We propose a new portfolio optimization framework, partially egalitarian portfolio selection (PEPS). Inspired by the celebrated LASSO regression and its recent variant partially egalitarian LASSO ...(PELASSO) developed in 1 in the context of the forecast combinations problem in econometrics in 1, we regularize the mean-variance portfolio optimization of Markowitz by adding two regularizing terms that essentially zero out portfolio weights of some of the assets in the portfolio and select and shrink portfolio weights of the remaining assets towards equal weights to hedge against parameter estimation risk. We solve our PEPS formulations by applying Gurobi 9.0 mixed integer optimization (MIO) solver that allow us to tackle large-scale portfolio problems. We test our PEPS portfolios against an array of classical portfolio optimization strategies on a number of datasets in the US equity markets. The PEPS portfolios exhibit the highest out-of-sample Sharpe ratios in all instances considered.
"Banks in Africa are weathering the COVID-19 pandemic well and showing a lot of creativity to overcome the crisis’s problems. But the war in Ukraine is causing new concerns. With interest rates ...rising in many countries and bond funding becoming more expensive, a significant number of banks are worried about rising financing costs. These issues and more are covered in the new Finance in Africa report, based on an annual survey of banks across the continent and supported by Making Finance Work for Africa, an initiative helping more people get loans across the continent. We surveyed 70 banks in sub-Saharan Africa from April to June in 2022 to find out if the war is hurting their business and to learn their views on climate lending, access to finance for women and the accelerating digitisation of the financial sector."
In this paper, we present a review of deterministic software for solving convex MINLP problems as well as a comprehensive comparison of a large selection of commonly available solvers. As a test set, ...we have used all MINLP instances classified as convex in the problem library MINLPLib, resulting in a test set of 335 convex MINLP instances. A summary of the most common methods for solving convex MINLP problems is given to better highlight the differences between the solvers. To show how the solvers perform on problems with different properties, we have divided the test set into subsets based on the continuous relaxation gap, the degree of nonlinearity, and the relative number of discrete variables. The results also provide guidelines on how well suited a specific solver or method is for particular types of MINLP problems.
Does Algorithmic Trading Improve Liquidity? HENDERSHOTT, TERRENCE; JONES, CHARLES M.; MENKVELD, ALBERT J.
The Journal of finance (New York),
February 2011, Volume:
66, Issue:
1
Journal Article
Peer reviewed
Open access
Algorithmic trading (AT) has increased sharply over the past decade. Does it improve market quality, and should it be encouraged? We provide the first analysis of this question. The New York Stock ...Exchange automated quote dissemination in 2003, and we use this change in market structure that increases AT as an exogenous instrument to measure the causal effect of AT on liquidity. For large stocks in particular, AT narrows spreads, reduces adverse selection, and reduces trade-related price discovery. The findings indicate that AT improves liquidity and enhances the informativeness of quotes.
We have devised two special modules for De Nederlandsche Bank (DNB) Household Survey to measure financial literacy and study its relationship to stock market participation. We find that the majority ...of respondents display basic financial knowledge and have some grasp of concepts such as interest compounding, inflation, and the time value of money. However, very few go beyond these basic concepts; many respondents do not know the difference between bonds and stocks, the relationship between bond prices and interest rates, and the basics of risk diversification. Most importantly, we find that financial literacy affects financial decision-making: Those with low literacy are much less likely to invest in stocks.
Discount-rate variation is the central organizing question of current asset-pricing research. I survey facts, theories, and applications. Previously, we thought returns were unpredictable, with ...variation in price-dividend ratios due to variation in expected cashflows. Now it seems all price-dividend variation corresponds to discount-rate variation. We also thought that the cross-section of expected returns came from the CAPM. Now we have a zoo of new factors. I categorize discount-rate theories based on central ingredients and data sources. Incorporating discount-rate variation affects finance applications, including portfolio theory, accounting, cost of capital, capital structure, compensation, and macroeconomics.
In this paper, we consider a frequency-dependent portfolio optimization problem with multiple assets using a control-theoretic approach. The expected logarithmic growth (ELG) rate of wealth is used ...as the objective performance metric. It is known that if the portfolio contains a special asset, the so-called dominant asset, then the optimal ELG level is achieved by investing all available funds in that asset. However, this “all-in” strategy is arguably too risky to implement. As a result, we study the case where the portfolio weights are chosen in a rather ad-hoc manner, and a linear buy-and-hold strategy is subsequently used. We show that if the underlying portfolio contains a dominant asset, buy and hold on that specific asset is asymptotically log-optimal with a logarithmic convergence rate. This result also extends to the scenario when a trader does not have a probabilistic model for returns or does not trust a model based on historical data. Specifically, we prove a version of the one fund theorem, which states that if a market contains a dominant asset, buying and holding a market portfolio with nonzero weights for each asset is asymptotically log-optimal. Additionally, we extend an existing result regarding the property called high-frequency maximality of an ELG-based portfolio from a single asset to a multi-asset portfolio case. This means that, in the absence of transaction costs, high-frequency rebalancing is unbeatable in terms of ELG. This result enables us to further improve the log-optimality obtained previously. Finally, we provide a result on the issue of how often a portfolio should be rebalanced, if needed. Examples using simulations with high-frequency historical trading data are included throughout to illustrate the theory.
I describe asset price dynamics caused by the slow movement of investment capital to trading opportunities. The pattern of price responses to supply or demand shocks typically involves a sharp ...reaction to the shock and a subsequent and more extended reversal. The amplitude of the immediate price impact and the pattern of the subsequent recovery can reflect institutional impediments to immediate trade, such as search costs for trading counterparties or time to raise capital by intermediaries. I discuss special impediments to capital formation during the recent financial crisis that caused asset price distortions, which subsided afterward. After presenting examples of price reactions to supply shocks in normal market settings, I offer a simple illustrative model of price dynamics associated with slow-moving capital due to the presence of inattentive investors.
It has become standard practice in the cross-sectional asset pricing literature to evaluate models based on how well they explain average returns on size-
B/
M portfolios, something many models seem ...to do remarkably well. In this paper, we review and critique the empirical methods used in the literature. We argue that asset pricing tests are often highly misleading, in the sense that apparently strong explanatory power (high cross-sectional
R
2s and small pricing errors) can provide quite weak support for a model. We offer a number of suggestions for improving empirical tests and evidence that several proposed models do not work as well as originally advertised.
There are several different economic barriers such as high up-front capital costs, high transaction costs and diverse risks (e.g. performance and technical, contract risks, market risks) that keep ...potential investors or institutional lenders from investing in decentralized renewable energy technologies (RETs). Therefore, suitable business models, specific financing concepts and advanced risk management tools to deal with issues concerning transaction costs and financial risks are required to support RET investments.
This article deals with this issue by introducing a Monte Carlo Simulation (MCS) approach to risk analysis based on an entire life-cycle representation of RET-investment projects. By doing this, the authors uncover considerable advantages regarding content and methodology compared to ordinary NPV-estimation or sensitivity analysis. It could be shown that the presented financial analysis combined with MCS aids in optimizing the conceptual design of an investment project with respect to capital returns and risk. Since both issues are decisive for lenders and investors, the double-criteria analysis method presented in this paper facilitates the raising of capital for project investments in decentralized RETs.
•We model a full life-cycle covering risk analysis of renewable energy projects.•We present a double-criteria analysis method for an investment project.•Monte Carlo Simulation generates analytical advantages for risk assessment.
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