Investment Time Horizon Research Articles

A Global-in-Time Neural Network Approach to Dynamic Portfolio Optimization

ABSTRACT We discuss a neural network approach, which does not rely on dynamic programming techniques, to solve dynamic portfolio optimization problems subject to multiple investment constraints. The approach allows for objectives of a very general form encompassing both time-consistent and time-inconsistent objectives, as well as objectives requiring multi-level optimization. The number of parameters of the neural network remains independent of the number of portfolio rebalancing events. Compared to reinforcement learning, this technique avoids the computation of high-dimensional conditional expectations. The approach remains practical when considering large numbers of underlying assets, long investment time horizons or very frequent rebalancing events. We prove convergence of the numerical solution to the theoretical optimal solution of a large class of problems under fairly general conditions, and present ground truth analyses for a number of popular formulations, including mean-variance, mean-semi-variance, and mean-conditional value-at-risk problems. Numerical experiments show that if the investment objective functional is separable in the sense of dynamic programming, the correct time-consistent optimal investment strategy is recovered, otherwise we obtain the correct pre-commitment (time-inconsistent) investment strategy. This method is agnostic as to the underlying data generating assumptions, and results are illustrated using (i) parametric models for underlying asset returns, (ii) stationary block bootstrap resampling of empirical returns, and (iii) generative adversarial network (GAN)-generated synthetic asset returns.

PERFORMANCE EVALUATION OF EXCHANGE TRADED FUNDS (ETFs) IN INDIA: A REVIEW OF SELECTED LITERATURE

The panorama of investment options provided to investors by Exchange Traded Funds (ETFs) and their remarkable rise since their introduction in the nation have made an assessment of existing literature on ETFs in India necessary. In the light of these developments, the present study focuses on previous research conducted by different researchers to comprehend the performance of equity based mutual funds in India. The aim of this paper is to examine the corpus of work that may be found in both national and international peer-reviewed journals, seminars and conference proceedings. It look at abroad range of research on topics such as risk return relationship, investment goals, investment time horizon, investment kind and market volatility that support investing in equity based mutual fund schemes. It has been found that most of the exchange traded funds (ETFs) fared better than their underlying indices.

An analysis of Risk Tolerance of Individual Investor using Gray Relational Analysis Method

Risk tolerance refers to an individual investor's inclination towards embracing fluctuations in investment values in pursuit of higher income. It reflects their capacity to endure such fluctuations and financial goals, time horizon and emotional comfort various including status influenced by factors. Personal preferences and investment choices with circumstances as it helps to regulate, informed investment decisions one's risk tolerance in taking understanding is important. financial situation, investment objectives, knowledge of the financial market, factors like experience, and emotional intelligence contribute to determining one's risk tolerance. Investors' risk tolerance is often assessed through questionnaires or evaluations conducted by financial advisors, which consider factors like attitudes towards risk, investment goals, time horizon, and financial situation. Based on the assessment, investors may be categorized into different risk profiles such as conservative, moderate, or aggressive, with corresponding investment strategies suggested accordingly. It's essential for investors to honestly evaluate and be realistic about their risk tolerance, as overestimating it may lead to excessive risk-taking and potential losses during market downturns, while underestimating it may result in overly conservative investment choices. Regular reviews of risk tolerance are recommended, especially in light of significant life events or market fluctuations. The concept of risk-return trade-off highlights that higher returns are typically associated with higher levels of risk, implying that investors expecting higher returns must be prepared to accept greater potential losses. However, risk tolerance isn't solely determined by financial factors; emotional elements such as fear, anxiety, and market sentiment also influence an investor's tolerance for risk and their behaviour during periods of volatility. Therefore, a comprehensive understanding of risk tolerance encompasses both financial considerations and emotional factors. the GRA (Grey Relational Analysis) approach at its inception, focusing on the concept of the gray gadget. This technique is particularly effective for selection problems involving multiple attributes within a component. The current literature highlights its applicability in addressing problems associated with multiple factors and variables, especially when dealing with complex relationships. The GRA approach is well-suited for resolving issues related to fixing problems, and various types of GRA techniques have been proposed in the field. The introduction of the GRA approach is both straightforward and environmentally friendly, making it a practical choice for addressing complex problems involving multiple variables. Investment Objectives, Time Horizon, Financial Situation, Knowledge and Experience and Liquidity Needs. Risk Assessment Questionnaires, Investment Experience and Knowledge, Financial Situation, Investment Goals and Emotional Attitude Towards Risk. the Rank of GRA for Risk Tolerance of Individual Investors. Liquidity Needs is got the first rank whereas the Low Risk Tolerance is having the Lowest rank.

GENERATIONAL INVESTMENT PATTERNS IN MUTUAL FUNDS: A COMPARATIVE STUDY OF GENERATION X AND MILLENNIALS

This Master’s thesis examines the investment patterns in mutual funds across two distinct generational cohorts: Generation X and Millennials. The primary aim of this study is to analyze and compare the investment behaviors and preferences between these two groups, focusing on factors such as risk tolerance, investment time horizon, and preferred fund types. Utilizing a mixed-methods approach that combines quantitative data analysis of investment portfolios with qualitative surveys and interviews, this research provides a comprehensive view of how generational differences influence investment decisions. The quantitative analysis involves a statistical examination of mutual fund holdings and transactions sourced from a proprietary database covering the period from 2010 to 2020. Meanwhile, the qualitative component features insights from over 200 individual interviews with representatives of each generation, supplemented by surveys to gauge attitudes towards various investment strategies and market outlooks. Initial findings suggest significant divergences in the investment strategies between Generation X and Millennials. Generation X investors tend to favor income-focused and balanced funds, displaying a preference for stability and long-term growth, influenced possibly by their proximity to retirement. In contrast, Millennials are more inclined towards equity-heavy portfolios and show a greater propensity for embracing emerging market funds and ESG (environmental, social, and governance) themed investments, driven by higher risk tolerance and a longer investment horizon. This thesis not only highlights the distinct investment preferences of these generations but also delves into the sociopolitical and economic factors that might influence these patterns. Furthermore, it discusses the implications of these generational differences for financial advisors, mutual fund managers, and policymakers. By identifying and understanding these generational investment behaviors, industry professionals can better tailor their approaches to meet the needs and preferences of each demographic group, enhancing client satisfaction and engagement. This comparative study contributes to the financial industry’s understanding of demographic shifts and their impacts on market trends, offering valuable insights for developing more targeted and effective investment products and strategies.

Home bias and the returns of strategic portfolios: Neither always so good nor so bad

Home Bias is a phenomenon that has been sufficiently addressed from many different perspectives, such as active management or structural investment constraints. However, there is little work about the economic effects of these situations. This work aims to quantify the impact of this effect on the returns of a set of strategic portfolios with the same allocation between fixed income and equity assets. The statistical information includes the daily values of all indices for the 2004–2021 period. Returns have been calculated for investment time horizons of between 5 and 15 years. Quantile regressions have been used to assess changes in returns in response to changes in portfolio composition. The results suggest that over-investment in local assets is not always positive and dependent on the local index selected.

The ESG premium in China’s a-share market: a time horizon perspective

ABSTRACT For ESG-oriented investors, the inquiry into whether companies with high-ESG scores can achieve excess returns holds significant significance. Existing research has generated some controversy regarding the existence of an ESG premium. In this study, we attempt to reconcile conflicting empirical findings by introducing a dimension of investment time horizons. We examine the impact of ESG scores (representing long-term values) and ESG (short-term changes) on subsequent stock returns. Our results indicate that high-ESG stocks tend to achieve higher expected returns, while high ESG has the opposite effect. One plausible explanation is that high-ESG firms tend to exhibit superior governance practices, making them more likely to create long-term value, while efforts to improve ESG may increase short-term costs, impacting profitability and exerting a negative influence on stock prices temporarily. The results based on financial data also support this conjecture.

The term structure of equity yields—a bottom-up approach

Abstract This article proposes a novel perspective on the term structure of market equity yields. Instead of using market dividend futures, we aggregate equity yields of individual firms to estimate the market equity yield curve. This approach allows studying the aggregation effect that shapes the market equity yield curve. During the period from 1990 to 2019, we find a positive aggregation effect: companies with high equity yields were expected to grow at higher rates than companies with low equity yields. Thus, high-yield companies were expected to generate an increasing share of total market dividends when expanding the investment time horizon. Under the assumption of flat firm-risk premia, this implies an upward-sloping term structure of equity risk premia. Together with the concave bond yield curve, the market equity yield curve was upward-sloping.

The Cost Realization on Time Frame Shift of Securities

Investors use a variety of investing methods depending on the time scales () of their short-term and long-term investment time horizons (ITH). The nature of the market varies greatly depending on the type of investment. Hurst exponents (H) and normalised variance (NV) techniques based on empirical mode decomposition (EMD) have been used to identify key features of the market over various time horizons. For the stock price's decomposed intrinsic mode functions (IMF), the values of H and NV have been estimated. We found H1 0.5 0.04 and H1 0.75 for the IMFs with time periods ranging from a few days to 3 months and 5 months, respectively. Two time series from the IMFs have been reconstructed based on the value of H1: a) Short-term time series [XST (t)] with H1 = 0.5.b) long-term time series [XLT (t)] with H1 0.75 and a time span of 5 months. The XST (t) and XLT (t) indicators reveal that market dynamics in short-term ITH are random and connected in long-term ITH. We also discovered that the NV is very low in short-term ITH and gradually increases in long-term ITH. The results also reveal that in the long run, stock prices are connected with the company's fundamental factors. The discovery may aid investors in developing investment and trading strategies for both short-term and long-term investment goals.

Across-time risk-aware strategies for outperforming a benchmark

We propose a novel objective function for constructing dynamic investment strategies with the goal of outperforming an investment benchmark at multiple points of evaluation during the investment time horizon. The proposed objective is intuitive, easy to parameterize, and directly targets a favorable tracking difference of the actively managed portfolio relative to the benchmark. Under stylized assumptions, we derive closed-form optimal investment strategies to guide the intuition in more realistic settings. In the case of discrete rebalancing with investment constraints, optimal strategies are obtained using a neural network-based numerical approach that does not rely on dynamic programming techniques. Compared to the targeting of a favorable tracking difference relative to the benchmark only at some fixed time horizon, our results show that the proposed objective offers a number of advantages: (i) The associated optimal strategies exhibit potentially more attractive asset allocation profiles, in that less extreme positions in individual assets are taken early in the investment time horizon, while achieving a similar terminal terminal wealth distribution. (ii) Across-time risk awareness leads to more robust performance and a higher probability of benchmark outperformance during the investment horizon in out-of-sample testing. The resulting strategies therefore exhibit desirable characteristics for active portfolio managers with periodic reporting requirements.

Asymmetric and time-frequency based networks of currency markets

We examine asymmetries in the volatility spillover of international currency markets over the short and long run, with a focus on the COVID-19 pandemic. In doing so, we propose partial quantile coherency network approach. Our results indicate heterogeneous behaviour of currencies’ volatility networks under various market conditions across investment time horizons. The volatility networks are driven by developed currency markets and by geographical proximity in Europe and Asia. We do not find asymmetry in the dependence structures of positive and negative currency volatilities. The dependence structure changes during COVID-19 especially in the long run. Many currencies show disentangled behaviour, which suggests their hedging and diversification potential.

Beating a Benchmark: Dynamic Programming May Not Be the Right Numerical Approach

We analyze dynamic investment strategies for benchmark outperformance using two widely used objectives of practical interest to investors: (i) maximizing the information ratio (IR), and (ii) obtaining a favorable tracking difference (cumulative outperformance) relative to the benchmark. In the case of the tracking difference, we propose a simple and intuitive objective function based on the quadratic deviation (QD) from an elevated benchmark. In order to gain some intuition about these strategies, we provide closed-form solutions for the controls under idealized assumptions. For more realistic cases, we represent the control using a neural network (NN) and directly solve a sampled optimization problem, which approximates the original optimal stochastic control formulation. Unlike the typical approach based on dynamic programming (DP), e.g., reinforcement learning, solving the sampled optimization with an NN as a control avoids computing conditional expectations and leads to an optimization problem with a small number of variables. In addition, our NN parameter size is independent of the number of portfolio rebalancing times. Under some assumptions, we prove that a traditional DP approach results in a high-dimensional problem, whereas directly solving for the control without using DP yields a low-dimensional problem. Our analytical and numerical results illustrate that, compared with IR-optimal strategies with the same expected value of terminal wealth, the QD-optimal investment strategies result in comparatively more diversified asset allocations during certain periods of the investment time horizon.

ESG, time horizons, risks and stock returns

Investment time horizon is an important part of significance of ESG factors. This research examines the role of ESG factors in returns and risks in short- and medium-term investment periods. It compares (a) the returns and risks of ESG portfolios between before scoring and after scoring, and (b) the returns and risks between ESG portfolios and their peers.The main results suggest that after scoring most short-term ESG portfolios have similar returns, but lower risks than before scoring. The returns of ESG portfolios are similar to those of nonESG portfolios for both short- and medium-term. There are more ESG portfolios, whose risks are different from nonESG portfolios, in the short-term investment than in the medium-term.ESG factors therefore play a greater role in risks than in returns, and in the short-term than in the medium-term. Additionally, the role of ESG factors in risks varies from industry to industry.

Model and upper–lower bound estimation scheme for portfolio optimisation considering uncertain investment time horizon

In this paper, we study the portfolio selection problem considering the uncertain investment time horizon. We employ the Period Value at Risk (PVaR) to characterise the risk in the special cases and establish a fundamental mixed integer linear programming (MILP) model to obtain the optimal portfolio solutions. Identifying that the symmetric property of PVaR can significantly reduce the computation burden of the CPLEX solver, an enhanced MILP model is proposed. To verify the quality of obtained solutions, we also develop fast lower and upper bound estimation schemes respectively. Using the real-world data sets from NYSE and NASDAQ stock markets, numerical results are provided to show the efficiency of the enhanced model.

STUDY ON MAJOR INVESTMENT PREFERENCES OF INDIAN INVESTORS

The purpose of this study was to analyse the preferences of investors in the Indian share market and other investment options available in the market. Many factors including risk awareness, investment time horizon, mode of trading, objectives of investment, usage of fundamental & technical analysis before investment, sources of information, etc. which affect the investment decisions are analysed through this research. The study is carried out using only the primary data collected by convenience sampling technique through a survey of 204 respondents from different regions of India but majorly from Gujarat State. Parametric and Non-parametric tests are applied to the data using SPSS software to analyse the effects of different variables. The study concluded that income levels and risk involvement highly affect the investment decision of an investor. Also, the usage of fundamental and technical analysis is important before making any investment. Another big conclusion is that returns are the major objective of maximum investors. Fixed deposit is the most preferred investment option according to the respondents.

Multiresolutional statistical machine learning for testing interdependence of power markets: A Variational Mode Decomposition-based approach

In the increasingly interconnected and digitized world, the field of electricity price forecasting has benefited from growing research especially due to the market liberalization and the connectedness between electrical systems. This study defines a novel multiscaled forecasting model based upon the Variational Mode Decomposition (VMD) to quantify multiscaled cross-correlation between two important European markets during COVID-19 pandemic. The VMD is known to be a strong information processing tool which is localized in both frequency and time, and is especially used for capturing nonstationary and nonlinear behaviors of time series. The set of new VMD techniques is applied on hourly electricity spot prices from the Nord Pool and MIBEL energy exchanges for the period ranging from January 2019 to March 2020. The sampled time series include a period of specific recession in the financial system, coinciding with the Brexit and COVID-19 event, which was accompanied by a significant collapse in the world’s economic sphere. The empirical results reveal a significant dependence between electricity markets across long- and medium-run investment time horizons, with evidence for dynamic lead–lag relationships at some frequency sub-bands. However, over the short-term (daily and intra-daily intervals), we notice a kind of independence between markets, especially in times of crisis, which offers investors different investment diversification opportunities. On the other hand, the accuracy of generated forecasts prove the interest of a conjoint modeling and the reliability of this new tool, in particular when the approach is adequately coupled with feedforward neural networks.

The impact of COVID-19 pandemic on the dynamic correlations between gold and U.S. equities: evidence from multifractal cross-correlation analysis.

This study exploits multifractal cross-correlation analysis (MFCCA) to investigate the impact of the COVID-19 pandemic on the cross-correlations between gold and U.S. equity markets using 1-min high-frequency data from January 1, 2019, to December 29, 2020. The MFCCA method shows that the pandemic caused an increase of multifractality in cross-correlations between the two markets. Specifically, the cross-correlations of small fluctuations became more persistent while those of large fluctuations became less persistent, explaining the source of multifractality. The findings of this study carry significant implications for investors, academicians, and policymakers. For example, the increase of multifractality of cross-correlation means that the non-linear relationship between gold and U.S. equity returns prevails more during economic downturns. Therefore, academicians may resort to non-linear techniques to evaluate the relationship between gold and U.S. equity markets during the health pandemic. Moreover, investors can know the value of hedging benefits over different investment time horizons during the pandemic. Finally, policymakers can better assess the economic downturns (i.e., those caused by health pandemics) over the dynamics of cross-correlation between gold and equity markets to make sound financial policies.

Implementation Paper on Identify Citizens Receiving Multiple Benefits Like Pension Under Different Schemes of the Central and State

Abstract: We build an effective, realistic retirement income plan for an individual investor. We propose real time frameworks with such mechanism that investor give some inputs such as number of investment instruments, income, and length of the time period before retirement using Modern Portfolio theory. This aim to develop a retirement framework using fundamentals of Modern Portfolio Theory as per investor’s needs on asset allocation assuming investor’s risk appetite reduces as investor ages in life and worries for real retirement income planning by comparing different statistical models scenarios. In each of the Scenarios we have 3 changing probability profile scenarios to allow for flexibility to the investor to withdraw from the portfolio for personal needs with increasing probability, decreasing probability and uniform probability of withdrawal throughout the portfolio investment time horizon. The results clearly reveal that there is no one best model for different investors as each investor is different with different objective functions. Keywords: Retirement, Social Securit, Modern Portfolio theory

Portfolio Management Services in India: Returns and Incentives

On the back of booming equity markets and falling interest rates, Indian high net worth investors have flocked to Portfolio Management Services (PMS) funds in India with substantial assets under management of INR 18,07,939 crores. However, there is scant academic literature on the performance of PMS funds in India which charge high fees compared to passive index funds. The purpose of this study is twofold. First, describe the performance of PMS funds in India as compared to their respective benchmark. Secondly, review the incentives fees structure of PMS funds and recommend regulatory changes needed to address the shortcomings. The principal results indicate that majority of PMS funds have failed to outperform the benchmark in the short-term and medium-term periods. In the long-term period, however, largely PMS funds have beaten the benchmark returns. The implications of this study are two-fold in the Indian market context. First, high net worth individuals in India must invest via PMS funds only if their investment time horizon is at least ten years. For the short to medium-term, it will be better to invest in index funds due to their meager expense ratio and returns, which are like the benchmark indices. Second, Indian policymakers must increase the minimum hurdle rate based on which the variable performance fee is charged to ensure portfolio managers are rewarded if they generate abnormal risk-adjusted returns. This study contributes to academic literature by focusing on the performance and incentives of PMS funds market in India, which is largely unexplored till now.

The Role of Institutional Investors in Corporate Governance of Their Portfolio Companies – The Case of Croatia

The goal of this research is to examine the involvement of institutional investors in the corporate governance of companies in their portfolio by analyzing characteristics of institutional investors with respect to the type of investment, investment time horizon, and degree of involvement in the process of managing a company. The paper will outline the attitudes of managers on the level of investors’ involvement in the governance process in order to identify determinants of investment decisions, decisions to take corrective actions to enhance corporate governance, or decisions to leave the ownership structure. A qualitative approach was chosen, consisting of a series of structural interviews with 25 fund managers of different types of institutional investors. The results indicate that institutional investors are involved in the corporate governance of their portfolio companies, and that control mechanisms they use include voting rights, direct communication with the management with the aim of discussing strategies for future development, and collaboration with other institutional investors in the ownership structure.

Replacement Analysis of Mutually Exclusive Projects of Unequal Lives Using Kelly Specific Real Option Criterion

This paper presents a model to address the uncertainty inherent in replacement problems, whereby a firm must select between mutually exclusive projects of unequal lifespans by applying the Kelly criterion (which is not well known to the engineering economics community) within a binomial lattice option-pricing environment. Assuming that only the interest rate, among many factors, is uncertain, Brown and Davis performed an economic analysis of this problem by employing a real option-pricing method and argued that their model yields results opposite to those yielded by the traditional approach. However, the results yielded by the model proposed herein are consistent with those by the traditional approach, unlike Brown and Davis’s model. The conclusion is that since the investment time horizon is infinite, a firm rationale pertaining to the selection of the best method for the investment problem of such types does not exist.