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The underlying concept behind the technical trading indicators based on moving averages of prices has remained unaltered for more than half of a century. The development in this field has consisted in proposing new ad-hoc rules and using more elaborate types of moving averages in the existing rules, without any deeper analysis of commonalities and differences between miscellaneous choices for trading rules and moving averages. The first contribution of this paper is to uncover the anatomy of market timing rules with moving averages. Our analysis offers a new and very insightful reinterpretation of the existing rules and demonstrates that the computation of every trading indicator can equivalently be interpreted as the computation of a weighted moving average of price changes. Therefore the performance of any moving average trading rule depends exclusively on the shape of the weighting function for price changes. The second contribution of this paper is a straightforward application of the useful knowledge revealed by our analysis. Specifically, we evaluate the out-of-sample performance of 300 various shapes of the weighting function for price changes using historical data on four financial market indices. The goal of this exercise is to suggest answers to long-standing questions about optimal types of moving averages and whether the best performing trading rule can beat the passive counterpart in out-of-sample tests.

In this article, the authors study the performance of trend-following investing across global markets since 1880, extending the existing evidence by more than 100 years using a novel data set. They find that in each decade since 1880, time series momentum has delivered positive average returns with low correlations to traditional asset classes. Further, time-series momentum has performed well in 8 out of 10 of the largest crisis periods over the century, defined as the largest drawdowns for a 60/40 stock/bond portfolio. Lastly, time series momentum has performed well across different macro environments, including recessions and booms, war and peacetime, high- and low-interest rate regimes, and high- and low-inflation periods.

This paper extends the work of Sloan (1996) by linking accrual reliability to earnings persistence. We construct a model showing that less reliable accruals lead to lower earnings persistence. We then develop a comprehensive balance sheet categorization of accruals and rate each category according to the reliability of the underlying accruals. Empirical tests generally confirm that less reliable categories of accruals lead to lower earnings persistence and that investors do not fully anticipate the lower earnings persistence, leading to significant security mispricing. We conclude that there are significant costs associated with the recognition of unreliable information in financial statements.

Bankers are under a great deal of stress and due to many antecedents of stress such as Overload, Role ambiguity, Role conflict, Responsibility for people, Participation, Lack of feedback, Keeping up with rapid technological change. Being in an innovative role, Career development, Organizational structure and climate, and Recent episodic events. One of the affected outcomes of stress is on job performance. This study examines the relationship between job stress and job performance on bank employees of banking sector in Pakistan. The study tests the purpose model in relation of job stress and its impact on job performance by using (n=144) data of graduate, senior employees including managers and customers services officers of well reputed growing bank in Pakistan. The data obtained through questioners was analyzed by statistical test correlation and regression and reliabilities were also confirmed. The results are significant with negative correlation between job stress and job performances and shows that job stress significantly reduce the performance of an individual. The results suggest that organization should facilitate supportive culture within the working atmosphere of the organization.

Spanish Abstract: Las fórmulas de valoración de opciones financieras se basan en el arbitraje (la posibilidad de formar una cartera réplica, esto es, que proporciona unos flujos idénticos a los de la opción financiera) y son muy exactas. Sin embargo, muy pocas veces tiene sentido utilizar directamente estas fórmulas para valorar opciones reales porque las opciones reales no son casi nunca replicables. Sin embargo, podemos modificar las fórmulas para tener en cuenta la no replicabilidad.Los principales problemas con los que nos encontramos al valorar opciones reales son:a) dificultad para definir los parámetros necesarios para identificar y valorar las opciones realesb) dificultad para definir y cuantificar la volatilidad de las fuentes de incertidumbre c) dificultad para calibrar la exclusividad de la opciónEstos tres factores hacen que la valoración de las opciones reales sea, en general, difícil, y casi siempre muchísimo menos exacta y más cuestionable que la valoración de las opciones financieras. Además, es mucho más difícil comunicar la valoración de las opciones reales que la de un proyecto de inversión ordinario por su mayor complejidad técnica.English Abstract: We show the difficulties of valuing real options and the errors encountered in some real option valuations.

The authors conduct the first systematic analysis of the determinants and impact of the sovereign credit ratings assigned by the two leading U.S. agencies, Moody's Investor Services and Standard and Poor's. Of the large number of criteria used by the two agencies, six factors appear to play an important role in determining a country's credit rating: per capita income, GDP growth, inflation, external debt, level of economic development, and default history. In addition, the authors find that sovereign ratings influence market yields - particularly those on non-investment-grade issues - independently of any correlation with publicly available information.

Empirically, standard, intuitive measures of risk like volatility and beta do not generate a positive correlation with average returns in most asset classes. It is possible that risk, however defined, is not positively related to return as an equilibrium in asset markets. This paper presents a survey of data across 20 different asset classes, and presents a model highlighting the assumptions consistent with no risk premium. The key is that when agents are concerned about relative wealth, risk taking is then deviating from the consensus or market portfolio. In this environment, all risk becomes like idiosyncratic risk in the standard model, avoidable so unpriced.

Traditional portfolio allocation methods are based on the assumption of known risk tolerance and parametric models of market returns. Deep reinforcement learning (DRL) is a non-parametric machine learning technique that can learn optimal policies for complex sequential decision-making problems. This paper conducts a comprehensive comparative analysis of traditional portfolio allocation methods and DRL. The paper finds that DRL outperforms traditional methods in terms of both risk-adjusted return and Sharpe ratio in a simulation study. The authors argue that DRL is a promising new approach to portfolio management with the potential to outperform traditional methods in a variety of market conditions.

Sell-side analysts have been the subject of hundreds of academic studies. In this paper, I offer perspectives on the state of our understanding of analysts based on prior academic research. Additionally, several observations are offered, which question how descriptive certain widely held beliefs are in light of the evidence. These observations on the literature serve as both criticisms and suggestions for future research.

Fueled in part by the wealth recently created from digital currencies, major art dealers such as Christie’s and Sotheby’s have embraced the sale of non-fungible tokens attached to unique digital works of art. What are non-fungible tokens, how is this related to the blockchain and what do we know about this ancient market for digital art? It now appears that digital art can be added to the growing list of uses for blockchain technology now becoming a part of modern life.This article proceeds in seven parts. First, is a discussion about the new and explosive market for digital art. Second, I explore the evolution of the digital world and virtual property. Third, is an explanation and historical account of the blockchain and virtual currencies. Fourth, non-fungible tokens are discussed. Fifth, is a brief look at unresolved issues impacting the law of NFTs and potential solutions are provided. Sixth, a few thoughts about the future of digital property are presented. And last, I conclude.This dramatic extension of blockchain and other digital technology to the world of art and music represents a new and exciting platform for creative expression. This paper is a valuable addition to the literature by providing a readable introduction and overview of what is now known about the likely impact of blockchain technology and non-fungible tokens to music and art. This important development should have a significant impact on the future of innovation and property law.

Regulatory concerns about the impact of structured claims on financial stability in times of stress are frequently too sweeping and indistinct for a judicious assessment of how structured finance transactions might propagate asset shocks across different capital market segments. This brief chapter defines structured finance in order to inform a more specific debate about possible regulatory challenges posed by complex forms of credit risk transfer.

We estimate the causal effect of online sports betting on households' investment, spending, and debt management decisions using household transaction data and a staggered difference-in-differences framework. Following legalization, sports betting spreads quickly, with both the number of participants and frequency of bets increasing over time. This increase does not displace other gambling or consumption but significantly reduces savings, as risky bets crowd out positive expected value investments. These effects concentrate among financially constrained households, as credit card debt increases, available credit decreases, and overdraft frequency rises. Our findings highlight the potential adverse effects of online sports betting on vulnerable households.

We survey managers of firms in seventeen European countries on their capital structure choice and its determinants. Our main objective is to explore the link between theory and practice of capital structure. Preliminary analysis of the survey shows some interesting findings. Financial flexibility, credit rating and tax advantage of debt are the most important factors influencing the debt policy while the earnings per share dilution is the most important concern in issuing equity. Evidence also supports that the level of interest rate and the share price are important considerations in selecting the timing of the debt and equity issues respectively. Hedging consideration are the primary factors influencing the selection of the maturity of debt or when raising capital abroad. We also propose to compare the responses of European managers with those of the U.S. in Graham and Harvey (2001) as well as across countries based on the English, French, German and Scandinavian law. This analysis would be completed by March 2002.

House prices generally depend on inflation, the yield curve and bank credit, but national differences in the mortgage markets also matter. House prices are more sensitive to short-term rates where floating rate mortgages are more widely used and more aggressive lending practices are associated with stronger feedback from prices to bank credit.

Market reactions to the 2019 novel coronavirus disease (COVID-19) provide new insights into how real shocks and financial policies drive firm value. Initially, internationally oriented firms, especially those more exposed to trade with China, underperformed. As the virus spread to Europe and the United States, corporate debt and cash holdings emerged as important value drivers, relevant even after the Fed intervened in the bond market. The content and tone of conference calls mirror this development over time. Overall, the results illustrate how anticipated real effects from the health crisis, a rare disaster, were amplified through financial channels.

Value investing, as defined by the Fama–French HML factor, has underperformed growth investing since 2007, producing a drawdown of 55% as of mid-2020. The underperformance leads many to argue that value is dead. Our analysis attributes value’s recent underperformance to two sources: the HML book-value-to-price definition fails to capture the increasingly important intangible assets, and valuations of value stocks relative to growth stocks have tumbled. Both arguments are inconsistent with value’s death arguments. We capitalize intangibles and show that this measure of value outperforms the traditional measure by a wide margin. We also perform a return decomposition and demonstrate that changes in the valuation spread between the growth and value portfolios explain the entire drawdown, with room to spare. The relative valuation of the value factor falls from the top quartile of the historical distribution at the start of 2007 to the bottom percentile as of June 2020.Financial Analysts Journal, 2021, 77(1): 44–67. Data for our iHML factor can be downloaded here.First posted to SSRN: November 17, 2019.

We offer empirical evidence that size, low-risk, value, and momentum factor portfolios generate economically meaningful and statistically significant alphas in the corporate bond market. Because the correlations between the single-factor portfolios are low, a combined multi-factor portfolio benefits from diversification among the factors: It has a lower tracking error and a higher information ratio than the individual factors. Our results are robust to transaction costs, alternative factor definitions, alternative portfolio construction settings, and constructing factor portfolios on a subsample of liquid bonds. Finally, allocating to corporate bond factors provides added value beyond allocating to equity factors in a multi-asset context.

We introduce "factors on demand", a modular, multi-asset-class return decomposition framework that extends beyond the standard systematic-plus-idiosyncratic approach. This framework, which rests on the conditional link between flexible bottom-up estimation factor models and flexible top-down attribution factor models, attains higher explanatory power, empirical accuracy and theoretical consistency than standard approaches. We explore applications stemming from factors on demand: - The joint use of a statistical model with non-idiosyncratic residual for return estimation and a cross-sectional model for return attribution - The optimal hedge of a portfolio of options, even when the investment horizon is close to the expiry and thus the securities are heavily non-linear - The "on demand" feature of FoD to extract a parsimonious set of few dominant attribution factors/hedges that change dynamically with time - Accommodating in the same platform global and regional models that give rise to the same, consistent risk numbers - Point-in-time style analysis, as opposed to the standard trailing regression - Risk attribution to select target portfolios to track the effect of incremental alpha signals on the allocation process Fully commented code supporting the above case studies is available at MATLAB Central File Exchange under the author's page.

This article outlines a different way to think about building a retirement income strategy, which moves dramatically away from the concepts of safe withdrawal rates and failure rates. The focus is how to best meet two competing financial objectives for retirement: satisfying spending goals and preserving financial assets. The process described here focuses on allocating assets between a portfolio of stocks and bonds, inflation-adjusted and fixed single-premium immediate annuities (SPIAs), and immediate variable annuities with guaranteed living benefit riders (VA/GLWBs). This process incorporates unique client circumstances, bases asset return assumptions on current market conditions, uses a consistent fee structure for a fair comparison between income tools, operationalizes the concept of diminishing returns from spending by incorporating a minimum needs threshold and a lifestyle spending goal, uses survival probabilities to calculate outcomes, and incorporates client preferences to balance the competing financial objectives for the final choice among the collection of allocations that define the efficient frontier for retirement income. Results are presented for a 65-year old couple whose lifestyle needs require a 4% inflation-adjusted withdrawal rate from retirement date assets. Their efficient frontier generally consists of combinations of stocks and fixed SPIAs. Perhaps surprisingly, bonds, inflation-adjusted SPIAs, and VA/GLWBs do not serve a useful role in the couple’s optimal retirement income portfolio.

We study the effect of adoption of enterprise risk management (ERM) principles on firms' long-term performance by examining how financial, asset and market characteristics change around the time of ERM adoption. Using a sample of 106 firms that announce the hiring of a Chief Risk Officer (an event frequently accompanied by adoption of Enterprise Risk Management) we find that firms adopting ERM experience a reduction in stock price volatility. We also find that firms hiring Chief Risk Officers (CRO) when compared to similar, non-CRO appointing firms in their industry group, exhibit increased asset opacity, a decreased market to book ratio and decreased earnings volatility. In addition, we find a negative relationship between the change in firms' market to book ratio and earnings volatility. We also find banks increase leverage after ERM adoption. Overall, our results fail to find support for the proposition that ERM is value creating, although further study is called for.

Regime changes planning in financial markets is well known to be hard to explain and interpret. Can an asset manager ex-plain clearly the intuition of his regime changes prediction on equity market ? To answer this question, we consider a gradi-ent boosting decision trees (GBDT) approach to plan regime changes on S&P 500 from a set of 150 technical, fundamen-tal and macroeconomic features. We report an improved ac-curacy of GBDT over other machine learning (ML) methods on the S&P 500 futures prices. We show that retaining fewer and carefully selected features provides improvements across all ML approaches. Shapley values have recently been intro-duced from game theory to the field of ML. This approach allows a robust identification of the most important variables planning stock market crises, and of a local explanation of the crisis probability at each date, through a consistent features attribution. We apply this methodology to analyse in detail the March 2020 financial meltdown, for which the model of-fered a timely out of sample prediction. This analysis unveils in particular the contrarian predictive role of the tech equity sector before and after the crash.

We identify a ‘slope’ factor in exchange rates. High interest rate currencies load more on this slope factor than low interest rate currencies. This factor accounts for most of the cross-sectional variation in average excess returns between high and low interest rate currencies. A standard, no-arbitrage model of interest rates with two factors – a country-specific factor and a global factor – can replicate these findings, provided there is sufficient heterogeneity in exposure to global or common innovations. We show that our slope factor identifies these common shocks, and we provide empirical evidence that it is related to changes in global equity market volatility. By investing in high interest rate currencies and borrowing in low interest rate currencies, US investors load up on global risk.

This paper studies the problem of default correlation. We first introduce a random variable called "time-until-default" to denote the survival time of each defaultable entity or financial instrument, and define the default correlation between two credit risks as the correlation coefficient between their survival times. Then we argue why a copula function approach should be used to specify the joint distribution of survival times after marginal distributions of survival times are derived from market information, such as risky bond prices or asset swap spreads. The definition and some basic properties of copula functions are given. We show that the current CreditMetrics approach to default correlation through asset correlation is equivalent to using a normal copula function. Finally, we give some numerical examples to illustrate the use of copula functions in the valuation of some credit derivatives, such as credit default swaps and first-to-default contracts.

We employ deep learning in forecasting high-frequency returns at multiple horizons for 115 stocks traded on Nasdaq using order book information at the most granular level. While raw order book states can be used as input to the forecasting models, we achieve state-of-the-art predictive accuracy by training simpler "off-the-shelf" artificial neural networks on stationary inputs derived from the order book. Specifically, models trained on order flow significantly outperform most models trained directly on order books. Using cross-sectional regressions we link the forecasting performance of a long short-term memory network to stock characteristics at the market microstructure level, suggesting that "information-rich" stocks can be predicted more accurately. Finally, we demonstrate that the effective horizon of stock specific forecasts is approximately two average price changes.

This paper explores the application of the Volume Weighted Average Price (VWAP) in detecting market imbalances and enhancing trading decisions across diverse market conditions. We introduce a straightforward VWAP-based day trading strategy, which initiates long positions when price is above the VWAP and short positions when it falls below the VWAP. Our analysis employs QQQ and TQQQ as primary trading instruments, covering the period from January 2, 2018, to September 28, 2023. This timeframe includes two bear markets and multiple high-volatility events, providing a comprehensive test of market variations. Our findings reveal that an initial investment of $25,000 in the VWAP Trend Trading strategy with QQQ would have grown to $192,656, net of commissions, yielding a 671% return. This performance is marked by a maximum drawdown of just 9.4% and a Sharpe Ratio of 2.1. In contrast, a passive buy-and-hold strategy in QQQ during the same period would have returned 126%, with a significantly higher maximum drawdown of 37% and a lower Sharpe Ratio of 0.7. Further enhancing our strategy with TQQQ (3x leveraged ETFs of QQQ), we observed extraordinary outcomes: a $25,000 investment surged to $2,085,417, net of commissions. This equates to an 8,242% total return, or an average annual return of 116%, maintaining a maximum drawdown comparable to the passive QQQ strategy. Although we do not regard it as a fully developed trading system, our findings highlight VWAP’s potential as a powerful tool for active traders and investors, emphasizing its superiority over standard buy-and-hold approaches in terms of profitability, risk-adjusted returns, and resilience during market fluctuations.