Superior Returns Research Articles

ESG and Smart Beta Investing: A Synergistic Approach to Indian Equity Investing using the NIFTY 100 ESG Index and Multi-Factor Weighted Smart Beta Strategies

Smart Beta Investing Strategies have acquired momentum in emerging markets over the past decade; however, they have yet to be investigated in academia, and researchers are unable to provide empirical evidence. Previous research on the Indian equity markets and the clever beta strategies and factors has indicated that there is some optimism in the field. The authors augment the current corpus of research by employing an ESG-filtered index in conjunction with the conventional factors of value, size, and momentum to identify the ten most optimal stocks for each feasible portfolio configuration. A single factor and multi-factor weighting scheme are implemented to determine the ultimate portfolio and return. The Smart Beta portfolio exhibits superior returns when contrasted with its passive counterparts, as evidenced by statistical tools and risk-return characteristics. The absence of empirical evidence and a scarcity of data result in certain limitations for the study.

The prescriptive nature of market timing and predictive portfolios

Abstract We explore the ability of market timing, model-based forecasts that include incremental information over and above momentum, to create long-short portfolios that consistently provide superior returns to traditional benchmarks, and frame our analysis in the idea of “prediction-led prescription” for forward-looking decision making. Our methodology consists of linear and non-linear forecasts with model selection, that predict the direction of future returns and apply then into the construction of portfolios. Our results strongly recommend that predictive-based portfolio allocation can produce excess returns and higher Sharpe ratios, above and beyond the individual return series that are utilized to create the forecasts and traditional portfolio benchmarks. The use of “predictive methods in portfolio construction reinforces the idea of prediction-led prescription” as portfolios are prescriptions for investment management and have an exclusively forward-looking nature.

Correction to: An experiment with ANNs and Long‑Tail Probability Ranking to Obtain Portfolios with Superior Returns

Correction to: An experiment with ANNs and Long‑Tail Probability Ranking to Obtain Portfolios with Superior Returns

Stock Trend Prediction with Machine Learning: Incorporating Inter-Stock Correlation Information through Laplacian Matrix

Predicting stock trends in financial markets is of significant importance to investors and portfolio managers. In addition to a stock’s historical price information, the correlation between that stock and others can also provide valuable information for forecasting future returns. Existing methods often fall short of straightforward and effective capture of the intricate interdependencies between stocks. In this research, we introduce the concept of a Laplacian correlation graph (LOG), designed to explicitly model the correlations in stock price changes as the edges of a graph. After constructing the LOG, we will build a machine learning model, such as a graph attention network (GAT), and incorporate the LOG into the loss term. This innovative loss term is designed to empower the neural network to learn and leverage price correlations among different stocks in a straightforward but effective manner. The advantage of a Laplacian matrix is that matrix operation form is more suitable for current machine learning frameworks, thus achieving high computational efficiency and simpler model representation. Experimental results demonstrate improvements across multiple evaluation metrics using our LOG. Incorporating our LOG into five base machine learning models consistently enhances their predictive performance. Furthermore, backtesting results reveal superior returns and information ratios, underscoring the practical implications of our approach for real-world investment decisions. Our study addresses the limitations of existing methods that miss the correlation between stocks or fail to model correlation in a simple and effective way, and the proposed LOG emerges as a promising tool for stock returns prediction, offering enhanced predictive accuracy and improved investment outcomes.

An experiment with ANNs and Long-Tail Probability Ranking to Obtain Portfolios with Superior Returns

An experiment with ANNs and Long-Tail Probability Ranking to Obtain Portfolios with Superior Returns

ANALYSIS OF MAGIC FORMULA FOR INVESTMENT RETURN IN INDIAN STOCK

Magic Formula Investing, popularized by Joel Greenblatt, is a strategy that focuses on selecting stocks based on two main criteria: high earnings yield and high return on capital. Earnings yield is the ratio of a company's earnings per share to its stock price, indicating how much profit a company is generating relative to its price. Return on capital, on the other hand, measures how efficiently a company is using its capital to generate profits. The goal of this strategy is to identify undervalued companies that exhibit strong financial performance. By targeting stocks with high earnings yield and return on capital, investors aim to uncover hidden gems in the stock market that may be overlooked by others. This methodical approach to value investing has garnered attention for its relative simplicity and effectiveness in outperforming the market. One of the key advantages of the Magic Formula is its straightforward nature, which makes it accessible to a wide range of investors. Unlike more complex investment strategies that require in-depth financial analysis, the Magic Formula provides a clear and systematic way to identify potential investment opportunities. Overall, Magic Formula Investing offers investors a disciplined and rules-based approach to stock selection, with the aim of achieving superior returns compared to the broader market. While past performance is not indicative of future results, the Magic Formula's track record

New Zealand long-term equity returns and their determinants

We document several aspects of New Zealand's long-term equity returns over the 156 years from 1867 to 2022. Remaining invested in the market has been an effective strategy. Investors with a 5-year (20-year) horizon lost money just 10% (<1%) of the time in nominal terms. Equities outperformed bonds in periods of moderate and high inflation, although bonds generated superior returns in deflationary periods. Returns over 5- and 10-year horizons can be predicted with a three-component model based on the “Buffett indicator”.

PRICING OF EQUITY INVESTMENT - AN EMPIRICAL ANALYSIS OF INDIAN CAPITAL MARKET

The investment is stock market is an art as well as science. The objective of stock market investment is to achieve superior returns comparing other investment options. The investment cost is one of the variable contributing towards the performance of the stock. The justified price of the stock is always a matter of discussion but no specific formula or theory exist to recognize the correct price. This paper is an attempt to decompose the market price of the stock listed on Indian exchanges. Using the data for 10 years, explanatory variables are identified which explain market price of the stock. The model identify the significant factors explaining 62 per cent changes in the market price. The findings of the study are useful to all academicians, researchers and market participants to understand the complex dynamics of market price of the stock and its explanatory factors.

Performance Evaluation of State-Owned Company Stocks in Indonesia

This study delves into the performance of Indonesian state-owned enterprises (BUMN) stocks from February 2021 to October 2023, attracting investor attention due to their strategic position in the national economy. However, the stock performance during this period exhibited suboptimal movements, failing to surpass the returns of the Indonesia Stock Exchange Composite Index (IHSG). To address this, a prudent investment strategy is essential, encompassing portfolio construction and management. In portfolio construction, the study employs proven smart beta strategies, known for delivering superior returns compared to traditional portfolios. Portfolio management involves testing two strategies: a passive approach utilizing the buy-and-hold technique and an active approach employing portfolio recomposition. This quantitative descriptive study utilizes historical closing price data, forming portfolios based on selected beta groups. Weighting is adjusted, with higher beta stocks receiving a larger allocation in the portfolio. The research compares two beta groups (high and low) and two investment strategies (active and passive) using the Sharpe, Treynor, and Jensen indices. The results indicate that the high-beta group and active strategy outperform, aligning with the investment adage "high risk, high return." The high-beta group yields higher returns due to its increased risk profile, consistent with the notion of high-risk, high-reward investments. The active strategy provides opportunities to discard underperforming stocks and enhance the weighting of well-performing ones during recomposition. These findings are valuable for investors seeking to capitalize on BUMN stocks at lower prices. The study aids in minimizing negative returns (losses) by identifying the most suitable strategy for BUMN portfolios. Investors can leverage this research to make informed decisions and navigate the challenges associated with investing in state-owned enterprises.

Portfolio optimization based on the pre-selection of stocks by the Support Vector Machine model

This study aims to analyze the performance of an investment portfolio using the Markowitz model, which maximizes the Sharpe ratio from a set of assets preselected through the Support Vector Machine (SVM) model using fundamental indicators in the Brazilian stock market. With an accuracy of 61% for the SVM model, the results indicate that preselecting assets based on fundamental indicators and subsequently optimizing them by maximizing the Sharpe ratio showed a superior return and faster recovery after drawdown periods compared to the benchmark or SVM (1/n) strategy. These results suggest the relevance of including the SVM in the optimization portfolio process.

An Evaluation of Large Cap, Mid Cap, and Small Cap Mutual Funds: Return, Risk, and Investor Considerations

Investing in mutual funds presents a straightforward entry point into the stock market, particularly for novice investors. Regardless of one’s familiarity with stock markets or age, mutual funds offer accessible investment opportunities. However, selecting the most suitable mutual fund scheme can be challenging, given investors’ varying risk appetites and financial goals. This study aims to assist such investors grappling with scheme selection dilemmas by analysing mutual fund schemes of many types and determining which ones offer superior returns at lower risks. The research evaluates the risk and return profiles of various mutual fund schemes taken up for the study. Utilising various ratio like Jensen’s, Sharpe, and Treynor, present discussion compares the returns by considering the systematic and unsystematic risk of selected schemes within each category. The analysis spans 5 years, from April 01, 2017, to March 31, 2022, drawing data from secondary sources including Moneycontrol, journals, books, and official websites of relevant financial bodies. BNP Paribas Large Cap Fund (G) emerges as a top performer in the sphere of large cap funds according to Sharpe and Jensen’s measures, while Edelweiss Large Cap Fund-A(G) excels by Treynor ratio. Among mid cap schemes, Edelweiss Mid Cap Fund (G) consistently outperforms its peers across all evaluated ratios. In the small cap category, Axis Small Cap Fund-Regular (G) stands out for delivering the highest returns on the basis of Jensen’s, Sharpe and Treynor ratios. Ultimately, this study serves to guide investors 2024in navigating the complexities of mutual fund selection, offering insights into which funds align best with their risk-return preferences and investment objectives. Findings reveal distinct return patterns across fund categories, with specific funds outperforming peers based on various return metrics. The study concludes that while BNP Paribas Large Cap mutual Fund (G) excels in large cap schemes, Edelweiss Mid Cap mutual Fund (G) stands out among mid cap funds, and Axis Small Cap mutual Fund-Regular (G) leads in the small cap mutual funds category. Implications suggest that investors can make informed decisions based on risk-return preferences, with large cap funds offering stability, mid cap funds providing moderate returns, and small cap funds presenting opportunities for better growth with higher risk acceptance capacity.

Brand Subsidy Strategies for Mitigating Agricultural Product Supply Risks Under Capital Limitation

This study investigates the application of brand subsidies to capitalize on limited capital in the context of branded agricultural products facing supply risks, aiming to identify a win-win subsidy decision-making model. We consider a supply chain for branded agricultural products comprising financially constrained farmers and financially robust retailers. Financially constrained farmers, who can produce without loans under normal conditions, resort to bank loans to supply branded agricultural products when faced with supply risks. We find that under capital constraints, brand subsidies consistently yield higher returns in terms of brand strength and market demand. Moreover, even after adjusting for supply risks through posterior probability, brand subsidies continue to deliver superior returns.

Enhancing daily stock trading with a novel fuzzy indicator: Performance analysis using Z-number based fuzzy TOPSIS method

In the dynamic realm of financial markets, developing effective strategies for stock exchange transactions is paramount. This research addresses this critical need by introducing a pioneering indicator for daily stock trading, leveraging a robust fuzzy inference system (FIS). The indicator ingeniously integrates key technical indicators including Moving Average Convergence and Divergence (MACD), Relative Strength Index (RSI), Stochastic Oscillator (SO), and On-Balance-Volume (OBV). The FIS is meticulously constructed based on expert opinions and gleaned fuzzy rules. The fuzzified values then serve as inputs to the FIS, which in turn generates signals indicating optimal actions: buy, hold, or sell stocks. To validate the importance of the FIS, a carefully curated selection of stocks from the NASDAQ stock exchange is employed for experimentation. To prove the efficiency of the FIS, the technical indicators and the fuzzy risk-adjusted returns are considered as alternatives and criteria, respectively. A novel Z-number-based technique for order preference by similarity to the ideal solution (TOPSIS) method is used to rank the technical indicators and the FIS. The comparative results unequivocally demonstrate that the developed FIS surpasses existing indicators, yielding superior returns.

Portfolio Management Strategy Based on LSTM

Mean-variance theory emphasizes the consideration of returns and risks simultaneously. However, traditional optimization of the risk-return trade-off may not always align with the preferences of individual investors. Given that outcomes about daily LSTM predictions have been inconclusive, this study uses weekly price dataset for prediction aiming to improve the signal-to-noise ratio. The objective is to develop a portfolio management strategy that prioritizes achieving reasonable returns while effectively managing risk. To achieve this, a selection process identifies 12 stocks from different sectors. LSTM models are trained using historical stock price data, and these models predict future stock prices. The predictions guide the weekly rebalancing of the portfolio based on a designed principle and the performance of the strategy-based portfolio is evaluated by comparing it to two benchmarks: the NASDAQ index and a 1/N investment portfolio. The results demonstrate that the strategy portfolio outperforms both benchmarks, achieving significant improvements in returns. The strategy portfolio exhibits a remarkable return of 116.21%, a Sharpe ratio of 2.944 and a maximum drawdown of 13.1%. Although there is a slight increase in volatility, the strategy effectively manages risk while delivering superior returns.

Dynamics of Venture Capital and Private Equity Investments in India: An Empirical Analysis

In this study, we explore the inter-dynamics among holding periods, return multiples, fund types, and exit routes of different VC and PE investments in the emerging economy context of India. We employ data spanning from January 2004 to March 2021, and our results indicate that there is a negative association between the holding period and return. The results also indicate that the average holding periods for India-dedicated and foreign funds are not significantly different. Furthermore, the results show that India-dedicated funds outperform foreign funds significantly in generating returns. Finally, the findings suggest that all exit routes can potentially yield similar results, contrary to the prevailing belief that certain exit routes guarantee superior returns. These findings provide useful insights for a spectrum of stakeholders, including entrepreneurs, practitioners, investors, financial analysts, and policymakers.

Washington Policy Analysts and the Propagation of Political Information

Washington policy research analysts (WAs) monitor political developments and produce research to interpret the impact of these events. We find institutional clients channel more commissions to brokerages providing policy research and commission-allocating institutional clients generate superior returns on their politically sensitive trades. We find that WA policy research reports are associated with significant price and volume reactions. Finally, we find sell-side analysts with access to WA issue superior stock recommendations on politically sensitive stocks. These effects are particularly acute during periods of high political uncertainty. Overall, we uncover a unique and an important conduit through which political information filters into asset prices. This paper was accepted by David Sraer, finance. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4919 .

Adjustable-band moving average learning strategies for technical analysis: evidence from the Dow Jones Industrial Average

ABSTRACT In this paper, we propose a unique moving average (MA) band parameter optimization algorithm. By comparing its performance with standard fixed-band trade rules, we show that adjustable band parameter MA rules provide relatively superior returns. We find the better performance of adjustable band trade rules in cases of high long-period moving average rules and in periods of general price decline. We provide evidence that average optimal adjustable bands have remained fairly constant in the DJIA for several decades, and that both standard MA technical analysis (TA) and band-optimization TA perform well in periods of price decline.

What drives shareholder returns in mining companies?

The mining industry has a long history of generating low shareholder returns. Over the past two decades, the average Canadian mining company has returned −8% per year, while the TSX composite index returned +7% per year. Prior studies indicate these low returns are driven primarily by volatile commodity prices and thus largely out of managers’ control. However, this research relies on small samples over short periods of time. To assess more robustly the distribution of shareholder returns across mining firms and to identify the relative impact of price versus other firm-specific factors on firm-level returns, we analyzed the performance of over 100 Canadian mining companies from 2003 to 2016. We find that commodity prices explained the majority of firm performance annually, but that over the long-run, mineral asset impairments had a much more significant influence on performance. Firms that significantly overperformed the industry experienced minimal impairments while firms that significantly underperformed experienced very large impairments. To deliver superior returns, it seems that mining firm managers and investors need to understand how to minimize impairments.

Networking effects of innovations: evidence from the Global South

PurposeThis paper aims to investigate the short- and long-run influence of core banking solutions (CBSs) on productive efficiency and identify the presence of potential network externalities arising from CBS adoption. This paper further examines the differential behaviour of long-term effects across the banking structure.Design/methodology/approachThis study uses a panel data set of Indian commercial banks from 2005 to 2021. Economic efficiency is quantified using VRS-based DEA programming algorithms. Productivity changes are measured through an input-oriented, DEA-based Malmquist productivity index. Short- and long-run effects are examined through a finite autoregressive distributed lag model, estimated through a pooled mean-group estimator.FindingsFindings suggest that CBS adoption negatively correlates with cost structure until the first year of adoption. Nevertheless, significant benefits are visible from the third year. Furthermore, such associations are highly susceptible to the industry structure. CBS results in higher incremental benefits for private banks vis-à-vis state-owned banks. Large banks receive significant and quicker productivity improvements from CBS vis-à-vis small banks. Bank age guides CBS–performance associations, highlighting that mature banks may face the issue of legacy infrastructure in CBS adoption. The resultant networking externalities are significant as they enhance the attractiveness of the network, which subsequently augments inter-branch and inter-bank communications.Originality/valueTo the best of the authors’ knowledge, this study is the first to recognise the stickiness of one of the most homogeneously adopted technological innovations in the Indian banking sector. The presence of a conjoint technological network has the potential to enhance the service delivery process and ensure superior returns for Indian banks.

Momentum in Low Carbon and Fossil Fuel Free Equity Investing

The Conference of the Parties to the United Nations Framework Convention on Climate Change (COP27) reiterated that climate change remains a critical issue for humanity. Especially, it stressed on the need to encourage a clean energy mix, including renewable and low-emission energies, as part of the continuing transition toward a cleaner and sustainable energy. Using daily indices data covering the period 1st January 2017 to 28th February 2023, this paper studies the performance of 2 family classes among sustainability indices, namely, low carbon and fossil fuel free indices. Specifically, this study sheds further light by assessing the performance of trading strategies which are based on the momentum of low carbon and fossil-fuel free based indices. The performance is based on a thorough analysis of the Relative Strength Index (RSI) and is captured through the Sharpe and Sharpe per trade measures. We decompose the analysis into pre and post COVID-19 to provide some insights how these sustainable energy investments were impacted by the coronavirus pandemic. Findings support an adjusted overbought/oversold RSI 75(25) model resulted in less false signals than the traditional 70 (30) model. Relative to the post COVID-19 period, all selected equity indices performed poorly in the pre- COVID-19 period, with negative returns, except for the MSCI World Low Carbon Leaders and the SPDR MSCI Emerging Markets Fossil Fuel Free equity indices. Comparatively, in the post COVID-19 period, all indices witnessed superior return performance, with also increased risk levels. SPDR MSCI Emerging Markets Fossil Fuel Free index ranked first, even after adjusting for number of trades. Investments post COVID-19 early impact period performed better than a naive buy-and-hold strategy for greener investments like low carbon and fossil fuel free equity indices.