Portfolio Management Decisions Research Articles

Short-Term Versus Long-Term Portfolio Management Strategies and the Selection of Securities

Purpose: This study examines the effectiveness of short-term versus long-term portfolio management strategies and the selection of securities. It aims to provide insights into how different strategies impact investment performance, considering risk tolerance and financial goals. Research Design and Methodology: The study synthesizes findings from academic journals and empirical research using a qualitative literature review approach. The methodology involves thematic analysis to identify key themes, patterns, and insights related to portfolio management strategies and security selection. Findings and Discussion: The research highlights that short-term strategies like momentum trading can capitalize on transient market inefficiencies but entail higher transaction costs and volatility. Conversely, long-term strategies, such as value investing, focus on fundamental analysis and offer more stable returns over time. The integration of ESG criteria into security selection is shown to enhance portfolio performance and align investments with sustainability objectives. Behavioral biases and technological advancements also significantly influence portfolio management decisions. Implications: The study underscores the importance of balancing short-term and long-term strategies based on investor risk tolerance and financial goals. Financial practitioners can leverage these insights to design diversified portfolios and offer tailored advice. Future research should explore the dynamic interplay between these strategies and the impact of technological and regulatory changes on portfolio management. Integrating ESG considerations is crucial for sustainable investing and aligning with evolving market dynamics.

Strategies to Improve Portfolio Management of New Products

Overview: This article presents the findings of an Innovation Research Interchange (IRI) study on practices and outcomes in new product (NP) portfolio management. Given the substantial investments in R&D and the consequential impact on the business’s future, effective portfolio management decisions play a pivotal role in achieving business success. Portfolio decisions influence R&D productivity, the success rates of new products, and several other performance metrics related to NPD.

Takahashi–Alexander Revisited: Modeling Private Equity Portfolio Outcomes Using Historical Simulations

In 2001, Dean Takahashi and Seth Alexander of the Yale University Investments Office developed a deterministic model for estimating future cash flows and valuations for the Yale endowment’s private equity portfolio. Their model, which is simple and intuitive, is still commonly used by investors to this day. However, the model possesses significant shortcomings, including its sensitivity to the assumptions necessary for the model to function and its failure to provide a range of possible portfolio outcomes. Both issues can result in investors developing overconfidence in forecasts, which can result in suboptimal—or even adverse—portfolio management decisions. To address these shortcomings, we propose a novel historical simulation approach that preserves the simplicity and intuition behind Takahashi and Alexander’s approach. However, our model, which leverages historical private equity cash flow data, requires no user assumptions and naturally provides a range of outcomes. We explore this model in some depth, highlighting the model’s power and flexibility across myriad use cases.

Modelling and Forecasting volatility in International financial markets

Modelling volatility using asset price returns has always been at the forefront of financial economics and option pricing. Observing the conditional variance properties in these asset returns, can be very useful for trend analysis and volatility predictions which are ever needed for trading, portfolio management and financial decision making. The aim of the study was to model and forecast volatility in stock markets. Six financial markets namely the Nasdaq, JSE, the DAX, the CAC 40 and the Nikkei 225 were used as samples with a sampling frame from January 29, 2018 to January 29, 2023. The findings of this study revealed that the variance for all the financial markets under consideration changes significantly with the passage of time. Also, volatility in the JSE, DAX & CAC 40 display fat tail distributions and it is expected to move by three standard deviations. Accordingly, volatility will persist in the Nasdaq, DAX and CAC 40 at an increasing rate but will persist at a decreasing rate in the JSE and Nikkei 225. Considering the peril involved in stock market investing, this study makes a notable contribution to estimating market volatility which is an integral component of asset pricing. With this knowledge, analyst and market traders will have a better understanding of the error distribution in stock markets which will assist in specifying predictive asset prices.

Can implied volatility predict returns on oil market? Evidence from Cross-Quantilogram Approach

This paper employs the Cross-Quantilogram methodology proposed by Han et al. (2016) to investigate whether the implied volatility of crude oil (OVX) ameliorates the directional predictability of the oil price returns. Our main result documents the existence of a quantile predictability from OVX to WTI returns when the crude oil implied volatility is in the upper conditional quantile. On the other hand, no sufficient evidence in directional predictability is found when the OVX is at the lower to intermediate level. As a part of robustness check, the same analysis was conducted to the Brent returns. The pattern of directional predictability (similar signs) broadly corresponds with those observed on the WTI returns, however, it seems that OVX is more predictive for Brent than WTI. Particularly, the predictive power of OVX is found to be significant during periods of moderate to high OVX and mainly concentrated on lower to intermediate variations in Brent returns. As a result, implied volatility can be considered as a driver with respect to the forthcoming variations of the returns in the spot oil market. Insights gleaned from this study could have important implications for investors and policymakers in terms of portfolio and risk management decisions.

Cryptocurrency portfolio allocation using a novel hybrid and predictive big data decision support system

Establishing a cryptocurrency portfolio management and allocation decision support is vital in enabling investors to thrive in this market. More than nearly 5000 cryptocurrencies are traded daily at cryptocurrency exchanges, and one of the primary challenges is the optimal selection of the assets. While research studies considered cryptocurrency price prediction as a typical time series forecasting problem, a portfolio allocation problem can be defined as a Multiple Criteria Decision-Making (MCDM) problem. To tackle this problem and generate a response to these dynamic challenges, this study developed an analytical and predictive approach to guide investors through their decisions. Time series forecasting was conducted using the Prophet Forecasting Model (PFM) to predict the prices. For the asset allocation stage, this study extended the Cluster analysis for improving the Multiple Criteria Decision Analysis (CLUS-MCDA) algorithm by adding additional features to this large-scale decision-making technique, including Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and the Višekriterijumsko kompromisno rangiranje (meaning: multi-criteria optimization and compromise solution) (VIKOR) methods. While the concept of Parallel Decision Making (PDM) in massive structured problems was previously introduced in the CLUS-MCDA algorithm, this study also utilized a target-based normalization technique that gives the decision-makers more control over the extended CLUS-MCDA II algorithm. To validate the reliability of the method, a cryptocurrency trade experiment considering more than 70 cryptocurrencies and multiple scenarios based on decision-makers' preferences was analysed. The outcomes of this study showed that the proposed decision support system is a reliable tool for such practical and real-world big data problems.

Exchange rates and the global transmission of equity market shocks

With the capacity to amplify or buffer the effect of shocks between equity markets in different countries, exchange rates play a crucial role in the transmission of shocks. By modelling the dependence structure between exchange rates and equity markets, we quantify the impact of an equity market shock on other equity markets through the cross-expected shortfall and assess the contribution of exchange rates to shock transmission. For emerging Latin American countries (Argentina, Brazil, Chile and Mexico) and two developed markets (the EU and USA), we document (a) that the contribution of exchange rates to shock transmission is time-varying and differs across countries; and (b) that exchange rates diversify (echo) shocks from abroad for investors based in emerging (developed) economies. Our results suggest that investors need to accurately measure the diversification role of their currency when making international portfolio and risk management decisions.

Degree and structure of return dependence among commodities, energy stocks and international equity markets during the post-COVID-19 period

This paper examines the safe-haven role of copper, iron, gold, silver, and energy stocks for international equity markets during the COVID-19 pandemic. Specifically, the degree and structure of return dependence at different points of conditional return distributions are examined for the pre-COVID and post-COVID periods. The results show that copper is a weak safe-haven for the US equity market at the upper-tail of conditional distribution of cooper returns during the post-COVID period. Gold loses its hedge status during the post-COVID period while silver is a strong safe-haven against international equity markets at the upper-tail of conditional return distribution of silver. Further, iron pose weak safe-haven properties against international equity markets when iron returns are extremely positive. However, neither conventional nor green energy stocks act as safe-haven against international equity markets. Current results may provide guidance for risk management, portfolio management and policy decisions during the post-COVID-19 period.

Corporate social responsibility and earnings quality in family firms

PurposeThe purpose of this paper is to investigate the relationship between corporate social responsibility and earnings quality, specifically in family firms.Design/methodology/approachBased on a sample of French-listed firms from the period 2005 to 2016, the authors use the instrumental variable approach based on a two-stage least-squares (2SLS) estimator.FindingsThe results show that Corporate Social Responsibility (CSR) performance is positively associated with the relevance and faithful representation of earnings. This means that companies that commit to CSR activities are more likely to provide high earnings quality. The results also show that the positive association between CSR performance and earnings quality is more prevalent in family firms suggesting that socially responsible family firms are willing to preserve their socio-emotional wealth by disclosing high quality earnings.Research limitations/implicationsThe results suggest that French firms commit to CSR to satisfy the interests of their stakeholders by disclosing high-quality information supporting the conflict resolution view of CSR. The findings also support the socio-emotional wealth perspective and suggest that family firms that engage in CSR activities provide a rich informational environment through high earnings quality.Practical implicationsThis study’s findings can be thus useful to investors for their portfolio management decisions by enabling them to identify the profile of companies with high earnings quality. These results may also help standard-setters and capital-market regulators improve market transparency by introducing new requirements to encourage investing in CSR.Originality/valueThis study extends the research on the relationship between CSR and earnings quality by focusing on two fundamental characteristics including relevance and faithful representation. This paper focuses on the effect of CSR on earnings quality in the specific context of family firms. This study offers then a better understanding of whether socially responsible family firms communicate stronger or weaker earnings quality than non-family firms based on the agency and socio-emotional wealth perspectives.

Handling the Use of Derivatives in Performance Attribution

In this article, the author demonstrates methods for processing derivatives exposures that are beneficial to investment portfolio performance and that accurately reflect the portfolio managers’ objectives. The article will help the reader evaluate the success of portfolio management decisions.

Graph-based stock correlation and prediction for high-frequency trading systems

In this paper, we have implemented a high-frequency quantitative system that can obtain stable returns for the Chinese A-share market, which has been running for more than 3 months (from March 27, 2020 to June 30, 2020) with the expected results. A number of rules and barriers exist in the Chinese A-share market such as trading restrictions and high fees, as well as scarce and expensive hedging tools. It is difficult to achieve stable absolute returns in such a market. Stock correlation analysis and price prediction play an important role to achieve any profitable trading. The portfolio management and subsequent trading decisions highly depend on the results of stock correlation analysis and price prediction. However, it is nontrivial to analyze and predict any stocks, being time-varying and affected by unlimited factors in a given market. Traditional methods only take some certain factors into consideration but ignore others that may be changed dynamically. In this paper, we propose a novel machine learning model named Graph Attention Long Short-Term Memory (GALSTM) to learn the correlations between stocks and predict their future prices automatically. First, a multi-Hawkes Process is used to initial a correlation graph between stocks. This procedure provides a good training start as the multi-Hawkes Processes will be studied on the most saint feature fluctuations with any correlations being statistically significant. Then an attention-based LSTM is built to learn the weighting matrix underlying the dynamic graph. In addition, we also build matching data process plus portfolio management modules to form a complete system. The proposed GALSTM enables us to expand the scope of stock selection under the premise of controlling risks with limited hedging tools in the A-share market, thereby effectively increasing high-frequency excess returns. We then construct a long and short positions combination, select long positions in the A shares of the entire market, and use stock index futures to short. With GALSTM model, the products managed by our fully automatic quantitative trading system achieved an absolute annual return rate of 44.71% and the standard deviation of daily returns is only 0.42% in three months of operation. Only 1 week loss in 13 weeks of running time.

Weak Supervision and Black-Litterman for Automated ESG Portfolio Construction

The authors propose an approach that combines modern machine learning techniques in natural language processing with portfolio optimization to incorporate views of companies’ environment, social, and governance (ESG) performance. This is automatically done through curating and subsequently converting large-scale news data into portfolio management decisions. They train a machine learning news data classifier to automatically identify several key ESG issues in news data over time. They then aggregate these issues over time to generate a views vector under the Black–Litterman portfolio framework and finally compare the performance of an ESG-tilted portfolio against a standard Black–Litterman portfolio. They also show how this can be achieved at scale, in a fully automated manner, and with consistency over large periods of time. Their methodology thus demonstrates a reasonable and agile method for asset managers to incorporate ESG considerations into their portfolios free of any exclusionary frameworks and without sacrificing performance. <b>TOPICS:</b>ESG investing, quantitative methods, statistical methods, big data/machine learning, portfolio construction <b>Key Findings</b> ▪ The authors describe a theoretical framework for using an automated NLP system to incorporate ESG criteria into portfolio optimization decisions. ▪ The authors demonstrate the technical implementation details for incorporating ESG signals into augmented portfolio weights. ▪ The authors demonstrate the competitive performance of such a portfolio through a long-term historical backtest with the S&amp;P 500 Index.

Harnessing the Potential of Private Assets: A Framework for Institutional Portfolio Construction

Institutional portfolios are increasing allocations to illiquid private assets seeking better returns and diversification. However, as allocations increase, a portfolio’s liquidity structure changes, sometimes abruptly. How can a CIO increase their confidence with private asset allocations and unlock their potential? Using a corporate defined benefit pension plan as our example, we present and illustrate a practical framework (OASIS TM) that can help CIOs analyze how their top-down asset allocation and their bottom-up private investing activity interact to affect their portfolio’s ability to respond to liquidity demands in a multi-asset, multi-period setting. This is exactly what a CIO needs to know. Besides scheduled benefit payments, corporate pension plans have many unexpected liquidity demands which should be accounted for when evaluating liquidity risk. For example, a plan should be able to rebalance when market movements cause allocations to exceed risk limits. A plan also needs liquidity to meet unexpected capital calls and be prepared for exogenous cash flow events driven by corporate actions (e.g., pension risk transfers, corporate contributions, and merger and acquisition activities). Many plans also have asset allocation glide paths, conditional on the plan’s funding ratio, that present additional liquidity strains as it may be difficult to sell illiquid assets to satisfy new allocation targets. The CIO’s challenge is to maximize expected portfolio performance while keeping liquidity risk under control. By measuring the potential tradeoff between asset allocation, portfolio performance and multiple dimensions of liquidity risk, the OASIS framework can help CIOs make more informed portfolio management decisions.

Food Dishes for Sustainable Development: A Swedish Food Retail Perspective.

Current dietary patterns contribute negatively to greenhouse gas emissions and to the increased prevalence of non-communicable diseases. Earlier research on sustainable food consumption mainly focuses on diets, rather than single meals. Diets are difficult to measure, which is usually executed through self-reporting. This paper aims to identify frequently prepared dishes in a home setting through sales statistics, and how they can be altered to reduce climate impact and increase nutritional value. Commonly prepared food dishes in a home setting among customers of a major food retailer in Sweden were identified through sales statistics. The dishes were altered to reach nutritional and climate impact guidelines. Commonly prepared food dishes exceeded goals for climate boundaries by more than threefold and were not in line with nutritional guidelines. The content of fat, including saturated fat, was too high. Vegetables, fruits, wholegrains and fiber need to be increased. To reduce climate impact and increase nutritional value, the amount of animal-based product need to be reduced and/or exchanged to plant-based alternatives. This research contributes empirically to understandings of how portfolio management decisions influence food consumption based on sales statistics and how nutritional and climate impact guidelines can be applied from a single meal perspective.

Geological probability of success assessment for amplitude-driven Prospects:A Nile Delta case study

Effective quantification and management of exploration risks and associated uncertainties remains a key challenge for the Oil and Gas industry. Geological probability of success evaluation is one of the key steps of the exploration process, which will lead - if properly managed - to enhanced project's value creation, as well as improved portfolio management and exploration decisions. This paper reviews and discusses a consistent, uniform and reproducible approach for evaluating seismic amplitude-supported prospects, weighted by the input data quality and availability. The method and workflows are further illustrated with an offshore Nile Delta case study. This approach enhances the confidence in the appropriate risk and associated resource estimate for less biased and more consistent probability estimation.

How does news flow affect cross-market volatility spillovers? Evidence from China’s stock index futures and spot markets

This paper examines how news flow affects cross-market volatility spillovers and price discovery process in China’s stock market and index futures market. We find robust evidence confirming dominant predicting power of the stock market in the price discovery process, and presence of asymmetric and persistent volatility effects. The results show that volatility spillovers are bidirectional between stock index futures and spot prices, and news release has significant and positive association with the dynamic conditional correlation between the index spot and the index futures markets. These have important implications for effective hedging and portfolio management decision in emerging markets.

A python-based multicriteria portfolio selection DSS

Our purpose in this article is to develop an integrated portfolio management decision support system, which takes into account the inherent multidimensional nature of the problem, while allowing the DM, i.e. investor, to incorporate his/her preferences in the decision process. The proposed DSS has been developed in Python programming language and consists of two components: The first component is associated with the security selection phase, while the second component is associated with the portfolio optimization phase. In the first phase, four discrete multicriteria methods are employed; the PROMETHEE II, the ELECTRE III, the MAUT and the TOPSIS. After the cumulative integration of the results, a series of mathematical programming models are applied in the second phase, that of multicriteria portfolio optimization; a mixed-integer quadratic programming model, a goal programming model, a genetic algorithm model, and a multiobjective PROMETHEE flow model. Finally, the proposed approach is tested through a large-scale illustrative application in several stock markets and various sectors, analyzing simultaneously a very large number of securities.

Using ESG to Enhance Fixed‐Income Returns: The Case of Inherent Group

The authors discuss how their investment firm uses environmental and social as well as financial considerations to produce superior fixed‐income returns for its limited partners. The firm is committed to the idea that, by making ESG analysis part of its deal sourcing, underwriting, monitoring, issuer engagement, and portfolio management decisions, such labor‐intensive practices end up providing valuable investment insights that help drive long‐term value creation.Using three of its investments as case studies—two involving short credit positions and one long position—the authors show how the incorporation of ESG in leveraged credit analysis has not only differentiated its approach, but proved effective in identifying undervalued (and distressed) debt as well as overvalued credits ripe for a fall. Conviction about the firm's two credit shorts rested on fundamental analysis reinforced by social and governance considerations—one involving opioids and price gouging, the other a conflicted governance structure. The third case involved investment in the distressed credit of a California utility premised on both environmental tailwinds from investments in renewables and a view of the bankruptcy process, triggered by California wildfire liabilities, as a social and economic forum for accomplishing an overall welfare‐increasing reassignment of risks and rewards—one likely to benefit society as a whole as well as (some) investors.

A Network Analysis of the Asia-Pacific and Other Developed Stock Markets: Pre and Post Global Financial Crisis

This paper examines the volatility spillover and connectedness between Asia-Pacific, US, UK, and eurozone stock markets. A spillover index is built using forecast error variance decomposition in a vector autoregression framework and the spillover index is used to build network diagrams. It shows evidence of how the increase in risk transfer (volatility spillover) between the markets led to the global financial crisis and of the higher level of connectedness since. Network diagrams show the direction and strength of the connectedness. The network strength estimation enables us to understand the risk associated with connectedness across the markets in the event of a trigger and its influence in portfolio management decisions of international funds. The Chinese market appears to be the most insulated, while the South Korean, Hong Kong, and Singapore stock markets dominate in terms of risk transfer. The US, UK, EU, Singapore and Hong Kong are the top five volatility spillover recipient markets, both during pre and post global financial crisis periods. We find the market size to be irrelevant in the determination of the level of connectedness, whereas the role of geographical proximity cannot be ruled out. The findings are relevant to multinational investment strategies and in understanding the relative risk of investment in the Asia-Pacific region.

Quantile causality and dependence between crude oil and precious metal prices

AbstractThis paper examines long‐run dependence and causality between oil and precious metal (gold, silver, platinum, palladium, steel, and titanium) prices across quantiles by exploiting their time series properties with the help of novel econometric techniques. The empirical results for the period 1990–2019 indicate that oil and metal prices are nonstationary across different quantiles and that cointegration patterns differ widely across quantiles. Causality running from oil to metal prices is quantile‐dependent and differs according to the metal, whereas upward and downward movements in metal prices have no causal effect on oil prices. These results have implications for investors and policymakers in terms of portfolio and risk management decisions.