Sharpe Ratio Portfolio Research Articles

CLUSTER ANALYSIS OF K-MEANS AND WARD METHOD IN FORMING A ROBUST PORTFOLIO: AN EMPIRICAL STUDY OF JAKARTA ISLAMIC INDEX

Building a portfolio is one method of reducing investment risk. Cluster analysis can shorten the time required to choose companies for a portfolio because it makes it easy to put firms in the same category together. To maintain the best state of the portfolio cluster analysis in the case of data containing outliers, K-means, and ward cluster analysis are employed in conjunction with a robust portfolio strategy. K-means clustering is a popular method for grouping data by assigning observations to clusters based on proximity to the cluster’s center meanwhile the Ward method is based on the size of the distance between clusters by minimizing the number of squares. This study seeks to determine the robust portfolio performance comparison outcomes produced by K-Means and Ward clustering utilizing the Sharpe ratio criterion. The Sharpe ratio is one of the most widely used methods to evaluate a portfolio’s risk-adjusted performance. The greater a portfolio's Sharpe ratio, the better its risk-adjusted performance. Stocks included in the Jakarta Islamic Index 70 (JII70) are used in this research. The results of the formation of a robust portfolio on K-Means clustering produce a return rate of 0.01038627 and risk of 0.1066364, while in the Ward cluster, the portfolio profit rate is obtained at 0.01632749 and the risk is 0.1340073. Based on the Sharpe ratio criteria, in this case, the robust portfolio with the Ward cluster is superior to the K-Means cluster because it produces a higher Sharpe value.

Portfolio Optimization Using Machine Learning Method and Monte Carlo Simulation

Investment portfolio optimization is a crucial aspect of quantitative finance, aiming to maximize returns and minimize risks. This study focuses on optimizing investment allocation among five selected stocks (BAIC Blue Valley, BYD, Chang’ an Automobile, Kweichow Moutai, Sunwoda) over 100 days using Monte Carlo simulation and machine learning methods. This study uses the sliding window approach with the linear regression model to predict future returns and calculate the variance-covariance matrix to determine the optimal portfolio weights, leading to two key strategies: maximizing the Sharpe ratio and minimizing the risk. The results reveal that the Max Sharpe Ratio portfolio achieved a cumulative return of 0.0909, significantly outperforming the CSI 300 index’s return of -0.0348. Additionally, the Min Risk portfolio exceeded the market index with a cumulative return of 0.0062. These findings demonstrate that both the Max Sharpe Ratio and Min Risk strategies are effective in achieving a balance between risk and return and surpassing the market, offering valuable insights for investors seeking optimized portfolio allocations.

Connectedness and systemic risk between FinTech and traditional financial stocks: Implications for portfolio diversification

The rapid growth in FinTech is attracting significant investor attention. There are concerns that FinTech stocks may be a source of systemic risk and this has implications for the stability of the financial sector. This study uses a TVP-VAR model to estimate the dynamic connectedness between global FinTech stocks and the stocks of large US banks, regional US banks, US insurance, and global private equity. The findings reveal that FinTech and insurance stocks are net receivers of shocks while large US banks are a major source of shocks. Thus, FinTech stocks are not a source of systemic risk but can be affected by financial contagion and systemic risk originating from large US banks. The highest connectedness was observed at the onset of the COVID-19 lockdowns illustrating the dramatic impact that a health pandemic can have on systemic risk. Systemic risk also increased during the 2023 US bank panic. Connectedness has implications for constructing investment portfolios. The equally weighted, risk parity, and risk parity connectedness portfolios have similar portfolio summary statistics, but lower risk adjusted returns than the maximum Sharpe ratio portfolio which better captures the low correlation between FinTech stocks and traditional financial stocks. These results are robust across various portfolio rebalancing periods (daily, weekly, monthly).

Implementation of machine learning in $$\ell _{\infty }$$-based sparse Sharpe ratio portfolio optimization: a case study on Indian stock market

Implementation of machine learning in $$\ell _{\infty }$$-based sparse Sharpe ratio portfolio optimization: a case study on Indian stock market

Bitcoin Return Prediction: Is It Possible via Stock-to-Flow, Metcalfe’s Law, Technical Analysis, or Market Sentiment?

Popular methods to value Bitcoin include the stock-to-flow model, Metcalfe’s Law, technical analysis, and sentiment-related measures. Within this paper, I test whether such models and variables are predictive of Bitcoin’s returns. I find that the stock-to-flow model predictions and Metcalfe’s Law help to explain Bitcoin’s returns in-sample but have limited to no ability to predict Bitcoin’s returns out-of-sample. In contrast, Bitcoin market sentiment and technical analysis measures are generally unrelated to Bitcoin’s returns in-sample and are poor predictors of Bitcoin’s returns out-of-sample. Despite the poor performance of Bitcoin return predictors within out-of-sample regressions, I demonstrate that a very successful out-of-sample Bitcoin tactical allocation or “market timing” strategy is formed via blending out-of-sample univariate model predictions. This OOS-blended model trading strategy, which algorithmically allocates between Bitcoin and cash (USD), significantly outperforms buying-and-holding or “HODL”ing Bitcoin, boosting CAPM alpha by almost 1300 basis points while also increasing portfolio Sharpe Ratio and Sortino Ratio and dramatically reducing portfolio maximum drawdown relative to buying-and-holding Bitcoin.

Empirical Analysis of Portfolio Allocation During Market Turbulence Based on Efficient Frontier

The global financial markets have experienced considerable turmoil due to the COVID-19 pandemic, in this context, portfolio management becomes critically important. This paper employs Monte Carlo Simulation to determine Efficient Frontier of a portfolio comprising fourteen US technology stocks in the period of COVID-19 pandemic by python, which also allocates portfolios for Minimum Volatility and Maximum Sharpe Ratio, along with their key parameters. According to this study, Maximum Sharpe Ratio portfolio outperforms Minimum Volatility portfolio in terms of cumulative return. However, its important to note that Maximum Sharpe Ratio portfolio also experiences a greater maximum drawdown. Additionally, this paper visually depicts the optimal allocation of asset weights across different levels of risk aversion using a chart. Furthermore, it identifies NVDA as the optimal choice for investors who have low degree of risk-aversion due to its high volatility and expected return. Conversely, IBM and CSCO emerge as the preferred choices for risk-averse investors, as they effectively mitigate the portfolios entire risk.

BRIDGING TRADITION AND INNOVATION: A LITERATURE REVIEW ON PORTFOLIO OPTIMIZATION

Portfolio optimization plays a crucial role in investment decision-making by balancing risk and return objectives. With the aim of improving portfolio performance, while enhancing risk management, this literature review explores traditional and artificial intelligence-powered approaches for portfolio optimization. From the traditional methods of portfolio optimization, methods such as random matrix theory, shrinkage estimators, correlation asymmetries and partial correlation networks are presented. While, from the artificial intelligence realm, techniques such as machine learning efficient frontiers, performance-based regularization, neural network predictors and deep learning models for direct optimization of portfolio Sharpe ratio are highlighted. Intertwining the traditional methods, with artificial intelligence techniques, this review highlights relevant portfolio optimization research useful for academics and practitioners alike.

Comparison of Portfolio Optimizations under Markowitz Model in Technology Sector and Financial Services Sector

In the period of Covid-19, different sectors received different levels of shocks, which gave investors a degree of caution when investing in various sectors. Therefore, portfolio optimization - using specific model to assign weights of stocks to achieve a higher return while reducing risk – becomes a popular strategy. This paper chooses the Markowitz model to find optimal sector-based portfolios, specifically in technology sector and financial services sector, as well as portfolios that contains stocks in both sectors. The study uses Python to do Monte Carlo simulation, finding two optimal portfolios with maximum Sharpe ratio and minimum volatility for each sector(s), and finally comparing performances to test if the sector-based portfolio works better than the inter-sector portfolio. According to results, the minimum volatility portfolio in combined sectors reaches the same return of 0.11 as the minimum volatility portfolio in technology sector, but with lower volatility. It means the inter-sectors portfolio is better off when seeking minimum volatility. On the other hand, the maximum Sharpe ratio portfolios in technology sector, financial services sector, and combined sectors have values of returns and volatility ordering from highest to lowest. As a result, with current information, without investors’ investment preference, the optimal maximum Sharpe ratio portfolio cannot be determined and needed further exploration.

Portfolio Optimization Based on U.S. Stock

In the financial domain, portfolio optimization is of immense significance due to its potential for risk diversification and return enhancement. Particularly in today's society where inflation is on the rise, a well-constructed portfolio can effectively assist investors in maintaining or increasing real assets. This study selected 10 U.S. stocks and analyzed their data for the past decade. Utilizing Monte Carlo simulation, the study identified the efficient frontier and constructed three types of portfolios: equal-weighted (1/N) portfolio, global minimum variance portfolio (hereinafter referred to as GMVP), and maximum Sharpe ratio portfolio. The findings show that Microsoft and Nvidia have the greatest investment weights in the portfolio with the maximum Sharpe ratio. In the GMVP, JNJ and KO have the highest investment weights. By contrasting the three portfolios' combined performance with the S&P 500 index, both the maximum Sharpe ratio and the 1/N portfolios outperformed the benchmark index, while the GMVP performed below the benchmark. The findings of this study may offer insights into portfolio management for investors looking to increase their real assets.

The Inconsistency between Model R2 and Portfolio Sharpe Ratio: A Perspective from Imbalanced Learning

The Inconsistency between Model R2 and Portfolio Sharpe Ratio: A Perspective from Imbalanced Learning

Estimation of an efficient benchmark portfolio for the Eastern European market

Purpose: This paper explores the mean-variance inefficiency of cap-weighted indices based on the CECE index as a benchmark. Methodology: For the period from March 2014 to September 2021, several proxies of efficient portfo-lios were estimated: the Global Minimum Variance (GMV) portfolio, the Maximum Sharpe Ratio (MSR) portfolio and the portfolio with equal weights of constituents (EW). Diversification of strategies was also considered by analyzing the performance of a portfolio consisting of GMV and MSR that were weighted equally. Based on monthly data, 90 out-of-sample estimations were made for each strategy in order to com-pare their risk-return characteristics. Furthermore, to confirm the differences in the riskiness and returns of the estimated portfolios, the F-test and the Welch test were performed, respectively. Results: The results show that all analyzed portfolios achieved superior performance compared to the CECE index with the GMV portfolio leading the way. Conclusion: Research findings highlight the importance of market development and liquidity when pursu-ing popular scientific diversification methods.

Portfolio Optimazation Based on 10 US Stocks

The US stock market has recently become volatile because of the increase in interest rates, which arouses investors focus on portfolio optimization. This study selected 10 representative US stocks from different industries listed on NASDAQ and NYSE. This paper applies Monte Carlo simulations to determine the efficient frontier and constructs portfolios with maximum Sharpe Ratio and minimum volatility. The result showed that Lululemon Athletica Inc. possesses the largest proportion of the maximum Sharpe ratio portfolio, and the Coca-Cola Company occupies the greatest weight in the min volatility portfolio. By comparing the cumulative return of the two portfolios with the NASDAQ Composite Index, the max Sharpe ratio portfolio is found to overperform the market benchmark. In the robustness test in which 8 stocks selected from the original group of assets are taken to construct a portfolio, the same result still holds. This result may demonstrate the feasibility of portfolio management for a few investors in that period.

Portfolio Optimization by Monte Carlo Simulation

In this paper, Monte Carlo simulation is used for constructing Efficient Frontier and optimizing the portfolio. Then the performance of the optimized portfolio had been evaluated and compared to the performance of the whole market, Firstly, this study collected the closing prices of five stocks in different industries that was listed in New York stock exchange between 2023/01/01 and 2023/04/12. Secondly, to testify if the construction of the portfolio can possibly mitigate the volatility, the correlation coefficient between these chosen stocks has been calculated. Then, Monte Carlo simulation has been used to construct the Efficient Frontier and find the weights of Maximum Sharpe Ratio portfolio and Minimum Variance portfolio. Lastly, this study put the market price data between 2023/03/12 and 2023/04/12 into the portfolios which had been built in the last step. The returns were compared to the S&P 500 subsequently. As the results shows, the Maximum Sharpe Ratio portfolio is performed better than S&P 500, Minimum Variance portfolio is performed worse than S&P 500. The results of this paper show the performance of these two portfolios compare to the market, which may help investors to decide which strategy to use when it comes to constructing a portfolio.

Portfolio Optimization Based on Six Chinese Stocks

China's capital markets started late and there are still many problems with securities trading, adding to the risks for investors. Establishing a portfolio in the A-share market and reducing non-systemic risks through diversification of investment not only reduces the volatility faced by investors, but also stimulates the enthusiasm of investors and enhances the liquidity of the capital market. Therefore, six representative stocks of Yahoo Finance are selected in this paper, and the adjusted closing price of stocks is collected. The mean-variance model is used to find the optimal weight ratio of these six stock portfolios through training sets and test sets. The actual returns are compared with the market to evaluate the outcome of the portfolio. The model results demonstrate that the Sharpe ratio of the portfolio with the highest Sharpe ratio is 68.70%, and the weight of Wuliangye is the highest at 73.55%, while Shenzhen Fountain is only 0.16%. The volatility of the least volatile portfolio is 26.48%, of which YUNNAN BAIYAO has the highest weight (37.78%), and Hithink RoyalFlush has the lowest weight (0.73%). The minimum volatility portfolio yields less than the maximum Sharpe ratio portfolio, but both outperform the market. After excluding the smallest stocks in the portfolio, the portfolio remains stable. The results displays that the mean-variance model can play an effective role in constructing the portfolio of A-share market.

Expert aggregation for financial forecasting

Machine learning algorithms dedicated to financial time series forecasting have gained a lot of interest. But choosing between several algorithms can be challenging, as their estimation accuracy may be unstable over time. Online aggregation of experts combine the forecasts of a finite set of models in a single approach without making any assumption about the models. In this paper, a Bernstein Online Aggregation (BOA) procedure is applied to the construction of long-short strategies built from individual stock return forecasts coming from different machine learning models. The online mixture of experts leads to attractive portfolio performances even in non-stationary environments. The inclusion of neural networks experts in the aggregation contributes to a better average return, while Ordinary Least Squares with Huber Loss experts contribute to lower risk. The aggregation outperforms individual algorithms, offering a higher portfolio Sharpe ratio, lower shortfall, with a similar turnover. Extensions to expert and aggregation specialisations are also proposed to improve the overall mixture on a family of portfolio evaluation metrics.

Portfolio Optimization Based on 7 Chinese Stocks

Since 2022, China has entered the post-pandemic era, with its strategic focus shifting from fighting the epidemic to economic recovery. The A-share market is under the influence of uncertain factors such as the novel coronavirus epidemic as well as the outbreak of conflict between Russia and Ukraine. In order to avoid risks and increase returns, optimizing investment portfolio has aroused people's attention and become the focus of research. This paper takes China's stock market as the research object, selects seven industries favored by investors, and selects a representative stock in each industry for portfolio construction and analysis. This paper collects data on the closing price of seven stocks from January 4 to April 1, 2022, and then randomly generates 100,000 different investment portfolios through Monte Carlo method. The portfolios with the lowest volatility and highest Sharpe ratio are built using the mean variance model. The effectiveness of these three portfolios was evaluated with real daily returns from April 6 to May 11, 2022, after acquiring the weights of these equities, and it was compared to the performance of the CSI 300 in the same time frame. The result indicates that the minimum variance portfolio has the largest cumulative return, which exceeds the cumulative return of CSI 300 in the same period, while equally weighted and the maximum Sharpe ratio portfolios generate lower yields than the market level.

Portfolio Optimization Based on 10 US Stocks

Focusing on the U.S. market, this research selects ten high-capitalization U.S. stocks in different industries and uses stock price forecasting in machine learning as well as Monte Carlo simulation to explore the efficient frontier of assets. Besides, this paper builds a portfolio with equal weight, maximum Sharpe ratio, and minimum volatility criteria, respectively. The results show that the Exxon Mobil Corporation possesses the largest proportion of the maximum Sharpe ratio portfolio, and the UnitedHealth Group Inc. accounts largest weights for the portfolio with the minimum volatility. In addition, this paper also compares the cumulative returns of the three investment portfolios with the important index NASDAQ of the US stock market. The results indicate that that the above mentioned three portfolios are all better than the benchmark index and can obtain a higher return. The results may shed light on some investors' approach to portfolio management during this extraordinary time.

Portfolio Optimization for 5 US Stocks

Nowadays, the rose of interest in the US and the changes in the value of the dollar case the public attention of investors on stocks. The US company stock was under a great change in this situation. So, the investors are now focusing on finding the portfolio optimization on the investment allocation. This study directly focuses on the selected five stocks of interest company to find the portfolio optimization using the Monte Carlo simulation and com-pare its return with the actual data. By Monte Carol simulation, the maxi-mum Sharpe ratio and the minimum volatility portfolios would be generat-ed, Netflix is regarded as the largest weight in the maximum Sharpe ratio portfolio, while Sunning is regarded as the largest proportion weight in the minimum volatility portfolio. Besides, after evaluating the results, the max-imum Sharpe ratio and minimum volatility portfolio perform better than the standard 1/N portfolio. Using this portfolio optimization, investors can find how to allocate their investment through several stocks to get the maximum returns with relatively low risk.

Optimized Portfolio Structured by 5 Stock Indexes

Equity market declines fiercely caused by the current liquidity crisis. Investors pay more attention to optimized portfolio in order to reduce losses. 5 representative indexes were chosen from capital market to construct financial portfolios. The sketch of the efficient frontier was plotted, and portfolios with the 1/N, the maximum sharpe ratio and the minimum volatility were acquired based on 10000 scipy optimization calculations, the FTSE100 accounts for the largest section of the portfolio with the maximum sharpe ratio while the SSE Composite Index possesses the biggest shares in the portfolio with minimum volatility. Comparing three portfolio returns of accumulations, it could be observed that the maximum sharpe ratio portfolios performance surpassed the 1/N and the minimum variance portfolio, several investors could make reasonable decisions refer to this result in the special tough period.

Portfolio Optimization Analysis -- Based on Six Stocks from Different Industries in the U.S. Stock Market

As the stock market becomes more and more unstable, investors all over the world pay more attention to risk management. Creating a portfolio is an effective way to make the trade-off between risk and return rate. This paper selects six stocks from the U.S. stock market in different industries, including the real estate industry, high technology, financial services company, pharmaceutical industry, automotive manufacturing industry and gas industry. This paper simulates 1,000,000 different investment portfolios by using the Monte Carlo simulation, then drawing the efficient frontier of the portfolio and figuring out the maximum Sharpe ratio and minimum volatility portfolio. As the results showed, McKesson Corporation accounted for the max proportion in both the maximum Sharpe ratio portfolio and the minimum volatility portfolio, which are 45.7149% and 60.8960% respectively. J.P. Morgan takes up 0.1232%, which is the min proportion in the maximum Sharpe ratio portfolio. However, Amazon accounted for the min proportion in the minimum volatility portfolio, which is 0.4828%. In the last, this paper compared the cumulative return of the three portfolios with the S&P 500 Index, only the return rate of the equal-weight portfolio and minimum volatility portfolio is better than the benchmark. Hence, this result may provide suggestions for picking certain kinds of portfolio in this special period.