Environmental Portfolios—Evidence from Screening and Passive Portfolio Management

Environmental portfolios via screening or optimization with respect to ecological criteria are not clear-cut concepts. Often, they urge investors to reduce the asset universe, which is accompanied by diversification losses. In this article, we show that a simple passive asset selection strategy based on environmental criteria allows ecological investors to adjust their portfolios without compromising or even reducing risk-adjusted financial performance. In detail, we show that screening does not lead to a significant financial performance reduction. Moreover, we propose an asset selection based on an environmental criteria that improves the portfolios’ financial performance, and further improves its potential positive environmental impact. Our results suggest that a combination of a screening and an environmental-scoring-based asset allocation seems to be a viable option for environmentally responsible investors leveraging the advantages of both strategies. Furthermore, we construct a risk factor CMP (clean minus polluting) and document a significant factor loading when added to the Fama–French five-factor model, suggesting the existence of a risk premium based on a firm’s environmental performance.

Evaluation of dynamic cointegration-based pairs trading strategy in the cryptocurrency market

Purpose This paper aims to demonstrate a dynamic cointegration-based pairs trading strategy, including an optimal look-back window framework in the cryptocurrency market and evaluate its return and risk by applying three different scenarios. Design/methodology/approach This study uses the Engle-Granger methodology, the Kapetanios-Snell-Shin test and the Johansen test as cointegration tests in different scenarios. This study calibrates the mean-reversion speed of the Ornstein-Uhlenbeck process to obtain the half-life used for the asset selection phase and look-back window estimation. Findings By considering the main limitations in the market microstructure, the strategy of this paper exceeds the naive buy-and-hold approach in the Bitmex exchange. Another significant finding is that this study implements a numerous collection of cryptocurrency coins to formulate the model’s spread, which improves the risk-adjusted profitability of the pairs trading strategy. Besides, the strategy’s maximum drawdown level is reasonably low, which makes it useful to be deployed. The results also indicate that a class of coins has better potential arbitrage opportunities than others. Originality/value This research has some noticeable advantages, making it stand out from similar studies in the cryptocurrency market. First is the accuracy of data in which minute-binned data create the signals in the formation period. Besides, to backtest the strategy during the trading period, this study simulates the trading signals using best bid/ask quotes and market trades. This study exclusively takes the order execution into account when the asset size is already available at its quoted price (with one or more period gaps after signal generation). This action makes the backtesting much more realistic.

Portfolio Construction Using Financial Ratio Indicators and Classification through Machine Learning

Financial Ratios have been a major indicator for financial asset selection. It’s seen that the decision taken to construct a portfolio based on financial ratio indicators has been able to make better returns than the random asset allocation process in the portfolio. This research will show multiple classifications based on unsupervised machine learning processes to satisfactorily determine investable assets or securities for portfolio contribution. Our suggested portfolio would then be compared with a random portfolio for a specific time frame in order to determine portfolio return, Sharpe ratio, and portfolio performance.

Replicating Private Equity with Value Investing, Homemade Leverage, and Hold-to-Maturity Accounting

The contributions of asset selection and incremental leverage to buyout investment performance are more important than typically assumed or estimated to be. Buyout funds select small firms with distinct value characteristics. Public equities with these characteristics have high risk-adjusted returns relative to common factors. Adding incremental leverage to a publicly traded stock portfolio increases both risks and mean returns in this sample. Direct investments in private equity funds earn lower mean returns than a replicating strategy designed to mimic these key economic features of their investment process with public equities and brokerage loans.

Intelligent portfolio asset prediction enabled by hybrid Jaya-based spotted hyena optimization algorithm

Purpose The purpose of this study is to provide a novel portfolio asset prediction by means of the modified deep learning and hybrid meta-heuristic concept. In the past few years, portfolio optimization has appeared as a demanding and fascinating multi-objective problem, in the area of computational finance. Yet, it is accepting the growing attention of fund management companies, researchers and individual investors. The primary issues in portfolio selection are the choice of a subset of assets and its related optimal weights of every chosen asset. The composition of every asset is chosen in a manner such that the total profit or return of the portfolio is improved thereby reducing the risk at the same time. Design/methodology/approach This paper provides a novel portfolio asset prediction using the modified deep learning concept. For implementing this framework, a set of data involving the portfolio details of different companies for certain duration is selected. The proposed model involves two main phases. One is to predict the future state or profit of every company, and the other is to select the company which is giving maximum profit in the future. In the first phase, a deep learning model called recurrent neural network (RNN) is used for predicting the future condition of the entire companies taken in the data set and thus creates the data library. Once the forecasting of the data is done, the selection of companies for the portfolio is done using a hybrid optimization algorithm by integrating Jaya algorithm (JA) and spotted hyena optimization (SHO) termed as Jaya-based spotted hyena optimization (J-SHO). This optimization model tries to get the optimal solution including which company has to be selected, and optimized RNN helps to predict the future return while using those companies. The main objective model of the J-SHO-based RNN is to maximize the prediction accuracy and J-SHO-based portfolio asset selection is to maximize the profit. Extensive experiments on the benchmark datasets from real-world stock markets with diverse assets in various time periods shows that the developed model outperforms other state-of-the-art strategies proving its efficiency in portfolio optimization. Findings From the analysis, the profit analysis of proposed J-SHO for predicting after 7 days in next month was 46.15% better than particle swarm optimization (PSO), 18.75% better than grey wolf optimization (GWO), 35.71% better than whale optimization algorithm (WOA), 5.56% superior to JA and 35.71% superior to SHO. Therefore, it can be certified that the proposed J-SHO was effective in providing intelligent portfolio asset selection and prediction when compared with the conventional methods. Originality/value This paper presents a technique for providing a novel portfolio asset prediction using J-SHO algorithm. This is the first work uses J-SHO-based optimization for providing a novel portfolio asset prediction using the modified deep learning concept.

“We’ll Always Have Paris”: Out-of-Country Buyers in the Housing Market

Previous research has shown that nonlocal household investors make suboptimal asset selection and market timing decisions. However, in real estate markets, heterogeneity in returns can exist even with identical ex ante investment (timing) choices, given that transaction prices are the outcome of a complex search-and-bargaining process. Analyzing notarial data for the Paris housing market, we find that “out-of-country” buyers indeed buy at higher prices and resell at substantially lower prices than do local investors, ceteris paribus. Furthermore, our evidence suggests that this pattern is not due to higher search costs and information asymmetries but instead stems from wealth-related differences in bargaining intensity. Finally, we estimate the causal effect of out-of-country demand shocks on property prices in Paris to be positive but small. This paper was accepted by Tomasz Piskorski, finance.