Investment Practitioners Research Articles

Mandatory climate disclosures: impacts on energy and agriculture markets

PurposeWith the rise of mandating climate-related disclosures (CRD), this paper aims to investigate how energy and agriculture markets are exposed to climate disclosure risk.Design/methodology/approachUsing the multivariable simultaneous quantile regression and data from 1 January 2017 to 29 February 2024, the authors examine daily and monthly responses of energy and agriculture markets to climate disclosure risk, energy risk, market sentiment, geopolitical risk and economic policy risk. The sample covers the global market, Australia, Canada, European Union (EU), Hong Kong, Japan, New Zealand, Singapore, the UK and the USA.FindingsThe results show that climate disclosure risk creates both positive and negative shocks in the energy and agriculture markets, and the impacts are asymmetric across quantiles in different economies. The higher the climate disclosure risk, the greater impact of crude oil future on the energy sector in North America (Canada and the USA) and Europe (EU and the UK), but no greater effects in Asia Pacific (Australia, New Zealand and Singapore). The agriculture sector can hedge against economic policy and geopolitical risks, but it is highly exposed to climate disclosure and energy risks.Originality/valueThis study timely contributes to the modest literature on the asymmetric effects of climate disclosure risk on the energy and agriculture markets at the global and national levels. The findings offer practical implications for policymakers and investment practitioners in understanding financial effects of mandating CRD to diversify risks depending upon market conditions and policy uncertainty.

Modelization and Calibration of the Power-Law Distribution in Stock Market by Maximization of Varma Entropy

Proper description of the return distribution is crucial for investment practitioners. The underestimation of the tail risk may lead to severe consequences, even for assets with moderate fluctuations. However, many empirical studies found that the distribution tails of many financial assets drop off more slowly than the Gaussian distributions. Therefore, we intend to model and calibrate the heavy tails observed in financial fluctuations in this study. By maximizing the Varma entropy with value-at-risk and expected shortfall constraints, we obtain the probability distribution of stock return and observe that the tail of stock return distribution is a power law. Since the variance of the real stock portfolio may be a random variable, using the mean-VaR-ES constraints to maximize the Varma entropy effectively avoids the problem of assuming that the variance is a constant value under the traditional mean-variance constraint. Therefore, the deduced theoretical model would be more consistent with the real market. Using high-frequency data from China’s stock markets, we calibrate our theoretical model and give the concrete form of probability density distribution p(x) for different time intervals. The calibration results show that the tail of the stock return distribution is a power law with most of the power-law orders between −2 and −7. We prove the robustness of our results by calibrating the Varma entropy for S&P 500 of the USA stock market and different stock market indices in China’s A-share market. Our research’s findings not only offer a theoretical perspective for researchers but also give investing professionals a theoretical foundation on which to base their decisions.

Machine learning and manager selection: evidence from South Africa

PurposeThis study investigates whether the skilled minority of active equity managers in emerging markets can be identified using a machine learning (ML) framework that incorporates a large set of performance characteristics.Design/methodology/approachThe study uses a cross-section of South African active equity managers from January 2002 to December 2021. The performance characteristics are analysed using ML models, with a particular focus on gradient boosters, and naïve selection techniques such as momentum and style alpha. The out-of-sample nominal, excess and risk-adjusted returns are evaluated, and precision tests are conducted to assess the accuracy of the performance predictions.FindingsA minority of active managers exhibit skill that results in generating alpha, even after accounting for fees, and show that ML models, particularly gradient boosters, are superior at identifying non-linearities. LightGBM (LG) achieves the highest out-of-sample nominal, excess and risk-adjusted return and proves to be the most accurate predictor of performance in precision tests. Naïve selection techniques, such as momentum and style alpha, outperform most ML models in forecasting emerging market active manager performance.Originality/valueThe authors contribute to the literature by demonstrating that a ML approach that incorporates a large set of performance characteristics can be used to identify skilled active equity managers in emerging markets. The findings suggest that both ML models and naïve selection techniques can be used to predict performance, but the former is more accurate in predicting ex ante performance. This study has practical implications for investment practitioners and academics interested in active asset manager performance in emerging markets.

The Potential of The Islamic Capital Market (ICM) as an Investment Model for the Baitul Mal

The mandate for investment in the Sharia Capital Market sector is clearly stated in the Aceh Governor Regulation No.8 of 2022 concerning the Management of Zakat and Infaq. the investment of infaq funds is carried out by the BMA Secretariat in the form and sector of shares, Sukuk, waqf empowerment, economic businesses, health, sharia deposits, education, and other investments. With the total BMA infaq funds from 2017 - 2022 around 153 billion, it provides an opportunity to invest in the Islamic financial market sector and provide benefits for the development of infaq funds aimed at the benefit of the economy. In this study, a qualitative method with a descriptive phenomenological approach was carried out. The sample was selected purposively, the informants of the research were investment practitioners, BMA commissioners, sharia supervisory boards, Islamic economics and Islamic banking academics, the government, the national committee for Islamic economics and finance (KNEK), and mustahik. The results of this study concluded that the sharia-based capital market provides a variety of sharia investment products that Baitul Mal Aceh can choose as a medium for investing infaq funds, namely sharia stocks, sukuk, and sharia mutual funds. Investment business models in Islamic financial markets can be done by considering risks carefully, and prudently choosing Islamic investment products that are low-risk and consistent returns.

A Model-Based Approach Machine Learning to Scalable Portfolio Selection

This study proposes a scalable asset selection and allocation approach using machine learning that integrates clustering methods into portfolio optimization models. The methodology applies the Uniform Manifold Approximation and Projection method and ensemble clustering techniques to preselect assets from the Ibovespa and S&P 500 indices. The research compares three allocation models and finds that the Hierarchical Risk Parity model outperformed the others, with a Sharpe ratio of 1.11. Despite the pandemic's impact on the portfolios, with drawdowns close to 30%, they recovered in 111 to 149 trading days. The portfolios outperformed the indices in cumulative returns, with similar annual volatilities of 20%. Preprocessing with UMAP allowed for finding clusters with higher discriminatory power, evaluated through internal cluster validation metrics, helping to reduce the problem's size during optimal portfolio allocation. Overall, this study highlights the potential of machine learning in portfolio optimization, providing a useful framework for investment practitioners.

Strategies for managing the political risk of investing in infrastructure projects, in developing countries

PurposePolitical risk has been identified as a major impediment to the success of foreign invested projects, in developing countries. Infrastructure projects are especially sensitive to host-country political climates. Governance in emerging economies can be unstable, which adversely impacts infrastructure projects, given their high capital-intensity, long operational periods and high asset specificity. While the detrimental impact of political risk is well documented, the mitigation of such impacts on infrastructure projects remains largely unexamined. This study, therefore, addresses this by exploring the available identified political risk management (PRM) strategies based on resilience theory and evaluating their effectiveness.Design/methodology/approachA mixed-method approach was employed to identify PRM strategies. Firstly, a comprehensive literature review identified 40 potential PRM strategies. However, the applicability of those 40 strategies was uncertain due to the scarcity of PRM studies. Thus, expert interviews, drawing on the insights of Chinese infrastructure industry professionals with experience in FII, were applied to review the identified strategies. This process reduced the pool of applicable strategies to 34. Subsequently, 356 questionnaires were sent out to investors from China, Australia and Singapore, with 218 valid responses returned. Based on the data collected from the surveys, statistical analysis was used to evaluate and classify applicable PRM strategies.FindingsResults reveal the most effective top five strategies for offsetting the detrimental effects of political risk on foreign infrastructure investment to be: (1) selection of suitable markets and projects; (2) maintaining good relationship with government; (3) purchasing political insurance; (4) utilizing capable contractors from both host country and home country; and (5) adopting an appropriate entry mode. The 34 strategies were further consolidated into four meta-strategies through factor analysis, resulting in the formulation of a strategy selection matrix.Originality/valueThe findings of this study offer a rational means by which infrastructure investment practitioners considering projects in developing countries, may arrive at an optimal political risk mitigation strategy. The findings also offer government of host countries directives to improving the political environment in order to attract foreign investment flows into local infrastructure projects.

Reference point formation: Does the market whisper in the background?

AbstractWell‐established studies in behavioral finance confirm that investors are highly influenced by subjective reference points when making economic decisions. Although the literature examines reference point formation for individual stocks based on observed price sequences, the stocks are often treated without considering market context. We extend this literature by incorporating market price information as background to the behavior experiment, and we consider both prospect theory and anticipation/disappointment‐based utility functions in a combined framework to investigate the impact of market context on reference point formation. Our article provides new evidence and insights for investment practitioners and behavioral researchers.

Is Being “Robust” Beneficial? A Perspective from the Indian Market

Motivated by the progress made towards incorporating robust optimization in the framework of risk minimization, this work focuses on assessing the practical usefulness of the robust optimization approaches for the minimization of downside risk measures, such as Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). Accordingly, we perform empirical analysis of the performance of VaR and CVaR models with respect to their robust counterparts, namely, Worst-Case VaR and Worst-Case CVaR, using both simulated data and market data involving the Indian indices of S&P BSE 30 and S&P BSE 100. Additionally, we provide relevant insights regarding the viability of these robust models over their classical formulations from the perspective of an investment practitioner. We conclude by noting the superior performance of Worst-Case VaR and Worst-Case CVaR with respect to their classical versions in the cases involving higher number of stocks and simulated setup respectively.

Distributed Ledger Technology Affordances for Financing Sustainable Development

Distributed Ledger Technology (DLT) has been heralded as one of the most significant innovations since the internet. This article explores the applicability of DLT to the issue of financing the UN Sustainable Development Goals (SDGs) by approaching the question of what opportunities DLT presents for aligning investments with SDGs-related outcomes within the financial sector through the concept of affordances. Our qualitative research inquiry combining an exhaustive analysis of practitioner literature and interviews with 15 leading sustainable and responsible investment (SRI) practitioners and 14 actors involved in DLT ventures in Sub-Saharan Africa shows that despite discourse around blockchain technology that focuses on generic perceptions of affordances relating to data transparency, actualization of these affordances within the sustainable finance field is contingent upon a number of socio-cognitive and institutional hurdles. As a result, while discourse around blockchain presents a galvanizing vision for innovation within the financial sector, its direct benefits for mobilizing additional contributions towards the SDGs by private sector investors remain ambiguous.

BANKING STOCK ABNORMAL RETURN ANALYSIS OF PRE AND POST MERGER AND ACQUISITION IN INDONESIA

The number of mergers and acquisitions (M&A) in Indonesia is growing because of government policy and also their usefulness as a corporate tool to pursue strategic growth and profit. This study aims to analyze the abnormal returns of banking industries pre and post-merger and acquisition in Indonesia. Using a sample of 7 M&A deals in Indonesia from 2018 to 2019, the event study methodology used in this study is Paired Sample T-Test to tell the difference between pre and post abnormal returns. The data that use for calculating is -30 until +30 of Merger and Acquisition. The result shows that from 7 mergers and acquisition there is only one bank that has a significant difference while the rest does not have a significant difference pre and post the event. This research hopefully can be used for further research, useful for investment practitioners.

What’s Priced? Estimating Market Mispricing of Macroeconomic News

Delineating between fundamental and nonfundamental price moves is vital for investment practitioners because the former tend to be more persistent than the latter. This article describes an approach to isolating price moves that are driven by fundamental macroeconomic news. The authors use an event-study methodology at the intraday frequency to estimate typical reactions to macro news releases for a set of seven equity and six government bond markets. These are calculated on an expanding-window and exponentially weighted basis to account for time variation in these reactions. The primary innovation of this work is a measure of market mispricing that looks at deviations of market returns from the returns expected based on the authors’ estimated coefficients, which they calculate for each of these markets from 2015 to 2019. This measure provides an input for discretionary tactical asset allocation decisions. The correlation of this market mispricing measure with future returns provides support for the thesis that initial time-series momentum and later time-series reversion of asset returns reflect nonfundamental factors. TOPICS:Derivatives, financial crises and financial market history Key Findings • Because nonfundamental price moves mean revert faster than fundamental price moves, the R2 of macro news is higher at a low frequency than at a high frequency. • Intraday estimates of market price responses to macro news are more accurate than estimates based on daily time series because a short event-study window reduces concerns around endogeneity. • These intraday estimates can be used to now-cast market return potential and help find the deviation of those now-casts from actual returns. Cumulating these deviations over months of data indicates whether markets are mispriced relative to macro fundamentals. • Our measure of market mispricing helps predict future asset returns and provides broader context to discretionary tactical asset allocation decisions. • This measure also reveals more cleanly than previous research the time-series properties of nonfundamental price moves, with initial time-series momentum and later time-series reversal.

Understanding drivers when investing for impact: an experimental study

In the last decade, a global interest in impact investing—whose goal is to generate social and environmental benefits alongside economic returns—has rapidly grown. In this context, this paper explores the socio-demographic characteristics of investors who choose impact investment options over traditional investments, and on the drivers promoting such choices. More specifically, the study consists of an experiment-based research in which 602 participants (non-experts and experts in the financial sector) took part in a multiple-choice game involving different investment scenarios and incentive conditions. The findings show that both expert investors and female participants are more likely to choose impact investment options and that the tendency to invest in social funds increases with age. Neither external and centrally planned incentives, such as fiscal incentives, nor the educational level of participants show a significant influence on investment choices. By contrast, information about the actual social impact achieved by funds plays a role in promoting socially oriented decisions. In this regard, visual aids appear to be an effective means of promoting impact investment. These findings may be of interest to policymakers, social campaigners and investment practitioners themselves when designing strategies for raising interest in impact investing or norms to strengthen a conducive and enabling environment for social entrepreneurship more broadly.

Improving market timing of time series momentum in the Chinese stock market

ABSTRACT This paper is the first study to present firm-level evidence that the time-series momentum (TSMOM) strategies with look-back-period k of 10 to 200 days outperform the buy-and-hold strategy (BH) on individual stocks in the Chinese stock market. We document that the optimal k* generating the best performance is different across assets and varies over time. We hence propose a model to predict the asset-specific and time-dependent k*, and examine the performance of the TSMOM strategies with the predicted k*. Our analysis shows that using the time-varying predicted k* substantially improves the predictability of the TSMOM strategies. Our new model and findings shed the light on trading strategy for both academia and applied investment practitioners.

Underlying risk preferences and analyst risk‐taking behavior

AbstractWe investigate the relationship between underlying risk preferences on analysts’ work‐related decisions. Specifically, we examine whether facial width‐to‐height ratio (fWHR), an innate personal characteristic that has been linked to financial risk tolerance, is associated with analysts’ stock coverage decisions and the boldness of their earnings forecasts and stock recommendations. We find that high‐fWHR analysts cover firms with lower earnings predictability, and issue bolder forecasts and recommendations. Our findings shed new light on the black box of analyst decision making, assisting investment practitioners in evaluating the information content produced by different types of analysts and understanding the observed dispersion in analyst forecasts.

Is Programme Evaluation the Same as Social Impact Measurement?

This study provides an analysis of the practical and theoretical differences in Social Impact Measurements (SIM) – as defined and is core to Impact Investing – and Programme Evaluation (PE) used in Monitoring and Evaluation (M&E). The study is the result of an inspiring effort to converge experiences of both Impact Investment practitioners and investment managers, with those of Programme Evaluators and M&E specialists. A meeting during the AEA’s (American Evaluation Association) ImpaCon conference in Atlanta in 2017. The effort was to facilitate co-learning in order to improve and grow the Impact Investing industry. The study analyses qualitative survey responses from a purposively selected panel of experts including: experienced SIM practitioners and scholars, impact investment managers, programme evaluators and evaluation scholars. Responses are deductively analysed to provide thematic reactions to the research questions. Even though there is a common theoretical intent to determine intervention worth in both SIM and PE, and a common adherence to principles of evaluative thinking; the study concludes that there are clear theoretical and practical differences in participatory and utilisation approach, efficiency and rigour.

Factors That Influence Investment Decision Making Among Potential Individual Investors in Malaysia

Individual investments behaviour is concerned with choices about purchases of small amounts of securities for his or her own account. Decision tools often support investment decisions. It is assumed that information structure and the factors in the market systematically influence individuals’ investment decisions as well as market outcomes. Decision tools often support investment decisions. It is assumed that information structure and the factors in the market systematically influence individuals’ investment decisions as well as market outcomes. Investor market behaviour derives from psychological principles of decision making to explain why people buy or sell stocks. These factors will focus upon how investors interpret and act on information to make investment decisions. The purpose of the study was to identify the factors that influence investment decision making among potential individual investors in Malaysia. Three behavioural factors might influence investment decision making which are accounting-information, firm-image coincidence and personal-financial-needs. A set of questionnaire was distributed to 384 potential investors in Malaysia specifically in housing area of Klang Valley as population of this study. Based on the findings, it showed that there is positive relationship between accounting-information, firm-image-coincidence and personal-financial-needs in investment decision making. Hence, between these three behavioural factors, accounting-information, firm-image coincidence and personal-financial-needs, the main influential factor is accounting-information. This study also proposed a future research for investment decision making and give implications to the potential investors, community, organization, policy makers and investment practitioners.

Determination a Reasonable Concession Period for (PPP) Projects

Public Private Partnership (PPP) are agreements where public bodies enter into long term contractual with private entities for construction or management the public sector facilities, or provision services to the community. Internal rate of return (IRR), pay back regime or tariff, and the concession period (CP) are essential items to success (PPP) projects. This research presents a systematic approach for a win-win partnership contract determined on a quantitative basis, by informing the partnership parties how long contract period should be made. Essence of the proposed methodology is that project completion time should allow a competent contractor to complete the project on schedule and operation period should be long enough to enable the concessionaire to achieve a reasonable return, but not too long such that concessionaire’s return is excessive and public sector’s interests are sacrificed. A case study of a PPP project in Mayoralty of Baghdad was conducted to evaluate performance of the developed mathematical models. The determined concession period (CP) has found to be approximately equal to actual concession period (CP) granted to the private sector. Evaluation shows the possibility to adopt the proposed approach to determine the concession period (CP) more effectively. Instead of opportunism policy, the proposed methodology enables local government of Baghdad province to enhance its policies of awarding the partnership projects to increase private sector participation in infrastructure development. Finally, the proposed method can be used by investment practitioners as a decision support tool for contract concession period (CP), and is worth popularizing to design the contracted concession period (CCP) for partnership projects in Iraq, and also can use as a methodology to assess the critical aspects which related to partnership projects in general.

Investor sentiment and the price-earnings ratio in the G7 stock markets

Investment practitioners often interpret an excessively high price-earnings ratio as a reflection of an overvalued market fuelled by optimistic investor sentiment. We examine the role of investor sentiment in explaining the P/E ratio in the G7 countries. The results suggest that after controlling for the effects of fundamental factors, the P/E ratio generally increases with an improvement in investor sentiment. The robustness of the findings to the use of forward P/E ratios, alternative data frequency, and controlling for financial crises is checked. Furthermore, the results from quantile regressions reveal that the effects of investor sentiment vary across the P/E quantiles.

When Short Sellers and Corporate Insiders Agree on Stock Pricing

The authors propose a strategy that uses trading information of both short sellers and corporate insiders. They find that the strategy earns statistically significant and economically meaningful risk-adjusted returns for at least one year, stemming mainly from the information asymmetry between informed and uninformed investors. Based on this finding, they then show that the strategy works best in high-information-asymmetry environments and during economic expansion periods. The results have important implications for investment practitioners. Investors interested in high-information-asymmetry firms can refer to the information on short interest and insider demand when making investment decisions.

Factors in Time: Fine-Tuning Hedge Fund Replication

Hedge fund replication has become a cottage industry in investing. Among the most popular hedge fund replication frameworks are factor models based on ordinary least squares (OLS) regression, a development that is no doubt due to its simplicity and familiarity among investment practitioners. Despite their widespread use, the OLS regression-based factor models that form the basis for many hedge fund replication programs are often overfitted to a single sample, severely undercutting their predictive effectiveness. As a remedy to the latter shortcoming, in this article the authors apply the regularization method known as “ridge regression” to the replication of hedge fund strategies. Ridge regression works by formally imbuing a regression with additional bias in exchange for a reduction in the variance between training and test samples. Using a simple yet robust methodology, the authors show how to dynamically calibrate the predictively optimal level of bias without significantly reducing the backward-looking explanatory power of a given model. In doing so, the authors demonstrate that ridge regression can help produce generalizable models that are useful in both the ex post risk analysis and ex ante replication of hedge fund strategies.