Sharpe Ratio Research Articles

Classical and Lagrangian mechanics in ray tracing: An optimizable framework for inhomogeneous media

Ray tracing is crucial for analyzing wave behaviors in diverse applications. This study presents an approach that extends beyond traditional methods, which typically involve solving second–order differential equations to determine ray trajectories. Leveraging classical momentum–impulse relations and the system’s Lagrangian, we establish a set of intuitive first integrals that circumvent the need for direct differential solutions, paving the way for optimization techniques. Our method employs the “shooting method” a technique for approximating solutions to differential equations by iteratively applying initial conditions. We introduce a novel momentum cost function that streamlines angle determination in anisotropic environments, a significant departure from conventional practices. Numerical validations demonstrate the robustness of our approach, confirming its efficacy in handling both sharp and gradual refractive index changes with high accuracy. The results also highlight the computational intensity required in anisotropic conditions, suggesting potential areas for efficiency improvements. This groundwork not only enhances current understanding but also opens avenues for future research into more complex media.

Limiting out-of-sample performance of optimal unconstrained portfolios

This paper studies the out-of-sample Sharpe ratio of an unconstrained portfolio that combines the global minimum-variance with a hedge portfolio. Furthermore, we investigate how this ratio behaves as the number of risky assets and observations approaches infinity while maintaining a constant ratio. Under these conditions, it becomes possible to simultaneously account for estimation risk and achieve analytical tractability when optimizing the out-of-sample Sharpe ratio. This analysis also provides valuable insights to enhance out-of-sample performance in the finite case by introducing additional deterministic factors to the portfolio components.

Portfolio optimization in the stock market under disruptions: Real case studies of COVID-19 pandemic and currency risk

The connection between risk and return is a critical concept in finance. Investors who take on more risk can earn higher returns. Disruptions are part of market risks, also known as systematic risks. Due to political and macroeconomic risks, their effects on the entire economic market, or a significant portion of its sectors, are not negligible. Among these sectors, the stock market is essential as a basis of the economy. The point is that the consequences of all disruptions are not merely unpleasant for companies. They may affect corporate profitability both negatively and positively. Hence, investors who trace companies' news and fundamental conditions react, and the closing price probably encounters volatility. Therefore, the common trend of several stocks may fluctuate. This research investigates how investors in the stock market should form their investment portfolio to see the minor losses when disruptions occur and take advantage of the potential provided profitability by turning threats into opportunities. A three-step framework is presented to achieve this goal. A prerequisite for this framework is the collection of relevant data. Ten stocks from distinct industries of the Securities and Exchange Organization of Iran are chosen for the examination. In the first step, multi-variate regression analysis categorizes selected stocks into the most and the least affected groups. At the same time, a comprehensive technical analysis is involved in this step. Then, in the second step, three well-known machine learning techniques, namely, stacked long short-term memory (Stacked LSTM), autoregressive integrated moving average (ARIMA), and Prophet, are compared from the point of view of predicting future closed prices of selected stocks. The method with the most minor statistical error in forecasting, i.e., root mean squared error (RMSE) and validation loss, estimates the next month's closed prices. Finally, based on the predicted closed prices, the third step empowers the investor to optimize their portfolio in different scenarios, containing various investment weights on the two obtained groups of the first step, using the Markowitz Modern Portfolio Theory. The COVID-19 pandemic and currency risk are the two examined real case studies to validate the proposed methodology. Numerical results based on the Sharp Ratio show the superiority of investing 75 percent of an asset in the most affected stocks and the rest in the least affected stocks in the case of a pandemic and vice versa for the case of currency risk, which reveals that according to the unique features of disruptions investors can benefit from both categories of stocks.

Investment Portfolio with Convex Optimization and Risk Adjustment Using Multi-Factor Model and Multi-Armed Bandit Algorithm

This paper examines the creation of investment portfolios through convex optimization, multifactor models, and the multi-armed bandit (MAB) algorithms, focusing on the KL-UCB strategy to optimize decisions in uncertain settings. It explores the impact of systematic risk factors using the Fama-French three-factor model, estimating the influence of market, size, and value premiums via linear regression. The use of Monte Carlo simulation is detailed for generating potential asset allocations and calculating their expected returns, volatility, and Sharpe ratios. The optimize minimize function from the SciPy library is employed to construct an efficient frontier and determine optimal asset allocation, aiming to maximize returns or minimize volatility across various risk levels. The findings suggest that the strategy of dynamic weight adjustments combined with the KL-UCB algorithm enhances portfolio returns, particularly during market volatility. The research also reveals a portfolio inclination towards large-cap growth stocks due to the negative impacts of size and value premiums. It concludes that dynamic weight adjustment strategies offer significant potential in optimizing portfolio performance in complex market conditions, though leveraging increases risk and should be carefully managed according to investor risk tolerance.

EVALUATING STOCK SELECTION IN THE SAAS INDUSTRY: THE EFFECTIVENESS OF THE RULE OF 40

The Rule of 40 is a popular financial guideline used by software-as-a-service (SaaS) industry participants to assess the operational health of the companies. This paper investigates the effectiveness of the Rule of 40 as a stock selection criterion. Our study analyses a sample of 1756 SaaS companies worldwide spanning the period 2003-2022. The findings demonstrate that the Rule of 40 adds value and delivers a moderately high Sharpe ratio as a stock selection tool. A modified rule, the SaaS Investing Rule of 65, is proposed and found to outperform the Rule of 40 in identifying relative winners and losers within the SaaS space. The effectiveness of the rules raises practical implications for investors and analysts. Additionally, we explore the effectiveness of alternative versions of the Rule of 40 using different measures of profitability, as well investigate whether the returns are driven by traditional style factors.

Water Impact Dynamics and Mechanism Analysis of Polypropylene and Polypropylene/poly(ethylene-co-octene) Replica Surfaces with Nanowires under Low Temperature Conditions.

In this work, polypropylene (PP) and PP/poly(ethylene-co-octene) (PP/POE) blend replica surfaces with densely arranged nanowires are fabricated using a molding process. Morphology analysis shows that the nanowires on these replica surfaces have sharp tips and high aspect ratios. The droplet impact behaviors on the surfaces of the PP/POE blend replicas and PP replicas are investigated before and after freezing at -20 °C for 4 h. The height of the nanowires on the PP/POE blend replica surfaces is higher than that on the PP surface before and after freezing. The static wettability and droplet impact tests on the surfaces of PP/POE blend replicas and PP replica show that adding POE into the PP matrix can significantly increase the toughness of the nanowires on the PP/POE blend replica surfaces during the droplet impact at low temperatures. These investigations are favorable to broaden the practical applications of superhydrophobic surfaces in some severe environments.

Combination of ESG scores and prediction-based returns using long short-term memory neural networks to generate responsible portfolios

ABSTRACT The significance of the environmental, social, and governance (ESG) factors has risen substantially among investors in recent years. Similarly, machine learning techniques have allowed for improvements in the prediction of stock prices. Our research combines ESG factors and prediction-based returns using recurrent neural networks to create profitable-sustainable portfolios that can consistently beat the market index in return and ESG scores. Our analysis focuses on the components of the EURO STOXX 50Ⓡ Index during the year 2021 and the first half of 2022, allowing us to analyze two different market scenarios, continuous growth and bear market, respectively. This paper provides empirical evidence that combining machine learning with ESG scores and its application in portfolio optimization can achieve higher returns and higher ESG performance depending on the macroeconomic context and presents the trade-off between the Sharpe Ratio and the ESG score of the optimized portfolios for different scenarios.

Commodity sectors and factor investment strategies

Commodity sectors exhibit heterogeneous characteristics owing to their limited supply and demand. To analyze these sector heterogeneities, we construct commodity factor portfolios without using a single sector and investigate whether commodity sectors play an important role in determining the risk premiums of commodity futures. We construct an equally weighted portfolio that includes momentum, basis, basis momentum, value, and hedging pressure while excluding the precious metal sector. We find that this portfolio generates a Sharpe ratio of 1.67. This finding indicates that the special status of precious metals employed as hedging tools for other assets distorts the performance of commodity factors.

Do clean energy stocks diversify the risk of FinTech stocks? Connectedness and portfolio implications

The FinTech sector is growing rapidly, prompting a need to explore effective investment diversification strategies for stocks in this sector. The existing literature has identified the benefits of using clean energy stocks to diversify stock portfolios and the purpose of this research is to estimate how useful clean energy stocks are for diversifying an investment in FinTech stocks. This study uses a QVAR model to estimate the dynamic return connectedness between FinTech stocks and clean energy stocks for the period September 2016 to April 2024. Total connectedness is time varying and is higher in the tails than at the median. The onset of the COVID-19 pandemic had a large but short-term impact on connectedness. Under normal market conditions, systemic risk increases by 3.5% per year. FinTech is a net transmitter of shocks to nuclear energy but is mostly unaffected by shocks from wind, solar, and nuclear energy stocks illustrating the diversification benefits of these sub-sectors. Portfolio analysis shows that adding solar, wind, and nuclear energy to a portfolio with FinTech can produce higher risk adjusted returns and lower drawdown than an investment solely in FinTech stocks. These results are robust across various portfolio rebalancing frequencies (daily, weekly, monthly). For example, a minimum connectedness portfolio rebalanced daily has an average annual return of 11% and a Sharpe ratio of 0.37. These values are higher than their respective values for an investment solely in FinTech stocks (5.4%, 0.11). Thus, clean energy stocks do provide diversification benefits for investments in FinTech stocks.

Covariance matrix filtering and portfolio optimisation: the average oracle vs non-linear shrinkage and all the variants of DCC-NLS

The Average Oracle, a simple and very fast covariance filtering method, is shown to yield superior Sharpe ratios than the current state-of-the-art (and complex) methods, Dynamic Conditional Covariance coupled to Non-Linear Shrinkage (DCC-NLS). We pit all the known variants of DCC-NLS (quadratic shrinkage, gross-leverage or turnover limitations, and factor-augmented NLS) against the Average Oracle in large-scale randomized experiments. We find generically that while some variants of DCC-NLS sometimes yield the lowest average realized volatility, albeit with a small improvement, their excessive gross leverage and investment concentration, and their 10-time larger turnover contribute to smaller average portfolio returns, which mechanically result in smaller realized Sharpe ratios than the Average Oracle. We also provide simple analytical arguments about the origin of the advantage of the Average Oracle over NLS in a changing world.

Global Equity Markets: An Empirical Study of Return Prediction and Diversification Benefits

My research delves into the equity market trends of China, Japan, and the UK in the post-2000 era. I analyze a dataset of macroeconomic variables from 2000 to 2022 to assess their ability to predict equity market returns. My study reveals that all three nations have seen an upward trajectory in their equity markets, with China's market experiencing the most substantial growth. In addition, I explore how macroeconomic variables affect stock prices and identify the variables that have the most significant impact on equity market returns. Finally, I conclude that a diversified investment strategy in multiple countries stock markets produces higher Sharpe ratios than any single-country investment.

Is style drift informative? Evidence from mutual funds in China

In this study, we investigate whether the style drift of portfolio managers is informative. We establish that style drift is based on public information rather than the imitation of peers. Fund managers are found to adjust their portfolios to align with market expectations, particularly in normal market conditions. However, we observe a pronounced shift towards irrational herd behavior during market turbulence and style rotation periods. While style drift can offer benefits in contemporary periods, such as positive risk-adjusted returns, the funds that exhibit excessive drift can hardly beat their peers in subsequent periods. More notably, sophisticated investors tend to abandon funds with more considerable style drift in favor of those adhering to a consistent investment strategy. Our findings indicate that while style drift may be a strategic response to market conditions, its advantages are transient and often unaligned with the preferences of sophisticated investors.

Do investors benefit from investing in stocks of green bond issuers?

We investigate the environmental and financial performance of portfolios comprising stocks from worldwide green bond issuers compared to matched non-green but otherwise similar portfolios. Portfolios formed by stocks of green bond issuing companies show superior environmental performance, notably reflected in reduced CO2 emissions over the medium to long term. Furthermore, these portfolios exhibit financial performance, as measured by the five-factor model of Fama and French (2015), at least comparable to their non-green counterparts, even outperforming them over a 12-month investment horizon. Overall, investing in stocks of green bond issuers seemingly offers competitive risk-adjusted returns.

Do Investment Funds Audited by the Big Four Firms Exhibit Different Performances? Evidence from Brazil

Investment funds manage a portfolio composed of financial instruments; therefore, their accounting reports should undergo a careful process of preparation and auditing. The main purpose of this study is to analyze the effect of being audited by a Big Four audit company on funds’ risk-adjusted performance. The database is composed of equity funds from the Brazilian financial market, with daily returns spanning from January 2005 to March 2023. The funds’ performance was measured based on three indicators, including the Sharpe Ratio and Jensen’s Alpha. Fama and MacBeth regressions were used to test the hypotheses. The main findings indicate that the benefits of audit quality also include a positive effect on the risk-adjusted performance of investment funds, as the coefficient of the variable “Big Four” was positive and significant based on the proxies for risk-adjusted performance. This study advances this area of research by demonstrating the effects of the type of audit on the risk-adjusted performance indicators of investment funds.

Trading strategies and Financial Performances: A simulation approach

This paper presents a comparative analysis of three major approaches to portfolio strategies: the maximization of the Sharpe ratio, the minimization of the Expected Shortfall and “zero–intelligence” trading. Data from financial time series and from a simulated order-book are used to analyse how various strategies affect investors’ portfolio performance and volatility. Results show, firstly, that the superiority of technical and analytical approaches over a random strategy is not obvious. Secondly, that strategies with lower and less risky profits may reveal preferable to those with higher returns and risk. Balancing this trade-off is crucial for stable financial growth.

Can multi-period auto-portfolio systems improve returns? Evidence from Chinese and U.S. stock markets

Current portfolios often underperform due to limited utilization of stock selection and a lack of attention to multi-period trading. To address this issue, we propose an auto-portfolio system that addresses these problems by integrating multi-class stock selection with portfolio optimization based on technical indicators. For stock selection, we combine Two-dimensional Convolutional Neural Network with Long and Short-term Memory to forecast the future trends of stocks and select potentially profitable stocks for investment. We then develop two portfolio models based on two technical indicators, which automatically perform multi-period investment. We establish a many-objective optimization problem including return, Conditional Value-at-Risk, skewness, kurtosis, and cost. To solve the optimization problem, we employ Non-dominated Sorting Genetic Algorithm III. The data of Chinese and the U.S. stock markets is used for verification, and a comparative analysis is discussed. In the out-of-sample period, two proposed multi-period portfolio models outperform the other models in both single and multi period, achieving higher Sharpe ratio of 1.021 and 1.052 in China, and 1.116 and 1.236 in the U.S., respectively.

Parameter optimization of the classifying device of straw micro-crusher based on neural network and CFD

To improve the classification performance of the straw micro-crusher's classifying device, the characteristic parameters impacting classification performance are identified through an analysis of the forces acting on the straw particles between the blades of the rotor cage and the motion characteristics of the fluid. Taguchi's experimental design is used to ascertain the combinations of computational fluid dynamics (CFD) simulation parameters for cut size and classifying sharpness index, while a neural network (NN) model is developed using CFD data to predict the optimal feature parameter combination for classification performance. The results reveal that the rotor cage speed holds the most significant impact on both the cut size and classifying sharpness index. The optimal combination of feature parameters recommended by the neural network is v=8m/s, n=1200r/min, z=36, θ=40°, at which the cut size is 28.3 μm, a reduction of 9.0 % compared to the Taguchi experiment. In addition, the relative error between the cut size predicted by the neural network model and the CFD simulation is less than 4.5 %, indicating the reliability of the neural network model in predicting the optimal parameter combination of the classifying device.

Understanding Investment Decision-making: A Qualitative Inquiry into High-Frequency Trading, Investment Strategies, and Portfolio Performance in the Financial Market

This qualitative study explores investment decision-making in the context of high-frequency trading (HFT), investment strategies, and portfolio performance within the financial market. The research aims to provide insights into the complex dynamics influencing investment decisions and their implications for market participants. Adopting a qualitative research design, the study conducts a comprehensive review and analysis of existing literature from academic journals, books, and conference proceedings. The data collection process involves synthesizing insights from diverse sources to uncover underlying themes, patterns, and contradictions surrounding investment decision-making. Through thematic analysis and constant comparison, the study identifies key findings related to the role of HFT, diversity of investment strategies, and evaluation of portfolio performance. The results highlight the transformative impact of HFT on market liquidity, efficiency, and stability, while also raising concerns about market fragmentation and systemic risks. Additionally, the study explores the evolution of investment strategies, ranging from traditional approaches like fundamental analysis to emerging techniques such as algorithmic trading and quantitative strategies. It underscores the importance of tailoring investment strategies to individual preferences and market conditions for optimizing portfolio performance. Furthermore, the study evaluates alternative frameworks such as post-modern portfolio theory (PMPT) and factor investing, offering promising avenues for enhancing portfolio resilience and risk-adjusted returns. Overall, the research contributes to a deeper understanding of investment decision-making processes and informs stakeholders in the financial industry about effective strategies for navigating the dynamic landscape of the financial market.

Science or scientism? On the momentum illusion

AbstractThis study explores the risk of the traditional momentum strategy in terms of its realized variance using various data frequencies. It is shown that momentum risk is infinite regardless of the data frequency, implying that (a) t-statistics for this strategy do not exist, (b) correlation-based metrics such as Sharpe ratios do not exist either, and (c) the momentum premium is not observable in reality. It is further shown that the time-honored lognormal distribution is unable to accurately model extreme events observed at various variance data frequencies. Finally, it is shown that the well-known effect of time aggregation does not work for this investment vehicle. Hence, the study is forced to conclude that momentum stories have no valid foundation for their claims.

Performance Analysis and Prospects of ESG Exchange-Traded Funds in the Mainland Chinese Market

This article delves into In this article, we conduct a comprehensive analysis of Environmental, Social, and Governance (ESG) Exchange-Traded Funds (ETFs) in the dynamic landscape of the Mainland Chinese market. The research categorizes existing academic perspectives into three distinct groups, evaluating whether ESG ETFs outperform, perform similarly, or underperform conventional ETFs. Employing a meticulous methodology, encompassing annualized volatility, daily average returns, Sharpe ratio, Information ratio, and tracking error, we delve into the nuanced performance metrics of these investment vehicles. The findings reveal a nuanced picture: within an annualized volatility range of 0.6% to 0.8%, ESG ETFs exhibit competitive performance, particularly demonstrating resilience in risk mitigation when market returns are negative. However, our analysis underscores crucial challenges in the current ESG landscape in Mainland China. The lack of clear ESG definitions and standardized evaluation criteria hampers a comprehensive assessment. Additionally, there is a notable disparity in ESG ratings for listed companies, adding complexity to accurate evaluations. Looking ahead, the study emphasizes the imperative of defining ESG standards clearly and establishing uniform evaluation systems and information disclosure mechanisms. These steps are essential to enhance the accuracy and reliability of ESG ETF evaluations, thereby fostering a more robust and stable investment environment. This research provides valuable insights for investors, policymakers, and market participants, illuminating the path toward optimizing the potential of ESG ETFs in the Mainland Chinese market.