Terms Of Risk-adjusted Returns Research Articles

Monte carlo simulation with bilstm for day-ahead stock portfolio management

<div>Predicting stock price movement and optimizing day-ahead stock portfolios are challenging tasks due to the inherent complexity and volatility of financial markets. This study proposes a novel approach that combines bidirectional long short-term memory (BiLSTM) neural networks with monte carlo simulation (MCS) to enhance day-ahead stock portfolio management. In the proposed methodology, historical data of the top-performing 10 stocks from different sectors of the National Stock Exchange of India (NSEI) is obtained from 1 January 2004 to 30 June 2023 and utilized to train a BiLSTM model. This model effectively extracts intricate patterns and trends from the time series, leading to more accurate and robust stock price predictions. MCS generates different scenarios, considering various market conditions and uncertainties. These scenarios provide a comprehensive view of the portfolio’s performance under different conditions, thus mitigating the risk of relying solely on a single prediction. The study evaluates the proposed framework and compares its performance against traditional portfolio management strategies. Results demonstrate that the MCS with the BiLSTM approach outperforms traditional methods in terms of risk-adjusted returns and portfolio stability.</div>

Comparative Analysis of Private and Public Mutual Funds: A Performance Evaluation

Abstract: The mutual fund industry is a popular investment avenue that offers investors the opportunity to invest in a diversified portfolio of securities. The industry has witnessed significant growth in recent years, with investors seeking to maximize their returns and reduce risk. This paper presents a comprehensive comparative analysis of private and public mutual funds in terms of their performance. The study aims to evaluate the risk-adjusted returns, expense ratios, fund size, asset allocation, and other relevant factors of both private and public mutual funds to determine which type of fund offers better performance. To conduct the analysis, a sample of four private and four public mutual funds was randomly selected, and their performance was evaluated using various performance metrics such as Sharpe ratio, Treynor ratio, and Jensen's alpha. The data was analyzed using statistical tools such as U test, t-test, and regression analysis. The study found that private sector mutual funds performed comparatively better than public sector mutual funds in terms of risk-adjusted returns, expense ratios, and asset allocation. Private sector mutual funds had higher Sharpe ratios and Treynor ratios compared to public sector mutual funds, indicating that they generated higher returns per unit of risk taken. Private sector mutual funds also had lower expense ratios, indicating that they charged lower fees to investors. In terms of asset allocation, private sector mutual funds had a higher allocation towards equity securities compared to public sector mutual funds, which had a higher allocation towards debt securities. The study also identified significant differences in the investment strategies, portfolio management, and investor base of private and public mutual funds. Private sector mutual funds had a more aggressive investment strategy, with a higher allocation towards equity securities, whereas public sector mutual funds had a more conservative investment strategy, with a higher allocation towards debt securities. Private sector mutual funds were also found to be more actively managed compared to public sector mutual funds. Private sector mutual funds had a higher percentage of assets under management from retail investors, while public sector mutual funds had a higher percentage of assets under management from institutional investors. The study provides valuable insights to investors, fund managers, and policymakers regarding the performance of private and public mutual funds. The findings suggest that investors may benefit from investing in private sector mutual funds due to their higher risk-adjusted returns, lower expense ratios, and higher allocation towards equity securities. Fund managers may also benefit from the study by gaining insights into the investment strategies and portfolio management practices of private and public mutual funds. Policymakers may use the study to design policies that promote the growth and development of the mutual fund industry. Overall, this study contributes to the existing literature on mutual funds by providing a comprehensive comparative analysis of private and public mutual funds in terms of their performance. The study adds to our understanding of the factors that affect the performance of mutual funds and provides insights into the investment strategies and portfolio management practices of private and public mutual funds.

A novel randomized weighted fuzzy AHP by using modified normalization with the TOPSIS for optimal stock portfolio selection model integrated with an effective sensitive analysis

Building an ideal stock portfolio is a difficult and important undertaking in the world of financial decision-making. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) technique and the Randomised Weighted Fuzzy Analytic Hierarchy Process (AHP) are combined in this research to create a powerful model for selecting the best stocks for a portfolio. To improve the portfolio's robustness under various market scenarios, our model also incorporates sensitivity analysis. Given the inherent uncertainty in financial markets, the suggested approach starts by using modified fuzzy AHP based on introducing the randomized weights and the novel normalization to get expert views and assign suitable weights to the underlying selection criteria. Based on the established criteria and associated fuzzy scores, the TOPSIS approach is then used to rank and choose the best stocks. Our model also includes sensitivity analysis to evaluate the performance of the portfolio under various market conditions. Extensive empirical tests are performed utilizing historical stock market data to assess the effectiveness of our strategy. The outcomes show the suggested model's superiority in building stock portfolios that beat conventional approaches in terms of risk-adjusted returns.

Diversification with globally integrated US stocks

We measure market integration at a firm-level for the US stock market with the rest of the world. The properties of firm-level integration are explored across time and industries and then stocks are sorted into high- and low-integration portfolios. The role of the least globally integrated US stocks in domestic and international portfolio diversification is assessed. We show that these stocks can statistically and economically augment diversification potential, especially after 2000. The same stocks can be combined with the US market index to create a portfolio that performs at least as well as an international index portfolio in terms of risk-adjusted returns and tail risk.

Application of machine learning in algorithmic investment strategies on global stock markets

The research undertakes the subject of machine learning based algorithmic investment strategies. Several technical analysis indicators were employed as inputs to machine learning models such as Neural Networks, K Nearest Neighbor, Regression Trees, Random Forests, Naïve Bayes classifiers, Bayesian Generalized Linear Models, and Support Vector Machines. Models were used to generate trading signals on WIG20, DAX, S&P500, and selected CEE indices in the period between 2002-01–01 and 2023–03–31. Strategies were compared with each other and with the benchmark buy-and-hold strategy in terms of achieved levels of risk and return. Sensitivity analysis was used to assess the quality of the estimation on independent subsets. The findings of the study showed that algorithmic strategies outperformed passive strategies in terms of risk-adjusted returns and that for the analyzed indices, Linear Support Vector Machine and Bayesian Generalized Linear Model were the best-performing models. The Linear Support Vector Machine was chosen as the model that, on average, produced the best results using a more thorough rank approach based on the outcomes for all examined models and indices.

Performance of Mutual Funds in India: A Study with Reference to Select Equity Multi-Cap Funds

Indian Mutual Fund Industry has witnessed enormous expansion in terms of growth of Assets under Management (AUM), from a meagre Rs. 25 crores in 1964 to Rs. 36.59 lakh crores in August 2021. Equity Multi-cap mutual funds tend to invest in stocks of companies across the stock market irrespective of sector and size. As a result, these funds provide much-needed diversification. In a direct plan, the investor decides to invest directly in mutual funds without routing the investment through any distributor or agent. Due to the absence of an intermediary commission, the direct plan has a lower expense ratio than a regular plan which leads to higher returns.
 Since the outbreak of COVID-19, the stock markets have experienced an extensive crisis with severe dampening effects in the entire global scenario. It has affected the Indian Mutual Fund Industry as well. However, irrespective of all negative signs, the impact of COVID-19 has created a ray of hope towards rebuilding self-confidence for Indian investors during the last year in this new normal landscape.
 In this backdrop, the present research paper focuses on examining the performance of direct plans of four open-ended Equity Multi-cap Mutual Funds [Baroda Multi-Cap Fund (BMF), ICICI Prudential Multi-Cap Fund (IPMF), Invesco India Multi-Cap Fund (IIMF) and Nippon India Multi-Cap Fund (NIMF)] based on certain parameters. This secondary data-based study covers 8 years (2013-2021). The criterion for selecting the funds was net assets above Rs.1,000 crores as of 31.08.2021. Results reveal that the funds provided double-digit returns during the entire study period. IPMF and NIMF performed poorly and remained riskier than the benchmark during the entire study period. Further, NIMF remained the riskiest fund throughout the study period. IIMF remained the best performer in the 1-year, 5-year and 8-year periods in terms of risk-adjusted return. NIMF remained the most aggressive fund and BMF remained the most defensive fund during the entire study period. The fund managers of IIMF succeeded in quality stock-picking in 1-year, 5-year and 8-year. The fund managers of NIMF performed miserably throughout the study period and failed in quality stock picking. RSQ values portray that BMF was the most successful fund in terms of diversification during the entire study period.

Portfolio diversification during the COVID-19 pandemic: Do vaccinations matter?

The COVID-19 vaccine rollout expects to mitigate the severe negative impacts of the pandemic on global financial markets. Our study provides supporting evidence for this expectation. We find robust evidence that vaccinations significantly reduce the cross-country stock volatility connectedness among G7 nations, suggesting that the diversification benefits of an international equity portfolio may be enhanced during the pandemic when vaccinations accelerate. We present two explanations for this result. First, the vaccine deployment improves stock market return and decreases individual stock market volatility. Second, the vaccine rollout helps a country’s stock market be more resilient to exogenous shocks. We further demonstrate that a global portfolio using a tactical allocation rule based on the intensity of vaccinations can outperform a buy-and-hold portfolio in terms of risk-adjusted returns.

Green bonds and implied volatilities: Dynamic causality, spillovers, and implications for portfolio management

The long-term and sustainable development focuses of green bond together with its increasing popularity drives the need for a better understanding of its hedging effects against market risks. Our study investigates whether and how green bond can act as a hedging instrument against implied volatility, a measure of forward-looking market uncertainty. We find evidence of significant time-varying connectedness between green bond and implied volatilities of the stock, energy, and commodity markets. Building on this characteristic, investors are required to adopt an active portfolio management strategy to ensure the hedging effectiveness of green bond against implied volatilities. Specifically, this strategy requires frequent switches between long and short positions in the green bond market. Our simple simulation study shows evidence that applying connectedness regime-dependent trading strategies can increase the hedging effectiveness of green bond against implied volatilities in terms of risk-adjusted returns.

Risk and return of heterogenous farmland locations and qualities

Using data on farmland values in Indiana and Iowa, this study examines the risk and return characteristics surrounding top, medium, and poor farmland qualities in different locations in these two states. We find that systematic risks of locations/qualities are very low (indistinguishable from 0). In terms of risk-adjusted return, our results show that Indiana farmland has more excess return and higher reward-to-risk ratios than Iowa. Also, adding the quality dimension to the geographic dimension in portfolio selection strategies improved the portfolio reward-to-risk ratio for Indiana but not for Iowa. Interestingly, we found that the average quality farmland has more weight in portfolios relative to top- and poor-quality farmland.

Economies of diversification and stochastic dominance analysis in French mixed sheep farms

AbstractFarm diversification is mainly driven by risk mitigation effects and economic gains related to complementarities between production activities. By combining these two aspects, we investigate diversification economies in a sample of French mixed sheep farming systems and rank these systems using stochastic dominance criteria. Partially diversified systems (Sheep-Grass, Sheep-Crop, Sheep-Landless) and fully diversified systems (Sheep-Grass-Crop-Landless) were evaluated. We find a high degree of diversification diseconomies in the sheep farming systems considered. The results also indicate that the fully diversified system is driven by its risk-reducing effects (including downside risk exposure) and that Sheep-Crop is the dominant system in terms of risk-adjusted returns.

Should investors include green bonds in their portfolios? Evidence for the USA and Europe

The green bond market has seen a rapid growth world widely in recent years. This paper explores the role of green bonds in asset allocation using the dynamic R-vine copula-based mean-CVaR approach. We compare the performance of portfolios including green bonds with that of portfolios including conventional bonds in the U.S. and European markets. Empirical results show that portfolios with green bonds outperform portfolios with conventional bonds in terms of risk-adjusted returns in the majority of cases in both markets. The benefit of green bonds comes from both the increase in the return and the decrease in the volatility for most of the cases. Overall, our findings suggest that green bonds are beneficial to investors.

In Search of Market Outperformance: Another Look at Berkshire Hathaway

In hindsight, few stocks are as acclaimed as Berkshire Hathaway (BRK) for long-term, wealth-generating, market-beating performance, even with its documented higher risk. Do the results apply across holding periods? Do the results hold in foresight? Addressing these questions, we compare the returns, volatilities, and Sharpe reward-to-variability ratios for BRK-A, the S&P 500, and an application of modern portfolio theory (MPT) for evidence of market outperformance in both hindsight and foresight. The period of study is from January 1, 1990 through December 31, 2019. Whether in hindsight or foresight across holding periods, evidence of BRK’s outperformance, in terms of risk-adjusted returns, is mostly elusive. Although investors may still view the stock as a significant wealth generator, they should be circumspect of it as a robust addition to a portfolio.

Managing downside risk of low-risk anomaly portfolios

In this paper, we present a novel risk-scaling strategy based on a measure of downside risk and investigate its performance on underlying portfolios that are formed on low-risk anomaly. The downside risk-scaling strategy addresses two challenges of the volatility-scaling strategy, namely, underestimation of and indirect management of downside risk. We demonstrate that our downside risk-scaled strategy improves the unscaled underlying low-risk anomaly strategy as well as outperforms volatility-scaled strategy in terms of risk-adjusted return and various performance metrics that are related to downside events.

Robust optimization of time series momentum portfolios

Mean-Variance Optimization (MVO) is well-known to be extremely sensitive to slight differences in the expected returns and covariances: if these measures change day to day, MVO can specify very different portfolios. Making wholesale changes in portfolio composition can cause the incremental gains to be negated by trading costs. We present a method for regularizing portfolio turnover by using the ℓ1 penalty, with the amount of penalization informed by recent historical data. We find that this method dramatically reduces turnover, while preserving the efficiency of mean-variance optimization in terms of risk-adjusted return. Factoring in reasonable estimates of transaction costs, the turnover-regularized MVO portfolio substantially outperforms a leverage-constrained MVO approach, in terms of risk-adjusted return.

The Diversification Benefits of Cryptocurrencies in Multi-Asset Portfolios: Cross-Country Evidence

Using a sample of 21 developing and developed countries, we analyze whether a well-diversified investor of traditional assets (stocks, bonds, real estate, and commodities) may benefit from investing in cryptocurrencies. Country-specific analyses indicate that cryptocurrencies usually fit in the tangent portfolio (maximum Sharpe ratio) but no -- or very little -- in the minimum variance portfolio (MVP). Out-of-sample analysis indicates that even global portfolios that already benefits from international diversification may enjoy investing marginally in cryptocurrencies: mean-variance optimal and naive with cryptocurrencies outperformed otherwise identical portfolios in terms of risk-adjusted returns. Besides, exchange rate movements do not drive this better performance -- it occurs for both local (all returns denominated in the local currency) and global perspectives (all returns in U.S. Dollars). We also find that cryptocurrencies' diversification benefits occur both before and after the COVID-19 pandemics, with the 1/N portfolio with cryptocurrencies presenting the higher risk-adjusted returns. Our paper adds to the literature by analyzing the marginal effects of adding cryptocurrencies on a sample of developing and developed economies and considering up-to-date data following the COVID-19 crisis.

The Effect of Regulatory Constraints on Fund Performance: New Evidence from UCITS Hedge Funds*

Abstract This article examines the effect of regulatory constraints on fund performance and risk by comparing conventional and UCITS hedge funds. Using a matching estimator approach, we estimate the indirect cost of UCITS regulation to be between 1.06% and 4.05% per annum in terms of risk-adjusted returns. These performance differences are likely to stem from UCITS constraints such as those governing eligible assets, diversification, and short selling, and cannot be explained by differences in redemption terms or level of leverage. We confirm that our performance results are not driven by management company characteristics, fund manager characteristics, or unobserved confounder bias.

Exploring the short-term momentum effect in the cryptocurrency market

This study explores the short-term momentum effect in the cryptocurrency market. Utilising a comprehensive cryptocurrency dataset and the portfolio construction methods of Fama and French (J Financ Econ 33:3–56, 1993) and Carhart (J Finance 52:57–82, 1997), we construct cryptocurrency portfolios and examine their performance. The main findings are: (1) the cryptocurrency market portfolio significantly outperforms major stock markets globally in terms of risk-adjusted return; (2) from an asset pricing perspective, short-term momentum effects are significantly priced in the cryptocurrency market, while size effects are controlled, suggesting that the short-term momentum effect explains variations in the returns of cryptocurrency portfolios; and (3) the portfolios constructed according to the short-term momentum effect do not outperform the cryptocurrency market portfolio.

A better criterion for forced selling in bond markets: Credit ratings versus credit spreads

During the global financial crisis, a bond portfolio manager using only credit ratings suffered from significant losses due to their lagging properties, and now focuses on market-based criteria as another credit risk measure for forced selling. In this study, we verify whether a market-based criterion outperforms in running the forced selling strategy for bond portfolios. In contrast to the market expectation, our empirical results show that the forced selling strategy with only market measures is inferior to the rating-based strategy in terms of risk-adjusted returns, although market measures precede changes in bond ratings. This result stems from the high volatility of market-based credit measures, which results in too frequent or too early sale of a bond with credit deterioration. The improvement of risk-return trade-off observed only when market measures are jointly considered with bond ratings. This implies that market-based credit measures have complementary benefits for detecting credit risk changes. In addition, these results are robust, even for credit-stressed bond portfolios, and for credit-stressed market conditions.

PERFORMANCE OF SHARIAH VERSUS CONVENTIONAL FUNDS: LESSONS FROM EMERGING MARKETS

This study compares the performance of Shariah and conventional mutual funds in emerging markets. The performance of 833 Shariah and conventional funds in 6 emerging markets from 2000 to 2015 was analyzed. We analyzed the Sharpe index, Treynor index, and Jensen’s alpha to compare the performance of Shariah and conventional funds. Jensen's alpha results conform to those of Sharpe’s in indicating that Shariah funds slightly outperform their conventional counterparts particularly in the case of Malaysia, Pakistan, and South Africa. Conventional funds perform exceptionally well in Egypt. Further investigation using the Henriksson–Merton model shows that fund managers’ performance relies nearly completely on their stock selection skills because they have either inferior or ineffective ability in timing the market. This study is the first cross-country attempt to compare the performance of Shariah and conventional funds in emerging markets in terms of risk-adjusted returns, security selectivity, and market timing capability. 
 
 Keywords: Emerging markets, Jensen’s alpha, mutual funds, risk-adjusted performances, Shariah mutual funds
 
 Cite as: Abdul-Rahim, R. Abdul-Rahman, A., & Ling, P-S. (2019). Performance of Shariah versus conventional funds: Lessons from emerging markets. Journal of Nusantara Studies, 4(2), 193-218. http://dx.doi.org/10.24200/jonus.vol4iss2pp193-218

Last Frontier Investments: The Case of Alpine Wines

AbstractWe identify and examine the performance of frontier investments from 2002 to 2017. Using fine wine as a setting, we find that the trade frequency and value of frontier investments in the form of Alpine wines have increased in recent years, leading to a rise in their prices above inflation rates. We further document that this frontier investment has been favorable in terms of risk-adjusted returns and volatility for investors. We also observe that the inclusion of frontier wines in a financial portfolio is favorable for investors, both in terms of returns and diversification benefits, due to low correlation coefficients. The identification and investment into frontier assets appears beneficial for investors looking for new opportunities. (JEL Classifications: C60, G11, Q11)