull markets. Key Findings ▪ The return profile of the volatility decile portfolio is time-varying. Its slope contains vital information on market condition—high-volatility portfolio outperforms low-volatility portfolio during good market condition, but underperforms during bad market condition. Since market regime and asset price behaviors are persistent, the slope parameter can be used to time volatility exposure. ▪ Holding the low-volatility portfolio benefits from the higher risk-adjusted return during general market condition. However, when the slope parameter is positive and statistically significant, it is optimal to hold the high-volatility portfolio for the subsequent period. This will ride on the higher return of high-volatility portfolio during strong growth periods. This leads to higher return and increased volatility, but both Sortini ratio and Information ratio exhibit statistically significant improvement. ▪ Stocks in the low-volatility portfolio are less correlated than stocks in the high-volatility portfolio. The outperformance of the volatility timing strategy formulated in this article can be attributed to holding a concentrated growth portfolio during good market conditions, and holding a diversified portfolio during bad market conditions, thus connecting the literature on low-volatility portfolio with studies on correlation structure and diversification.

Facing increasingly severe environmental problems, green technology (GT) innovation has become an effective way to achieve the sustainable development of firms. Many manufacturers often choose and invest in appropriate GTs from GT-suppliers to improve the environmental impact of production. But others abandon GTs because they are too expensive to invest in. This paper thereby studies the dynamic investment strategy of GT in a manufacturer-supplier supply chain and explores the optimal government subsidy incentive and its impact on investment and sustainable production decisions. Firstly, an original two-sided matching game model (including two-sided matching analysis and Nash equilibrium analysis) is presented to study the matching process between a manufacturer with a limited investment budget and a GT-supplier with GTs and to determine the optimal GT investment-production strategy. Then, a government-manufacturer Stackelberg game model integrating differential game is constructed to determine the optimal government subsidy strategy and to investigate the effect of government subsidy on investment-production decisions. The results show that stable matching between the manufacturer and GT-supplier can be obtained. Numerical simulations verify the feasibility and rationality of the proposed matching game mechanism and demonstrate that GT investment profit is higher than non-investment profit, and subsidy profit is better than non-subsidy profit. The increase in the carbon price leads to an increase in the GT investment intensity. The increase in the carbon price can cause an increase in product greenness and green demand. The contributions of this paper are in providing an infrastructure for studying how managers can obtain the optimal GT investment-production strategy in the supply chain, and how the government formulates the optimal subsidy strategy to stimulate managers’ GT innovation behavior.

The financial planning and advice industry has been experiencing healthy growth for the past five years and is expected to accelerate this growth following the Covid-19 pandemic (IBISWorld, 2021). The pandemic has led to higher equity yields and appreciating asset value, directly increasing the total value of assets under management (AUM) held by financial planners and advisors. The industry in the US alone has surpassed $52.9 billion in 2021. As the economy is expected to improve, this figure is expected to follow suit. Not included in these figures are the explosion of online personal finance bloggers and influencers. Some YouTube and TikTok videos have raked in billions of views regarding personal finance (Smith, 2021). Many of these online contents have benefitted viewers and prompted them to start making good decisions regarding their personal wealth, spreading financial literacy to the masses. However, poor financial advice may be spread out as easily to viewers. The Wall Street Journal has reported on this issue back in 2005 where blogs and magazines have been found to give both good and bad advice on budgeting, saving, and overall personal finance management (Cullen, 2005). Whatever the net effect of this phenomenon, the easy access through social media has amplified it. This article briefly journeys through the evolution of personal finance management and personal financial planning, including the new trends this industry is moving towards. Subsequently, this article will look into the risk and rewards of the current personal financial planning and advice industry, including certified financial planners and uncertified personnel (social media influencers, financial gurus), as to whether consumers are benefitting as a whole, or otherwise. A disclaimer to this research is that the findings and opinions towards the industry do not encompass all the service providers in the business as there are many other influencing factors such as business models, individual agenda, and unique circumstances of each provider and consumer. Keywords: Conflict of interest; financial planning; financial experts; Influencers; Personal finance

PurposeWhen a fund manager leaves, the investment strategy of the fund changes or remains the same. The departing fund manager's resignation is either forced or voluntary. The study investigates the relationship between the portfolio manager's transition and the fund's investment strategy and how the change affects the mutual fund returns in the subsequent period.Design/methodology/approachThe authors examine 148 fund manager changes in India between April 2005–March 2018 using three performance measures: abnormal return (fund return minus benchmark return), Jensen's alpha and Carhart four-factor alpha. The analysis includes an event study methodology, followed by a two-step Fama–MacBeth regression approach.FindingsContrary to the previous studies conducted in the developed markets, the authors find that fund performance improves irrespective of whether the fund manager change is forced or voluntary. The outperformance after the fund manager's exit is significant for funds belonging to the larger fund families.Originality/valueIn the context of investment management, the authors provide a conceptual framework to understand the effect of fund manager exit on mutual fund performance. The authors substantiate their arguments with empirical evidence. To the best of the authors' understanding, this is the first research to examine the effect of changing mutual fund managers in an emerging market setting.

The study examines optimal pollution control R&D investment strategy of firms under asymmetric information and further analyzes the impact of government incentive mechanism on it. We use stochastic optimal control theory to get the exact solution of R&D investment strategy and incentive mechanism. Our analysis reveals that if there is no supervisor, firms choose not to cooperate, but the government can take appropriate incentive compensation to make firms reach Nash equilibrium. If there are supervisors, the optimal strategy of the enterprise is to choose cooperation, and there will be Pareto optimum among the firms. Furthermore, the R&D investment level decreases with increasing environmental uncertainty.

The aim of the paper is to evaluate the impact of novel COVID-19 on the returns and volatility of Indian stock markets with special reference to equity investment strategies of the Bombay Stock Exchange. For the purpose of evaluating the impact, the study has applied GARCH. The research has considered a time frame from March 2015 to January 2021. Prior to implementing GARCH model, pre-estimation tests (i.e., augmented Dickey-Fuller and ARCH-Lagrange multiplier) were conducted. Outcomes clearly indicate that the returns during the crisis for all the strategy indices have been negative, which means that the COVID-19 outbreak resulted in massive losses. Additionally, 'during crisis' period showed an increase in volatility for all the strategy indices depicting that the pandemic has a long-lasting effect and will take time to fade off. This research will help the investors in the investment decision process by giving them insights about the different strategies. © 2021.

This article proposes a simple and effective rotation strategy. The strategy rotates between the value-weighted market portfolio (VW) and the equal-weighted counterpart (EW) based on an implicit market signal—the lagged one-month market return. The authors report a statistically significant relation between the lagged one-month market return and the future 1/N premium, which is the return difference between the EW and VW portfolios. Building on the predictive quality and exploiting the time-varying nature of the 1/N premium, they introduce a transparent and robust investment strategy that yields superior absolute and risk-adjusted returns. TOPICS:Portfolio construction, statistical methods, security analysis and valuation, performance measurement Key Findings ▪ The authors propose a simple market-based signal to rotate between value- and equal-weighted portfolios. ▪ The signal quality is statistically significant and robust across the world. ▪ The strategy generates significant alpha after accounting for transaction costs.

Over the past decade robo-advisors have gone from offerings from a handful of start-ups to an established and fast-growing segment of the wealth management industry. Robo-advisors use technology to translate core retail investing principles—establishing an investment plan, seeking broad diversification, weighting cost and value, and accounting for taxes—into automated platforms with easy-to-use interfaces. Intuitive user experience backed by well-established investment processes allow individual investors to create and execute investment strategies with little or no interaction with a financial professional, resulting in large economies of scale for robo-advisors and low costs for clients. In this article, we discuss how robo-advisors assess client risk tolerance, build, and recommend portfolios, manage risk, and optimize taxes. We discuss the implementation of goal-based investing, socially responsible (ESG) investing, and smart beta strategies on robo-advisory platforms. Additionally, we examine robo-advisor performance during the market downturn in March 2020, the first significant market drawdown since their introduction, and find portfolio performance in agreement with broadly diversified stock and bond holdings. Consistent with expectations, robo-advisors also reported increased tax-loss harvesting activity during this downturn. TOPICS:Big data/machine learning, legal/regulatory/public policy, portfolio construction, performance measurement Key Findings ▪ The authors provide an up-to-date view of online platforms referred to as robo-advisors, that offer automated investment and portfolio management services to retail investors. ▪ They describe and characterize key elements and features of robo-advisory service offerings, including automated tax management, goal-based investing, socially responsible (ESG) investing, and smart beta strategies. ▪ They examine robo-advisor performance during the market downturn in March 2020, and find portfolio performance in agreement with broadly diversified stock and bond holdings.