This paper introduces a technical trading framework that features trend-following, conditional active trading, stop-loss mechanisms and trading volume in formulating trading strategies. Unlike analyses focusing on a single indicator, this framework reflects a comprehensive approach, integrating multiple trading principles into a technical analysis. The implementation in this study mainly relies on common technical indicators such as moving average crossovers and moving average convergence/ divergence. Empirical results on data from 2007 to 2023 demonstrate that trading strategies derived from this framework outperforms the buy-and-hold approach on US index exchange-traded funds such as the SPDR S&P 500 ETF Trust and Invesco QQQ Trust (Series 1). Using a large, survivorship-bias-free US stock sample from 2000 to 2023, strategies selected through machine learning exhibit higher average returns and reduced drawdowns compared with buy and hold. An optimized multilayer perceptron neural network is employed to support strategy parameter selection. In addition, moving average gap volatility and downside price volatility prove valuable in parameter selection for the trading strategies.
Japan is renowned for its cutting-edge technology industry and significant investment in research and development (R&D). However, Japanese nonfinancial enterprises face challenges that affect their investment decisions, including high capital costs and stringent regulations. This study uses pecking-order theory to assess the determinants of investment strategies. We meticulously followed inclusion criteria and collected data through purposive sampling from Thomson Reuters Eikon DataStream, focusing on 304 nonfinancial institutions listed on the Tokyo Stock Exchange for the 18 years from 2006 to 2023. The primary estimation methods employed to analyze the relationships between the study's variables were the augmented mean group estimator and the two-step generalized method of moments. The study found that interest rates had a negative and significant impact on both R&D and capital expenditures (CAPEX). In addition, while the tax rate negatively and significantly influenced R&D, it had a positive and significant effect on CAPEX. The investigation also revealed that total debt and stock prices positively and significantly impacted both R&D and CAPEX. Further, the moderating relationship between firm size and interest rates showed a positive and significant effect on R&D but a negative and sig-nificant impact on CAPEX. Companies should consider restructuring their activities to minimize tax liabilities by allocating investments to areas with more favorable tax rates or implementing tax-efficient financing arrangements.
This study evaluates the performance and portfolio role of US-domiciled, US dollar-denominated Indian equity exchange-traded funds (ETFs) relative to US and global benchmarks between April 2008 and August 2023. Using return-based measures, multifactor models and portfolio optimization techniques, we examine both standalone performance and the contribution of Indian ETFs within diversified allocations. The results show that Indian equity ETFs underperform US and global equities on a risk-adjusted basis, with higher volatility and no statistically significant alpha. Their performance improved during the Covid-19 pandemic and postvaccination periods, though gains proved temporary. Portfolio tests reveal that when paired with US equities the Indian ETFs reduce efficiency, with Sharpe-maximizing allocations assigning zero weight to Indian equity ETFs. By contrast, modest allocations to Indian equity ETFs enhance diversification when paired with non-US global equities, and risk-parity approaches ascribe nearly one-third weight to Indian equity ETFs. Measures of tail risk (value-at-risk, conditional value-at-risk) indicate that Indian ETFs have significantly higher tail risk, strengthening the argument for their use on a limited basis. The study finds that Indian equity ETFs are best used as satellite exposures that provide conditional diversification benefits to global portfolios, rather than as sources of consistent alpha or core efficiency.
This paper investigates the volatility behavior of gold and oil prices during the Covid-19 pandemic using advanced econometric models, including the autoregressive integrated moving average (ARIMA), autoregressive conditional heteroscedasticity (ARCH), generalized ARCH (GARCH), exponential GARCH and threshold GARCH. Covering the period from January 9, 2019, to December 9, 2022, the study explores how these commodities responded to heightened global uncertainty. The results show that gold, unlike oil, consistently acted as a safe-haven asset, preserving value during market disruptions caused by geopolitical tensions, economic shocks and the health crisis. Our findings reveal a strong interdependence between gold and oil, with gold serving as a hedge against oil price volatility. A diversified portfolio including both assets proved more resilient than one reliant on either commodity alone. Volatility models also underscore gold's role as a hedge against geopolitical risk, enhancing its value for portfolio diversification. However, the quality of data available during the pandemic may affect the precision of statistical inferences. Despite such constraints, the study has practical implications. Investors are encouraged to increase gold exposure during volatile periods, while policy makers could monitor gold prices as indicators of systemic risk. Overall, gold emerges as both a resilient investment and a strategic indicator during a global crisis.
The purpose of this study is to provide clarity on the formulation of short-selling scenarios, facilitating informed decision-making and strong market strategies. Our study addresses a significant gap in the literature on short selling by providing a comprehensive bibliometric review of research in areas such as the integration of short selling with the emerging concept of cryptocurrency, the role of information asymmetry in short-selling activities, market fluctuations and market indifference. In identifying these areas, the paper maps the current state of the literature and provides new directions for further research. We analyze the current status of publications on short selling to identify research patterns and themes that can contribute to the development of a more efficient, transparent and reliable financial system. We use a bibliometric approach along with news analysis to study 209 Scopus-indexed articles published prior to May 2024. Software such as VOSVIEWER, BIBLIOSHINY by R and the science mapping analysis tool (SCIMAT) is used to identify clusters, themes and the strength of connections between short selling and its variables as well as to provide a news analysis. Our findings reveal four thematic clusters: investment dynamics, investment strategies, optimization strategies and investment efficiency. Internal and external links between the themes are identified through thematic mapping, and cluster network strength is revealed through cluster mapping. This study suggests future directions and provides scope for alternative approaches in the context of short selling, by including unexplored research themes on which future studies could be based.
This paper examines the investment signals generated from a combination of large language models (LLMs). The algorithm is applied to two sources of text: YouTube social media posts, and the business and economic news from more than 100 mainstream news sources such as CBS News, CNBC and Forbes. Investment signals based on news outlets outperform the Standard & Poor's 500 (S&P 500) index, chosen as a benchmark, in relative and risk-adjusted terms, from September 2020 to April 2023. The fine-tuned LLM model clearly outperforms the base LLM model as well as a bidirectional encoder representations from transformers (BERT) model combination, which was chosen as a benchmark and comprises entity recognition from the BERT base model and sentiment analysis from FinBERT. Classification of LLM sentence embeddings with a novel approach using uniform manifold approximation and projection (UMAP) dimensionality reduction and hierarchical density-based spatial clustering of applications with noise (HDBSCAN) also generates investment signals that outperform the S&P 500 index on a risk-adjusted basis over the period from June 2018 to June 2023. All investment strategies are demonstrated to be unique by means of a standard regression against the Fama-French investment factors.
In an attempt to unravel the intricate web of interconnectedness in the Indian equity market during periods of crisis, this study delves into the volatility spillover in three distinct crises: the Covid-19 pandemic (health), the Russia-Ukraine war (geopolitics) and the collapse of Silicon Valley Bank (financial), as well as a noncrisis period. Employing the time-varying parameter vector autoregression method, it analyzes both intrasectoral and intersectoral dynamics. The findings present a compelling picture: the auto, capital goods and realty sectors consistently amplify risk across events, while the banking, fast-moving consumer goods and health care sectors invariably play the role of shock absorbers. Interestingly, the other sectors exhibit a dual nature, acting as both transmitters and receivers of risk turbulence. This granular understanding of sector-specific risk dynamics empowers portfolio managers to strategically adjust asset allocation during times of crisis. Notably, this study not only fills a critical gap in our understanding of emerging market resilience but also advances the field by comparing sector interconnectedness across these distinct crises.
We contribute to the literature by introducing an explanation of the variation in idiosyncratic volatility (IVOL) anomaly return and its application to minimum-variance investing in the Canadian stock market. First, we find that the variation in minimum-variance exchange-traded fund excess return is better explained by the crowdedness of factor investing (and the following overpricing and market friction) than by the IVOL factor, which expands the state-of-the-art model. Second, we identify the crowdedness of factor investment by defining a novel time series indicator. Our indicator has a high predictive power for future factor returns and for the IVOL factor. Third, using the indicator and proxy for leverage constraint, we suggest a strategy of rotation between the Canadian minimum-variance exchange-traded fund portfolio and the Canadian stock market portfolio. This rotation strategy shows an excess compound annual growth rate that is more than 1% higher than the ex post high-performing spread between the two assets and is robust to high transaction costs. The long-only strategy is also considered to have a moderate index outperformance, enabling more practical implementation.
The rapid development of quantitative investing has heightened the need for high- quality data and sophisticated methods for interpreting complex information, particularly through advanced data visualization techniques. This paper introduces a robust visualization model developed by Premialab's quant research team, leveraging their extensive database of more than 5000 single-asset and multiasset quantitative investment strategies (QIS) sourced from 18 global investment banks. Utilizing uniform manifold approximation and projection (UMAP) for dimensionality reduction, high-dimensional time series of quantitative strategies are transformed into a twodimensional, risk-premium-segmented space that preserves up to 90% of the original data structure. The model is capable of identifying nonlinear relationships and clustering strategies with similar risk factor exposures, enabling an insightful comparison of their relative performance. An application to equity strategies provides further insights into the positioning of each strategy within its peer group. Further, the model's capacity to detect diversifying strategies enhances portfolio completion by projecting and visualizing clusters that can complement an existing portfolio setup in order to ultimately target a higher degree of diversification. The results demonstrate the robustness of this approach in mapping complex investment strategies, providing investors with an intuitive and actionable framework for strategy selection and risk assessment.
Option trading provides rich sets of data that may be used to trade their underlying assets effectively. Strategies for this purpose are discussed in this paper. These strategies use the call and put prices of option chains to estimate the consensus features of the future probability distribution of the price of the underlying asset. This enables a trader to predict the expected profit and expected loss that may be experienced when trading in the asset. The expected profit and expected loss are metrics of reward and risk for such trades. Trading rules based on these metrics are shown to be effective when applied dynamically to historical data of the option chains underlain by the Standard & Poor's 500, Dow Jones and Nasdaq indexes.
This study aims to provide empirical insight into the efficiency of major pure-play internet banks in South Korea, Japan and China (that is, KakaoBank, K Bank, Sony Bank, Jibun Bank and WeBank). We employed the data envelopment analysis model and adopted a profit-oriented approach to measuring the efficiency of the banks between 2017 and 2020. In addition, we ran fixed cross-section regression to measure the relationship between net profit and the input and output variables. We found that in 2017 Sony Bank was run efficiently, but KakaoBank, K Bank and Jibun Bank were run inefficiently. In 2018 WeBank and Sony Bank were efficient, and the efficiency scores of KakaoBank and Jibun Bank were close to 1, meaning they ran fairly efficiently; however, K Bank was run inefficiently. In 2019 KakaoBank, WeBank and Sony Bank were run efficiently, and Jibun Bank's efficiency score was close to 1, meaning it was run fairly efficiently, but K Bank was run inefficiently. In 2020 KakaoBank and Sony Bank were run efficiently but K Bank was run inefficiently. In addition, Jibun Bank's efficiency score decreased slightly compared with 2019. Fixed cross-section regression showed the strongest relationship between net profit and noninterest expense. However, the relationship between net profit and noninterest income is also strong. We recommend that banks prioritize managing noninterest expenses and income to achieve sustainable profits.
Behavioral finance combines the psychological aspects of human behavior with tra-ditional finance concepts. This study's objective is to identify determinants influ-encing the investment decisions of an individual investor. The study is based onthe five cognitive biases that impact investment decisions. Adopting a quantitativeand descriptive research approach, the study uses data from 400 investors to iden-tify the variables that significantly impact investment decisions. Factor analysis isused to determine the influential critical factors. Five cognitive biases are measuredusing 12 different variables. The results indicate that herding, overconfidence and mental accounting are the main cognitive biases that significantly impact investmentdecision-making. Investor overconfidence is the most critical of these parameters,which together effectively govern investment decisions. The paper provides portfoliomanagers with a helpful tool for understanding the different ways in which investorsbehave, and for improving the quality of advice for their investors. It also providesguidelines to investors on the critical biases that hinder investment growth, and howto identify these weaknesses and avoid them. Financial services providers will alsofind their improved knowledge of their clients helpful when designing products
This research empirically investigates the impact of investor sentiment on equitymarket volatility during periods of economic turbulence, with a focus on theCovid-19 pandemic. Daily data from the Bombay Stock Exchange Sensitive Indexand proxies for investor sentiment from November 23, 2017 to March 31, 2022 is col-lected and split into pre- and post-Covid-19 periods. To quantify investor sentiment,a comprehensive sentiment index is developed using principal component analy-sis. The study employs generalized autoregressive conditional heteroscedasticity(GARCH), threshold GARCH and exponential GARCH models to evaluate the influ-ence of sentiment on stock market volatility. These models enable the assessment ofhow positive news and negative news affect volatility differently, considering theirasymmetric impacts. The results of the analysis highlight the significant influence ofinvestor sentiment on volatility, particularly during the post-Covid-19 period. Postpandemic, volatility shocks are found to be more persistent and enduring, indicating heightened market instability. Negative news has a more pronounced impact onvolatility than positive news of equivalent magnitude. This disparity can be attributedto the widespread concern, fear and uncertainty prevalent during the post-Covid-19period, which contributed to the increase in market volatility
This paper discusses the application of information-theoretic concepts to the back-ward filtering of time series using moving averages. We identify moving averagesas time-dependent expectation values derived from maximum entropy probabilitykernels that are subject to relevant constraints. Constraining the width of the kernelresults in the simple moving average, while constraining the typical timescale yieldsthe exponential moving average. With a martingale constraint, we derive a movingaverage corresponding to a risk-neutral valuation scheme for financial time series.By expanding this framework to generalized forms of entropy, we introduce a broadfamily of maximum-entropy-based moving averages
In this paper we investigate the investment performance of collectible watches traded on the secondary market using a novel data set of more than 60 000 watch auction results over the period 1999-2020. The risk and return characteristics of the as yet un investigated collectible category of watches are determined. The annualized real US dollar geometric mean returns of collectible watches was 5.5% (7.7% nominal)over the two full decades between 1999 and 2020, outperforming the Standard &Poor's 500 index and other collectible assets such as art and classic cars. Comparably low standard deviations in the prices of collectible watches led to attractive risk-adjusted nominal Sharpe ratios that were inferior only to those of gold. Moreover, the returns of the series were positively correlated with both inflation and equity investments. In a further detailed analysis, a hedonic pricing model provides detailed insights into price-driving determinants in luxury watches, such as authenticity, rarity, type of watch, brand, famous pre-owners and many more
This paper examines the performance of specialty unaligned exchange-traded funds (UETFs) in comparison with US and global equities from February 2000 to April 2023. Our analysis reveals a significant positive correlation between the average monthly returns of UETFs and both US and global equities. Across the sample period, UETFs demonstrated superior absolute performance, as evidenced by their average monthly returns outpacing those of US and global equities. Moreover, our investigation into risk-adjusted performance indicates that UETFs surpassed US and global stocks. Notably, our findings also indicate that UETFs generated positive alpha. In addition, this study integrates rolling monthly alphas, computed in successive intervals, revealing the sustained outperformance of UETFs compared with global equities. However, it is crucial to note that, despite these advantages, UETFs remain susceptible to stock market fluctuations and are unable to completely evade volatility. This study sheds light on the nuanced dynamics of UETFs and their performance relative to broader equity markets.
