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This paper introduces a real-time, continuous measure of national sentiment that is language-free and thus comparable globally: the positivity of songs that individuals choose to listen to. This is a direct measure of mood that does not pre-specify certain mood-affecting events nor assume the extent of their impact on investors. We validate our music-based sentiment measure by correlating it with mood swings induced by seasonal factors, weather conditions, and COVID-related restrictions. We find that music sentiment is positively correlated with same-week equity market returns and negatively correlated with next-week returns, consistent with sentiment-induced temporary mispricing. Results also hold under a daily analysis and are stronger when trading restrictions limit arbitrage. Music sentiment also predicts increases in net mutual fund flows, and absolute sentiment precedes a rise in stock market volatility. It is negatively associated with government bond returns, consistent with a flight to safety.

This paper presents a dynamic model of takeovers based on the stock market valuations of merging firms. The model incorporates competition and imperfect information and determines the terms and timing of takeovers by solving option exercise games between bidding and target shareholders. The implications of the model for returns to stockholders are consistent with the available evidence. Notably, the model predicts that (1) returns to target shareholders should be larger than returns to bidding shareholders, and (2) returns to bidding shareholders can be negative if there is competition for the acquisition of the target. In addition, the model generates new predictions relating these returns to the drift, volatility and correlation coefficient of the bidder and the target stock returns and to the dispersion of beliefs regarding the benefits of the takeover.

It is well known that the cost of a call and put option is equal to its intrinsic value plus the cost of a stop loss strategy. This stop loss strategy can be re-expressed in terms of the local time. It provides easily closed forms solution for model like Black Scholes [8] or [3]. This paper examines the theory of local time for stochastic volatility models and in particular the SABR model [5]. It gives an approximated formula for the local time in SABR and shows that this model can be valued using a Black Scholes formula but where all the terms are complex number. This formula turns out to be more robust for low and high strikes. This solves in particular the problem of valuing the whole smile in SABR as required in the replication method for CMS and the copula integration for CMS spread options.

The BP Deepwater Horizon drilling rig exploded on April 20, 2010, leading to an unprecedented environmental and financial disaster. This paper details responses in the financial markets for BP securities, including stock, bonds, options, and credit default swaps. Following the disaster BP shares dropped more than 50 percent in value, with high volatility. BP share trading volume increased thirteen-fold, and option trading volume increased twenty-fold. The implied volatility of BP shares also jumped, ranging between two and four times its earlier levels. Interest rate spreads on BP bonds widened and the prices of credit default swaps exploded. Finally, on June 16, the company announced that cash dividends were suspended. We provide evidence from options markets that this dividend suspension was anticipated. From late June through September, there was a partial reversion to pre-explosion levels in all markets. We detail the degree of integration across markets, as wide swings in BP’s outlook were simultaneously absorbed in the various markets.

Over the past few years, commodity prices have experienced the biggest boom in half a century. In this paper we investigate whether it is possible by active asset management to take advantage of the unique risk-return characteristics of commodities, while avoiding their excessive volatility. We show that observing (and learning from) the actions of different groups of market participants enables an active asset manager to successfully 'time' the commodities market. We focus on the information contained in the Commitment of Traders report, published by the CFTC. This report summarizes the size and direction of the positions taken by different types of traders in different markets. Our findings indicate that there is indeed significant informational content in this report, which can be exploited by an active portfolio manager. Our dynamically managed strategies exhibit superior out-of-sample performance, achieving Sharpe ratios in excess of 1.0 and annualized alphas relative to the S&P 500 of around 15%.

There are, at least, seven aspects relating to financial regulation where the recent, and still current, financial turmoil has thrown up issues for discussion. These include: 1. The scale and scope of deposit insurance; 2. Bank insolvency regimes, also known as 'prompt corrective action'; 3. Money market operations by Central Banks; 4. Commercial bank liquidity risk management; 5. Procyclicality of capital adequacy requirements (and mark-to-market), Basel II; lack of counter-cyclical instruments; 6. Boundaries of regulation, conduits, SIVs and reputational risk; 7. Crisis management: - (a) domestic, within countries, e.g. UK Tripartite Committee; (b) cross-border; how to bear the burden of cross-border defaults? This paper describes how the current crisis has exposed regulatory failings, drawing largely on recent UK experience, and suggests what remedial action might be undertaken.

We use the introduction of satellite coverage of major retailers to study the capital market implications of unequal access to big data. Satellite data enabled sophisticated investors with access to such data to formulate profitable trading strategies, especially by targeting the upcoming reports of retailers with bad news for the quarter. The introduction of satellite data led to more informed short-selling activity, less informed individual buying activity, and lower stock liquidity around the reports of retailers with satellite coverage. We conclude that unequal access to big data can increase information asymmetry among market participants without immediately enhancing price discovery.

This paper tests whether momentum-based strategies remain profitable after considering market frictions, in particular price concessions induced by trading. Alternative measures of price impact are estimated and applied to alternative momentum-based trading rules. The performance of traditional momentum strategies, in addition to strategies designed to reduce the cost of trades, is evaluated. We find that, after taking into account the price impact induced by trades, as much as 5 billion dollars (relative to December 1999 market capitalization) may be invested in some momentum-based strategies before the apparent profit opportunities vanish. Other, extensively studied, momentum strategies are not implementable on a large scale. The persistence of momentum returns exhibited in the data remains an important challenge to the asset-pricing literature.

We examine the effect of securities laws on stock market development in 49 countries. We find almost no evidence that public enforcement benefits stock markets, and strong evidence that laws facilitating private enforcement through disclosure and liability rules benefit stock markets.

Asset swaps provide a form of asset financing, where investors borrow funds to purchase an asset, typically a bond. Asset swaps are also a good bond rich-cheap analysis tool. Such swaps can of course be used for speculative purposes. In this paper we provide a brief overview of asset swaps and derive a par-par asset swap spread formula incorporating bond accrued interest. Finally we illustrate how to calculate both the yield-yield and par-par asset swap spread using the liquid 10 year German Bund.

This article studies how stock price efficiency and the distribution of returns are affected by short-sale constraints. The study is based on a global data set that includes more than 12,600 stocks from 26 countries between 2005 and 2008. Our main findings are as follows. First, lending supply has a significant impact on efficiency. Stocks with higher short-sale constraints, measured by low lending supply, have lower price efficiency. Second, relaxing short-sales constraints is not associated with an increase in either price instability or occurrence of extreme negative returns.

Bitcoin is a major virtual currency. Using weekly data over the 2010-2013 period, we analyze a Bitcoin investment from the standpoint of a U.S. investor with a diversified portfolio including both traditional assets (worldwide stocks, bonds, hard currencies) and alternative investments (commodities, hedge funds, real estate). Over the period under consideration, Bitcoin investment had highly distinctive features, including exceptionally high average return and volatility. Its correlation with other assets was remarkably low. Spanning tests confirm that Bitcoin investment offers significant diversification benefits. We show that the inclusion of even a small proportion of Bitcoins may dramatically improve the risk-return trade-off of well-diversified portfolios. Results should however be taken with caution as the data may reflect early-stage behavior which may not last in the medium or long run.

Abstract This paper considers several popular portfolio implementation techniques that maximize exposure to value and/or momentum stocks while taking into account transaction costs. Our analysis of long-only strategies illustrates how a strategy that simultaneously incorporates both value and momentum outperforms a strategy that combines pure-play value and momentum portfolios that are formed independently. There are two advantages of the simultaneous strategy. The first is the reduction in transaction costs; the second is better utilization of unfavorable value and momentum information in a long-only portfolio. Our analysis also addresses the optimal way to combine the faster-moving momentum signal with the slower-moving value signal.

We develop a new high-performance spectral collocation method for the computation of American put and call option prices. The proposed algorithm involves a carefully posed Jacobi-Newton iteration for the optimal exercise boundary, aided by Gauss-Legendre quadrature and Chebyshev polynomial interpolation on a certain transformation of the boundary. The resulting scheme is straightforward to implement and converges at a speed several orders of magnitude faster than existing approaches. Computational effort depends on required accuracy; at precision levels similar to, say, those computed by finite difference grids with several hundred steps, the computational throughput of the algorithm in the Black-Scholes model is typically close to 100,000 option prices per second per CPU. For benchmarking purposes, Black-Scholes American option prices can generally be computed to at 10 or 11 significant digits in less than one-tenth of a second.

We analyze the three components of active management (asset allocation, market timing and security selection) in the net performance of U.S. pension funds and relate these to fund size and the liquidity of the investments. On average, the funds in our sample have an annual net alpha of 89 basis points that is evenly distributed across the asset allocation, market timing, and security selection components. Stock momentum fully explains the positive alpha in security selection, whereas “time series momentum” drives market timing. While larger pension funds have lower investment costs, this does not lead to better net performance. Rather, all three components of active management exhibit substantial diseconomies of scale directly related to illiquidity. Our results suggest that especially the larger pension funds would have done better if they invested more in passive mandates without frequent rebalancing across asset classes.

We construct size and value factors in China. The size factor excludes the smallest 30% of firms, which are companies valued significantly as potential shells in reverse mergers that circumvent tight IPO constraints. The value factor is based on the earnings-price ratio, which subsumes the book-to-market ratio in capturing all Chinese value effects. Our three-factor model strongly dominates a model formed by just replicating the Fama and French (1993) procedure in China. Unlike that model, which leaves a 17% annual alpha on the earnings-price factor, our model explains most reported Chinese anomalies, including profitability and volatility anomalies.

This paper shows that investments based on deep learning signals extract profitability from difficult-to-arbitrage stocks and during high limits-to-arbitrage market states. In particular, excluding microcaps, distressed stocks, or episodes of high market volatility considerably attenuates profitability. Machine learning-based performance further deteriorates in the presence of reasonable trading costs due to high turnover and extreme positions in the tangency portfolio implied by the pricing kernel. Despite their opaque nature, machine learning methods successfully identify mispriced stocks consistent with most anomalies. Beyond economic restrictions, deep learning signals are profitable in long positions and recent years and command low downside risk.

Using the CEM pension fund data set, we document the cost structure and performance of a large sample of US pension funds. To date, self-reporting biases and a deficiency of comprehensive return and cost data have severely hindered pension fund performance studies. The bias-free CEM dataset resolves these issues and provides detailed information on fund-specific returns, benchmarks and costs for all types of pension plans and equity mandates. We find that pension fund cost levels are substantially lower than mutual fund fees. The domestic equity investments of US pension funds tend to generate abnormal returns (after expenses and trading costs) close to zero or slightly positive, contrasting the average underperformance of mutual funds. However, small cap mandates of defined benefit funds have outperformed their benchmarks by about 3% a year. While larger scale brings costs advantages, liquidity limitations seem to allow only smaller funds, and especially small cap mandates, to outperform their benchmarks.

Previous studies on the effect of International Financial Reporting Standards (IFRS) on accounting quality often have difficulties to control for confounding factors on accounting quality. As a result, the observed changes in accounting quality could not be attributed mainly to IFRS. We use a unique research setting to address this issue by comparing the accounting quality of publicly listed companies in 15 member states of the European Union before and after the full adoption of IFRS in 2005. We use five indicators as proxies for accounting quality. We find that the majority of accounting quality indicators improved after IFRS adoption in the European Union. That is, there is less of managing earnings towards a target, a lower magnitude of absolute discretionary accruals, and higher accruals quality. But our results also show that firms engage in more earnings smoothing and recognize large losses in a less timely manner in post-IFRS periods. In addition, we examine the effects of institutional variables on financial reporting quality. Our contribution to the literature is that we show the improved accounting quality is attributable to IFRS, rather than changes in managerial incentives, institutional features of capital markets, and general business environment, etc.

Stock market participation is monotonically related to IQ, controlling for wealth, income, age, and other demographic and occupational information. The high correlation between IQ, measured early in adult life, and participation, exists even among the affluent. Supplemental data from siblings, studied with an instrumental variables approach and regressions that control for family effects, demonstrate that IQ’s influence on participation extends to females and does not arise from omitted familial and non-familial variables. High-IQ investors are more likely to hold mutual funds and larger numbers of stocks, experience lower risk, and earn higher Sharpe ratios. We discuss implications for policy and finance research.

Taking into account the apart role in the market economy, the capital market occupies an important place, through their specific mechanisms, succeeding to give its contribution to the economic development of the society. In consequence, the public authorities must notice the importance of the capital market in the national economy and, on the other hand, to make the efforts for insuring the necessary framework for the normal functioning of its specific mechanisms. The valences of the capital could be even more interesting in the case of emerging markets - like the Romanian capital market - being well-known its contribution in reorienting financial resources to efficient activities, contributing to the economic reform, but also being interesting in the privatisation process.

Mean-variance criteria remain prevalent in multi-period problems, and yet not much is known about their dynamically optimal policies. We provide a fully analytical characterization of the optimal dynamic mean-variance portfolios within a general incomplete-market economy, and recover a simple structure that also inherits several conventional properties of static models. We also identify a probability measure that incorporates intertemporal hedging demands and facilitates much tractability in the explicit computation of portfolios. We solve the problem by explicitly recognizing the time-inconsistency of the mean-variance criterion and deriving a recursive representation for it, which makes dynamic programming applicable. We further show that our time-consistent solution is generically different from the pre-commitment solutions in the extant literature, which maximize the mean-variance criterion at an initial date and which the investor commits to follow despite incentives to deviate. We illustrate the usefulness of our analysis by explicitly computing dynamic mean-variance portfolios under various stochastic investment opportunities in a straightforward way, which does not involve solving a Hamilton-Jacobi-Bellman differential equation. A calibration exercise shows that the mean-variance hedging demands may comprise a significant fraction of the investor's total risky asset demand.

The primary goal of this Article is to bring empirical evidence to bear on the heretofore largely theoretical law and economics debate about insider trading. The Article first summarizes various agency, market, and contractual (or "Coasian") theories of insider trading propounded over the course of this longstanding debate. The Article then proposes three testable hypotheses regarding the relationship between insider trading laws and several measures of stock market performance. Exploiting the natural variation of international data, the Article finds that more stringent insider trading laws are generally associated with more dispersed equity ownership, greater stock price accuracy and greater stock market liquidity, controlling for various economic, legal and institutional factors. These results cast doubt on pure "Coasian" theories of insider trading and suggest the appropriate locus of academic and policy inquiries about the efficiency implications of insider trading and its regulation. Further empirical research is necessary, however, in order conclusively to resolve the perennial insider trading debate.

The London Interbank Offered Rate (LIBOR) is a widely used indicator of funding conditions in the interbank market. As of 2013, LIBOR underpins more than $300 trillion of financial contracts, including swaps and futures, in addition to trillions more in variable-rate mortgage and student loans. LIBOR's volatile behavior during the financial crisis provoked questions surrounding its credibility. Ongoing regulatory investigations have uncovered misconduct by a number of financial institutions. Policymakers across the globe now face the task of reforming LIBOR in the aftermath of the scandal and crisis.

In business research, firm size is both ubiquitous and readily measured. Complexity, another firm-related construct, is also relevant, but difficult to measure and not well defined. As a result, complexity is less frequently incorporated in empirical designs. We argue that most extant measures of complexity are one-dimensional, have limited availability, and/or are frequently misspecified. Using both machine learning and an application specific lexicon, we develop a text solution that uses widely available data and provides an omnibus measure of complexity. Our proposed measure, used in tandem with 10-K file size, provides a useful proxy that dominates traditional measures.