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We study the stock market reaction to aggregate earnings news. Previous research shows that, for individual firms, stock prices react positively to earnings news but require several quarters to fully reflect the information in earnings. We find that the relation between returns and earnings is substantially different in aggregate data. First, returns are unrelated to past earnings, suggesting that prices neither underreact nor overreact to aggregate earnings news. Second, aggregate returns are negatively correlated with concurrent earnings; over the last 30 years, stock prices increased 6.5% in quarters with negative earnings growth and only 1.9% otherwise. This finding suggests that earnings and discount rates move together over time, and provides new evidence that discount-rate shocks explain a significant fraction of aggregate stock returns.

Quantitative Meta-Strategies (QMS) are quantitative strategies designed to manage investment strategies. As a field, QMS can be defined as the mathematical study of the decisions made by the supervisor of a team of investment managers, regardless of whether their investment style is systematic or discretionary. Algorithmized investment processes can be tested and improved before being applied to a business. They provide objective and consistent oversight, and help prevent repeated mistakes. They are scalable and speed up quality improvement by limiting managerial frictions and biases.

Based on an asset demand system, we develop a framework to quantify the impact of market trends and changes in regulation on asset prices, price informativeness, and the wealth distribution. Our leading applications are the transition from active to passive investment management and climate-induced shifts in asset demand. The transition from active to passive investment management had a large impact on equity prices but a small impact on price informativeness because capital did not flow from more to less informed investors on average. This finding is based on a new measure of investor-level informativeness that identifies which investors are more informed about future profitability. Climate-induced shifts in asset demand have a potentially large impact on equity prices and the wealth distribution, implying capital gains for passive investment advisors, pension funds, insurance companies, and private banking and capital losses for active investment advisors and hedge funds.

Model-Free Reinforcement Learning has achieved meaningful results in stable environments but, to this day, it remains problematic in regime changing environments like financial markets. In contrast, model-based RL is able to capture some fundamental and dynamical concepts of the environment but suffer from cognitive bias. In this work, we propose to combine the best of the two techniques by selecting various model-based approaches thanks to Model-Free Deep Reinforcement Learning. Using not only past performance and volatility, we include additional contextual information such as macro and risk appetite signals to account for implicit regime changes. We also adapt traditional RL methods to real-life situations by considering only past data for the training sets. Hence, we cannot use future information in our training data set as implied by K-fold cross validation. Building on traditional statistical methods, we use the traditional "walk-forward analysis", which is defined by successive training and testing based on expanding periods, to assert the robustness of the resulting agent. Finally, we present the concept of statistical difference's significance based on a two-tailed T-test, to highlight the ways in which our models differ from more traditional ones. Our experimental results show that our approach outperforms traditional financial baseline portfolio models such as the Markowitz model in almost all evaluation metrics commonly used in financial mathematics, namely net performance, Sharpe and Sortino ratios, maximum drawdown, maximum drawdown over volatility.

This paper draws upon the modern science of finance to address several important practical issues in personal finance. Chief among these is how much to save for retirement and how to invest those savings. The paper suggests ways that advances in the theory of finance combined with innovations in financial contracting technology might be used to improve social welfare by designing and producing a new generation of user-friendly life-cycle products for consumers. It contrasts the old Markowitz single-period paradigm of efficient diversification with a new Mertonian paradigm that takes account of multi-period hedging, labor supply flexibility, and habit formation.

A mechanical rebalancing strategy, such as a monthly or quarterly reallocation towards fixed portfolio weights, is an active strategy. Winning asset classes are sold and losers are bought. During crises, when markets are often trending, this can lead to substantially larger drawdowns than a buy-and-hold strategy. Our paper shows that the negative convexity induced by rebalancing can be substantially mitigated, taking the popular 60-40 stock-bond portfolio as our use case. One alternative is an allocation to a trend-following strategy. The positive convexity of this overlay tends to counter the impact on drawdowns of the mechanical rebalancing strategy. The second alternative we call strategic rebalancing, which uses smart rebalancing timing based on trend-following signals – without a direct allocation to a trend-following strategy. For example, if the trend-following model suggests that stock markets are in a negative trend, rebalancing is delayed.

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. In this course, we discuss scientifically sound ML tools that have been successfully applied to the management of large pools of funds.

We provide an easy method to identify purchases and sales initiated by retail investors using recent, widely available U.S. equity transactions data. Individual stocks with net buying by retail investors outperform stocks with negative imbalances by approximately 10 basis points over the following week. Less than half of the predictive power of marketable retail order imbalances is attributable to order flow persistence; contrarian trading (a proxy for liquidity provision) and public news sentiment explain little of the remaining predictability. There is suggestive (but only suggestive) evidence that retail marketable orders contain firm-level information that is not yet incorporated into prices.

This paper reviews the literature on one of the most meaningful concepts in modern behavioural finance, the overconfidence phenomenon. Overconfidence is presented as a well-developed psychological theory, with main facets comprising miscalibration, better-than-average effect, illusion of control and unrealistic optimism. The primary applications of overconfidence in contemporary finance are analysed, from the perspective of financial markets and corporate behaviour. Experimental studies, formal models and analyses of market data demonstrate that overconfidence at least partially solves some financial market puzzles that cannot be accounted for by standard economic theory. Overconfidence in the corporate context may affect not only a company's internal financing structure, but also its interactions with other market participants through merger and acquisition activity.

Finance is widely seen as an obstacle to a better world. Principles of Sustainable Finance explains how the financial sector can be mobilized to counter this and provides many examples and tools. Using finance as a means to achieve social goals we can divert the planet and its economy from its current path to a world that is sustainable for all.Written for undergraduate, graduate, and executive students of finance, economics, business, and sustainability, this textbook combines theory, empirical data, and policy to explain the sustainability challenges for corporate investment. It shows how investors and bankers can steer funding to sustainable companies and projects without sacrificing return and thus speed up the transition to a sustainable economy.

Human resource accounting (HRA) is an attempt to identify, quantify and report investment made in Human resources of an organization that are not presently accounted for under conventional accounting practice. Businesses which require a considerable creativity or are science-based show a significant difference between market value and net book value. This difference is for intangible assets (including human skills). However the Human Resources are yet to get recognition in Balance Sheet. Businesses are not properly accounting for it in Books of Accounts. Auditor certifies in his report that balance sheet shows true position of business in spite of the fact that it is not showing the value of human resources. Researches in this field have been slow and researchers are not able to develop a model which are free from major limitations. Major limitation of existing models is that they are not able to identify two effects on Human Capital creation which is back bone of accounting. In this article I have discussed efforts done in this field by researchers and proposed a model for valuation and accounting of Human resources. This proposed model is not altogether new model but it is an extension of Lev and Schwartz Model (L&S) because at one point it uses Lev & Schwartz Valuation principles. It also remove major weakness of L&S model such as it is able to account for Human Resource in balance-sheet. This model also encourages employee to achieve high level of performance.

The size effect in finance literature refers to the observation that smaller firms have higher returns than larger firms, on average over long horizons. It also describes the contribution that firm size has in explaining stock returns. Discovered by Banz (1981) in testing the Sharpe-Lintner Capital Asset Pricing Model, subsequent research finds the size effect has diminished or disappeared since the 1980s in the U.S., UK, and elsewhere following Banz's announcement and launches of small-cap funds. Firm size is thought to proxy for underlying risk factors associated with smaller firms. Observed variations in the size effect can be explained by such underlying factors like market liquidity that change over time. Related research finds the size effect is seasonal. It occurs primarily during January in the U.S. and has had little or no presence in the other 11 months, which confounds empirical research on risk-reward relationships. Research also finds the size effect is concentrated in smaller listed firms, making the effect nonlinear.

Each of the most recent accords of the Basel Committee on Banking Regulation, known as Basel II, 2.5, and II, has embraced a different primary measure of market risk in global banking regulation: traditional value-at-risk (VaR), stressed VaR, and expected shortfall. After introducing the mathematics of VaR and expected shortfall, this note will evaluate how well the reforms embraced by Basel 2.5 and III - stressed VaR and expected shortfall - have addressed longstanding regulatory concerns with traditional VaR. Part I describes the calculation of VaR in its conventional form. For illustrative purposes, Part I will describe parametric VaR on a Gaussian distribution. Part II summarizes known weaknesses in VaR, from inherent model and estimation risk to VaR’s failure to perform under extreme economic stress and VaR’s failure to satisfy the theoretical constraints on “coherent” measurements of risk. Part III describes how to calculate expected shortfall as an extension of conditional VaR. It further describes how expected shortfall, but not VaR, provides a coherent measure of risk. Part III then reverses field. It explains how VaR, but not expected shortfall (or, for that matter, nearly every other general spectral measure of risk), satisfies the mathematical requirement of “elicitability.” Mathematical limitations on measures of risk therefore force regulators and bankers to choose between coherence and elicitability, between theoretically sound consolidation of diverse risks (on one hand) and reliable backtesting of risk forecasts against historical observations.

This study takes a novel approach to testing the efficacy of technical analysis. Rather than testing specific trading rules as is typically done in the literature, we rely on institutional portfolio managers’ statements about whether and how intensely they use technical analysis, irrespective of the form in which they implement it. In our sample of more than 10,000 portfolios, about one-third of actively managed equity and balanced funds use technical analysis. We compare the investment performance of funds that use technical analysis versus those that do not using five metrics. Mean and median (3 and 4-factor) alpha values are generally slightly higher for a cross section of funds using technical analysis, but performance volatility is also higher. Benchmark-adjusted returns are also higher, particularly when market prices are declining. The most remarkable finding is that portfolios with greater reliance on technical analysis have elevated skewness and kurtosis levels relative to portfolios that do not use technical analysis. Funds using technical analysis appear to have provided a meaningful advantage to their investors, albeit in an unexpected way.

This paper documents a significantly negative cross-sectional relation between left-tail risk and future returns on individual stocks trading in the U.S. and international countries. We provide a behavioral explanation to this anomaly based on the idea that investors underestimate the persistence in left-tail risk and overprice stocks with large recent losses. Thus, low returns in the left-tail of the distribution persist into the future causing left-tail return momentum. We find that the left-tail risk anomaly is stronger for stocks that are more likely to be held by retail investors, that receive less investor attention and that are costlier to arbitrage.

This paper develops a simple technique that controls for “false discoveries,” or mutual funds that exhibit significant alphas by luck alone. Our approach precisely separates funds into (1) unskilled, (2) zero-alpha, and (3) skilled funds, even with dependencies in cross-fund estimated alphas. We find that 75% of funds exhibit a zero alpha (net of expenses), consistent with the Berk and Green (2004) equilibrium. Further, we find a significant proportion of skilled (positive alpha) funds prior to 1996, but almost none by 2006. We also show that controlling for false discoveries substantially improves the ability to find funds with persistent performance.

In this chapter we provide an overview of the concept of blockchain technology and its potential to disrupt the world of banking through facilitating global money remittance, smart contracts, automated banking ledgers and digital assets. In this regard, we first provide a brief overview of the core aspects of this technology, as well as the second-generation contract-based developments. From there we discuss key issues that must be considered in developing such ledger based technologies in a banking context.

We introduce the copula-marginal algorithm (CMA), a commercially viable technique to generate and manipulate a much wider variety of copulas than those commonly used by practitioners. CMA consists of two steps: separation, to decompose arbitrary joint distributions into their copula and marginals; and combination, to glue arbitrary copulas and marginals into new joint distributions. Unlike traditional copula techniques, CMA a) is not restricted to few parametric copulas such as elliptical or Archimedean; b) never requires the explicit computation of marginal cdf’s or quantile functions; c) does not assume equal probabilities for all the scenarios, and thus allows for advanced techniques such as importance sampling or entropy pooling; d) allows for arbitrary transformations of copulas. Furthermore, the implementation of CMA is also computationally very efficient in arbitrary large dimensions. To illustrate benefits and applications of CMA, we propose two case studies: stress-testing with a panic copula which hits non-symmetrically the downside and displays non-equal, risk-premium adjusted probabilities; and arbitrary rotations of the panic copula. Documented code for all the algorithms and the applications is available for download.

We develop a profile of overvalued equity, and show that firms meeting this profile experience abnormal stock returns net of transaction costs of -22 to -25 percent over the twelve months following portfolio formation. We show our model is distinct from predictors proposed in prior work, and our results robust to alternative measurements of expected returns. We also show that overvaluation is not confined to small firms and that institutions do not trade as if they identify overvalued equity. The profitable predictability we document suggests a pricing anomaly relating to the 2.5% of the firms in the population that our model identifies as substantially overvalued. Although we believe markets are generally efficient within the bounds of transaction costs, our evidence suggests that violations of minimally rational use of publicly available information do occur. To the extent that anomalies disappear or attenuate once documented in the literature (Doukas et al. 2002, Schwert 2003), our results are of interest to financial economists and investors.

A standard critique of valuation models, in general, and discounted cash flow models in particular is that they fail to fully account for the many intangible assets possessed by firms. There have been attempts to value brand name, trade marks and copyrights and bring them on to the balance sheet. Other intangible assets include patents and customer lists. We would expand this list to consider the flexibility that a firm may preserve to expand its market or enter new markets. In this paper, we consider a variety of ways in which these assets can be valued and the consequences for investors.

This paper studies the "overpriced puts puzzle'' -- the finding that historical prices of the S&P 500 put options have been too high and incompatible with the canonical asset-pricing models, such as CAPM and Rubinstein (1976) model. To investigate whether put returns could be rationalized by another, possibly nonstandard equilibrium model, we implement the model-free methodology of Bondarenko (2003a). The methodology requires no parametric assumptions on investors' preferences. Furthermore, it can be applied even when the sample is affected by certain selection biases (such as the Peso problem) and when investors' beliefs are incorrect. The main finding of the paper is that no model within a studied class of models can possibly explain the put anomaly.

This paper revisits the controversy on regulation and applies its insights to the debate on corporate governance and mutual funds. The general result of this exercise is that a strong case can be made in favor of more mandatory disclosure. While theoretically there is scope also for other mandatory regulation, it is unclear whether its benefits exceed its costs. Furthermore, it is difficult to see how this ideal regulation could emerge from the political process, which tends to be dominated by incumbent firms. I propose a mechanism to reduce this bias.

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. In this course, we discuss scientifically sound ML tools that have been successfully applied to the management of large pools of funds.

This paper reviews the current state of ChatGPT technology in finance and its potential to improve existing NLP-based financial applications. We discuss the ethical and regulatory considerations, as well as potential future research directions in the field. The literature suggests that ChatGPT has the potential to improve NLP-based financial applications, but also raises ethical and regulatory concerns that need to be addressed. The paper highlights the need for research in robustness, interpretability, and ethical considerations to ensure responsible use of ChatGPT technology in finance.

In this article, we show how to calibrate the widely-used SVI parameterization of the implied volatility smile in such a way as to guarantee the absence of static arbitrage. In particular, we exhibit a large class of arbitrage-free SVI volatility surfaces with a simple closed-form representation. We demonstrate the high quality of typical SVI fits with a numerical example using recent SPX options data.