Robust stock and bond allocation with end-of-horizon effects

2019 ◽  
Vol 53 (1) ◽  
pp. 1-28
Author(s):  
Michael Dziecichowicz ◽  
Aurélie C. Thiele

We propose an approach to portfolio management over a finite time horizon that (i) does not require the precise knowledge of the underlying probability distributions, instead relying on range forecasts for the stock returns, and (ii) allows the fund manager to capture the degree of the investor’s risk aversion through a single, intuitive parameter called the budget of uncertainty. This budget represents the worst-case number of time periods with poor performance that the investor is willing to plan for. An application of this setting is target-date funds for pension fund management. We describe an efficient procedure to compute the dynamic allocation between (riskless) bonds and (riskier) stocks at each time period, and we illustrate the risk-to-time-horizon tradeoff on optimal allocation tables, which can easily be provided to fund participants to help them select their strategy. The proposed approach refines rules implemented by practitioners and provides an intuitive framework to incorporate risk in applications with end of horizon effects. In contrast with existing literature providing robust fund management approaches to mathematically sophisticated finance professionals, our goal is to provide a simple framework for less quantitative fund participants who seek to understand how stock return uncertainty and planned retirement date affect the optimal stock-vs-bond allocation in their portfolio. We extend our procedure to the case when the investor’s wealth is penalized for falling short of performance benchmarks across the time horizon. We also discuss the case where the manager can invest in multiple stocks. Numerical results are provided.

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1446
Author(s):  
Min Wang ◽  
Xi Chen ◽  
Ayetiguli Sidike ◽  
Liangzhong Cao ◽  
Philippe DeMaeyer ◽  
...  

Water users in the Amudarya River Basin in Uzbekistan are suffering severe water use competition and uneven water allocation, which seriously threatens ecosystems, as shown, for example, in the well-known Aral Sea catastrophe. This study explores the optimized water allocation schemes in the study area at the provincial level under different incoming flow levels, based on the current water distribution quotas among riparian nations, which are usually ignored in related research. The optimization model of the inexact two-stage stochastic programming method is used, which is characterized by probability distributions and interval values. Results show that (1) water allocation is redistributed among five different sectors. Livestock, industrial, and municipality have the highest water allocation priority, and water competition mainly exists in the other two sectors of irrigation and ecology; (2) water allocation is redistributed among six different provinces, and allocated water only in Bukhara and Khorezm can satisfy the upper bound of water demand; (3) the ecological sector can receive a guaranteed water allocation of 8.237–12.354 km3; (4) under high incoming flow level, compared with the actual water distribution, the total allocated water of four sectors (except for ecology) is reduced by 3.706 km3 and total economic benefits are increased by USD 3.885B.


2021 ◽  
Vol 68 (4) ◽  
pp. 1-25
Author(s):  
Thodoris Lykouris ◽  
Sergei Vassilvitskii

Traditional online algorithms encapsulate decision making under uncertainty, and give ways to hedge against all possible future events, while guaranteeing a nearly optimal solution, as compared to an offline optimum. On the other hand, machine learning algorithms are in the business of extrapolating patterns found in the data to predict the future, and usually come with strong guarantees on the expected generalization error. In this work, we develop a framework for augmenting online algorithms with a machine learned predictor to achieve competitive ratios that provably improve upon unconditional worst-case lower bounds when the predictor has low error. Our approach treats the predictor as a complete black box and is not dependent on its inner workings or the exact distribution of its errors. We apply this framework to the traditional caching problem—creating an eviction strategy for a cache of size k . We demonstrate that naively following the oracle’s recommendations may lead to very poor performance, even when the average error is quite low. Instead, we show how to modify the Marker algorithm to take into account the predictions and prove that this combined approach achieves a competitive ratio that both (i) decreases as the predictor’s error decreases and (ii) is always capped by O (log k ), which can be achieved without any assistance from the predictor. We complement our results with an empirical evaluation of our algorithm on real-world datasets and show that it performs well empirically even when using simple off-the-shelf predictions.


2021 ◽  
Vol 275 ◽  
pp. 01005
Author(s):  
Ruipeng Tan

This paper focuses on comparing portfolio management and construction before and after the coronavirus. First, this paper presents the importance of building up portfolios for investors to diversify their risks. Theories on portfolio management are discussed in this section to show how they have been developed to help on investing and reduce risk. Then, the paper moves on to show the impact of the pandemic on the financial market and portfolio management. Sample data on tech stock returns are collected to perform a Monte Carlo simulation on portfolio construction to find out the efficient portfolio before and after the COVID-19 outbreak. The efficient portfolio is build based on the Markowitz theory to find the combination. Comparisons between these portfolio constructions are made to find out the changes in portfolio management and construction under the pandemic era. In conclusion, this paper presents how pandemic has changed and impacted the investments and lists recommendations on future portfolio management and construction.


Author(s):  
Nandan Sudarsanam ◽  
Ramya Chandran ◽  
Daniel D. Frey

Abstract This research studies the use of predetermined experimental plans in a live setting with a finite implementation horizon. In this context, we seek to determine the optimal experimental budget in different environments using a Bayesian framework. We derive theoretical results on the optimal allocation of resources to treatments with the objective of minimizing cumulative regret, a metric commonly used in online statistical learning. Our base case studies a setting with two treatments assuming Gaussian priors for the treatment means and noise distributions. We extend our study through analytical and semi-analytical techniques which explore worst-case bounds and the generalization to k treatments. We determine theoretical limits for the experimental budget across all possible scenarios. The optimal level of experimentation that is recommended by this study varies extensively and depends on the experimental environment as well as the number of available units. This highlights the importance of such an approach which incorporates these factors to determine the budget.


2018 ◽  
Vol 10 (10) ◽  
pp. 3361 ◽  
Author(s):  
Junru Zhang ◽  
Hadrian Djajadikerta ◽  
Zhaoyong Zhang

This paper examines the impact of firms’ sustainability engagement on their stock returns and volatility by employing the EGARCH and FIGARCH models using data from the major financial firms listed in the Chinese stock market. We find evidence of a positive association between sustainability engagement and stock returns, suggesting firms’ sustainability news release in favour of the market. Although volatility persistence can largely be explained by news flows, the results show that sustainability news release has the significant and largest drop in volatility persistence, followed by popularity in Google search engine and the general news. Sustainability news release is found to affect positively stock return volatility. We also find evidence that market expectation can be driven by the dominant social paradigm when sustainability is included. These findings have important implications for market efficiency and effective portfolio management decisions.


2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Aifan Ling ◽  
Le Tang

Recently, active portfolio management problems are paid close attention by many researchers due to the explosion of fund industries. We consider a numerical study of a robust active portfolio selection model with downside risk and multiple weights constraints in this paper. We compare the numerical performance of solutions with the classical mean-variance tracking error model and the naive1/Nportfolio strategy by real market data from China market and other markets. We find from the numerical results that the tested active models are more attractive and robust than the compared models.


2018 ◽  
Vol 17 (1) ◽  
pp. 78-108 ◽  
Author(s):  
Tatiana Fedyk ◽  
Natalya Khimich

Purpose The purpose of this paper is to link valuation of different accounting items to research and development (R&D) investment decisions and investigate how suboptimal R&D choices during initial public offering (IPO) are linked to future operating and market underperformance. Design/methodology/approach For firms with substantial growth opportunities, accounting net income is a poor measure of the firm’s performance (Smith and Watts, 1992). Therefore, other metrics such as R&D intensity are used by investors to evaluate firms’ performance. This leads to a coexistence of two strategies: if earnings are the main value driver, firms tend to underinvest in R&D; and if R&D expenditures are the main value driver, firms tend to overinvest in R&D. Findings The authors show that the R&D investment decision varies systematically with cross-sectional characteristics: firms that are at the growth stage, unprofitable or belong to science-driven industries are more likely to overinvest, while firms that are able to avoid losses by decreasing R&D expenditure are more likely to underinvest. Finally, they find that R&D overinvestment leads to future underperformance as evidenced by poor operating return on assets, lower product market share, higher frequency of delisting due to poor performance and negative abnormal stock returns. Originality/value While prior literature concentrates on R&D underinvestment as a tool of reporting higher net income, the authors demonstrate the existence of an alternative strategy used by many IPO firms – R&D overinvestment.


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